has_attentions - consistent test skipping logic and tf tests (#17495)
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amyeroberts
@@ -485,123 +485,119 @@ class ModelTesterMixin:
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loss.backward()
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def test_attention_outputs(self):
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if not self.has_attentions:
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pass
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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else:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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if self.is_encoder_decoder:
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correct_outlen = 5
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if chunk_length is not None:
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self.assertListEqual(
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list(attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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# Question Answering model returns start_logits and end_logits
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if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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correct_outlen += 1 # start_logits and end_logits instead of only 1 output
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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if self.is_encoder_decoder:
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correct_outlen = 5
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self.assertEqual(out_len, correct_outlen)
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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# Question Answering model returns start_logits and end_logits
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if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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correct_outlen += 1 # start_logits and end_logits instead of only 1 output
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
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)
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self.assertEqual(out_len, correct_outlen)
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_attention_heads,
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decoder_seq_length,
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encoder_key_length,
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],
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)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
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)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_attention_heads,
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decoder_seq_length,
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encoder_key_length,
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],
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)
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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(self_attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(self_attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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@slow
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def test_torchscript_simple(self):
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