# Copyright 2023 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import unittest from parameterized import parameterized from transformers import set_seed from transformers.generation.configuration_utils import ALL_CACHE_IMPLEMENTATIONS from transformers.testing_utils import ( CaptureStderr, cleanup, get_gpu_count, is_torch_available, require_read_token, require_torch, require_torch_accelerator, require_torch_gpu, require_torch_multi_gpu, slow, torch_device, ) from transformers.utils import is_optimum_quanto_available, is_torch_greater_or_equal if is_torch_available(): import torch from transformers import ( AutoModelForCausalLM, AutoTokenizer, Cache, ClvpForCausalLM, DynamicCache, GenerationConfig, LlamaConfig, StaticCache, convert_and_export_with_cache, ) TEST_CACHE_IMPLEMENTATIONS = [ cache_name for cache_name in ALL_CACHE_IMPLEMENTATIONS # TODO (joao): Mamba is not compatible with most models, remove from `ALL_CACHE_IMPLEMENTATIONS`? if cache_name != "mamba" # TODO (joao): offloaded_hybrid == offloaded_hybrid_chunked, deprecate one of them if cache_name != "offloaded_hybrid" ] @require_torch class CacheTest(unittest.TestCase): """Cache tests that don't require loading models""" def test_dynamic_cache_retrocompatibility(self): """Tests that we can convert back and forth between the legacy cache format and DynamicCache""" legacy_cache = () new_cache = DynamicCache() # Creates a new cache with 10 layers in both formats for layer_idx in range(10): new_key = torch.rand((2, 4, 8, 16)) new_value = torch.rand((2, 4, 8, 16)) new_cache.update(new_key, new_value, layer_idx) legacy_cache += ((new_key, new_value),) # Sanity check 1: they must have the same shapes self.assertTrue(len(legacy_cache), len(new_cache)) for layer_idx in range(10): self.assertTrue(len(legacy_cache[layer_idx]), len(legacy_cache[layer_idx])) for key_value_idx in range(2): self.assertTrue( legacy_cache[layer_idx][key_value_idx].shape == new_cache[layer_idx][key_value_idx].shape ) # Sanity check 2: we can get the sequence length in multiple ways with DynamicCache, and they return the # expected value self.assertTrue(legacy_cache[0][0].shape[-2] == new_cache[0][0].shape[-2] == new_cache.get_seq_length() == 8) # Sanity check 3: they must be equal, and both support indexing for layer_idx in range(10): for key_value_idx in range(2): self.assertTrue( torch.allclose(new_cache[layer_idx][key_value_idx], legacy_cache[layer_idx][key_value_idx]) ) # Test 1: We can convert from legacy to new with no changes from_legacy = DynamicCache.from_legacy_cache(legacy_cache) for layer_idx in range(10): for key_value_idx in range(2): self.assertTrue( torch.allclose(from_legacy[layer_idx][key_value_idx], legacy_cache[layer_idx][key_value_idx]) ) # Test 2: We can convert from new to legacy with no changes to_legacy = new_cache.to_legacy_cache() for layer_idx in range(10): for key_value_idx in range(2): self.assertTrue( torch.allclose(to_legacy[layer_idx][key_value_idx], new_cache[layer_idx][key_value_idx]) ) def test_reorder_cache_retrocompatibility(self): """Tests that Cache.reorder_cache is retrocompatible with the legacy code path""" legacy_reorder_fn = ClvpForCausalLM._reorder_cache # An example of a legacy `_reorder_cache` function legacy_cache = () new_cache = DynamicCache() # Creates a new cache with 10 layers in both formats for layer_idx in range(10): new_key = torch.rand((4, 4, 8, 16)) new_value = torch.rand((4, 4, 8, 16)) new_cache.update(new_key, new_value, layer_idx) legacy_cache += ((new_key, new_value),) # Let's create some dummy beam indices. From the shape above, it is equivalent to the case where num_beams=4 # and batch_size=1 beam_idx = torch.randint(low=0, high=4, size=(4,)) legacy_cache_reordered = legacy_reorder_fn(legacy_cache, beam_idx) new_cache.reorder_cache(beam_idx) # Let's check that the results are the same for layer_idx in range(10): for key_value_idx in range(2): self.assertTrue( torch.allclose( new_cache[layer_idx][key_value_idx], legacy_cache_reordered[layer_idx][key_value_idx] ) ) def test_static_cache_mha_mqa_gqa(self): """ Tests that static cache works with multi-head attention (MHA), grouped query attention (GQA), and multi-query attention (MQA) """ def _random_kvs(config): # shape for key and values: (batch_size, num_heads, seq_len, head_dim) random_keys = torch.rand( (1, config.num_key_value_heads, 1, config.hidden_size // config.num_attention_heads), device=torch_device, ) random_values = torch.rand( (1, config.num_key_value_heads, 1, config.hidden_size // config.num_attention_heads), device=torch_device, ) return random_keys, random_values mha_config = LlamaConfig(num_attention_heads=32) mha_static_cache = StaticCache(config=mha_config, max_batch_size=1, max_cache_len=10, device=torch_device) cached_keys, cached_values = mha_static_cache.update( *_random_kvs(mha_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)} ) self.assertTrue(cached_keys.shape == (1, 32, 10, 128)) self.assertTrue(cached_values.shape == (1, 32, 10, 128)) gqa_config = LlamaConfig(num_attention_heads=32, num_key_value_heads=4) gqa_static_cache = StaticCache(config=gqa_config, max_batch_size=1, max_cache_len=10, device=torch_device) cached_keys, cached_values = gqa_static_cache.update( *_random_kvs(gqa_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)} ) self.assertTrue(cached_keys.shape == (1, 4, 10, 128)) self.assertTrue(cached_values.shape == (1, 4, 10, 128)) mqa_config = LlamaConfig(num_attention_heads=32, num_key_value_heads=1) mqa_static_cache = StaticCache(config=mqa_config, max_batch_size=1, max_cache_len=10, device=torch_device) cached_keys, cached_values = mqa_static_cache.update( *_random_kvs(mqa_config), 0, cache_kwargs={"cache_position": torch.arange(1).to(torch_device)} ) self.assertTrue(cached_keys.shape == (1, 1, 10, 128)) self.assertTrue(cached_values.shape == (1, 1, 10, 128)) class CacheIntegrationTest(unittest.TestCase): """Fast cache integration tests that share the same small model""" @classmethod def setUpClass(cls): # Load once and reuse across tests cls.tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct", padding_side="left") cls.model = AutoModelForCausalLM.from_pretrained( "HuggingFaceTB/SmolLM2-135M-Instruct", device_map="auto", torch_dtype=torch.float16 ) cls.model.config.sliding_window = 256 # hack to enable the use of caches with sliding windows def _skip_on_uninstalled_cache_dependencies(self, cache_implementation): """Function to skip tests on missing cache dependencies, given a cache implementation""" if cache_implementation == "quantized" and not is_optimum_quanto_available(): self.skipTest("Quanto is not available") if "offloaded" in cache_implementation: has_accelerator = torch_device is not None and torch_device != "cpu" if not has_accelerator: self.skipTest("Offloaded caches require an accelerator") @parameterized.expand(TEST_CACHE_IMPLEMENTATIONS) def test_cache_batched(self, cache_implementation): """Sanity check: caches' `.update` function expects batched inputs""" self._skip_on_uninstalled_cache_dependencies(cache_implementation) EXPECTED_GENERATION = ["A sequence: 1, 2, 3, 4, 5, 6, 7, 8,", "A sequence: A, B, C, D, E, F, G, H"] inputs = self.tokenizer( ["A sequence: 1, 2, 3, 4, 5", "A sequence: A, B, C"], padding=True, return_tensors="pt" ) inputs = inputs.to(self.model.device) gen_out = self.model.generate( **inputs, do_sample=False, max_new_tokens=10, return_dict_in_generate=True, cache_implementation=cache_implementation, disable_compile=True, ) # Sanity check: a cache was used self.assertIsInstance(gen_out.past_key_values, Cache) # Confirm that the output matches expectations decoded = self.tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) self.assertListEqual(decoded, EXPECTED_GENERATION) @parameterized.expand(TEST_CACHE_IMPLEMENTATIONS) def test_cache_beam_search(self, cache_implementation): """ Sanity check: caches' `reorder_cache` is operational. We can confirm this by looking at the beam indices (an output sequence contains multiple beam indices). """ self._skip_on_uninstalled_cache_dependencies(cache_implementation) if cache_implementation == "offloaded_hybrid_chunked": # TODO (joao, cyril): something is off with `offloaded_hybrid_chunked` aka `OffloadedHybridCache`: the # output sequence (and the corresponding beam scores, if we add `output_scores=True`) are significantly # different from the other caches. self.skipTest("`offloaded_hybrid_chunked` fails this test") EXPECTED_GENERATION = [ "Blue is the color of the sky, and the color of", "Blue is the color of the sky, and the second is", ] inputs = self.tokenizer(["Blue is"], return_tensors="pt").to(self.model.device) gen_out = self.model.generate( **inputs, do_sample=False, max_new_tokens=10, num_beams=2, num_return_sequences=2, cache_implementation=cache_implementation, disable_compile=True, return_dict_in_generate=True, ) # Sanity check: a cache was used self.assertIsInstance(gen_out.past_key_values, Cache) # At least one of the sequences requires multiple beam indices -> `reorder_cache` had to shift things around self.assertTrue(any(len(set(beams_in_sequence)) > 1 for beams_in_sequence in gen_out.beam_indices)) # Confirm that the output matches expectations decoded = self.tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) self.assertListEqual(decoded, EXPECTED_GENERATION) @parameterized.expand(TEST_CACHE_IMPLEMENTATIONS) def test_cache_extra_left_padding(self, cache_implementation): """Tests that adding extra left-padding does not affect the generation with the cache""" self._skip_on_uninstalled_cache_dependencies(cache_implementation) EXPECTED_GENERATION = ["The cat's whiskers are also a sign of anxiety."] inputs = self.tokenizer(["The cat"], padding=True, return_tensors="pt").to(self.model.device) generation_kwargs = { "do_sample": False, "max_new_tokens": 10, "cache_implementation": cache_implementation, "disable_compile": True, } gen_out = self.model.generate(**inputs, **generation_kwargs) decoded = self.tokenizer.batch_decode(gen_out, skip_special_tokens=True) self.assertListEqual(decoded, EXPECTED_GENERATION) # Now with extra left-padding inputs_expanded = self.tokenizer(["The cat"], padding=True, return_tensors="pt", pad_to_multiple_of=32) inputs_expanded = inputs_expanded.to(self.model.device) self.assertTrue(inputs.input_ids.shape[1] < inputs_expanded.input_ids.shape[1]) gen_out = self.model.generate(**inputs_expanded, **generation_kwargs) decoded = self.tokenizer.batch_decode(gen_out, skip_special_tokens=True) self.assertListEqual(decoded, EXPECTED_GENERATION) @require_torch_accelerator class CacheHardIntegrationTest(unittest.TestCase): """Hard cache integration tests that require loading different models""" def setUp(self): # Clears memory before each test. Some tests use large models, which might result in suboptimal torch # re-allocation if we run multiple tests in a row without clearing memory. cleanup(torch_device, gc_collect=True) @classmethod def tearDownClass(cls): # Clears memory after the last test. See `setUp` for more details. cleanup(torch_device, gc_collect=True) @slow def test_dynamic_cache_hard(self): """Hard test for base cache implementation -- minor numerical fluctuations will cause this test to fail""" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B", padding_side="left") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B", device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer(["Here's everything I know about cats. Cats"], return_tensors="pt").to(model.device) set_seed(0) gen_out = model.generate( **inputs, do_sample=True, max_new_tokens=256, return_dict_in_generate=True, output_scores=True ) decoded = tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) # sum of the scores for the generated tokens input_length = inputs.input_ids.shape[1] score_sum = sum( [score[0][gen_out.sequences[0][input_length + idx]] for idx, score in enumerate(gen_out.scores)] ) EXPECTED_GENERATION = ( "Here's everything I know about cats. Cats are mammals, they have four legs, they have a tail, they have " "a face with a nose, eyes, and mouth. They have fur, they have claws, and they have a body that is " "covered in fur. They are carnivores, so they eat meat. They are also very clean animals, they groom " "themselves. They have a lot of different breeds. Some are small, some are large. Some are friendly, " "some are not. They have a lot of different personalities. They can be very independent, or they can be " "very affectionate. They can be very playful, or they can be very lazy. They can be very intelligent, or " "they can be very silly. They have a lot of different behaviors. They can be very curious, or they can " "be very cautious. They can be very vocal, or they can be very quiet. They can be very social, or they " "can be very solitary. They can be very active, or they can be very inactive. They can be very " "affectionate, or they can be very aloof. They can be very playful, or they can be very lazy. They can " "be very intelligent, or they can be very silly. They have a lot of different behaviors. They can be " "very curious, or they can" ) EXPECTED_SCORE_SUM = 11017.4971 self.assertEqual(decoded[0], EXPECTED_GENERATION) self.assertAlmostEqual(score_sum, EXPECTED_SCORE_SUM, places=2) self.assertIsInstance(gen_out.past_key_values, DynamicCache) # sanity check @parameterized.expand([("eager"), ("sdpa")]) @require_torch_gpu @slow def test_static_cache_greedy_decoding_pad_left(self, attn_implementation): """Tests that different cache implementations work well with eager and SDPA inference""" EXPECTED_GENERATION = [ "The best color is the one that is most suitable for the purpose.", "We should not undermind the issues at hand, but instead, we should focus on the things", ] tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B", padding_side="left") model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-4B", torch_dtype=torch.bfloat16, attn_implementation=attn_implementation, device_map="auto", ) inputs = tokenizer( ["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt" ).to(model.device) generation_kwargs = {"do_sample": False, "max_new_tokens": 10, "return_dict_in_generate": True} set_seed(0) gen_out = model.generate(**inputs, **generation_kwargs) decoded = tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) with self.subTest(f"{attn_implementation}, dynamic"): self.assertListEqual(decoded, EXPECTED_GENERATION) self.assertIsInstance(gen_out.past_key_values, DynamicCache) # sanity check set_seed(0) gen_out = model.generate(**inputs, **generation_kwargs, cache_implementation="static", disable_compile=True) decoded = tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) with self.subTest(f"{attn_implementation}, static, eager"): self.assertListEqual(decoded, EXPECTED_GENERATION) self.assertIsInstance(gen_out.past_key_values, StaticCache) # sanity check set_seed(0) gen_out = model.generate(**inputs, **generation_kwargs, cache_implementation="static") decoded = tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True) with self.subTest(f"{attn_implementation}, static, compiled"): self.assertListEqual(decoded, EXPECTED_GENERATION) self.assertIsInstance(gen_out.past_key_values, StaticCache) # sanity check @require_torch_accelerator @slow def test_offloaded_cache_uses_less_memory_than_dynamic_cache(self): """Tests that OffloadedCache uses less memory than the default DynamicCache""" model_name = "microsoft/Phi-3-mini-4k-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16) device = model.device if not is_torch_greater_or_equal("2.7", accept_dev=True) and device.type == "xpu": self.skipTest(reason="This test requires torch >= 2.7 to run on xpu.") input_text = "Fun fact:" inputs = tokenizer(input_text, return_tensors="pt").to(device) common = { "num_beams": 4, "num_beam_groups": 2, "num_return_sequences": 4, "diversity_penalty": 1.0, "max_new_tokens": 20, "early_stopping": True, } original = GenerationConfig(**common) offloaded = GenerationConfig(cache_implementation="offloaded", **common) torch_accelerator_module = None if device.type == "cuda": torch_accelerator_module = torch.cuda elif device.type == "xpu": torch_accelerator_module = torch.xpu torch_accelerator_module.reset_peak_memory_stats(device) model.generate(generation_config=original, **inputs) original_peak_memory = torch_accelerator_module.max_memory_allocated(device) torch_accelerator_module.reset_peak_memory_stats(device) model.generate(generation_config=offloaded, **inputs) offloaded_peak_memory = torch_accelerator_module.max_memory_allocated(device) self.assertTrue(offloaded_peak_memory < original_peak_memory) @require_torch_gpu @slow def test_cache_copy(self): """Tests that we can manually set a cache, copy, and reuse it for generation""" # TODO (joao): test for all cache implementations in `CacheIntegrationTest` after standardizing the # lazy init of cache layers model_name = "microsoft/Phi-3-mini-4k-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype=torch.bfloat16) prompt_cache = StaticCache( config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16 ) INITIAL_PROMPT = "You are a helpful assistant. " inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda") # This is the common prompt cached, we need to run forward without grad to be able to copy with torch.no_grad(): prompt_cache = model(**inputs_initial_prompt, past_key_values=prompt_cache).past_key_values prompts = ["Help me to write a blogpost about travelling.", "What is the capital of France?"] responses = [] for prompt in prompts: new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda") past_key_values = copy.deepcopy(prompt_cache) outputs = model.generate( **new_inputs, past_key_values=past_key_values, max_new_tokens=40, disable_compile=True ) response = tokenizer.batch_decode(outputs)[0] responses.append(response) EXPECTED_DECODED_TEXT = [ "You are a helpful assistant. Help me to write a blogpost about travelling.\n\nTraveling is an " "enriching experience that broadens our horizons and allows us to explore the world beyond our comfort " "zones. Whether it's a short weekend getaway", "You are a helpful assistant. What is the capital of France?\n\n\n## Response:Paris is the capital " "of France.\n\n\n\n\n\n\n<|endoftext|>", ] self.assertEqual(responses, EXPECTED_DECODED_TEXT) @require_torch_multi_gpu def test_data_parallel_dynamic_cache(self): """ Tests that the dynamic cache works with nn.DataParallel. Under the hood, `DynamicCache` is rebuilt from multiple `DynamicCache` in the gather step. """ model_repo = "hf-internal-testing/tiny-random-MistralForCausalLM" model = AutoModelForCausalLM.from_pretrained(model_repo).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_repo) # w/o DP: batch_size = num_gpu # w DP: batch_size = 1 (with num_gpus replicas) num_gpus = get_gpu_count() model_inputs = tokenizer(["foo bar"] * num_gpus, return_tensors="pt").to(model.device) # w/o DP no_parallelism_cache = model(**model_inputs).past_key_values self.assertIsInstance(no_parallelism_cache, DynamicCache) # w DP model = torch.nn.DataParallel(model) parallelism_cache = model(**model_inputs).past_key_values self.assertIsInstance(parallelism_cache, DynamicCache) # Check that the caches are the same for layer_idx in range(len(no_parallelism_cache)): for kv_idx in range(2): # 0 = key, 1 = value torch.testing.assert_close( actual=parallelism_cache[layer_idx][kv_idx], expected=no_parallelism_cache[layer_idx][kv_idx] ) @require_torch_gpu def test_static_cache_no_cuda_graph_skips(self): """ Tests generating with static cache and compilation doesn't skip cuda graphs. Regression test for #36543. (? We set `fullgraph=True`, which according to torch docs means it should raise an exception. Instead, messages are being thrown to stderr?) """ model_repo = "hf-internal-testing/tiny-random-MistralForCausalLM" model = AutoModelForCausalLM.from_pretrained(model_repo).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_repo) inputs = tokenizer(["foo bar"], return_tensors="pt").to(torch_device) # on `main`, prior to #36543, this would send stderr messages about cuda graphs being skipped. with CaptureStderr() as cap: model.generate(**inputs, max_new_tokens=2, cache_implementation="static") self.assertNotIn("cuda", cap.err.lower()) @require_torch_multi_gpu @slow @require_read_token def test_static_cache_multi_gpu(self): """Regression test for #35164: static cache with multi-gpu""" model_id = "google/gemma-2-2b-it" tokenizer = AutoTokenizer.from_pretrained(model_id) device_map = {"model.embed_tokens": 0, "model.norm": 1, "model.rotary_emb": 1, "lm_head": 0} num_hidden_layers = 26 for i in range(num_hidden_layers): device_map[f"model.layers.{i}"] = 0 if i < 13 else 1 model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="bfloat16", device_map=device_map, ) inputs = tokenizer("Today is a beautiful day!", return_tensors="pt").to(0) _ = model(**inputs) _ = model.generate(**inputs, max_new_tokens=2, cache_implementation="hybrid") @require_torch class CacheExportIntegrationTest(unittest.TestCase): """Cache tests that rely on `torch.export()` and model loading""" def test_dynamic_cache_exportability(self): model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM") model = model.eval() tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM") prompt = "What is the best way to debug python script?" inputs = tokenizer(prompt, return_tensors="pt") attention_mask = inputs.attention_mask input_ids = inputs.input_ids past_key_values = DynamicCache() ep = torch.export.export( model, (), { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": True, }, strict=False, ) res = ep.module()( input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values, use_cache=True, ) self.assertTrue(len(res.past_key_values.key_cache) == model.config.num_hidden_layers) self.assertEqual(2 * model.config.num_hidden_layers + 1, len(ep.graph_signature.output_specs)) self.assertEqual( 3, len( [ x for x in ep.graph_signature.input_specs if x.kind == torch.export.graph_signature.InputKind.USER_INPUT ] ), ) past_key_values_eager = DynamicCache() res_eager = model( input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values_eager, use_cache=True, ) self.assertTrue(torch.allclose(res.logits, res_eager.logits)) for k1, k2 in zip(res.past_key_values.key_cache, res_eager.past_key_values.key_cache): self.assertTrue(torch.allclose(k1, k2)) for v1, v2 in zip(res.past_key_values.value_cache, res_eager.past_key_values.value_cache): self.assertTrue(torch.allclose(v1, v2)) def test_static_cache_exportability(self): """ Tests that static cache works with `torch.export()` """ if not is_torch_greater_or_equal("2.3"): self.skipTest(reason="This test requires torch >= 2.3 to run.") set_seed(0) device = "cpu" dtype = "bfloat16" cache_implementation = "static" attn_implementation = "sdpa" # Export and ExecuTorch only works for SdpaAttention batch_size = 1 max_cache_len = 1234 model_id = "hf-internal-testing/tiny-random-LlamaForCausalLM" model = AutoModelForCausalLM.from_pretrained( model_id, device_map=device, torch_dtype=dtype, attn_implementation=attn_implementation, generation_config=GenerationConfig( use_cache=True, cache_implementation=cache_implementation, max_length=max_cache_len, cache_config={ "batch_size": batch_size, "max_cache_len": max_cache_len, "device": device, }, ), ) # Check if cache config is passed through correctly self.assertEqual(model.generation_config.use_cache, True) self.assertEqual(model.generation_config.cache_implementation, cache_implementation) self.assertEqual(model.generation_config.max_length, max_cache_len) self.assertTrue(model.generation_config.cache_config is not None) self.assertEqual(model.generation_config.cache_config.batch_size, batch_size) self.assertEqual(model.generation_config.cache_config.max_cache_len, max_cache_len) exported_program = convert_and_export_with_cache(model) # Check if the exported model is configured with the `StaticCache` correctly n_static_key_caches = n_static_value_caches = 0 for buffer_name, buffer in exported_program.named_buffers(): if buffer_name.startswith("key_cache"): self.assertTrue(buffer.shape[0] == batch_size) self.assertTrue(buffer.shape[2] == max_cache_len) n_static_key_caches = n_static_key_caches + 1 if buffer_name.startswith("value_cache"): self.assertTrue(buffer.shape[0] == batch_size) self.assertTrue(buffer.shape[2] == max_cache_len) n_static_value_caches = n_static_value_caches + 1 self.assertEqual(n_static_key_caches, model.config.num_hidden_layers) self.assertEqual(n_static_value_caches, model.config.num_hidden_layers) # Export with dynamic shapes using Dim.AUTO tokenizer = AutoTokenizer.from_pretrained(model_id) input_ids = tokenizer("Here's everything I know", return_tensors="pt").input_ids dynamic_shapes = {"input_ids": {1: torch.export.Dim.AUTO}, "cache_position": None} exported_program = convert_and_export_with_cache( model, example_input_ids=input_ids, dynamic_shapes=dynamic_shapes, strict=False, ) def test_hybrid_cache_exportability(self): """ Tests that static cache works with `torch.export()` """ if not is_torch_greater_or_equal("2.6"): self.skipTest(reason="This test requires torch >= 2.6 to run.") from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM set_seed(0) model_id = "hf-internal-testing/tiny-random-Gemma3ForCausalLM" model = AutoModelForCausalLM.from_pretrained(model_id) model.eval() self.assertEqual(model.config.use_cache, True) self.assertEqual(model.config.cache_implementation, "hybrid") # Export + HybridCache model.eval() max_batch_size = 1 max_cache_len = 23 exportable_module = TorchExportableModuleForDecoderOnlyLM(model, max_batch_size, max_cache_len) exported_program = exportable_module.export() n_g_key_caches = n_g_value_caches = 0 for buffer_name, buffer in exported_program.named_buffers(): if buffer_name.startswith("key_cache"): self.assertTrue(buffer.shape[0] == max_batch_size) self.assertTrue(buffer.shape[2] == max_cache_len) n_g_key_caches = n_g_key_caches + 1 if buffer_name.startswith("value_cache"): self.assertTrue(buffer.shape[0] == max_batch_size) self.assertTrue(buffer.shape[2] == max_cache_len) n_g_value_caches = n_g_value_caches + 1 self.assertEqual(n_g_key_caches, model.config.num_hidden_layers) self.assertEqual(n_g_value_caches, model.config.num_hidden_layers) # Export with dynamic shapes using Dim.AUTO tokenizer = AutoTokenizer.from_pretrained(model_id) input_ids = tokenizer("Here's everything I know", return_tensors="pt").input_ids dynamic_shapes = {"input_ids": {1: torch.export.Dim.AUTO}, "cache_position": None} exported_program = exportable_module.export( input_ids=input_ids, dynamic_shapes=dynamic_shapes, strict=False, )