[Use cache] Align logic of use_cache with output_attentions and output_hidden_states (#5194)

* fix use cache

* add bart use cache

* fix bart

* finish bart

src/transformers/modeling_bart.py

@@ -815,14 +815,19 @@ class BartModel(PretrainedBartModel):
         encoder_outputs: Optional[Tuple] = None,
         decoder_attention_mask=None,
         decoder_cached_states=None,
-        use_cache=False,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):
+
+        if decoder_input_ids is None:
+            use_cache = False
+
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
 
         # make masks if user doesn't supply
         if not use_cache:
@@ -915,7 +920,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         decoder_attention_mask=None,
         decoder_cached_states=None,
         labels=None,
-        use_cache=False,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         **unused,
@@ -968,6 +973,9 @@ class BartForConditionalGeneration(PretrainedBartModel):
             )
             labels = unused.pop("lm_labels")
 
+        if labels is not None:
+            use_cache = False
+
         outputs = self.model(
             input_ids,
             attention_mask=attention_mask,
@@ -1070,6 +1078,7 @@ class BartForSequenceClassification(PretrainedBartModel):
         labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        use_cache=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
@@ -1106,6 +1115,9 @@ class BartForSequenceClassification(PretrainedBartModel):
         loss, logits = outputs[:2]
 
         """
+        if labels is not None:
+            use_cache = False
+
         outputs = self.model(
             input_ids,
             attention_mask=attention_mask,
@@ -1114,6 +1126,7 @@ class BartForSequenceClassification(PretrainedBartModel):
             encoder_outputs=encoder_outputs,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
         )
         x = outputs[0]  # last hidden state
         eos_mask = input_ids.eq(self.config.eos_token_id)
@@ -1159,6 +1172,7 @@ class BartForQuestionAnswering(PretrainedBartModel):
         end_positions=None,
         output_attentions=None,
         output_hidden_states=None,
+        use_cache=None,
     ):
         r"""
         start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
@@ -1206,6 +1220,8 @@ class BartForQuestionAnswering(PretrainedBartModel):
         answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
 
         """
+        if start_positions is not None and end_positions is not None:
+            use_cache = False
 
         outputs = self.model(
             input_ids,
@@ -1215,6 +1231,7 @@ class BartForQuestionAnswering(PretrainedBartModel):
             encoder_outputs=encoder_outputs,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
         )
 
         sequence_output = outputs[0]

src/transformers/modeling_ctrl.py

@@ -335,7 +335,7 @@ class CTRLModel(CTRLPreTrainedModel):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):
@@ -374,6 +374,7 @@ class CTRLModel(CTRLPreTrainedModel):
 
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
@@ -519,7 +520,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         head_mask=None,
         inputs_embeds=None,
         labels=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):

src/transformers/modeling_gpt2.py

@@ -379,7 +379,7 @@ class GPT2Model(GPT2PreTrainedModel):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):
@@ -420,6 +420,7 @@ class GPT2Model(GPT2PreTrainedModel):
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
 
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
@@ -562,7 +563,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         head_mask=None,
         inputs_embeds=None,
         labels=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):
@@ -671,7 +672,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         mc_token_ids=None,
         labels=None,
         mc_labels=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         **kwargs

src/transformers/modeling_t5.py

@@ -659,11 +659,12 @@ class T5Stack(T5PreTrainedModel):
         inputs_embeds=None,
         head_mask=None,
         past_key_value_states=None,
-        use_cache=False,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
     ):
 
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -854,6 +855,7 @@ class T5Model(T5PreTrainedModel):
         self.shared = nn.Embedding(config.vocab_size, config.d_model)
 
         encoder_config = copy.deepcopy(config)
+        encoder_config.use_cache = False
         self.encoder = T5Stack(encoder_config, self.shared)
 
         decoder_config = copy.deepcopy(config)
@@ -893,7 +895,7 @@ class T5Model(T5PreTrainedModel):
         decoder_input_ids=None,
         decoder_attention_mask=None,
         decoder_past_key_value_states=None,
-        use_cache=True,
+        use_cache=None,
         inputs_embeds=None,
         decoder_inputs_embeds=None,
         head_mask=None,
@@ -933,6 +935,7 @@ class T5Model(T5PreTrainedModel):
         last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 
         """
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
 
         # Encode if needed (training, first prediction pass)
         if encoder_outputs is None:
@@ -985,6 +988,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         self.shared = nn.Embedding(config.vocab_size, config.d_model)
 
         encoder_config = copy.deepcopy(config)
+        encoder_config.use_cache = False
         self.encoder = T5Stack(encoder_config, self.shared)
 
         decoder_config = copy.deepcopy(config)
@@ -1021,7 +1025,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         decoder_input_ids=None,
         decoder_attention_mask=None,
         decoder_past_key_value_states=None,
-        use_cache=True,
+        use_cache=None,
         labels=None,
         inputs_embeds=None,
         decoder_inputs_embeds=None,
@@ -1086,6 +1090,8 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
             labels = kwargs.pop("lm_labels")
         assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
 
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
         # Encode if needed (training, first prediction pass)
         if encoder_outputs is None:
             # Convert encoder inputs in embeddings if needed

src/transformers/modeling_tf_ctrl.py

@@ -186,6 +186,7 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
         super().__init__(**kwargs)
         self.output_hidden_states = config.output_hidden_states
         self.output_attentions = config.output_attentions
+        self.use_cache = config.use_cache
 
         self.d_model_size = config.n_embd
         self.num_layers = config.n_layer
@@ -235,7 +236,7 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         training=False,
@@ -270,6 +271,7 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
 
         output_attentions = output_attentions if output_attentions is not None else self.output_attentions
         output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.use_cache
 
         # If using past key value states, only the last tokens
         # should be given as an input

src/transformers/modeling_tf_gpt2.py

@@ -215,6 +215,8 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
         super().__init__(*inputs, **kwargs)
         self.output_attentions = config.output_attentions
         self.output_hidden_states = config.output_hidden_states
+        self.use_cache = config.use_cache
+
         self.num_hidden_layers = config.n_layer
         self.vocab_size = config.vocab_size
         self.n_embd = config.n_embd
@@ -254,10 +256,10 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        use_cache=True,
+        use_cache=None,
-        training=False,
         output_attentions=None,
         output_hidden_states=None,
+        training=False,
     ):
         if isinstance(inputs, (tuple, list)):
             input_ids = inputs[0]
@@ -288,6 +290,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
 
         output_attentions = output_attentions if output_attentions is not None else self.output_attentions
         output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.use_cache
 
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
@@ -622,7 +625,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
         head_mask=None,
         inputs_embeds=None,
         mc_token_ids=None,
-        use_cache=True,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         training=False,

src/transformers/modeling_tf_t5.py

@@ -518,6 +518,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
         super().__init__(**kwargs)
         self.output_hidden_states = config.output_hidden_states
         self.output_attentions = config.output_attentions
+        self.use_cache = config.use_cache
 
         self.embed_tokens = embed_tokens
         self.is_decoder = config.is_decoder
@@ -556,7 +557,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
         inputs_embeds=None,
         head_mask=None,
         past_key_value_states=None,
-        use_cache=False,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         training=False,
@@ -586,6 +587,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
 
         output_attentions = output_attentions if output_attentions is not None else self.output_attentions
         output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.use_cache
 
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both inputs and inputs_embeds at the same time")
@@ -874,6 +876,7 @@ class TFT5Model(TFT5PreTrainedModel):
         embed_tokens = _NoLayerEmbedTokens(self.shared, abs_scope_name=shared_abs_scope_name)
 
         encoder_config = copy.deepcopy(config)
+        encoder_config.use_cache = False
         self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
 
         decoder_config = copy.deepcopy(config)
@@ -952,11 +955,13 @@ class TFT5Model(TFT5PreTrainedModel):
         decoder_attention_mask = kwargs.get("decoder_attention_mask", None)
         decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None)
         decoder_past_key_value_states = kwargs.get("decoder_past_key_value_states", None)
-        use_cache = kwargs.get("use_cache", True)
+        use_cache = kwargs.get("use_cache", None)
         head_mask = kwargs.get("head_mask", None)
         output_attentions = kwargs.get("output_attentions", None)
         output_hidden_states = kwargs.get("output_hidden_states", None)
 
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
         # Encode if needed (training, first prediction pass)
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
@@ -1014,6 +1019,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
         embed_tokens = _NoLayerEmbedTokens(self.shared, abs_scope_name=shared_abs_scope_name)
 
         encoder_config = copy.deepcopy(config)
+        encoder_config.use_cache = False
         self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
 
         decoder_config = copy.deepcopy(config)
@@ -1095,13 +1101,15 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
         encoder_outputs = kwargs.get("encoder_outputs", None)
         decoder_attention_mask = kwargs.get("decoder_attention_mask", None)
         decoder_past_key_value_states = kwargs.get("decoder_past_key_value_states", None)
-        use_cache = kwargs.get("use_cache", True)
+        use_cache = kwargs.get("use_cache", None)
         inputs_embeds = kwargs.get("inputs_embeds", None)
         decoder_inputs_embeds = kwargs.get("decoder_inputs_embeds", None)
         head_mask = kwargs.get("head_mask", None)
         output_attentions = kwargs.get("output_attentions", None)
         output_hidden_states = kwargs.get("output_hidden_states", None)
 
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
         # Encode if needed (training, first prediction pass)
         if encoder_outputs is None:
             # Convert encoder inputs in embeddings if needed

tests/test_modeling_bart.py

@@ -153,6 +153,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
 
     def test_advanced_inputs(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.use_cache = False
         inputs_dict["input_ids"][:, -2:] = config.pad_token_id
         decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_bart_decoder_inputs(
             config, inputs_dict["input_ids"]

tests/test_modeling_gpt2.py

@@ -168,7 +168,14 @@ class GPT2ModelTester:
         model.eval()
 
         # first forward pass
-        output, past = model(input_ids, token_type_ids=token_type_ids)
+        outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
+        outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past = outputs
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

tests/test_modeling_t5.py

@@ -193,7 +193,14 @@ class T5ModelTester:
         model.eval()
 
         # first forward pass
-        output, past_key_value_states = model(input_ids, use_cache=True)
+        outputs = model(input_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids)
+        outputs_no_past = model(input_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past_key_value_states = outputs
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

tests/test_modeling_tf_common.py

@@ -126,6 +126,7 @@ class TFModelTesterMixin:
             if "T5" in main_layer_class.__name__:
                 # Take the same values than in TFT5ModelTester for this shared layer
                 shared = TFSharedEmbeddings(99, 32, name="shared")
+                config.use_cache = False
                 main_layer = main_layer_class(config, embed_tokens=shared)
             else:
                 main_layer = main_layer_class(config)

tests/test_modeling_tf_gpt2.py

@@ -143,7 +143,14 @@ class TFGPT2ModelTester:
         model = TFGPT2Model(config=config)
 
         # first forward pass
-        output, past = model(input_ids, token_type_ids=token_type_ids)
+        outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
+        outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past = outputs
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

tests/test_modeling_tf_t5.py

@@ -135,7 +135,15 @@ class TFT5ModelTester:
         self.batch_size = 1
 
         # first forward pass
-        _, past_key_value_states = model(input_ids, use_cache=True)
+        outputs = model(input_ids, use_cache=True)
+
+        outputs_use_cache_conf = model(input_ids)
+        outputs_no_past = model(input_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past_key_value_states = outputs
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)