# Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # 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. """Testing suite for the PyTorch DiffLlama model.""" import gc import tempfile import unittest import pytest from transformers import AutoTokenizer, BitsAndBytesConfig, DiffLlamaConfig, StaticCache, is_torch_available from transformers.testing_utils import ( backend_empty_cache, cleanup, require_bitsandbytes, require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( DiffLlamaForCausalLM, DiffLlamaForQuestionAnswering, DiffLlamaForSequenceClassification, DiffLlamaForTokenClassification, DiffLlamaModel, ) class DiffLlamaModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, pad_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device)) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return DiffLlamaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DiffLlamaModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class DiffLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DiffLlamaModel, DiffLlamaForCausalLM, DiffLlamaForSequenceClassification, DiffLlamaForQuestionAnswering, DiffLlamaForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": DiffLlamaModel, "text-classification": DiffLlamaForSequenceClassification, "text-generation": DiffLlamaForCausalLM, "zero-shot": DiffLlamaForSequenceClassification, "token-classification": DiffLlamaForTokenClassification, } if is_torch_available() else {} ) # Need to use `0.8` instead of `0.9` for `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.7, 0.8] # used in `test_torch_compile_for_training` _torch_compile_train_cls = DiffLlamaForCausalLM if is_torch_available() else None def setUp(self): self.model_tester = DiffLlamaModelTester(self) self.config_tester = ConfigTester(self, config_class=DiffLlamaConfig, hidden_size=32) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_diffllama_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = DiffLlamaForSequenceClassification(config) model.to(torch_device) model.eval() 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)) def test_diffllama_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = DiffLlamaForSequenceClassification(config) model.to(torch_device) model.eval() 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)) def test_diffllama_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = DiffLlamaForSequenceClassification(config) model.to(torch_device) model.eval() 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)) def test_diffllama_token_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels) model = DiffLlamaForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=token_labels) self.assertEqual( result.logits.shape, (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels), ) def test_model_loading_old_rope_configs(self): def _reinitialize_config(base_config, new_kwargs): # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation # steps. base_config_dict = base_config.to_dict() new_config = DiffLlamaConfig.from_dict(config_dict={**base_config_dict, **new_kwargs}) return new_config # from untouched config -> ✅ base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common() original_model = DiffLlamaForCausalLM(base_config).to(torch_device) original_model(**model_inputs) # from a config with the expected rope configuration -> ✅ config = _reinitialize_config(base_config, {"rope_parameters": {"rope_type": "linear", "factor": 10.0}}) original_model = DiffLlamaForCausalLM(config).to(torch_device) original_model(**model_inputs) # from a config with the old rope configuration ('type' instead of 'rope_type') -> ✅ we gracefully handle BC config = _reinitialize_config(base_config, {"rope_parameters": {"type": "linear", "factor": 10.0}}) original_model = DiffLlamaForCausalLM(config).to(torch_device) original_model(**model_inputs) # from a config with both 'type' and 'rope_type' -> ✅ they can coexist (and both are present in the config) config = _reinitialize_config( base_config, {"rope_parameters": {"type": "linear", "rope_type": "linear", "factor": 10.0}} ) self.assertTrue(config.rope_parameters["type"] == "linear") self.assertTrue(config.rope_parameters["rope_type"] == "linear") original_model = DiffLlamaForCausalLM(config).to(torch_device) original_model(**model_inputs) # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: config = _reinitialize_config(base_config, {"rope_parameters": {"rope_type": "linear", "factor": -999.0}}) original_model = DiffLlamaForCausalLM(config).to(torch_device) original_model(**model_inputs) self.assertEqual(len(logs.output), 1) self.assertIn("factor field", logs.output[0]) # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: config = _reinitialize_config( base_config, {"rope_parameters": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}} ) original_model = DiffLlamaForCausalLM(config).to(torch_device) original_model(**model_inputs) self.assertEqual(len(logs.output), 1) self.assertIn("Unrecognized keys", logs.output[0]) # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception with self.assertRaises(KeyError): config = _reinitialize_config( base_config, {"rope_parameters": {"rope_type": "linear"}} ) # missing "factor" @require_flash_attn @require_torch_accelerator @require_bitsandbytes @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): """ Overwriting the common test as the test is flaky on tiny models """ model = DiffLlamaForCausalLM.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map={"": 0}, ) tokenizer = AutoTokenizer.from_pretrained("kajuma/DiffLlama-0.3B-handcut") texts = ["hi", "Hello this is a very long sentence"] tokenizer.padding_side = "right" tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0) output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_native = tokenizer.batch_decode(output_native) model = DiffLlamaForCausalLM.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map={"": 0}, attn_implementation="flash_attention_2", ) output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_fa_2 = tokenizer.batch_decode(output_fa_2) self.assertListEqual(output_native, output_fa_2) @require_flash_attn @require_torch_accelerator @slow @pytest.mark.flash_attn_test def test_use_flash_attention_2_true(self): """ NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended. """ config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) model.save_pretrained(tmp_dir) new_model = DiffLlamaForCausalLM.from_pretrained( tmp_dir, attn_implementation="flash_attention_2", dtype=torch.float16 ).to(torch_device) self.assertTrue(new_model.config._attn_implementation == "flash_attention_2") has_flash = False for name, submodule in new_model.named_modules(): if "FlashAttention" in submodule.__class__.__name__: has_flash = True break if not has_flash: raise ValueError("The flash model should have flash attention layers") @slow def test_eager_matches_sdpa_generate(self): """ Overwriting the common test as the test is flaky on tiny models """ max_new_tokens = 30 tokenizer = AutoTokenizer.from_pretrained("kajuma/DiffLlama-0.3B-handcut") model_sdpa = DiffLlamaForCausalLM.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", dtype=torch.float16, ).to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = DiffLlamaForCausalLM.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", dtype=torch.float16, attn_implementation="eager", ).to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: raise ValueError("The eager model should not have SDPA attention layers") has_sdpa = False for name, submodule in model_sdpa.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: has_sdpa = True break if not has_sdpa: raise ValueError("The SDPA model should have SDPA attention layers") texts = [ "hi here's a longer context, getting longer and", "Hello this is a very long sentence my friend, very long for real", "Today I am in Paris and", ] for padding_side in ["left", "right"]: tokenizer.padding_side = padding_side tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device) res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) with self.subTest(f"{padding_side}"): torch.testing.assert_close( res_eager, res_sdpa, msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}", ) @require_torch_accelerator class DiffLlamaIntegrationTest(unittest.TestCase): def tearDown(self): # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed. cleanup(torch_device, gc_collect=False) @slow @require_torch_accelerator @pytest.mark.torch_compile_test def test_compile_static_cache(self): NUM_TOKENS_TO_GENERATE = 40 # Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test # was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs. EXPECTED_TEXT_COMPLETION = [ "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial " "reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe " "theory of relativ", "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, " "my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", ] prompts = [ "Simply put, the theory of relativity states that ", "My favorite all time favorite condiment is ketchup.", ] tokenizer = AutoTokenizer.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", pad_token="", padding_side="right" ) model = DiffLlamaForCausalLM.from_pretrained( "kajuma/DiffLlama-0.3B-handcut", device_map=torch_device, dtype=torch.float16 ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) # Dynamic Cache generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) # Static Cache generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Static Cache + compile model._cache = None # clear cache object, initialized when we pass `cache_implementation="static"` model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text) @slow @require_torch_accelerator class Mask4DTestHard(unittest.TestCase): def tearDown(self): gc.collect() backend_empty_cache(torch_device) def setUp(self): model_name = "kajuma/DiffLlama-0.3B-handcut" self.model_dtype = torch.float32 self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = DiffLlamaForCausalLM.from_pretrained(model_name, dtype=self.model_dtype).to(torch_device) def get_test_data(self): template = "my favorite {}" items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item batch_separate = [template.format(x) for x in items] # 3 separate lines batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device) input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device) mask_shared_prefix = torch.tensor( [ [ [ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1], ] ] ], device=torch_device, ) position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device) # building custom positions ids based on custom mask position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1) # effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device) # inverting the mask min_dtype = torch.finfo(self.model_dtype).min mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix def test_stacked_causal_mask(self): ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # single forward run with 4D custom mask logits_shared_prefix = self.model.forward( input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix ).logits logits_shared_prefix_last = logits_shared_prefix[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], : ] # last three tokens decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)] self.assertEqual(decoded, decoded_shared_prefix) def test_partial_stacked_causal_mask(self): # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # 2 forward runs with custom 4D masks part_a = 3 # split point input_1a = input_ids_shared_prefix[:, :part_a] position_ids_1a = position_ids_shared_prefix[:, :part_a] mask_1a = mask_shared_prefix[:, :, :part_a, :part_a] outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a) past_key_values_a = outs_1a["past_key_values"] # Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len]) input_1b = input_ids_shared_prefix[:, part_a:] position_ids_1b = position_ids_shared_prefix[:, part_a:] mask_1b = mask_shared_prefix[:, :, part_a:, :] outs_1b = self.model.forward( input_1b, attention_mask=mask_1b, position_ids=position_ids_1b, past_key_values=past_key_values_a, ) decoded_1b = [ self.tokenizer.decode(t) for t in outs_1b.logits.argmax(-1)[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a ] ] self.assertEqual(decoded, decoded_1b) def test_stacked_causal_mask_static_cache(self): """same as above but with StaticCache""" ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # upgrade the model with StaticCache max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1] past_key_values = StaticCache(config=self.model.config, max_cache_len=max_cache_len) padded_attention_mask = torch.nn.functional.pad( input=mask_shared_prefix, pad=(0, max_cache_len - mask_shared_prefix.shape[-1]), mode="constant", value=torch.finfo(self.model_dtype).min, ) # single forward run with 4D custom mask logits_shared_prefix = self.model.forward( input_ids_shared_prefix, attention_mask=padded_attention_mask, position_ids=position_ids_shared_prefix, past_key_values=past_key_values, ).logits logits_shared_prefix_last = logits_shared_prefix[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], : ] # last three tokens decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)] self.assertEqual(decoded, decoded_shared_prefix) def test_partial_stacked_causal_mask_static_cache(self): # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks # we pass a 4D attention mask shaped [..., seq_len, full_static_cache_len]) ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self.get_test_data() # regular batch logits = self.model.forward(input_ids, position_ids=position_ids).logits logits_last = logits[:, -1, :] # last tokens in each batch line decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)] # upgrade the model with StaticCache max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1] past_key_values = StaticCache(config=self.model.config, max_cache_len=max_cache_len) # forward run for the first part of input part_a = 3 # split point input_1a = input_ids_shared_prefix[:, :part_a] position_ids_1a = position_ids_shared_prefix[:, :part_a] mask_1a = mask_shared_prefix[:, :, :part_a, :part_a] padded_mask_1a = torch.nn.functional.pad( input=mask_1a, pad=(0, max_cache_len - mask_1a.shape[-1]), mode="constant", value=torch.finfo(self.model_dtype).min, ) _ = self.model.forward( input_1a, attention_mask=padded_mask_1a, position_ids=position_ids_1a, past_key_values=past_key_values, ) # forward run for the second part of input input_1b = input_ids_shared_prefix[:, part_a:] position_ids_1b = position_ids_shared_prefix[:, part_a:] mask_1b = mask_shared_prefix[:, :, part_a:, :] padded_mask_1b = torch.nn.functional.pad( input=mask_1b, pad=(0, max_cache_len - mask_1b.shape[-1]), mode="constant", value=0 ) outs_1b = self.model.forward( input_1b, attention_mask=padded_mask_1b, position_ids=position_ids_1b, past_key_values=past_key_values, ) decoded_1b = [ self.tokenizer.decode(t) for t in outs_1b.logits.argmax(-1)[ 0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a ] ] self.assertEqual(decoded, decoded_1b)