Generate: return `past_key_values` (#25086)

src/transformers/generation/utils.py

@@ -104,12 +104,20 @@ class GreedySearchDecoderOnlyOutput(ModelOutput):
         hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
     scores: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -140,6 +148,13 @@ class ContrastiveSearchEncoderDecoderOutput(ModelOutput):
         decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -149,6 +164,7 @@ class ContrastiveSearchEncoderDecoderOutput(ModelOutput):
     decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -169,15 +185,23 @@ class ContrastiveSearchDecoderOnlyOutput(ModelOutput):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
         hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is
-        passed or when `config.output_hidden_states=True`):
-            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
-            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+            passed or when `config.output_hidden_states=True`): Tuple (one element for each generated token) of tuples
+            (one element for each layer of the decoder) of `torch.FloatTensor` of shape `(batch_size, generated_length,
+            hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
     scores: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -211,6 +235,13 @@ class GreedySearchEncoderDecoderOutput(ModelOutput):
         decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -220,6 +251,7 @@ class GreedySearchEncoderDecoderOutput(ModelOutput):
     decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -243,12 +275,20 @@ class SampleDecoderOnlyOutput(ModelOutput):
         hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
     scores: Optional[Tuple[torch.FloatTensor]] = None
     attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -283,6 +323,13 @@ class SampleEncoderDecoderOutput(ModelOutput):
         decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -292,6 +339,7 @@ class SampleEncoderDecoderOutput(ModelOutput):
     decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -319,6 +367,13 @@ class BeamSearchDecoderOnlyOutput(ModelOutput):
         hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -327,6 +382,7 @@ class BeamSearchDecoderOnlyOutput(ModelOutput):
     beam_indices: Optional[torch.LongTensor] = None
     attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -366,6 +422,13 @@ class BeamSearchEncoderDecoderOutput(ModelOutput):
         decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -377,6 +440,7 @@ class BeamSearchEncoderDecoderOutput(ModelOutput):
     decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -404,6 +468,13 @@ class BeamSampleDecoderOnlyOutput(ModelOutput):
         hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -412,6 +483,7 @@ class BeamSampleDecoderOnlyOutput(ModelOutput):
     beam_indices: Optional[torch.LongTensor] = None
     attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 @dataclass
@@ -450,6 +522,13 @@ class BeamSampleEncoderDecoderOutput(ModelOutput):
         decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
             Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
             `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
+        past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            NOTE: some models have a different `past_key_values` format, confirm with the model's documentation.
+            Usually a Tuple (one element for each layer of the decoder) of tuples (two elements, key tensor and value
+            tensor). The first Tuple is of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
     """
 
     sequences: torch.LongTensor = None
@@ -461,6 +540,7 @@ class BeamSampleEncoderDecoderOutput(ModelOutput):
     decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    past_key_values: Optional[Tuple[Tuple[Tuple[torch.FloatTensor]]]] = None
 
 
 GreedySearchOutput = Union[GreedySearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput]
@@ -2148,8 +2228,8 @@ class GenerationMixin:
                         items.append(item.repeat_interleave(1, dim=0))
                     else:
                         items.append(item.repeat_interleave(top_k, dim=0))
-                new_key_values.append(items)
-            model_kwargs["past_key_values"] = new_key_values
+                new_key_values.append(tuple(items))
+            model_kwargs["past_key_values"] = tuple(new_key_values)
 
             if sequential:
                 all_outputs = {key: [] for key in outputs}  # defined in first loop iteration
@@ -2330,6 +2410,17 @@ class GenerationMixin:
             streamer.end()
 
         if return_dict_in_generate:
+            # Contrastive search works by forward looking at the next token, so we need to exclude it from
+            # `past_key_values` to be consistent with the other decoding methods
+            if model_kwargs.get("past_key_values") is not None:
+                past_key_values = []
+                for layer in model_kwargs["past_key_values"]:
+                    layer_past_key_values = []
+                    for item in layer:
+                        layer_past_key_values.append(item[..., :-1, :])
+                    past_key_values.append(tuple(layer_past_key_values))
+                model_kwargs["past_key_values"] = tuple(past_key_values)
+
             if self.config.is_encoder_decoder:
                 return ContrastiveSearchEncoderDecoderOutput(
                     sequences=input_ids,
@@ -2339,6 +2430,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return ContrastiveSearchDecoderOnlyOutput(
@@ -2346,6 +2438,7 @@ class GenerationMixin:
                     scores=scores,
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return input_ids
@@ -2598,6 +2691,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return GreedySearchDecoderOnlyOutput(
@@ -2605,6 +2699,7 @@ class GenerationMixin:
                     scores=scores,
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return input_ids
@@ -2880,6 +2975,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return SampleDecoderOnlyOutput(
@@ -2887,6 +2983,7 @@ class GenerationMixin:
                     scores=scores,
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return input_ids
@@ -3201,6 +3298,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return BeamSearchDecoderOnlyOutput(
@@ -3210,6 +3308,7 @@ class GenerationMixin:
                     beam_indices=sequence_outputs["beam_indices"],
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return sequence_outputs["sequences"]
@@ -3530,6 +3629,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return BeamSampleDecoderOnlyOutput(
@@ -3539,6 +3639,7 @@ class GenerationMixin:
                     beam_indices=sequence_outputs["beam_indices"],
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return sequence_outputs["sequences"]
@@ -3909,6 +4010,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return BeamSearchDecoderOnlyOutput(
@@ -3918,6 +4020,7 @@ class GenerationMixin:
                     beam_indices=sequence_outputs["beam_indices"],
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return sequence_outputs["sequences"]
@@ -4244,6 +4347,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return BeamSearchDecoderOnlyOutput(
@@ -4253,6 +4357,7 @@ class GenerationMixin:
                     beam_indices=sequence_outputs["beam_indices"],
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return sequence_outputs["sequences"]
@@ -4672,6 +4777,7 @@ class GenerationMixin:
                     decoder_attentions=decoder_attentions,
                     cross_attentions=cross_attentions,
                     decoder_hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
             else:
                 return GreedySearchDecoderOnlyOutput(
@@ -4679,6 +4785,7 @@ class GenerationMixin:
                     scores=scores,
                     attentions=decoder_attentions,
                     hidden_states=decoder_hidden_states,
+                    past_key_values=model_kwargs.get("past_key_values"),
                 )
         else:
             return input_ids

tests/generation/test_utils.py

@@ -1829,6 +1829,85 @@ class GenerationTesterMixin:
                 outputs_from_embeds_wo_ids[:, 1:].tolist(),
             )
 
+    def test_generate_continue_from_past_key_values(self):
+        # Tests that we can continue generating from past key values, returned from a previous `generate` call
+        for model_class in self.all_generative_model_classes:
+            # won't fix: old models with unique inputs/caches/others
+            if any(model_name in model_class.__name__.lower() for model_name in ["imagegpt"]):
+                return
+            # may fix in the future: needs modeling or test input preparation fixes for compatibility
+            if any(model_name in model_class.__name__.lower() for model_name in ["umt5"]):
+                return
+
+            config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # If it doesn't support cache, pass the test
+            if not hasattr(config, "use_cache"):
+                return
+
+            # Let's make it always:
+            # 1. use cache (for obvious reasons)
+            # 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
+            #    would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
+            #    continuation would force it to generate beyond an EOS token)
+            # 3. ignore `token_type_ids` for simplicity
+            # 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
+            #    active by default on some models
+            config.use_cache = True
+            if "token_type_ids" in inputs:
+                del inputs["token_type_ids"]
+
+            model = model_class(config).to(torch_device)
+            model.eval()
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
+            model.generation_config.forced_eos_token_id = None
+
+            # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
+            outputs = model(**inputs)
+            if "past_key_values" not in outputs:
+                return
+
+            # Traditional way of generating text, with `return_dict_in_generate` to return the past key values
+            outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
+
+            # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
+            # inputs may need to be tweaked across `generate` calls (like the attention mask).
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
+
+            # Continue from the tokens generated above, preparing the inputs accordingly
+            inputs["past_key_values"] = outputs_cached.past_key_values
+            new_attention_len = outputs_cached.sequences.shape[-1]
+            if config.is_encoder_decoder:
+                inputs["decoder_input_ids"] = outputs_cached.sequences
+                if "decoder_attention_mask" in inputs:
+                    inputs["decoder_attention_mask"] = torch.nn.functional.pad(
+                        inputs["decoder_attention_mask"],
+                        (0, new_attention_len - inputs["decoder_attention_mask"].shape[1]),
+                        mode="constant",
+                        value=1,
+                    )
+            else:
+                inputs["input_ids"] = outputs_cached.sequences
+                if "attention_mask" in inputs:
+                    inputs["attention_mask"] = torch.nn.functional.pad(
+                        inputs["attention_mask"],
+                        (0, new_attention_len - inputs["attention_mask"].shape[1]),
+                        mode="constant",
+                        value=1,
+                    )
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
+
+            # The two sets of generated text and past kv should be equal to each other
+            self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
+            for layer_idx in range(len(outputs_cached.past_key_values)):
+                for kv_idx in range(len(outputs_cached.past_key_values[layer_idx])):
+                    self.assertTrue(
+                        torch.allclose(
+                            outputs.past_key_values[layer_idx][kv_idx],
+                            outputs_cached.past_key_values[layer_idx][kv_idx],
+                        )
+                    )
+
     def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
         batch_size, seq_length = input_ids.shape
         num_sequences_in_output = batch_size * num_return_sequences
@@ -1894,6 +1973,24 @@ class GenerationTesterMixin:
                 use_cache=use_cache,
             )
 
+        # Past Key Value States -- two notes here:
+        # 1. Its inner sequence length is with respect to the inputs of the latest forward pass, hence the "-1"
+        # 2. Some old models still return `output.past_key_values` even without `use_cache=True`
+        # 3. TODO (joao): A few models have different formats, skipping those until the cache refactor is complete
+        models_without_standard_cache = ("bloom", "ctrl", "fsmt", "gptbigcode", "mega", "reformer")
+        has_standard_cache = not any(
+            model_name in config.__class__.__name__.lower() for model_name in models_without_standard_cache
+        )
+        if use_cache and has_standard_cache:
+            past_key_values = output.past_key_values
+            past_sequence_length = output.sequences.shape[-1] - 1
+            self._check_past_key_values_for_generate(
+                num_sequences_in_output,
+                past_key_values,
+                seq_length=past_sequence_length,
+                config=config,
+            )
+
     def _check_scores(self, batch_size, scores, length, config):
         expected_shape = (batch_size, config.vocab_size)
         self.assertIsInstance(scores, tuple)
@@ -1959,6 +2056,30 @@ class GenerationTesterMixin:
             [encoder_expected_shape] * len(hidden_states),
         )
 
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config, num_beam_groups=1):
+        self.assertIsInstance(past_key_values, tuple)
+        self.assertListEqual(
+            [isinstance(iter_past_key_values, tuple) for iter_past_key_values in past_key_values],
+            [True] * len(past_key_values),
+        )
+
+        # (batch, head, seq_length, head_features)
+        expected_shape = (
+            batch_size * num_beam_groups,
+            config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
+            seq_length,
+            config.hidden_size // config.num_attention_heads,
+        )
+        # check shape key, value
+        self.assertListEqual(
+            [layer_past_key_values[0].shape for layer_past_key_values in past_key_values],
+            [expected_shape] * len(past_key_values),
+        )
+        self.assertListEqual(
+            [layer_past_key_values[1].shape for layer_past_key_values in past_key_values],
+            [expected_shape] * len(past_key_values),
+        )
+
     def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
         # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
         # set to same device. we don't care what device.