Fix inverted conditional in TF common test! (#22540)

* Fix inverted conditional in TF common test!

* Make the same change in the PT tests file

* Make sure hidden states for GPT2 have the same output shape in PT/TF

* Minor fix to PT implementation of token classification loss

* Skip loss equivalence test for TFHubert because it keeps overflowing to inf

* Compute LM loss for TF the (weird) way it's computed in PT

* Skip loss equivalence test for Wav2Vec2 for the same reason as Hubert

* Fix - don't try to access the hidden states property when output is a tuple

src/transformers/models/esm/modeling_esm.py

@@ -1228,16 +1228,7 @@ class EsmForTokenClassification(EsmPreTrainedModel):
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
-            # Only keep active parts of the loss
-            if attention_mask is not None:
-                active_loss = attention_mask.view(-1) == 1
-                active_logits = logits.view(-1, self.num_labels)
-                active_labels = torch.where(
-                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
-                )
-                loss = loss_fct(active_logits, active_labels)
-            else:
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/gpt2/modeling_tf_gpt2.py

@@ -1051,6 +1051,12 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
         )
         hidden_states = transformer_outputs[0]
         hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
+        if return_dict and output_hidden_states:
+            # We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
+            # input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
+            all_hidden_states = transformer_outputs.hidden_states[:-1] + (hidden_states,)
+        else:
+            all_hidden_states = None
         lm_logits = self.transformer.wte(hidden_states, mode="linear")
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training)
         mc_logits = tf.squeeze(mc_logits, axis=-1)
@@ -1062,7 +1068,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
             logits=lm_logits,
             mc_logits=mc_logits,
             past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
+            hidden_states=all_hidden_states,
             attentions=transformer_outputs.attentions,
         )
 

src/transformers/models/xglm/modeling_tf_xglm.py

@@ -953,9 +953,11 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
         loss = None
         if labels is not None:
             # shift labels to the left and cut last logit token
-            shifted_logits = lm_logits[:, :-1]
-            labels = labels[:, 1:]
-            loss = self.hf_compute_loss(labels, shifted_logits)
+            labels = tf.concat(
+                [labels[:, 1:], tf.fill((labels.shape[0], 1), tf.cast(self.config.pad_token_id, labels.dtype))],
+                axis=-1,
+            )
+            loss = self.hf_compute_loss(labels, lm_logits)
 
         if not return_dict:
             output = (lm_logits,) + outputs[1:]

tests/models/hubert/test_modeling_tf_hubert.py

@@ -17,13 +17,15 @@
 import copy
 import inspect
 import math
+import os
+import tempfile
 import unittest
 
 import numpy as np
 import pytest
 
 from transformers import is_tf_available
-from transformers.testing_utils import require_soundfile, require_tf, slow
+from transformers.testing_utils import is_pt_tf_cross_test, require_soundfile, require_tf, slow
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
@@ -333,6 +335,62 @@ class TFHubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCa
         # TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
+        # We override the base test here to skip loss calculation for Hubert models because the loss is massive with
+        # the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
+        import torch
+
+        import transformers
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Output all for aggressive testing
+            config.output_hidden_states = True
+            config.output_attentions = self.has_attentions
+
+            # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
+            # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
+            # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
+            self._make_attention_mask_non_null(inputs_dict)
+
+            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
+            pt_model_class = getattr(transformers, pt_model_class_name)
+
+            tf_model = model_class(config)
+            pt_model = pt_model_class(config)
+
+            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
 
 @require_tf
 class TFHubertRobustModelTest(TFModelTesterMixin, unittest.TestCase):
@@ -458,6 +516,62 @@ class TFHubertRobustModelTest(TFModelTesterMixin, unittest.TestCase):
         # TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
+        # We override the base test here to skip loss calculation for Hubert models because the loss is massive with
+        # the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
+        import torch
+
+        import transformers
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Output all for aggressive testing
+            config.output_hidden_states = True
+            config.output_attentions = self.has_attentions
+
+            # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
+            # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
+            # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
+            self._make_attention_mask_non_null(inputs_dict)
+
+            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
+            pt_model_class = getattr(transformers, pt_model_class_name)
+
+            tf_model = model_class(config)
+            pt_model = pt_model_class(config)
+
+            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
 
 @require_tf
 class TFHubertUtilsTest(unittest.TestCase):

tests/models/wav2vec2/test_modeling_tf_wav2vec2.py

@@ -19,6 +19,8 @@ import glob
 import inspect
 import math
 import multiprocessing
+import os
+import tempfile
 import traceback
 import unittest
 
@@ -31,6 +33,7 @@ from transformers import Wav2Vec2Config, is_tf_available
 from transformers.testing_utils import (
     CaptureLogger,
     is_flaky,
+    is_pt_tf_cross_test,
     require_librosa,
     require_pyctcdecode,
     require_tf,
@@ -397,6 +400,62 @@ class TFWav2Vec2ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.Test
         # TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
+        # We override the base test here to skip loss calculation for Wav2Vec2 models because the loss is massive with
+        # the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
+        import torch
+
+        import transformers
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Output all for aggressive testing
+            config.output_hidden_states = True
+            config.output_attentions = self.has_attentions
+
+            # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
+            # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
+            # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
+            self._make_attention_mask_non_null(inputs_dict)
+
+            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
+            pt_model_class = getattr(transformers, pt_model_class_name)
+
+            tf_model = model_class(config)
+            pt_model = pt_model_class(config)
+
+            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
 
 @require_tf
 class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
@@ -524,6 +583,62 @@ class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
         # TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
+        # We override the base test here to skip loss calculation for Wav2Vec2 models because the loss is massive with
+        # the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
+        import torch
+
+        import transformers
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Output all for aggressive testing
+            config.output_hidden_states = True
+            config.output_attentions = self.has_attentions
+
+            # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
+            # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
+            # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
+            self._make_attention_mask_non_null(inputs_dict)
+
+            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
+            pt_model_class = getattr(transformers, pt_model_class_name)
+
+            tf_model = model_class(config)
+            pt_model = pt_model_class(config)
+
+            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+
+            # Check we can load pt model in tf and vice-versa with model => model functions
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
+            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
+                torch.save(pt_model.state_dict(), pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
+                tf_model.save_weights(tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
+
+            # Original test: check without `labels`
+            self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
+
 
 @require_tf
 class TFWav2Vec2UtilsTest(unittest.TestCase):

tests/test_modeling_common.py

@@ -2030,7 +2030,7 @@ class ModelTesterMixin:
 
             # For some models (e.g. base models), there is no label returned.
             # Set the input dict to `None` to avoid check outputs twice for the same input dicts.
-            if set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()):
+            if not set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()):
                 pt_inputs_dict_with_labels = None
 
             # Check we can load pt model in tf and vice-versa with model => model functions

tests/test_modeling_tf_common.py

@@ -699,7 +699,7 @@ class TFModelTesterMixin:
 
             # For some models (e.g. base models), there is no label returned.
             # Set the input dict to `None` to avoid check outputs twice for the same input dicts.
-            if set(tf_inputs_dict_with_labels.keys()).symmetric_difference(tf_inputs_dict.keys()):
+            if not set(tf_inputs_dict_with_labels.keys()).symmetric_difference(tf_inputs_dict.keys()):
                 tf_inputs_dict_with_labels = None
 
             # Check we can load pt model in tf and vice-versa with model => model functions