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| # Copyright 2024 The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """PyTorch - Flax general utilities.""" | |
| from pickle import UnpicklingError | |
| import jax | |
| import jax.numpy as jnp | |
| import numpy as np | |
| from flax.serialization import from_bytes | |
| from flax.traverse_util import flatten_dict | |
| from ..utils import logging | |
| logger = logging.get_logger(__name__) | |
| ##################### | |
| # Flax => PyTorch # | |
| ##################### | |
| # from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352 | |
| def load_flax_checkpoint_in_pytorch_model(pt_model, model_file): | |
| try: | |
| with open(model_file, "rb") as flax_state_f: | |
| flax_state = from_bytes(None, flax_state_f.read()) | |
| except UnpicklingError as e: | |
| try: | |
| with open(model_file) as f: | |
| if f.read().startswith("version"): | |
| raise OSError( | |
| "You seem to have cloned a repository without having git-lfs installed. Please" | |
| " install git-lfs and run `git lfs install` followed by `git lfs pull` in the" | |
| " folder you cloned." | |
| ) | |
| else: | |
| raise ValueError from e | |
| except (UnicodeDecodeError, ValueError): | |
| raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ") | |
| return load_flax_weights_in_pytorch_model(pt_model, flax_state) | |
| def load_flax_weights_in_pytorch_model(pt_model, flax_state): | |
| """Load flax checkpoints in a PyTorch model""" | |
| try: | |
| import torch # noqa: F401 | |
| except ImportError: | |
| logger.error( | |
| "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see" | |
| " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation" | |
| " instructions." | |
| ) | |
| raise | |
| # check if we have bf16 weights | |
| is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values() | |
| if any(is_type_bf16): | |
| # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16 | |
| # and bf16 is not fully supported in PT yet. | |
| logger.warning( | |
| "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` " | |
| "before loading those in PyTorch model." | |
| ) | |
| flax_state = jax.tree_util.tree_map( | |
| lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state | |
| ) | |
| pt_model.base_model_prefix = "" | |
| flax_state_dict = flatten_dict(flax_state, sep=".") | |
| pt_model_dict = pt_model.state_dict() | |
| # keep track of unexpected & missing keys | |
| unexpected_keys = [] | |
| missing_keys = set(pt_model_dict.keys()) | |
| for flax_key_tuple, flax_tensor in flax_state_dict.items(): | |
| flax_key_tuple_array = flax_key_tuple.split(".") | |
| if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4: | |
| flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] | |
| flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1)) | |
| elif flax_key_tuple_array[-1] == "kernel": | |
| flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] | |
| flax_tensor = flax_tensor.T | |
| elif flax_key_tuple_array[-1] == "scale": | |
| flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"] | |
| if "time_embedding" not in flax_key_tuple_array: | |
| for i, flax_key_tuple_string in enumerate(flax_key_tuple_array): | |
| flax_key_tuple_array[i] = ( | |
| flax_key_tuple_string.replace("_0", ".0") | |
| .replace("_1", ".1") | |
| .replace("_2", ".2") | |
| .replace("_3", ".3") | |
| .replace("_4", ".4") | |
| .replace("_5", ".5") | |
| .replace("_6", ".6") | |
| .replace("_7", ".7") | |
| .replace("_8", ".8") | |
| .replace("_9", ".9") | |
| ) | |
| flax_key = ".".join(flax_key_tuple_array) | |
| if flax_key in pt_model_dict: | |
| if flax_tensor.shape != pt_model_dict[flax_key].shape: | |
| raise ValueError( | |
| f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected " | |
| f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." | |
| ) | |
| else: | |
| # add weight to pytorch dict | |
| flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor | |
| pt_model_dict[flax_key] = torch.from_numpy(flax_tensor) | |
| # remove from missing keys | |
| missing_keys.remove(flax_key) | |
| else: | |
| # weight is not expected by PyTorch model | |
| unexpected_keys.append(flax_key) | |
| pt_model.load_state_dict(pt_model_dict) | |
| # re-transform missing_keys to list | |
| missing_keys = list(missing_keys) | |
| if len(unexpected_keys) > 0: | |
| logger.warning( | |
| "Some weights of the Flax model were not used when initializing the PyTorch model" | |
| f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing" | |
| f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture" | |
| " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This" | |
| f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect" | |
| " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a" | |
| " FlaxBertForSequenceClassification model)." | |
| ) | |
| if len(missing_keys) > 0: | |
| logger.warning( | |
| f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly" | |
| f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to" | |
| " use it for predictions and inference." | |
| ) | |
| return pt_model | |