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# coding=utf-8
# Copyright 2021 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."""
import os
from pickle import UnpicklingError
import numpy as np
import jax.numpy as jnp
import transformers
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from .utils import logging
logger = logging.get_logger(__name__)
#####################
# PyTorch => Flax #
#####################
def load_pytorch_checkpoint_in_flax_state_dict(flax_model, pytorch_checkpoint_path, allow_missing_keys=False):
"""Load pytorch checkpoints in a flax model"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a PyTorch model in Flax, 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
pt_path = os.path.abspath(pytorch_checkpoint_path)
logger.info(f"Loading PyTorch weights from {pt_path}")
pt_state_dict = torch.load(pt_path, map_location="cpu")
logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")
flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
return flax_state_dict
def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
# convert pytorch tensor to numpy
pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
random_flax_state_dict = flatten_dict(flax_model.params)
flax_state_dict = {}
remove_base_model_prefix = (flax_model.base_model_prefix not in flax_model.params) and (
flax_model.base_model_prefix in set([k.split(".")[0] for k in pt_state_dict.keys()])
)
add_base_model_prefix = (flax_model.base_model_prefix in flax_model.params) and (
flax_model.base_model_prefix not in set([k.split(".")[0] for k in pt_state_dict.keys()])
)
# Need to change some parameters name to match Flax names so that we don't have to fork any layer
for pt_key, pt_tensor in pt_state_dict.items():
pt_tuple_key = tuple(pt_key.split("."))
has_base_model_prefix = pt_tuple_key[0] == flax_model.base_model_prefix
require_base_model_prefix = (flax_model.base_model_prefix,) + pt_tuple_key in random_flax_state_dict
if remove_base_model_prefix and has_base_model_prefix:
pt_tuple_key = pt_tuple_key[1:]
elif add_base_model_prefix and require_base_model_prefix:
pt_tuple_key = (flax_model.base_model_prefix,) + pt_tuple_key
# Correctly rename weight parameters
if pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
elif pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and pt_tuple_key not in random_flax_state_dict:
# conv layer
pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
elif pt_tuple_key[-1] == "weight" and pt_tuple_key not in random_flax_state_dict:
# linear layer
pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
pt_tensor = pt_tensor.T
elif pt_tuple_key[-1] == "gamma":
pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
elif pt_tuple_key[-1] == "beta":
pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key in random_flax_state_dict:
if pt_tensor.shape != random_flax_state_dict[pt_tuple_key].shape:
raise ValueError(
f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
f"{random_flax_state_dict[pt_tuple_key].shape}, but is {pt_tensor.shape}."
)
# also add unexpected weight so that warning is thrown
flax_state_dict[pt_tuple_key] = jnp.asarray(pt_tensor)
return unflatten_dict(flax_state_dict)
#####################
# Flax => PyTorch #
#####################
def load_flax_checkpoint_in_pytorch_model(model, flax_checkpoint_path):
"""Load flax checkpoints in a PyTorch model"""
flax_checkpoint_path = os.path.abspath(flax_checkpoint_path)
logger.info(f"Loading Flax weights from {flax_checkpoint_path}")
# import correct flax class
flax_cls = getattr(transformers, "Flax" + model.__class__.__name__)
# load flax weight dict
with open(flax_checkpoint_path, "rb") as state_f:
try:
flax_state_dict = from_bytes(flax_cls, state_f.read())
except UnpicklingError:
raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")
return load_flax_weights_in_pytorch_model(model, flax_state_dict)
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 a 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
flax_state_dict = flatten_dict(flax_state)
pt_model_dict = pt_model.state_dict()
remove_base_model_prefix = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in set([k.split(".")[0] for k in pt_model_dict.keys()])
)
add_base_model_prefix = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in set([k.split(".")[0] for k in pt_model_dict.keys()])
)
# 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():
has_base_model_prefix = flax_key_tuple[0] == pt_model.base_model_prefix
require_base_model_prefix = ".".join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if remove_base_model_prefix and has_base_model_prefix:
flax_key_tuple = flax_key_tuple[1:]
elif add_base_model_prefix and require_base_model_prefix:
flax_key_tuple = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(flax_key_tuple) not in pt_model_dict:
# conv layer
flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
elif flax_key_tuple[-1] == "kernel" and ".".join(flax_key_tuple) not in pt_model_dict:
# linear layer
flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
flax_tensor = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
flax_key = ".".join(flax_key_tuple)
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 "
f"initializing the PyTorch model {pt_model.__class__.__name__}: {unexpected_keys}\n"
f"- This IS expected if you are initializing {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"
f"- This 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)."
)
else:
logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model "
f"and are newly initialized: {missing_keys}\n"
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
else:
logger.warning(
f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
"If your task is similar to the task the model of the checkpoint was trained on, "
f"you can already use {pt_model.__class__.__name__} for predictions without further training."
)
return pt_model
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