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# coding=utf-8 | |
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. | |
# Copyright (c) 2018, NVIDIA CORPORATION. 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. | |
""" PyTorch - TF 2.0 general utilities.""" | |
import os | |
import re | |
import numpy | |
from .utils import ExplicitEnum, expand_dims, is_numpy_array, is_torch_tensor, logging, reshape, squeeze, tensor_size | |
from .utils import transpose as transpose_func | |
logger = logging.get_logger(__name__) | |
class TransposeType(ExplicitEnum): | |
""" | |
Possible ... | |
""" | |
NO = "no" | |
SIMPLE = "simple" | |
CONV1D = "conv1d" | |
CONV2D = "conv2d" | |
def convert_tf_weight_name_to_pt_weight_name( | |
tf_name, start_prefix_to_remove="", tf_weight_shape=None, name_scope=None | |
): | |
""" | |
Convert a TF 2.0 model variable name in a pytorch model weight name. | |
Conventions for TF2.0 scopes -> PyTorch attribute names conversions: | |
- '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch) | |
- '_._' is replaced by a new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) | |
return tuple with: | |
- pytorch model weight name | |
- transpose: `TransposeType` member indicating whether and how TF2.0 and PyTorch weights matrices should be | |
transposed with regards to each other | |
""" | |
if name_scope is not None: | |
if not tf_name.startswith(name_scope): | |
raise ValueError( | |
f"Weight name {tf_name} does not start with name_scope {name_scope}. This is an internal error " | |
"in Transformers, so (unless you were doing something really evil) please open an issue to report it!" | |
) | |
tf_name = tf_name[len(name_scope) :] | |
tf_name = tf_name.lstrip("/") | |
tf_name = tf_name.replace(":0", "") # device ids | |
tf_name = re.sub( | |
r"/[^/]*___([^/]*)/", r"/\1/", tf_name | |
) # '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch) | |
tf_name = tf_name.replace( | |
"_._", "/" | |
) # '_._' is replaced by a level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList) | |
tf_name = re.sub(r"//+", "/", tf_name) # Remove empty levels at the end | |
tf_name = tf_name.split("/") # Convert from TF2.0 '/' separators to PyTorch '.' separators | |
# Some weights have a single name without "/" such as final_logits_bias in BART | |
if len(tf_name) > 1: | |
tf_name = tf_name[1:] # Remove level zero | |
tf_weight_shape = list(tf_weight_shape) | |
# When should we transpose the weights | |
if tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 4: | |
transpose = TransposeType.CONV2D | |
elif tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 3: | |
transpose = TransposeType.CONV1D | |
elif bool( | |
tf_name[-1] in ["kernel", "pointwise_kernel", "depthwise_kernel"] | |
or "emb_projs" in tf_name | |
or "out_projs" in tf_name | |
): | |
transpose = TransposeType.SIMPLE | |
else: | |
transpose = TransposeType.NO | |
# Convert standard TF2.0 names in PyTorch names | |
if tf_name[-1] == "kernel" or tf_name[-1] == "embeddings" or tf_name[-1] == "gamma": | |
tf_name[-1] = "weight" | |
if tf_name[-1] == "beta": | |
tf_name[-1] = "bias" | |
# The SeparableConv1D TF layer contains two weights that are translated to PyTorch Conv1D here | |
if tf_name[-1] == "pointwise_kernel" or tf_name[-1] == "depthwise_kernel": | |
tf_name[-1] = tf_name[-1].replace("_kernel", ".weight") | |
# Remove prefix if needed | |
tf_name = ".".join(tf_name) | |
if start_prefix_to_remove: | |
tf_name = tf_name.replace(start_prefix_to_remove, "", 1) | |
return tf_name, transpose | |
def apply_transpose(transpose: TransposeType, weight, match_shape=None, pt_to_tf=True): | |
""" | |
Apply a transpose to some weight then tries to reshape the weight to the same shape as a given shape, all in a | |
framework agnostic way. | |
""" | |
if transpose is TransposeType.CONV2D: | |
# Conv2D weight: | |
# PT: (num_out_channel, num_in_channel, kernel[0], kernel[1]) | |
# -> TF: (kernel[0], kernel[1], num_in_channel, num_out_channel) | |
axes = (2, 3, 1, 0) if pt_to_tf else (3, 2, 0, 1) | |
weight = transpose_func(weight, axes=axes) | |
elif transpose is TransposeType.CONV1D: | |
# Conv1D weight: | |
# PT: (num_out_channel, num_in_channel, kernel) | |
# -> TF: (kernel, num_in_channel, num_out_channel) | |
weight = transpose_func(weight, axes=(2, 1, 0)) | |
elif transpose is TransposeType.SIMPLE: | |
weight = transpose_func(weight) | |
if match_shape is None: | |
return weight | |
if len(match_shape) < len(weight.shape): | |
weight = squeeze(weight) | |
elif len(match_shape) > len(weight.shape): | |
weight = expand_dims(weight, axis=0) | |
if list(match_shape) != list(weight.shape): | |
try: | |
weight = reshape(weight, match_shape) | |
except AssertionError as e: | |
e.args += (match_shape, match_shape) | |
raise e | |
return weight | |
##################### | |
# PyTorch => TF 2.0 # | |
##################### | |
def load_pytorch_checkpoint_in_tf2_model( | |
tf_model, | |
pytorch_checkpoint_path, | |
tf_inputs=None, | |
allow_missing_keys=False, | |
output_loading_info=False, | |
_prefix=None, | |
tf_to_pt_weight_rename=None, | |
): | |
"""Load pytorch checkpoints in a TF 2.0 model""" | |
try: | |
import tensorflow as tf # noqa: F401 | |
import torch # noqa: F401 | |
except ImportError: | |
logger.error( | |
"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see " | |
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions." | |
) | |
raise | |
# Treats a single file as a collection of shards with 1 shard. | |
if isinstance(pytorch_checkpoint_path, str): | |
pytorch_checkpoint_path = [pytorch_checkpoint_path] | |
# Loads all shards into a single state dictionary | |
pt_state_dict = {} | |
for path in pytorch_checkpoint_path: | |
pt_path = os.path.abspath(path) | |
logger.info(f"Loading PyTorch weights from {pt_path}") | |
pt_state_dict.update(torch.load(pt_path, map_location="cpu")) | |
logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters") | |
return load_pytorch_weights_in_tf2_model( | |
tf_model, | |
pt_state_dict, | |
tf_inputs=tf_inputs, | |
allow_missing_keys=allow_missing_keys, | |
output_loading_info=output_loading_info, | |
_prefix=_prefix, | |
tf_to_pt_weight_rename=tf_to_pt_weight_rename, | |
) | |
def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False): | |
"""Load pytorch checkpoints in a TF 2.0 model""" | |
pt_state_dict = pt_model.state_dict() | |
return load_pytorch_weights_in_tf2_model( | |
tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys | |
) | |
def load_pytorch_weights_in_tf2_model( | |
tf_model, | |
pt_state_dict, | |
tf_inputs=None, | |
allow_missing_keys=False, | |
output_loading_info=False, | |
_prefix=None, | |
tf_to_pt_weight_rename=None, | |
): | |
"""Load pytorch state_dict in a TF 2.0 model.""" | |
try: | |
import tensorflow as tf # noqa: F401 | |
import torch # noqa: F401 | |
except ImportError: | |
logger.error( | |
"Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see " | |
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions." | |
) | |
raise | |
pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()} | |
return load_pytorch_state_dict_in_tf2_model( | |
tf_model, | |
pt_state_dict, | |
tf_inputs=tf_inputs, | |
allow_missing_keys=allow_missing_keys, | |
output_loading_info=output_loading_info, | |
_prefix=_prefix, | |
tf_to_pt_weight_rename=tf_to_pt_weight_rename, | |
) | |
def load_pytorch_state_dict_in_tf2_model( | |
tf_model, | |
pt_state_dict, | |
tf_inputs=None, | |
allow_missing_keys=False, | |
output_loading_info=False, | |
_prefix=None, | |
tf_to_pt_weight_rename=None, | |
ignore_mismatched_sizes=False, | |
): | |
"""Load a pytorch state_dict in a TF 2.0 model. pt_state_dict can be either an actual dict or a lazy-loading | |
safetensors archive created with the safe_open() function.""" | |
import tensorflow as tf | |
from keras import backend as K | |
if tf_inputs is None: | |
tf_inputs = tf_model.dummy_inputs | |
if _prefix is None: | |
_prefix = "" | |
if tf_inputs: | |
with tf.name_scope(_prefix): | |
tf_model(tf_inputs, training=False) # Make sure model is built | |
# Convert old format to new format if needed from a PyTorch state_dict | |
tf_keys_to_pt_keys = {} | |
for key in pt_state_dict.keys(): | |
new_key = None | |
if "gamma" in key: | |
new_key = key.replace("gamma", "weight") | |
if "beta" in key: | |
new_key = key.replace("beta", "bias") | |
if "running_var" in key: | |
new_key = key.replace("running_var", "moving_variance") | |
if "running_mean" in key: | |
new_key = key.replace("running_mean", "moving_mean") | |
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030 | |
key_components = key.split(".") | |
name = None | |
if key_components[-3::2] == ["parametrizations", "original0"]: | |
name = key_components[-2] + "_g" | |
elif key_components[-3::2] == ["parametrizations", "original1"]: | |
name = key_components[-2] + "_v" | |
if name is not None: | |
key_components = key_components[:-3] + [name] | |
new_key = ".".join(key_components) | |
if new_key is None: | |
new_key = key | |
tf_keys_to_pt_keys[new_key] = key | |
# Matt: All TF models store the actual model stem in a MainLayer class, including the base model. | |
# In PT, the derived models (with heads) use the base model class as the stem instead, | |
# and there is no MainLayer class. This means that TF base classes have one | |
# extra layer in their weight names, corresponding to the MainLayer class. This code block compensates for that. | |
start_prefix_to_remove = "" | |
if not any(s.startswith(tf_model.base_model_prefix) for s in tf_keys_to_pt_keys.keys()): | |
start_prefix_to_remove = tf_model.base_model_prefix + "." | |
symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights | |
tf_loaded_numel = 0 | |
all_pytorch_weights = set(tf_keys_to_pt_keys.keys()) | |
missing_keys = [] | |
mismatched_keys = [] | |
is_safetensor_archive = hasattr(pt_state_dict, "get_tensor") | |
for symbolic_weight in symbolic_weights: | |
sw_name = symbolic_weight.name | |
name, transpose = convert_tf_weight_name_to_pt_weight_name( | |
sw_name, | |
start_prefix_to_remove=start_prefix_to_remove, | |
tf_weight_shape=symbolic_weight.shape, | |
name_scope=_prefix, | |
) | |
if tf_to_pt_weight_rename is not None: | |
name = tf_to_pt_weight_rename(name) | |
# Find associated numpy array in pytorch model state dict | |
if name not in tf_keys_to_pt_keys: | |
if allow_missing_keys: | |
missing_keys.append(name) | |
continue | |
elif tf_model._keys_to_ignore_on_load_missing is not None: | |
# authorized missing keys don't have to be loaded | |
if any(re.search(pat, name) is not None for pat in tf_model._keys_to_ignore_on_load_missing): | |
continue | |
raise AttributeError(f"{name} not found in PyTorch model") | |
state_dict_name = tf_keys_to_pt_keys[name] | |
if is_safetensor_archive: | |
array = pt_state_dict.get_tensor(state_dict_name) | |
else: | |
array = pt_state_dict[state_dict_name] | |
try: | |
array = apply_transpose(transpose, array, symbolic_weight.shape) | |
except tf.errors.InvalidArgumentError as e: | |
if not ignore_mismatched_sizes: | |
error_msg = str(e) | |
error_msg += ( | |
"\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method." | |
) | |
raise tf.errors.InvalidArgumentError(error_msg) | |
else: | |
mismatched_keys.append((name, array.shape, symbolic_weight.shape)) | |
continue | |
tf_loaded_numel += tensor_size(array) | |
K.set_value(symbolic_weight, array) | |
del array # Immediately free memory to keep peak usage as low as possible | |
all_pytorch_weights.discard(name) | |
logger.info(f"Loaded {tf_loaded_numel:,} parameters in the TF 2.0 model.") | |
unexpected_keys = list(all_pytorch_weights) | |
if tf_model._keys_to_ignore_on_load_missing is not None: | |
for pat in tf_model._keys_to_ignore_on_load_missing: | |
missing_keys = [k for k in missing_keys if re.search(pat, k) is None] | |
if tf_model._keys_to_ignore_on_load_unexpected is not None: | |
for pat in tf_model._keys_to_ignore_on_load_unexpected: | |
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] | |
if len(unexpected_keys) > 0: | |
logger.warning( | |
"Some weights of the PyTorch model were not used when initializing the TF 2.0 model" | |
f" {tf_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing" | |
f" {tf_model.__class__.__name__} from a PyTorch model trained on another task or with another architecture" | |
" (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n- This IS" | |
f" NOT expected if you are initializing {tf_model.__class__.__name__} from a PyTorch model that you expect" | |
" to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a" | |
" BertForSequenceClassification model)." | |
) | |
else: | |
logger.warning(f"All PyTorch model weights were used when initializing {tf_model.__class__.__name__}.\n") | |
if len(missing_keys) > 0: | |
logger.warning( | |
f"Some weights or buffers of the TF 2.0 model {tf_model.__class__.__name__} were not initialized from the" | |
f" PyTorch model and are newly initialized: {missing_keys}\nYou 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 {tf_model.__class__.__name__} were initialized from the PyTorch model.\n" | |
"If your task is similar to the task the model of the checkpoint was trained on, " | |
f"you can already use {tf_model.__class__.__name__} for predictions without further training." | |
) | |
if len(mismatched_keys) > 0: | |
mismatched_warning = "\n".join( | |
[ | |
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" | |
for key, shape1, shape2 in mismatched_keys | |
] | |
) | |
logger.warning( | |
f"Some weights of {tf_model.__class__.__name__} were not initialized from the model checkpoint" | |
f" are newly initialized because the shapes did not" | |
f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be able" | |
" to use it for predictions and inference." | |
) | |
if output_loading_info: | |
loading_info = { | |
"missing_keys": missing_keys, | |
"unexpected_keys": unexpected_keys, | |
"mismatched_keys": mismatched_keys, | |
} | |
return tf_model, loading_info | |
return tf_model | |
##################### | |
# TF 2.0 => PyTorch # | |
##################### | |
def load_tf2_checkpoint_in_pytorch_model( | |
pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False, output_loading_info=False | |
): | |
""" | |
Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer learning (see | |
https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357). | |
""" | |
try: | |
import tensorflow as tf # noqa: F401 | |
import torch # noqa: F401 | |
except ImportError: | |
logger.error( | |
"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see " | |
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions." | |
) | |
raise | |
import transformers | |
from .modeling_tf_utils import load_tf_weights | |
logger.info(f"Loading TensorFlow weights from {tf_checkpoint_path}") | |
# Instantiate and load the associated TF 2.0 model | |
tf_model_class_name = "TF" + pt_model.__class__.__name__ # Add "TF" at the beginning | |
tf_model_class = getattr(transformers, tf_model_class_name) | |
tf_model = tf_model_class(pt_model.config) | |
if tf_inputs is None: | |
tf_inputs = tf_model.dummy_inputs | |
if tf_inputs is not None: | |
tf_model(tf_inputs, training=False) # Make sure model is built | |
load_tf_weights(tf_model, tf_checkpoint_path) | |
return load_tf2_model_in_pytorch_model( | |
pt_model, tf_model, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info | |
) | |
def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False, output_loading_info=False): | |
"""Load TF 2.0 model in a pytorch model""" | |
weights = tf_model.weights | |
return load_tf2_weights_in_pytorch_model( | |
pt_model, weights, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info | |
) | |
def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False, output_loading_info=False): | |
"""Load TF2.0 symbolic weights in a PyTorch model""" | |
try: | |
import tensorflow as tf # noqa: F401 | |
import torch # noqa: F401 | |
except ImportError: | |
logger.error( | |
"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see " | |
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions." | |
) | |
raise | |
tf_state_dict = {tf_weight.name: tf_weight.numpy() for tf_weight in tf_weights} | |
return load_tf2_state_dict_in_pytorch_model( | |
pt_model, tf_state_dict, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info | |
) | |
def load_tf2_state_dict_in_pytorch_model(pt_model, tf_state_dict, allow_missing_keys=False, output_loading_info=False): | |
import torch | |
new_pt_params_dict = {} | |
current_pt_params_dict = dict(pt_model.named_parameters()) | |
# Make sure we are able to load PyTorch base models as well as derived models (with heads) | |
# TF models always have a prefix, some of PyTorch models (base ones) don't | |
start_prefix_to_remove = "" | |
if not any(s.startswith(pt_model.base_model_prefix) for s in current_pt_params_dict.keys()): | |
start_prefix_to_remove = pt_model.base_model_prefix + "." | |
# Build a map from potential PyTorch weight names to TF 2.0 Variables | |
tf_weights_map = {} | |
for name, tf_weight in tf_state_dict.items(): | |
pt_name, transpose = convert_tf_weight_name_to_pt_weight_name( | |
name, start_prefix_to_remove=start_prefix_to_remove, tf_weight_shape=tf_weight.shape | |
) | |
tf_weights_map[pt_name] = (tf_weight, transpose) | |
all_tf_weights = set(tf_weights_map.keys()) | |
loaded_pt_weights_data_ptr = {} | |
missing_keys_pt = [] | |
for pt_weight_name, pt_weight in current_pt_params_dict.items(): | |
# Handle PyTorch shared weight ()not duplicated in TF 2.0 | |
if pt_weight.data_ptr() in loaded_pt_weights_data_ptr: | |
new_pt_params_dict[pt_weight_name] = loaded_pt_weights_data_ptr[pt_weight.data_ptr()] | |
continue | |
pt_weight_name_to_check = pt_weight_name | |
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030 | |
key_components = pt_weight_name.split(".") | |
name = None | |
if key_components[-3::2] == ["parametrizations", "original0"]: | |
name = key_components[-2] + "_g" | |
elif key_components[-3::2] == ["parametrizations", "original1"]: | |
name = key_components[-2] + "_v" | |
if name is not None: | |
key_components = key_components[:-3] + [name] | |
pt_weight_name_to_check = ".".join(key_components) | |
# Find associated numpy array in pytorch model state dict | |
if pt_weight_name_to_check not in tf_weights_map: | |
if allow_missing_keys: | |
missing_keys_pt.append(pt_weight_name) | |
continue | |
raise AttributeError(f"{pt_weight_name} not found in TF 2.0 model") | |
array, transpose = tf_weights_map[pt_weight_name_to_check] | |
array = apply_transpose(transpose, array, pt_weight.shape, pt_to_tf=False) | |
if numpy.isscalar(array): | |
array = numpy.array(array) | |
if not is_torch_tensor(array) and not is_numpy_array(array): | |
array = array.numpy() | |
if is_numpy_array(array): | |
# Convert to torch tensor | |
array = torch.from_numpy(array) | |
new_pt_params_dict[pt_weight_name] = array | |
loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = array | |
all_tf_weights.discard(pt_weight_name) | |
missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False) | |
missing_keys += missing_keys_pt | |
# Some models may have keys that are not in the state by design, removing them before needlessly warning | |
# the user. | |
if pt_model._keys_to_ignore_on_load_missing is not None: | |
for pat in pt_model._keys_to_ignore_on_load_missing: | |
missing_keys = [k for k in missing_keys if re.search(pat, k) is None] | |
if pt_model._keys_to_ignore_on_load_unexpected is not None: | |
for pat in pt_model._keys_to_ignore_on_load_unexpected: | |
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] | |
if len(unexpected_keys) > 0: | |
logger.warning( | |
"Some weights of the TF 2.0 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 TF 2.0 model trained on another task or with another architecture" | |
" (e.g. initializing a BertForSequenceClassification model from a TFBertForPreTraining model).\n- This IS" | |
f" NOT expected if you are initializing {pt_model.__class__.__name__} from a TF 2.0 model that you expect" | |
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a" | |
" TFBertForSequenceClassification model)." | |
) | |
else: | |
logger.warning(f"All TF 2.0 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 TF 2.0 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." | |
) | |
else: | |
logger.warning( | |
f"All the weights of {pt_model.__class__.__name__} were initialized from the TF 2.0 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." | |
) | |
logger.info(f"Weights or buffers not loaded from TF 2.0 model: {all_tf_weights}") | |
if output_loading_info: | |
loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys} | |
return pt_model, loading_info | |
return pt_model | |