Update notebooks

run_medclip.sh

@@ -1,4 +1,4 @@
-python src/hybrid_clip/run_hybrid_clip.py \
+python src/medclip/run_medclip.py \
     --output_dir ./snapshots/vision_augmented_biobert \
     --text_model_name_or_path="allenai/scibert_scivocab_uncased" \
     --vision_model_name_or_path="openai/clip-vit-base-patch32" \

src/medclip/run_medclip.py

@@ -0,0 +1,570 @@
+#!/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.
+"""
+Training a CLIP like dual encoder models using text and vision encoders in the library.
+
+The script can be used to train CLIP like models for languages other than english by using
+a text encoder pre-trained in the desired language. Currently this script support the following vision
+and text models:
+Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip)
+Text models: BERT, ROBERTa (https://huggingface.co/models?filter=masked-lm)
+"""
+
+import json
+import logging
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Callable, Optional
+
+import torch
+from torchvision.datasets import VisionDataset
+from torchvision.io import ImageReadMode, read_image
+from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
+from torchvision.transforms.functional import InterpolationMode
+from torchvision.transforms.transforms import GaussianBlur, RandomAutocontrast, RandomHorizontalFlip
+from tqdm import tqdm
+
+import jax
+import jax.numpy as jnp
+import optax
+import transformers
+from flax import jax_utils
+from flax.jax_utils import unreplicate
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, shard, shard_prng_key
+from modeling_hybrid_clip import FlaxHybridCLIP
+from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, is_tensorboard_available, set_seed
+
+
+logger = logging.getLogger(__name__)
+
+# Cache the result
+has_tensorboard = is_tensorboard_available()
+if has_tensorboard:
+    try:
+        from flax.metrics.tensorboard import SummaryWriter
+    except ImportError as ie:
+        has_tensorboard = False
+        print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
+
+else:
+    print(
+        "Unable to display metrics through TensorBoard because the package is not installed: "
+        "Please run pip install tensorboard to enable."
+    )
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    text_model_name_or_path: str = field(
+        metadata={
+            "help": "The text model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    vision_model_name_or_path: str = field(
+        metadata={
+            "help": "The vision model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    from_pt: bool = field(
+        default=True,
+        metadata={"help": "whether to load the text and vision model using PyTorch checkpoints."},
+    )
+    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]`."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input 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)."},
+    )
+    max_seq_length: Optional[int] = field(
+        default=72,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    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"}
+    )
+    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."},
+    )
+
+    def __post_init__(self):
+        if 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 == "json", "`train_file` should be a json file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension == "json", "`validation_file` should be a json file."
+
+
+# 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):
+        super().__init__()
+        self.transforms = torch.nn.Sequential(
+            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
+            CenterCrop(image_size),
+            GaussianBlur(3, sigma=(0.05, 0.2)),
+            RandomAutocontrast(p=0.5),
+            RandomHorizontalFlip(p=0.5),
+            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.
+            `image_path`: 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,
+        root: str,
+        file_path: str,
+        captions_per_image=2,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        transforms: Optional[Callable] = None,
+    ):
+        super().__init__(root, transforms, transform, target_transform)
+
+        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"])
+            # self.image_paths.append(example["image_path"])
+            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 = 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_metric(summary_writer, train_metrics, eval_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)
+
+    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
+
+
+def main():
+    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:
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        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}")
+
+    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.text_model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.text_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."
+        )
+
+    model = FlaxHybridCLIP.from_text_vision_pretrained(
+        model_args.text_model_name_or_path,
+        model_args.vision_model_name_or_path,
+        seed=training_args.seed,
+        dtype=getattr(jnp, model_args.dtype),
+        text_from_pt=model_args.from_pt,
+        vision_from_pt=model_args.from_pt,
+    )
+    config = model.config
+    # set seed for torch dataloaders
+    set_seed(training_args.seed)
+
+    # Initialize torchvision transforms and jit them for faster processing
+    preprocess = Transform(config.vision_config.image_size)
+    preprocess = torch.jit.script(preprocess)
+
+    # Initialize the image-text dataset
+    train_dataset = ImageTextDataset(
+        data_args.data_dir,
+        data_args.train_file,
+        captions_per_image=1,
+        transform=preprocess,
+    )
+
+    eval_dataset = ImageTextDataset(
+        data_args.data_dir,
+        data_args.validation_file,
+        captions_per_image=1,
+        transform=preprocess,
+    )
+
+    # 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()
+    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
+
+    # 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]).permute(0, 2, 3, 1).numpy()
+        # pixel_values = torch.stack([example[0] for example in examples]).numpy()
+        captions = [example[1] for example in examples]
+        inputs = tokenizer(captions, max_length=data_args.max_seq_length, 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
+
+    # 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 tensorboard only on the master node
+    if has_tensorboard and jax.process_index() == 0:
+        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
+
+    # 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,
+    )
+
+    # create adam optimizer
+    adamw = 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,
+    )
+
+    # Setup train state
+    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
+
+    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)}
+        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) = {train_batch_size}")
+    logger.info(f"  Total optimization steps = {total_train_steps}")
+
+    train_time = 0
+    # Create sampling rng
+    rng, input_rng = jax.random.split(rng)
+
+    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)
+        train_metrics = []
+
+        steps_per_epoch = len(train_dataset) // train_batch_size
+        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
+        # train
+        for batch in train_loader:
+            batch = shard(batch)
+            state, train_metric = p_train_step(state, batch)
+            train_metrics.append(train_metric)
+
+            train_step_progress_bar.update(1)
+
+        train_time += time.time() - train_start
+
+        train_metric = unreplicate(train_metric)
+
+        train_step_progress_bar.close()
+        epochs.write(
+            f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
+        )
+
+        # ======================== Evaluating ==============================
+        eval_metrics = []
+        eval_steps = len(eval_dataset) // eval_batch_size
+        eval_step_progress_bar = tqdm(total=eval_steps, desc="Evaluating...", position=2, leave=False)
+        for batch in eval_loader:
+            # Model forward
+            batch = shard(batch)
+            metrics = p_eval_step(state.params, batch)
+            eval_metrics.append(metrics)
+
+            eval_step_progress_bar.update(1)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+
+        eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
+
+        # Print metrics and update progress bar
+        eval_step_progress_bar.close()
+        desc = f"Epoch... ({epoch + 1}/{num_epochs} | 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_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
+
+        # save checkpoint after each epoch and push checkpoint to the hub
+        if jax.process_index() == 0:
+            params = jax.device_get(unreplicate(state.params))
+            model.save_pretrained(
+                training_args.output_dir,
+                params=params,
+                push_to_hub=training_args.push_to_hub,
+                commit_message=f"Saving weights and logs of epoch {epoch+1}",
+            )
+
+
+if __name__ == "__main__":
+    main()