CLI: tool to convert PT into TF weights and open hub PR (#17497)

.circleci/config.yml

@@ -78,7 +78,8 @@ jobs:
                   keys:
                       - v0.4-torch_and_tf-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng git-lfs
+            - run: git lfs install
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
@@ -117,7 +118,8 @@ jobs:
                   keys:
                       - v0.4-torch_and_tf-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng git-lfs
+            - run: git lfs install
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html

src/transformers/commands/pt_to_tf.py

@@ -0,0 +1,186 @@
+# Copyright 2022 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.
+
+import os
+from argparse import ArgumentParser, Namespace
+
+import numpy as np
+from datasets import load_dataset
+
+from huggingface_hub import Repository, upload_file
+
+from .. import AutoFeatureExtractor, AutoModel, AutoTokenizer, TFAutoModel, is_tf_available, is_torch_available
+from ..utils import logging
+from . import BaseTransformersCLICommand
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    tf.config.experimental.enable_tensor_float_32_execution(False)
+
+if is_torch_available():
+    import torch
+
+
+MAX_ERROR = 5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
+TF_WEIGHTS_NAME = "tf_model.h5"
+
+
+def convert_command_factory(args: Namespace):
+    """
+    Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
+
+    Returns: ServeCommand
+    """
+    return PTtoTFCommand(args.model_name, args.local_dir, args.no_pr)
+
+
+class PTtoTFCommand(BaseTransformersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        """
+        Register this command to argparse so it's available for the transformer-cli
+
+        Args:
+            parser: Root parser to register command-specific arguments
+        """
+        train_parser = parser.add_parser(
+            "pt-to-tf",
+            help=(
+                "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
+                " Can also be used to validate existing weights without opening PRs, with --no-pr."
+            ),
+        )
+        train_parser.add_argument(
+            "--model-name",
+            type=str,
+            required=True,
+            help="The model name, including owner/organization, as seen on the hub.",
+        )
+        train_parser.add_argument(
+            "--local-dir",
+            type=str,
+            default="",
+            help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
+        )
+        train_parser.add_argument(
+            "--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights."
+        )
+        train_parser.set_defaults(func=convert_command_factory)
+
+    def __init__(self, model_name: str, local_dir: str, no_pr: bool, *args):
+        self._logger = logging.get_logger("transformers-cli/pt_to_tf")
+        self._model_name = model_name
+        self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name)
+        self._no_pr = no_pr
+
+    def get_text_inputs(self):
+        tokenizer = AutoTokenizer.from_pretrained(self._local_dir)
+        sample_text = ["Hi there!", "I am a batch with more than one row and different input lengths."]
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        pt_input = tokenizer(sample_text, return_tensors="pt", padding=True, truncation=True)
+        tf_input = tokenizer(sample_text, return_tensors="tf", padding=True, truncation=True)
+        return pt_input, tf_input
+
+    def get_audio_inputs(self):
+        processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
+        num_samples = 2
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
+        raw_samples = [x["array"] for x in speech_samples]
+        pt_input = processor(raw_samples, return_tensors="pt", padding=True)
+        tf_input = processor(raw_samples, return_tensors="tf", padding=True)
+        return pt_input, tf_input
+
+    def get_image_inputs(self):
+        feature_extractor = AutoFeatureExtractor.from_pretrained(self._local_dir)
+        num_samples = 2
+        ds = load_dataset("cifar10", "plain_text", split="test")[:num_samples]["img"]
+        pt_input = feature_extractor(images=ds, return_tensors="pt")
+        tf_input = feature_extractor(images=ds, return_tensors="tf")
+        return pt_input, tf_input
+
+    def run(self):
+        # Fetch remote data
+        # TODO: implement a solution to pull a specific PR/commit, so we can use this CLI to validate pushes.
+        repo = Repository(local_dir=self._local_dir, clone_from=self._model_name)
+        repo.git_pull()  # in case the repo already exists locally, but with an older commit
+
+        # Load models and acquire a basic input for its modality.
+        pt_model = AutoModel.from_pretrained(self._local_dir)
+        main_input_name = pt_model.main_input_name
+        if main_input_name == "input_ids":
+            pt_input, tf_input = self.get_text_inputs()
+        elif main_input_name == "pixel_values":
+            pt_input, tf_input = self.get_image_inputs()
+        elif main_input_name == "input_features":
+            pt_input, tf_input = self.get_audio_inputs()
+        else:
+            raise ValueError(f"Can't detect the model modality (`main_input_name` = {main_input_name})")
+        tf_from_pt_model = TFAutoModel.from_pretrained(self._local_dir, from_pt=True)
+
+        # Extra input requirements, in addition to the input modality
+        if hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder"):
+            decoder_input_ids = np.asarray([[1], [1]], dtype=int) * pt_model.config.decoder_start_token_id
+            pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
+            tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})
+
+        # Confirms that cross loading PT weights into TF worked.
+        pt_last_hidden_state = pt_model(**pt_input).last_hidden_state.detach().numpy()
+        tf_from_pt_last_hidden_state = tf_from_pt_model(**tf_input).last_hidden_state.numpy()
+        crossload_diff = np.max(np.abs(pt_last_hidden_state - tf_from_pt_last_hidden_state))
+        if crossload_diff >= MAX_ERROR:
+            raise ValueError(
+                "The cross-loaded TF model has different last hidden states, something went wrong! (max difference ="
+                f" {crossload_diff})"
+            )
+
+        # Save the weights in a TF format (if they don't exist) and confirms that the results are still good
+        tf_weights_path = os.path.join(self._local_dir, TF_WEIGHTS_NAME)
+        if not os.path.exists(tf_weights_path):
+            tf_from_pt_model.save_weights(tf_weights_path)
+        del tf_from_pt_model, pt_model  # will no longer be used, and may have a large memory footprint
+        tf_model = TFAutoModel.from_pretrained(self._local_dir)
+        tf_last_hidden_state = tf_model(**tf_input).last_hidden_state.numpy()
+        converted_diff = np.max(np.abs(pt_last_hidden_state - tf_last_hidden_state))
+        if converted_diff >= MAX_ERROR:
+            raise ValueError(
+                "The converted TF model has different last hidden states, something went wrong! (max difference ="
+                f" {converted_diff})"
+            )
+
+        if not self._no_pr:
+            # TODO: remove try/except when the upload to PR feature is released
+            # (https://github.com/huggingface/huggingface_hub/pull/884)
+            try:
+                self._logger.warn("Uploading the weights into a new PR...")
+                hub_pr_url = upload_file(
+                    path_or_fileobj=tf_weights_path,
+                    path_in_repo=TF_WEIGHTS_NAME,
+                    repo_id=self._model_name,
+                    create_pr=True,
+                    pr_commit_summary="Add TF weights",
+                    pr_commit_description=(
+                        f"Validated by the `pt_to_tf` CLI. Max crossload hidden state difference={crossload_diff:.3e};"
+                        f" Max converted hidden state difference={converted_diff:.3e}."
+                    ),
+                )
+                self._logger.warn(f"PR open in {hub_pr_url}")
+            except TypeError:
+                self._logger.warn(
+                    f"You can now open a PR in https://huggingface.co/{self._model_name}/discussions, manually"
+                    f" uploading the file in {tf_weights_path}"
+                )

src/transformers/commands/transformers_cli.py

@@ -21,6 +21,7 @@ from .convert import ConvertCommand
 from .download import DownloadCommand
 from .env import EnvironmentCommand
 from .lfs import LfsCommands
+from .pt_to_tf import PTtoTFCommand
 from .run import RunCommand
 from .serving import ServeCommand
 from .user import UserCommands
@@ -40,6 +41,7 @@ def main():
     AddNewModelCommand.register_subcommand(commands_parser)
     AddNewModelLikeCommand.register_subcommand(commands_parser)
     LfsCommands.register_subcommand(commands_parser)
+    PTtoTFCommand.register_subcommand(commands_parser)
 
     # Let's go
     args = parser.parse_args()

tests/utils/test_cli.py

@@ -13,10 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import shutil
 import unittest
 from unittest.mock import patch
 
-from transformers.testing_utils import CaptureStd
+from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test
 
 
 class CLITest(unittest.TestCase):
@@ -30,3 +32,16 @@ class CLITest(unittest.TestCase):
         self.assertIn("Python version", cs.out)
         self.assertIn("Platform", cs.out)
         self.assertIn("Using distributed or parallel set-up in script?", cs.out)
+
+    @is_pt_tf_cross_test
+    @patch(
+        "sys.argv", ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]
+    )
+    def test_cli_pt_to_tf(self):
+        import transformers.commands.transformers_cli
+
+        shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj", ignore_errors=True)  # cleans potential past runs
+        transformers.commands.transformers_cli.main()
+
+        # The original repo has no TF weights -- if they exist, they were created by the CLI
+        self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"))