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Add TFViTModel (#13778)

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* Start the work for TFViTModel

* Convert to TF code - need to check in the follow up commits

* Clean up model code

* Expose TFViTModel

* make style

* make quality

* Add test

* make style & quality

* Fix some imports

* fix wrong usage - *kwargs => ** kwargs

* Fix Conv2D weight loading (PT->TF) issue

* Add tests for images with different sizes + fix model

* Fix some common tests for TFViTModel

* Use inputs instead of input_ids in test_compile_tf_model

* Add a comment about transpose and Conv2D in convert_tf_weight_name_to_pt_weight_name

* Avoid transpose in TFViT call

* Fix Conv2D issue in load_tf2_weights_in_pytorch_model

* Use tf.keras.layers.Conv2D instead of tf.nn.conv2d

* Using simpler heuristic to detect Conv2D layer

* Change convert_tf_weight_name_to_pt_weight_name to return TransposeType

* Check tf_weight_shape is not None before using it

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix missing comma

* fix input dtype

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2021-11-09 13:54:37 +01:00
committed by GitHub
parent 6326aa4bf0
commit be4a6c64dc
13 changed files with 1420 additions and 19 deletions
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39
tests/test_modeling_tf_common.py
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@@ -49,6 +49,7 @@ if is_tf_available():
from transformers import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
@@ -126,7 +127,10 @@ class TFModelTesterMixin:
elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
elif model_class in [
*get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
]:
inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
@@ -460,6 +464,8 @@ class TFModelTesterMixin:
pt_inputs_dict[name] = key
elif name == "input_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
elif name == "pixel_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
else:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
@@ -504,6 +510,8 @@ class TFModelTesterMixin:
pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
elif name == "input_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
elif name == "pixel_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
else:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
# need to rename encoder-decoder "inputs" for PyTorch
@@ -605,7 +613,7 @@ class TFModelTesterMixin:
for model_class in self.all_model_classes:
if self.is_encoder_decoder:
input_ids = {
inputs = {
"decoder_input_ids": tf.keras.Input(
batch_shape=(2, max_input),
name="decoder_input_ids",
@@ -613,10 +621,22 @@ class TFModelTesterMixin:
),
"input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
}
# TODO: A better way to handle vision models
elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification"]:
inputs = tf.keras.Input(
batch_shape=(
3,
self.model_tester.num_channels,
self.model_tester.image_size,
self.model_tester.image_size,
),
name="pixel_values",
dtype="float32",
)
elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
input_ids = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
else:
input_ids = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")
inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")
# Prepare our model
model = model_class(config)
@@ -626,14 +646,14 @@ class TFModelTesterMixin:
model.save_pretrained(tmpdirname, saved_model=False)
model = model_class.from_pretrained(tmpdirname)
outputs_dict = model(input_ids)
outputs_dict = model(inputs)
hidden_states = outputs_dict[0]
# Add a dense layer on top to test integration with other keras modules
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
def test_keyword_and_dict_args(self):
@@ -647,6 +667,8 @@ class TFModelTesterMixin:
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_ids = inputs_keywords.pop("input_ids", None)
if input_ids is None:
input_ids = inputs_keywords.pop("pixel_values", None)
outputs_keywords = model(input_ids, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
@@ -1236,7 +1258,8 @@ class TFModelTesterMixin:
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
input_ids = prepared_for_class.pop("input_ids")
input_name = "input_ids" if "input_ids" in prepared_for_class else "pixel_values"
input_ids = prepared_for_class.pop(input_name)
loss = model(input_ids, **prepared_for_class)[0]
self.assertEqual(loss.shape, [loss_size])
@@ -1255,7 +1278,7 @@ class TFModelTesterMixin:
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: "input_ids"}
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key