Revert "Add FlaxWhisperForAudioClassification model" (#23154)
Revert "Add FlaxWhisperForAudioClassification model (#22883)"
This reverts commit c8f2c5c56e.
This commit is contained in:
Sylvain Gugger
@@ -12,6 +12,8 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import inspect
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import tempfile
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@@ -39,7 +41,6 @@ if is_flax_available():
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from transformers import (
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FLAX_MODEL_MAPPING,
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FlaxWhisperForAudioClassification,
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FlaxWhisperForConditionalGeneration,
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FlaxWhisperModel,
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WhisperFeatureExtractor,
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@@ -703,205 +704,3 @@ class FlaxWhisperModelIntegrationTest(unittest.TestCase):
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transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True)
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self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
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class FlaxWhisperEncoderModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=60,
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is_training=True,
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use_labels=True,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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input_channels=1,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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max_source_positions=30,
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num_mel_bins=80,
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num_conv_layers=1,
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suppress_tokens=None,
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begin_suppress_tokens=None,
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classifier_proj_size=4,
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num_labels=2,
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is_encoder_decoder=False,
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is_decoder=False,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.input_channels = input_channels
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.num_mel_bins = num_mel_bins
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self.max_position_embeddings = max_position_embeddings
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self.max_source_positions = max_source_positions
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self.num_conv_layers = num_conv_layers
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self.suppress_tokens = suppress_tokens
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self.begin_suppress_tokens = begin_suppress_tokens
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self.classifier_proj_size = classifier_proj_size
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self.num_labels = num_labels
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self.is_encoder_decoder = is_encoder_decoder
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self.is_decoder = is_decoder
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def get_config(self):
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return WhisperConfig(
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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input_channels=self.input_channels,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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max_source_positions=self.max_source_positions,
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decoder_ffn_dim=self.hidden_size,
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encoder_ffn_dim=self.hidden_size,
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suppress_tokens=self.suppress_tokens,
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begin_suppress_tokens=self.begin_suppress_tokens,
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classifier_proj_size=self.classifier_proj_size,
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num_labels=self.num_labels,
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is_encoder_decoder=self.is_encoder_decoder,
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is_decoder=self.is_decoder,
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)
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def prepare_whisper_encoder_inputs_dict(
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self,
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input_features,
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):
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return {
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"input_features": input_features,
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}
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def prepare_config_and_inputs(self):
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input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length])
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config = self.get_config()
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inputs_dict = self.prepare_whisper_encoder_inputs_dict(
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input_features=input_features,
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)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def get_subsampled_output_lengths(self, input_lengths):
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"""
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Computes the output length of the convolutional layers
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"""
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for i in range(self.num_conv_layers):
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input_lengths = (input_lengths - 1) // 2 + 1
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return input_lengths
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@property
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def encoder_seq_length(self):
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return self.get_subsampled_output_lengths(self.seq_length)
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@require_flax
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class WhisperEncoderModelTest(FlaxModelTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxWhisperForAudioClassification,) if is_flax_available() else ()
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is_encoder_decoder = False
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fx_compatible = False
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test_pruning = False
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test_missing_keys = False
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input_name = "input_features"
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def setUp(self):
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self.model_tester = FlaxWhisperEncoderModelTester(self)
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_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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self.init_shape = (1,) + inputs_dict["input_features"].shape[1:]
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self.all_model_classes = (
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make_partial_class(model_class, input_shape=self.init_shape) for model_class in self.all_model_classes
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)
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self.config_tester = ConfigTester(self, config_class=WhisperConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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# overwrite because of `input_features`
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def test_jit_compilation(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def model_jitted(input_features, **kwargs):
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return model(input_features=input_features, **kwargs)
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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# overwrite because of `input_features`
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.__call__)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_features", "attention_mask", "output_attentions"]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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def test_inputs_embeds(self):
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pass
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# WhisperEncoder has no inputs_embeds and thus the `get_input_embeddings` fn is not implemented
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def test_model_common_attributes(self):
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pass
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# WhisperEncoder cannot resize token embeddings since it has no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# WhisperEncoder does not have any base model
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def test_save_load_to_base(self):
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pass
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# WhisperEncoder does not have any base model
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def test_save_load_from_base(self):
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pass
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# WhisperEncoder does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_from_base_pt(self):
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pass
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# WhisperEncoder does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_to_base_pt(self):
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pass
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# WhisperEncoder does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_bf16_to_base_pt(self):
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pass
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