Cache: models return input cache type (#30716)

src/transformers/models/cohere/modeling_cohere.py

@@ -881,7 +881,9 @@ class CohereModel(CoherePreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         past_seen_tokens = 0
+        return_legacy_cache = False
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
@@ -943,11 +945,10 @@ class CohereModel(CoherePreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(

src/transformers/models/dbrx/modeling_dbrx.py

@@ -1115,7 +1115,9 @@ class DbrxModel(DbrxPreTrainedModel):
 
         inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training)
 
+        return_legacy_cache = False
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
@@ -1182,13 +1184,10 @@ class DbrxModel(DbrxPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, DynamicCache)
-                else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(
                 v

src/transformers/models/gemma/modeling_gemma.py

@@ -865,7 +865,9 @@ class GemmaModel(GemmaPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        return_legacy_cache = False
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
@@ -933,13 +935,10 @@ class GemmaModel(GemmaPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, DynamicCache)
-                else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(

src/transformers/models/llama/modeling_llama.py

@@ -960,7 +960,9 @@ class LlamaModel(LlamaPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        return_legacy_cache = False
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
@@ -1021,13 +1023,10 @@ class LlamaModel(LlamaPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, DynamicCache)
-                else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(

src/transformers/models/olmo/modeling_olmo.py

@@ -938,7 +938,9 @@ class OlmoModel(OlmoPreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        return_legacy_cache = False
         if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
@@ -999,13 +1001,10 @@ class OlmoModel(OlmoPreTrainedModel):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, DynamicCache)
-                else next_decoder_cache
-            )
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(

tests/models/cohere/test_modeling_cohere.py

@@ -16,8 +16,6 @@
 
 import unittest
 
-from parameterized import parameterized
-
 from transformers import CohereConfig, is_torch_available
 from transformers.testing_utils import (
     require_bitsandbytes,
@@ -296,11 +294,6 @@ class CohereModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    @unittest.skip("TODO @gante fix this for Cohere")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
     def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)

tests/models/dbrx/test_modeling_dbrx.py

@@ -17,8 +17,6 @@
 
 import unittest
 
-from parameterized import parameterized
-
 from transformers import DbrxConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
@@ -357,11 +355,6 @@ class DbrxModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
     def test_tied_weights_keys(self):
         pass
 
-    @unittest.skip("TODO @gante fix this for Llama")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
 
 @require_torch
 class DbrxModelIntegrationTest(unittest.TestCase):

tests/models/gemma/test_modeling_gemma.py

@@ -17,7 +17,6 @@ import tempfile
 import unittest
 
 import pytest
-from parameterized import parameterized
 
 from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available
 from transformers.testing_utils import (
@@ -367,11 +366,6 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
         self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
 
-    @unittest.skip("TODO @gante fix this for Llama")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
     @unittest.skip("Gemma buffers include complex numbers, which breaks this test")
     def test_save_load_fast_init_from_base(self):
         pass

tests/models/llama/test_modeling_llama.py

@@ -591,11 +591,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
                     msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
                 )
 
-    @unittest.skip("TODO @gante fix this for Llama")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
 
 @require_torch_gpu
 class LlamaIntegrationTest(unittest.TestCase):

tests/models/olmo/test_modeling_olmo.py

@@ -353,11 +353,6 @@ class OlmoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         # The output should be different for long inputs
         self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
 
-    @unittest.skip("TODO @gante fix this for OLMo")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
 
 @require_torch
 class OlmoIntegrationTest(unittest.TestCase):

tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

@@ -15,8 +15,6 @@
 """ Testing suite for the PyTorch RecurrentGemma model. """
 import unittest
 
-from parameterized import parameterized
-
 from transformers import AutoModelForCausalLM, AutoTokenizer, RecurrentGemmaConfig, is_torch_available, set_seed
 from transformers.testing_utils import (
     require_bitsandbytes,
@@ -330,11 +328,6 @@ class RecurrentGemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
             config_and_inputs[0].position_embedding_type = type
             self.model_tester.create_and_check_model(*config_and_inputs)
 
-    @unittest.skip("Recurrent gemma does not use legacy cache")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
     def test_save_load_fast_init_from_base(self):
         pass