# coding=utf-8 # Copyright 2019 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 glob import json import os import os.path import subprocess import sys import tempfile import textwrap import threading import unittest import unittest.mock as mock import uuid import warnings from pathlib import Path import requests from huggingface_hub import HfApi, HfFolder from parameterized import parameterized from pytest import mark from requests.exceptions import HTTPError from transformers import ( AutoConfig, AutoModel, AutoModelForImageClassification, AutoModelForSequenceClassification, CLIPTextModelWithProjection, DynamicCache, LlavaForConditionalGeneration, MistralForCausalLM, OwlViTForObjectDetection, PretrainedConfig, is_torch_available, logging, ) from transformers.modeling_flash_attention_utils import is_flash_attn_available from transformers.testing_utils import ( TOKEN, CaptureLogger, LoggingLevel, TemporaryHubRepo, TestCasePlus, hub_retry, is_staging_test, require_accelerate, require_flax, require_read_token, require_safetensors, require_tf, require_torch, require_torch_accelerator, require_torch_gpu, require_torch_multi_accelerator, require_usr_bin_time, slow, torch_device, ) from transformers.utils import ( SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ) from transformers.utils.import_utils import ( is_flash_attn_2_available, is_flax_available, is_tf_available, is_torch_npu_available, is_torch_sdpa_available, is_torchdynamo_available, ) sys.path.append(str(Path(__file__).parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402 if is_torch_available(): import torch from safetensors.torch import save_file as safe_save_file from test_module.custom_modeling import CustomModel, NoSuperInitModel from torch import nn from transformers import ( AutoModelForCausalLM, AutoTokenizer, BertConfig, BertModel, CLIPTextModel, GenerationMixin, PreTrainedModel, T5Config, T5ForConditionalGeneration, ) from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, _create_4d_causal_attention_mask, _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask, ) from transformers.modeling_utils import ( _find_disjoint, _find_identical, dtype_byte_size, ) from transformers.pytorch_utils import isin_mps_friendly # Fake pretrained models for tests class BaseModel(PreTrainedModel): base_model_prefix = "base" config_class = PretrainedConfig def __init__(self, config): super().__init__(config) self.linear = nn.Linear(5, 5) self.linear_2 = nn.Linear(5, 5) def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithTiedWeights(PreTrainedModel): config_class = PretrainedConfig def __init__(self, config): super().__init__(config) self.linear = nn.Linear(5, 5) self.linear_2 = nn.Linear(5, 5) def forward(self, x): return self.linear_2(self.linear(x)) def tie_weights(self): self.linear_2.weight = self.linear.weight class ModelWithHead(PreTrainedModel): base_model_prefix = "base" config_class = PretrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.base = BaseModel(config) # linear is a common name between Base and Head on purpose. self.linear = nn.Linear(5, 5) self.linear2 = nn.Linear(5, 5) def forward(self, x): return self.linear2(self.linear(self.base(x))) class ModelWithHeadAndTiedWeights(PreTrainedModel): base_model_prefix = "base" config_class = PretrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.base = BaseModel(config) self.decoder = nn.Linear(5, 5) def forward(self, x): return self.decoder(self.base(x)) def tie_weights(self): self.decoder.weight = self.base.linear.weight class Prepare4dCausalAttentionMaskModel(nn.Module): def forward(self, inputs_embeds): batch_size, seq_length, _ = inputs_embeds.shape past_key_values_length = 4 attention_mask = _prepare_4d_causal_attention_mask( None, (batch_size, seq_length), inputs_embeds, past_key_values_length ) return attention_mask class Create4dCausalAttentionMaskModel(nn.Module): def forward(self, inputs_embeds): batch_size, seq_length, _ = inputs_embeds.shape past_key_values_length = 4 attention_mask = _create_4d_causal_attention_mask( (batch_size, seq_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) return attention_mask class Prepare4dAttentionMaskModel(nn.Module): def forward(self, mask, inputs_embeds): attention_mask = _prepare_4d_attention_mask(mask, dtype=inputs_embeds.dtype) return attention_mask class TestOffline(unittest.TestCase): def test_offline(self): # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported from huggingface_hub import constants from transformers.utils import hub offlfine_env = hub._is_offline_mode hub_cache_env = constants.HF_HUB_CACHE hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE default_cache = constants.default_cache_path transformers_cache = hub.TRANSFORMERS_CACHE try: hub._is_offline_mode = True with tempfile.TemporaryDirectory() as tmpdir: LOG.info("Temporary cache dir %s", tmpdir) constants.HF_HUB_CACHE = tmpdir constants.HUGGINGFACE_HUB_CACHE = tmpdir constants.default_cache_path = tmpdir hub.TRANSFORMERS_CACHE = tmpdir # First offline load should fail try: AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None ) except OSError: LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF) else: self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF)) # Download model -> Huggingface Hub not concerned by our offline mode LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF) hub_api = HfApi() local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir) AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None ) finally: # Tear down: reset env as it was before calling this test hub._is_offline_mode = offlfine_env constants.HF_HUB_CACHE = hub_cache_env constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1 constants.default_cache_path = default_cache hub.TRANSFORMERS_CACHE = transformers_cache def test_local_files_only(self): # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported from huggingface_hub import constants from transformers.utils import hub hub_cache_env = constants.HF_HUB_CACHE hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE default_cache = constants.default_cache_path transformers_cache = hub.TRANSFORMERS_CACHE try: with tempfile.TemporaryDirectory() as tmpdir: LOG.info("Temporary cache dir %s", tmpdir) constants.HF_HUB_CACHE = tmpdir constants.HUGGINGFACE_HUB_CACHE = tmpdir constants.default_cache_path = tmpdir hub.TRANSFORMERS_CACHE = tmpdir try: AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True ) except OSError: LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF) else: self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF)) LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF) hub_api = HfApi() local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir) AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True ) finally: # Tear down: reset env as it was before calling this test constants.HF_HUB_CACHE = hub_cache_env constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1 constants.default_cache_path = default_cache hub.TRANSFORMERS_CACHE = transformers_cache if is_flax_available(): from transformers import FlaxBertModel if is_tf_available(): from transformers import TFBertModel TINY_T5 = "patrickvonplaten/t5-tiny-random" TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification" TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM" TINY_IMAGE_CLASSIF = "hf-internal-testing/tiny-random-SiglipForImageClassification" TINY_LLAVA = "hf-internal-testing/tiny-random-LlavaForConditionalGeneration" LOG = logging.get_logger(__name__) def check_models_equal(model1, model2): models_are_equal = True for model1_p, model2_p in zip(model1.parameters(), model2.parameters()): if model1_p.data.ne(model2_p.data).sum() > 0: models_are_equal = False return models_are_equal @require_torch class ModelUtilsTest(TestCasePlus): def setUp(self): self.old_dtype = torch.get_default_dtype() super().setUp() def tearDown(self): torch.set_default_dtype(self.old_dtype) super().tearDown() def test_hub_retry(self): @hub_retry(max_attempts=2) def test_func(): # First attempt will fail with a connection error if not hasattr(test_func, "attempt"): test_func.attempt = 1 raise requests.exceptions.ConnectionError("Connection failed") # Second attempt will succeed return True self.assertTrue(test_func()) @slow def test_model_from_pretrained(self): model_name = "google-bert/bert-base-uncased" config = BertConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, PretrainedConfig) model = BertModel.from_pretrained(model_name) model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, PreTrainedModel) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 8) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) # Not sure this is the intended behavior. TODO fix Lysandre & Thom config.name_or_path = model_name model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True) self.assertEqual(model.config.output_hidden_states, True) self.assertEqual(model.config, config) def test_model_from_pretrained_subfolder(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder)) with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_manually_shared_disjointed_tensors_optimum(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) # Let's fuse qkv attn = model.encoder.layer[0].attention.self q = attn.query.weight k = attn.key.weight v = attn.value.weight # Force some shared storage qkv = torch.stack([q, k, v], dim=0) attn.query.weight = torch.nn.Parameter(qkv[0]) attn.key.weight = torch.nn.Parameter(qkv[1]) attn.value.weight = torch.nn.Parameter(qkv[2]) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_subfolder_sharded(self): config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") model = BertModel(config) subfolder = "bert" with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB") with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir) model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder) self.assertTrue(check_models_equal(model, model_loaded)) def test_model_from_pretrained_hub_subfolder(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_hub_subfolder_sharded(self): subfolder = "bert" model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder" with self.assertRaises(OSError): _ = BertModel.from_pretrained(model_id) model = BertModel.from_pretrained(model_id, subfolder=subfolder) self.assertIsNotNone(model) def test_model_from_pretrained_with_different_pretrained_model_name(self): model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertIsNotNone(model) logger = logging.get_logger("transformers.configuration_utils") with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: BertModel.from_pretrained(TINY_T5) self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out) @require_accelerate def test_model_from_pretrained_with_none_quantization_config(self): # Needs a device_map for to enter the low_cpu_mem branch. We also load AutoModelForSequenceClassification # deliberately to enter the missing keys branch. model = AutoModelForSequenceClassification.from_pretrained( TINY_MISTRAL, device_map="auto", quantization_config=None ) self.assertIsNotNone(model) def test_model_from_config_torch_dtype(self): # test that the model can be instantiated with dtype of user's choice - as long as it's a # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the # model from the config object. config = T5Config.from_pretrained(TINY_T5) model = AutoModel.from_config(config) # XXX: isn't supported # model = T5ForConditionalGeneration.from_config(config) self.assertEqual(model.dtype, torch.float32) model = AutoModel.from_config(config, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type with self.assertRaises(ValueError): model = AutoModel.from_config(config, torch_dtype=torch.int64) def test_model_from_config_torch_dtype_str(self): # test that from_pretrained works with torch_dtype being strings like "float32" for PyTorch backend model = AutoModel.from_pretrained(TINY_T5, torch_dtype="float32") self.assertEqual(model.dtype, torch.float32) self.assertIsInstance(model.config.torch_dtype, torch.dtype) model = AutoModel.from_pretrained(TINY_T5, torch_dtype="float16") self.assertEqual(model.dtype, torch.float16) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type with self.assertRaises(ValueError): model = AutoModel.from_pretrained(TINY_T5, torch_dtype="int64") def test_model_from_config_torch_dtype_composite(self): """ Test that from_pretrained works with torch_dtype being as a dict per each sub-config in composite config Tiny-Llava has saved auto dtype as `torch.float32` for all modules. """ # Load without dtype specified model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA) self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.float32) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # should be able to set torch_dtype as a simple string and the model loads it correctly model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype="float32") self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.float32) self.assertIsInstance(model.config.torch_dtype, torch.dtype) model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype=torch.float16) self.assertEqual(model.language_model.dtype, torch.float16) self.assertEqual(model.vision_tower.dtype, torch.float16) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # should be able to set torch_dtype as a dict for each sub-config model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, torch_dtype={"text_config": "float32", "vision_config": "float16", "": "bfloat16"} ) self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.float16) self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # should be able to set the values as torch.dtype (not str) model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, torch_dtype={"text_config": torch.float32, "vision_config": torch.float16, "": torch.bfloat16} ) self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.float16) self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # should be able to set the values in configs directly and pass it to `from_pretrained` config = copy.deepcopy(model.config) config.text_config.torch_dtype = torch.float32 config.vision_config.torch_dtype = torch.bfloat16 config.torch_dtype = torch.float16 model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, config=config, torch_dtype="auto") self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.bfloat16) self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.float16) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # but if the model has `_keep_in_fp32_modules` then those modules should be in fp32 no matter what LlavaForConditionalGeneration._keep_in_fp32_modules = ["multi_modal_projector"] model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, config=config, torch_dtype="auto") self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.bfloat16) self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.float32) self.assertIsInstance(model.config.torch_dtype, torch.dtype) # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type with self.assertRaises(ValueError): model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype="int64") model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, torch_dtype={"text_config": "float32", "vision_config": "int64", "": "float16"} ) def test_model_from_pretrained_torch_dtype(self): # test that the model can be instantiated with dtype of either # 1. explicit from_pretrained's torch_dtype argument # 2. via autodiscovery by looking at model weights (torch_dtype="auto") # so if a model.half() was saved, we want it to be instantiated as such. # # test an explicit model class, but also AutoModel separately as the latter goes through a different code path model_path = self.get_auto_remove_tmp_dir() # baseline - we know TINY_T5 is fp32 model model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertEqual(model.dtype, torch.float32) def remove_torch_dtype(model_path): file = f"{model_path}/config.json" with open(file, "r", encoding="utf-8") as f: s = json.load(f) s.pop("torch_dtype") with open(file, "w", encoding="utf-8") as f: json.dump(s, f) # test the default fp32 save_pretrained => from_pretrained cycle model.save_pretrained(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path) self.assertEqual(model.dtype, torch.float32) # 1. test torch_dtype="auto" via `config.torch_dtype` model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # 2. test torch_dtype="auto" via auto-derivation # now remove the torch_dtype entry from config.json and try "auto" again which should # perform auto-derivation from weights remove_torch_dtype(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test forced loading in fp16 (even though the weights are in fp32) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test fp16 save_pretrained, loaded with auto-detection model = model.half() model.save_pretrained(model_path) # 1. test torch_dtype="auto" via `config.torch_dtype` model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.config.torch_dtype, torch.float16) self.assertEqual(model.dtype, torch.float16) # tests `config.torch_dtype` saving with open(f"{model_path}/config.json") as f: config_dict = json.load(f) self.assertEqual(config_dict["torch_dtype"], "float16") # 2. test torch_dtype="auto" via auto-derivation # now same with using config info remove_torch_dtype(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float16) # 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration model = AutoModel.from_pretrained(model_path, torch_dtype="auto") self.assertEqual(model.dtype, torch.float16) # test fp16 save_pretrained, loaded with the explicit fp16 model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test AutoModel separately as it goes through a different path # test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto") # test that the config object didn't get polluted with torch_dtype="auto" # there was a bug that after this call we ended up with config.torch_dtype=="auto" self.assertNotEqual(model.config.torch_dtype, "auto") # now test the outcome self.assertEqual(model.dtype, torch.float32) model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # test model whose first param is not of a floating type, but int model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto") self.assertEqual(model.dtype, torch.float32) # test model that init the model with _from_config model = CLIPTextModelWithProjection.from_pretrained( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", subfolder="text_encoder", torch_dtype=torch.bfloat16, ) self.assertEqual(model.dtype, torch.bfloat16) def test_model_from_pretrained_attn_implementation(self): # test that the model can be instantiated with attn_implementation of either # 1. explicit from_pretrained's attn_implementation argument # 2. explicit from_pretrained's attn_implementation argument with a config argument attn_implementation_available = ["eager"] if is_torch_sdpa_available(): attn_implementation_available.append("sdpa") if is_flash_attn_available(): attn_implementation_available.append("flash_attention_2") for requested_attn_implementation in attn_implementation_available: model = AutoModelForCausalLM.from_pretrained( TINY_MISTRAL, attn_implementation=requested_attn_implementation ) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) config = AutoConfig.from_pretrained(TINY_MISTRAL) model = AutoModelForCausalLM.from_pretrained( TINY_MISTRAL, config=config, attn_implementation=requested_attn_implementation ) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) def test_model_from_config_attn_implementation(self): # test that the model can be instantiated with attn_implementation of either # 1. config created with explicit attn_implementatation and from_config # 2. explicit from_config's attn_implementation argument with a config argument # 3. config created with explicit attn_implementatation and from_config overriding with explicit attn_implementation argument attn_implementation_available = ["eager"] if is_torch_sdpa_available(): attn_implementation_available.append("sdpa") if is_flash_attn_available(): attn_implementation_available.append("flash_attention_2") for requested_attn_implementation in attn_implementation_available: config = AutoConfig.from_pretrained(TINY_MISTRAL, attn_implementation=requested_attn_implementation) # Ensure the config was set correctly self.assertEqual(config._attn_implementation, requested_attn_implementation) self.assertEqual(config._attn_implementation_internal, requested_attn_implementation) model = AutoModelForCausalLM.from_config(config) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) config = AutoConfig.from_pretrained(TINY_MISTRAL) # When the config is not set, the default is "eager" self.assertEqual(config._attn_implementation, "eager") self.assertEqual(config._attn_implementation_internal, None) model = AutoModelForCausalLM.from_config(config=config, attn_implementation=requested_attn_implementation) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) # Set a nonsense attn_implementation in the config, which should be overridden by the explicit argument config = AutoConfig.from_pretrained(TINY_MISTRAL, attn_implementation="foo-bar-baz") self.assertEqual(config._attn_implementation, "foo-bar-baz") self.assertEqual(config._attn_implementation_internal, "foo-bar-baz") model = AutoModelForCausalLM.from_config(config=config, attn_implementation=requested_attn_implementation) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) def test_torch_dtype_byte_sizes(self): torch_dtypes_and_bytes = [ (torch.double, 8), (torch.float64, 8), (torch.float, 4), (torch.float32, 4), (torch.half, 2), (torch.float16, 2), (torch.bfloat16, 2), (torch.long, 8), (torch.int64, 8), (torch.int, 4), (torch.int32, 4), (torch.short, 2), (torch.int16, 2), (torch.uint8, 1), (torch.int8, 1), (torch.float8_e4m3fn, 1), (torch.float8_e5m2, 1), (torch.bool, 0.125), ] for torch_dtype, bytes_per_element in torch_dtypes_and_bytes: self.assertEqual(dtype_byte_size(torch_dtype), bytes_per_element) def test_no_super_init_config_and_model(self): config = NoSuperInitConfig(attribute=32) model = NoSuperInitModel(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = NoSuperInitModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_checkpoint_sharding_local_bin(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: # We use the same folder for various sizes to make sure a new save erases the old checkpoint. for max_size in ["50kB", "100kB", "200kB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size, safe_serialization=False) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".bin"): shard_file = os.path.join(tmp_dir, shard) shard_to_size[shard_file] = os.path.getsize(shard_file) index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME) # Check there is an index but no regular weight file self.assertTrue(os.path.isfile(index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) # Check a file is bigger than max_size only when it has a single weight for shard_file, size in shard_to_size.items(): max_size_int = int(max_size[:-2]) * 10**3 # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than # the size asked for (since we count parameters) if size >= max_size_int + 50000: state_dict = torch.load(shard_file, weights_only=True) self.assertEqual(len(state_dict), 1) # Check the index and the shard files found match with open(index_file, "r", encoding="utf-8") as f: index = json.loads(f.read()) all_shards = set(index["weight_map"].values()) shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".bin")} self.assertSetEqual(all_shards, shards_found) # Finally, check the model can be reloaded new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_sharding_from_hub(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # the model above is the same as the model below, just a sharded version. ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.parameters(), ref_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_variant_local_bin(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, variant="v2", safe_serialization=False) weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) weights_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained(tmp_dir) new_model = BertModel.from_pretrained(tmp_dir, variant="v2") for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_variant_local_sharded_bin(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=False) weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"]) weights_index_file = os.path.join(tmp_dir, weights_index_name) self.assertTrue(os.path.isfile(weights_index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME))) for i in range(1, 5): weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["bin"]) weights_name_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_name_file)) with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained(tmp_dir) new_model = BertModel.from_pretrained(tmp_dir, variant="v2") for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) @require_safetensors def test_checkpoint_variant_local_safe(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True) weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"]) weights_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained(tmp_dir) new_model = BertModel.from_pretrained(tmp_dir, variant="v2") for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) @require_safetensors def test_checkpoint_variant_local_sharded_safe(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True) weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"]) weights_index_file = os.path.join(tmp_dir, weights_index_name) self.assertTrue(os.path.isfile(weights_index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))) for i in range(1, 5): weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["safetensors"]) weights_name_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_name_file)) with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained(tmp_dir) new_model = BertModel.from_pretrained(tmp_dir, variant="v2") for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_loading_only_safetensors_available(self): # Test that the loading behaviour is as expected when only safetensor checkpoints are available # - We can load the model with use_safetensors=True # - We can load the model without specifying use_safetensors i.e. we search for the available checkpoint, # preferring safetensors # - We cannot load the model with use_safetensors=False model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="50kB", safe_serialization=True) weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["json"]) weights_index_file = os.path.join(tmp_dir, weights_index_name) self.assertTrue(os.path.isfile(weights_index_file)) for i in range(1, 5): weights_name = f"model-0000{i}-of-00005" + ".safetensors" weights_name_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_name_file)) # Setting use_safetensors=False should raise an error as the checkpoint was saved with safetensors=True with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir, use_safetensors=False) # We can load the model with use_safetensors=True new_model = BertModel.from_pretrained(tmp_dir, use_safetensors=True) # We can load the model without specifying use_safetensors new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_loading_only_pytorch_bin_available(self): # Test that the loading behaviour is as expected when only pytorch checkpoints are available # - We can load the model with use_safetensors=False # - We can load the model without specifying use_safetensors i.e. we search for the available checkpoint, # preferring safetensors but falling back to pytorch # - We cannot load the model with use_safetensors=True model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="50kB", safe_serialization=False) weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["json"]) weights_index_file = os.path.join(tmp_dir, weights_index_name) self.assertTrue(os.path.isfile(weights_index_file)) for i in range(1, 5): weights_name = WEIGHTS_NAME.split(".")[0].split("_")[0] + f"_model-0000{i}-of-00005" + ".bin" weights_name_file = os.path.join(tmp_dir, weights_name) self.assertTrue(os.path.isfile(weights_name_file)) # Setting use_safetensors=True should raise an error as the checkpoint was saved with safetensors=False with self.assertRaises(OSError): _ = BertModel.from_pretrained(tmp_dir, use_safetensors=True) # We can load the model with use_safetensors=False new_model = BertModel.from_pretrained(tmp_dir, use_safetensors=False) # We can load the model without specifying use_safetensors new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) def test_checkpoint_variant_hub(self): with tempfile.TemporaryDirectory() as tmp_dir: with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir) model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2" ) self.assertIsNotNone(model) def test_checkpoint_variant_hub_sharded(self): with tempfile.TemporaryDirectory() as tmp_dir: with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir ) model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2" ) self.assertIsNotNone(model) @require_safetensors def test_checkpoint_variant_hub_safe(self): with tempfile.TemporaryDirectory() as tmp_dir: with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir) model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2" ) self.assertIsNotNone(model) @require_safetensors def test_checkpoint_variant_hub_sharded_safe(self): with tempfile.TemporaryDirectory() as tmp_dir: with self.assertRaises(EnvironmentError): _ = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir ) model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2" ) self.assertIsNotNone(model) def test_checkpoint_variant_save_load_bin(self): with tempfile.TemporaryDirectory() as tmp_dir: model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2" ) weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) model.save_pretrained(tmp_dir, variant="v2", safe_serialization=False) # saving will create a variant checkpoint self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name))) model.save_pretrained(tmp_dir, safe_serialization=False) # saving shouldn't delete variant checkpoints weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"]) self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name))) # there should be a normal checkpoint self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) self.assertIsNotNone(model) @require_accelerate @mark.accelerate_tests def test_from_pretrained_low_cpu_mem_usage_functional(self): # test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and # sharded models mnames = [ "hf-internal-testing/tiny-random-bert-sharded", "hf-internal-testing/tiny-random-bert", ] for mname in mnames: _ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True) @slow @require_usr_bin_time @require_accelerate @mark.accelerate_tests def test_from_pretrained_low_cpu_mem_usage_equal(self): # Before this would test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default # Now though these should be around the same. # TODO: Look for good bounds to check that their timings are near the same mname = "HuggingFaceTB/SmolLM-135M" preamble = "from transformers import AutoModel" one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)' # Save this output as `max_rss_normal` if testing memory results max_rss_normal = self.python_one_liner_max_rss(one_liner_str) one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)' # Save this output as `max_rss_low_mem` if testing memory results max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str) # Should be within 5MBs of each other (overhead) self.assertAlmostEqual( max_rss_normal / 1024 / 1024, max_rss_low_mem / 1024 / 1024, delta=5, msg="using `low_cpu_mem_usage` should incur the same memory usage in both cases.", ) # if you want to compare things manually, let's first look at the size of the model in bytes # model = AutoModel.from_pretrained(mname, low_cpu_mem_usage=False) # total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()) # total_bytes = total_numel * 4 # Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent. # The easiest way to test this is to switch the model and torch.load to do all the work on # gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add # functionality to load models directly on gpu, this test can be rewritten to use torch's # cuda memory tracking and then we should be able to do a much more precise test. @require_accelerate @mark.accelerate_tests @require_torch_multi_accelerator @slow def test_model_parallelism_gpt2(self): device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1} for i in range(12): device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1 model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", device_map=device_map) tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") inputs = tokenizer("Hello, my name is", return_tensors="pt") output = model.generate(inputs["input_ids"].to(f"{torch_device}:0")) text_output = tokenizer.decode(output[0].tolist()) self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm") @require_accelerate @mark.accelerate_tests @require_torch_accelerator def test_from_pretrained_disk_offload_task_model(self): model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2") device_map = { "transformer.wte": f"{torch_device}:0", "transformer.wpe": f"{torch_device}:0", "transformer.h.0": "cpu", "transformer.h.1": "cpu", "transformer.h.2": "cpu", "transformer.h.3": "disk", "transformer.h.4": "disk", "transformer.ln_f": f"{torch_device}:0", "lm_head": f"{torch_device}:0", } with tempfile.TemporaryDirectory() as tmp_dir: inputs = torch.tensor([[1, 2, 3]]).to(f"{torch_device}:0") model.save_pretrained(tmp_dir) new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(f"{torch_device}:0") outputs1 = new_model.to(f"{torch_device}:0")(inputs) offload_folder = os.path.join(tmp_dir, "offload") new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder ) outputs2 = new_model_with_offload(inputs) torch.testing.assert_close(outputs1.logits.cpu(), outputs2.logits.cpu()) # With state dict temp offload new_model_with_offload = AutoModelForCausalLM.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder, offload_state_dict=True, ) outputs2 = new_model_with_offload(inputs) torch.testing.assert_close(outputs1.logits.cpu(), outputs2.logits.cpu()) @require_accelerate @mark.accelerate_tests @require_torch_accelerator def test_from_pretrained_disk_offload_derived_to_base_model(self): derived_model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2") device_map = { "wte": f"{torch_device}:0", "wpe": f"{torch_device}:0", "h.0": "cpu", "h.1": "cpu", "h.2": "cpu", "h.3": "disk", "h.4": "disk", "ln_f": f"{torch_device}:0", } with tempfile.TemporaryDirectory() as tmp_dir: inputs = torch.tensor([[1, 2, 3]]).to(f"{torch_device}:0") derived_model.save_pretrained(tmp_dir, use_safetensors=True) base_model = AutoModel.from_pretrained(tmp_dir) outputs1 = base_model.to(f"{torch_device}:0")(inputs) # with disk offload offload_folder = os.path.join(tmp_dir, "offload") base_model_with_offload = AutoModel.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder ) outputs2 = base_model_with_offload(inputs) torch.testing.assert_close(outputs1[0].cpu(), outputs2[0].cpu()) # With state dict temp offload new_model_with_offload = AutoModel.from_pretrained( tmp_dir, device_map=device_map, offload_folder=offload_folder, offload_state_dict=True, ) outputs2 = new_model_with_offload(inputs) torch.testing.assert_close(outputs1[0].cpu(), outputs2[0].cpu()) @slow @require_torch def test_from_pretrained_non_contiguous_checkpoint(self): # See: https://github.com/huggingface/transformers/pull/28414 # Tiny models on the Hub have contiguous weights, contrarily to google/owlvit model = OwlViTForObjectDetection.from_pretrained("fxmarty/owlvit-tiny-non-contiguous-weight") self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous()) model = OwlViTForObjectDetection.from_pretrained( "fxmarty/owlvit-tiny-non-contiguous-weight", device_map="auto" ) self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous()) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=False) model.save_pretrained(tmp_dir, safe_serialization=True) def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.Session.request", return_value=response_mock) as mock_head: _ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() @require_accelerate @mark.accelerate_tests def test_save_model_with_device_map_cpu(self): model_id = "hf-internal-testing/tiny-random-gpt2" inputs = torch.tensor([[1, 2, 3]]) with tempfile.TemporaryDirectory() as tmp_dir: model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu") output = model(inputs)[0] model.save_pretrained( tmp_dir, max_shard_size="200KB" ) # model is 1.6MB, max shard size is allocated to cpu by default saved_model = AutoModelForCausalLM.from_pretrained(tmp_dir, device_map="cpu") saved_model_output = saved_model(inputs)[0] torch.testing.assert_close(output, saved_model_output) @require_accelerate @mark.accelerate_tests @require_torch_accelerator def test_save_offloaded_model(self): device_map = { "transformer.wte": f"{torch_device}:0", "transformer.wpe": f"{torch_device}:0", "transformer.h.0": "cpu", "transformer.h.1": "cpu", "transformer.h.2": "cpu", "transformer.h.3": "disk", "transformer.h.4": "disk", "transformer.ln_f": f"{torch_device}:0", "lm_head": f"{torch_device}:0", } # check_models_equal requires onloaded tensors model_id = "hf-internal-testing/tiny-random-gpt2" onloaded_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu").to(f"{torch_device}:0") inputs = torch.tensor([[1, 2, 3]]).to(f"{torch_device}:0") output = onloaded_model(inputs)[0] with tempfile.TemporaryDirectory() as tmp_dir: offload_folder = os.path.join(tmp_dir, "offload") offloaded_model = AutoModelForCausalLM.from_pretrained( model_id, device_map=device_map, offload_folder=offload_folder ) presaved_output = offloaded_model(inputs)[0] offloaded_model.save_pretrained( tmp_dir, max_shard_size="200KB" ) # model is 1.6MB, max shard size is allocated to cpu by default saved_model = AutoModelForCausalLM.from_pretrained(tmp_dir, device_map=device_map) postsaved_output = saved_model(inputs)[0] torch.testing.assert_close(output, presaved_output, rtol=1e-4, atol=1e-4) torch.testing.assert_close(presaved_output, postsaved_output) @require_safetensors def test_use_safetensors(self): # Should not raise anymore AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True) # test that error if only safetensors is available with self.assertRaises(OSError) as env_error: BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False) self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception)) # test that only safetensors if both available and use_safetensors=False with tempfile.TemporaryDirectory() as tmp_dir: CLIPTextModel.from_pretrained( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", subfolder="text_encoder", use_safetensors=False, cache_dir=tmp_dir, ) all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*")) self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files)) self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files)) # test that no safetensors if both available and use_safetensors=True with tempfile.TemporaryDirectory() as tmp_dir: CLIPTextModel.from_pretrained( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", subfolder="text_encoder", use_safetensors=True, cache_dir=tmp_dir, ) all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*")) self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files)) self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files)) # test no model file found when use_safetensors=None (default when safetensors package available) with self.assertRaises(OSError) as missing_model_file_error: BertModel.from_pretrained("hf-internal-testing/config-no-model") self.assertTrue( "does not appear to have a file named pytorch_model.bin, model.safetensors," in str(missing_model_file_error.exception) ) with self.assertRaises(OSError) as missing_model_file_error: with tempfile.TemporaryDirectory() as tmp_dir: with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write("{}") f.close() BertModel.from_pretrained(tmp_dir) self.assertTrue( "Error no file named pytorch_model.bin, model.safetensors" in str(missing_model_file_error.exception) ) @require_safetensors def test_safetensors_save_and_load(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) # No pytorch_model.bin file, only a model.safetensors self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) @require_safetensors def test_safetensors_load_from_hub(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): torch.testing.assert_close(p1, p2) @require_safetensors def test_safetensors_save_and_load_sharded(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB") # No pytorch_model.bin index file, only a model.safetensors index self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME))) self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))) # No regular weights file self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) new_model = BertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.parameters(), new_model.parameters()): torch.testing.assert_close(p1, p2) @require_safetensors def test_safetensors_load_from_hub_sharded(self): safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors") pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") # Check models are equal for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()): torch.testing.assert_close(p1, p2) def test_base_model_to_head_model_load(self): base_model = BaseModel(PretrainedConfig()) with tempfile.TemporaryDirectory() as tmp_dir: base_model.save_pretrained(tmp_dir, safe_serialization=False) # Can load a base model in a model with head model = ModelWithHead.from_pretrained(tmp_dir) for p1, p2 in zip(model.base.parameters(), base_model.parameters()): torch.testing.assert_close(p1, p2) # It doesn't work if the state dict has a mix of keys of the head and base without prefix though. base_state_dict = base_model.state_dict() head_state_dict = model.state_dict() base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"] base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"] safe_save_file(base_state_dict, os.path.join(tmp_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"}) with self.assertRaisesRegex( ValueError, "The state dictionary of the model you are trying to load is corrupted." ): _ = ModelWithHead.from_pretrained(tmp_dir) def test_tied_weights_reload(self): # Base model = BaseModelWithTiedWeights(PretrainedConfig()) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = BaseModelWithTiedWeights.from_pretrained(tmp_dir) self.assertIs(new_model.linear.weight, new_model.linear_2.weight) state_dict = model.state_dict() # Remove tied weight from state_dict -> model should load with no complain of missing keys del state_dict["linear_2.weight"] torch.save(state_dict, os.path.join(tmp_dir, WEIGHTS_NAME)) new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) self.assertListEqual(load_info["missing_keys"], []) self.assertIs(new_model.linear.weight, new_model.linear_2.weight) # With head model.save_pretrained(tmp_dir) new_model, load_info = ModelWithHeadAndTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) self.assertIs(new_model.base.linear.weight, new_model.decoder.weight) # Should only complain about the missing bias self.assertListEqual(load_info["missing_keys"], ["decoder.bias"]) def test_unexpected_keys_warnings(self): model = ModelWithHead(PretrainedConfig()) logger = logging.get_logger("transformers.modeling_utils") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) # Loading the model with a new class, we don't get a warning for unexpected weights, just an info with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: _, loading_info = BaseModel.from_pretrained(tmp_dir, output_loading_info=True) self.assertNotIn("were not used when initializing ModelWithHead", cl.out) self.assertEqual( set(loading_info["unexpected_keys"]), {"linear.weight", "linear.bias", "linear2.weight", "linear2.bias"}, ) # Loading the model with the same class, we do get a warning for unexpected weights state_dict = model.state_dict() state_dict["added_key"] = copy.deepcopy(state_dict["linear.weight"]) safe_save_file(state_dict, os.path.join(tmp_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"}) with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: _, loading_info = ModelWithHead.from_pretrained(tmp_dir, output_loading_info=True) self.assertIn("were not used when initializing ModelWithHead: ['added_key']", cl.out) self.assertEqual(loading_info["unexpected_keys"], ["added_key"]) def test_warn_if_padding_and_no_attention_mask(self): logger = logging.get_logger("transformers.modeling_utils") with self.subTest("Ensure no warnings when pad_token_id is None."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config_no_pad_token = PretrainedConfig() config_no_pad_token.pad_token_id = None model = ModelWithHead(config_no_pad_token) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out) with self.subTest("Ensure no warnings when there is an attention_mask."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]]) attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out) with self.subTest("Ensure no warnings when there are no pad_token_ids in the input_ids."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[1, 345, 232, 328, 740, 140, 1695, 69, 6078, 2341, 25]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out) with self.subTest("Ensure a warning is shown when the input_ids start with a pad_token_id."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out) with self.subTest("Ensure a warning is shown when the input_ids end with a pad_token_id."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[432, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out) with self.subTest("Ensure that the warning is shown at most once."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertEqual(cl.out.count("We strongly recommend passing in an `attention_mask`"), 1) with self.subTest("Ensure a different warning is shown when the pad_token_id is equal to the bos_token_id."): logger.warning_once.cache_clear() with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: config = PretrainedConfig() config.pad_token_id = 0 config.bos_token_id = config.pad_token_id model = ModelWithHead(config) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]]) model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) self.assertIn("You may ignore this warning if your `pad_token_id`", cl.out) if not is_torchdynamo_available(): self.skipTest(reason="torchdynamo is not available") with self.subTest("Ensure that the warning code is skipped when compiling with torchdynamo."): logger.warning_once.cache_clear() from torch._dynamo import config, testing config = PretrainedConfig() config.pad_token_id = 0 model = ModelWithHead(config) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]]) def f(input_ids): model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None) compile_counter = testing.CompileCounter() opt_fn = torch.compile(f, dynamic=True, backend=compile_counter) opt_fn(input_ids) self.assertEqual(compile_counter.frame_count, 0) @require_torch_accelerator @slow def test_pretrained_low_mem_new_config(self): # Checking for 1 model(the same one which was described in the issue) . model_ids = ["openai-community/gpt2"] for model_id in model_ids: model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id) model_config.n_layer = 48 model_config.n_head = 25 model_config.n_embd = 1600 model = AutoModelForCausalLM.from_pretrained( pretrained_model_name_or_path=model_id, config=model_config, ignore_mismatched_sizes=True, torch_dtype=torch.float16, low_cpu_mem_usage=True, ) model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id) self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__) def test_generation_config_is_loaded_with_model(self): # Note: `hf-internal-testing/tiny-random-MistralForCausalLM` has a `generation_config.json` # containing `bos_token_id: 1` # 1. Load without further parameters model = AutoModelForCausalLM.from_pretrained(TINY_MISTRAL) self.assertEqual(model.generation_config.bos_token_id, 1) # 2. Load with `device_map` model = AutoModelForCausalLM.from_pretrained(TINY_MISTRAL, device_map="auto") self.assertEqual(model.generation_config.bos_token_id, 1) @require_safetensors def test_safetensors_torch_from_torch(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) @require_safetensors @require_flax def test_safetensors_torch_from_flax(self): hub_model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(hub_model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) @require_tf @require_safetensors def test_safetensors_torch_from_tf(self): hub_model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") model = TFBertModel.from_pretrained("hf-internal-testing/tiny-bert-tf-only") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(hub_model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) @require_safetensors def test_safetensors_torch_from_torch_sharded(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB") new_model = BertModel.from_pretrained(tmp_dir) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_modifying_model_config_gets_moved_to_generation_config(self): """ Calling `model.save_pretrained` should move the changes made to `generate` parameterization in the model config to the generation config. """ model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") # Initially, the repetition penalty has its default value in `model.config`. The `model.generation_config` will # have the exact same default self.assertTrue(model.config.repetition_penalty == 1.0) self.assertTrue(model.generation_config.repetition_penalty == 1.0) # If the user attempts to save a custom generation parameter: model.config.repetition_penalty = 3.0 with warnings.catch_warnings(record=True) as warning_list: with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) # 1 - That parameter will be removed from `model.config`. We don't want to use `model.config` to store # generative parameters, and the old default (1.0) would no longer relect the user's wishes. self.assertTrue(model.config.repetition_penalty is None) # 2 - That parameter will be set in `model.generation_config` instead. self.assertTrue(model.generation_config.repetition_penalty == 3.0) # 3 - The user will see a warning regarding the custom parameter that has been moved. self.assertTrue(len(warning_list) == 1) self.assertTrue("Moving the following attributes" in str(warning_list[0].message)) self.assertTrue("repetition_penalty" in str(warning_list[0].message)) @require_safetensors def test_model_from_pretrained_from_mlx(self): from safetensors import safe_open model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-mistral-mlx") self.assertIsNotNone(model) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) with safe_open(os.path.join(tmp_dir, "model.safetensors"), framework="pt") as f: metadata = f.metadata() self.assertEqual(metadata.get("format"), "pt") new_model = AutoModelForCausalLM.from_pretrained(tmp_dir) input_ids = torch.randint(100, 1000, (1, 10)) with torch.no_grad(): outputs = model(input_ids) outputs_from_saved = new_model(input_ids) torch.testing.assert_close(outputs_from_saved["logits"], outputs["logits"]) def test_warning_for_beta_gamma_parameters(self): class TestGammaBetaNorm(torch.nn.Module): def __init__(self): super().__init__() self.gamma = torch.nn.Parameter(torch.ones(1)) self.beta = torch.nn.Parameter(torch.zeros(1)) def forward(self): return self.gamma.sum() + self.beta.sum() class TestModelGammaBeta(PreTrainedModel): def __init__(self, config): super().__init__(config) self.LayerNorm = TestGammaBetaNorm() self.post_init() def forward(self): return self.LayerNorm() logger = logging.get_logger("transformers.modeling_utils") config = PretrainedConfig() warning_msg_gamma = "`LayerNorm.gamma` -> `LayerNorm.weight`" warning_msg_beta = "`LayerNorm.beta` -> `LayerNorm.bias`" model = TestModelGammaBeta(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) with LoggingLevel(logging.INFO): with CaptureLogger(logger) as cl1: _, loading_info = TestModelGammaBeta.from_pretrained( tmp_dir, config=config, output_loading_info=True ) missing_keys = loading_info["missing_keys"] unexpected_keys = loading_info["unexpected_keys"] self.assertIn("`TestModelGammaBeta`", cl1.out) self.assertIn(warning_msg_gamma, cl1.out) self.assertIn(warning_msg_beta, cl1.out) self.assertIn("LayerNorm.gamma", missing_keys) self.assertIn("LayerNorm.weight", unexpected_keys) self.assertIn("LayerNorm.beta", missing_keys) self.assertIn("LayerNorm.bias", unexpected_keys) def test_isin_mps_friendly(self): """tests that our custom `isin_mps_friendly` matches `torch.isin`""" random_ids = torch.randint(0, 100, (100,)) # We can match against an integer random_test_integer = torch.randint(0, 100, (1,)).item() self.assertTrue( torch.equal( torch.isin(random_ids, random_test_integer), isin_mps_friendly(random_ids, random_test_integer) ) ) # We can match against an 0D tensor random_test_tensor = torch.randint(0, 100, (1,)).squeeze() self.assertTrue( torch.equal(torch.isin(random_ids, random_test_tensor), isin_mps_friendly(random_ids, random_test_tensor)) ) # We can match against an 1D tensor (with many items) random_test_tensor = torch.randint(0, 100, (10,)) self.assertTrue( torch.equal(torch.isin(random_ids, random_test_tensor), isin_mps_friendly(random_ids, random_test_tensor)) ) def test_can_generate(self): """Tests the behavior of `PreTrainedModel.can_generate` method.""" logger = logging.get_logger("transformers.modeling_utils") logger.warning_once.cache_clear() # 1 - By default, a model CAN'T generate can_generate = BertModel.can_generate() self.assertFalse(can_generate) # 2 - The most common case for a model to be able to generate is to inherit from `GenerationMixin` directly class DummyBertWithMixin(BertModel, GenerationMixin): pass with CaptureLogger(logger) as cl: can_generate = DummyBertWithMixin.can_generate() self.assertTrue("" == cl.out) self.assertTrue(can_generate) # 3 - Finally, it can inherit from a model that can generate class DummyBertWithParent(DummyBertWithMixin): pass with CaptureLogger(logger) as cl: can_generate = DummyBertWithParent.can_generate() self.assertTrue("" == cl.out) self.assertTrue(can_generate) # 4 - BC: models with a custom `prepare_inputs_for_generation` can generate (it was assumed they inherited # `GenerationMixin`) class DummyBertWithPrepareInputs(BertModel): def prepare_inputs_for_generation(self): pass with CaptureLogger(logger) as cl: can_generate = DummyBertWithPrepareInputs.can_generate() self.assertTrue("it doesn't directly inherit from `GenerationMixin`" in cl.out) self.assertTrue(can_generate) def test_save_and_load_config_with_custom_generation(self): """ Regression test for the ability to save and load a config with a custom generation kwarg (i.e. a parameter that gets moved to the generation config and reset on the model config) """ model = T5ForConditionalGeneration.from_pretrained(TINY_T5) # The default for `num_beams` is 1 and `early_stopping` is False self.assertTrue(model.config.num_beams == 1) self.assertTrue(model.config.early_stopping is False) # When we save the model, this custom parameter should be moved to the generation config AND the model # config should contain `None` model.config.num_beams = 2 model.config.early_stopping = True self.assertTrue(model.generation_config.num_beams == 1) # unmodified generation config with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = T5ForConditionalGeneration.from_pretrained(tmp_dir) # moved to generation config self.assertTrue(new_model.generation_config.num_beams == 2) self.assertTrue(new_model.generation_config.early_stopping is True) # reset in the model config self.assertTrue(new_model.config.num_beams is None) self.assertTrue(new_model.config.early_stopping is None) # Sanity check: We can run `generate` with the new model without any warnings random_ids = torch.randint(0, 100, (1, 5)) with warnings.catch_warnings(record=True) as w: new_model.generate(random_ids, max_new_tokens=3) self.assertTrue(len(w) == 0) def test_load_model_with_state_dict_only(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") state_dict = model.state_dict() config = model.config model_loaded = BertModel.from_pretrained( pretrained_model_name_or_path=None, config=config, state_dict=state_dict ) self.assertTrue(check_models_equal(model, model_loaded)) def test_load_model_with_state_dict_only_low_cpu_mem_usage(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") state_dict = model.state_dict() config = model.config model_loaded = BertModel.from_pretrained( pretrained_model_name_or_path=None, config=config, state_dict=state_dict, low_cpu_mem_usage=True ) self.assertTrue(check_models_equal(model, model_loaded)) def test_cache_when_needed_at_train_time(self): """ Some fine-tuning methods require the use of cache, like prefix tuning in PEFT. This test checks that a cache is at train time used if we request it. Related issue: #35648 """ model = AutoModelForCausalLM.from_pretrained(TINY_MISTRAL) tokenizer = AutoTokenizer.from_pretrained(TINY_MISTRAL) model_inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") # By default it is not training, we have to set it self.assertFalse(model.training) model.train() # If we set `use_cache=True` while training, then a cache is returned model_outputs = model(**model_inputs, use_cache=True) self.assertIsInstance(model_outputs.past_key_values, DynamicCache) self.assertTrue(model.training) # simulate injecting virtual tokens like in prefix tuning num_virtual_tokens = 3 past_key_values = [torch.randn(2, 1, 2, num_virtual_tokens, 8)] * 2 past_key_values = DynamicCache.from_legacy_cache(past_key_values) model_inputs["attention_mask"] = torch.cat( ( model_inputs["attention_mask"], torch.ones(1, num_virtual_tokens).to(model_inputs["attention_mask"].device), ), dim=1, ) model_outputs = model(**model_inputs, past_key_values=past_key_values, use_cache=True) self.assertTrue(model.training) # We can also disable the cache to skip a few operations, if the training loop doesn't need cache model_outputs = model(**model_inputs, use_cache=False) self.assertIsNone(model_outputs.past_key_values) self.assertTrue(model.training) def test_restore_default_torch_dtype_from_pretrained(self): """ Tests that the default torch dtype is restored when an error happens during the loading of a model. """ old_dtype = torch.get_default_dtype() # set default type to float32 torch.set_default_dtype(torch.float32) # Mock injection point which is right after the call to `_set_default_torch_dtype` original_set_default_torch_dtype = MistralForCausalLM._set_default_torch_dtype def debug(*args, **kwargs): # call the method as usual, than raise a RuntimeError original_set_default_torch_dtype(*args, **kwargs) raise RuntimeError with mock.patch( "transformers.models.mistral.modeling_mistral.MistralForCausalLM._set_default_torch_dtype", side_effect=debug, ): with self.assertRaises(RuntimeError): _ = AutoModelForCausalLM.from_pretrained(TINY_MISTRAL, device_map="auto", torch_dtype=torch.float16) # default should still be float32 assert torch.get_default_dtype() == torch.float32 torch.set_default_dtype(old_dtype) def test_restore_default_torch_dtype_from_config(self): """ Tests that the default torch dtype is restored when an error happens during the loading of a model. """ old_dtype = torch.get_default_dtype() # set default type to float32 torch.set_default_dtype(torch.float32) config = AutoConfig.from_pretrained( TINY_MISTRAL, ) # Mock injection point which is right after the call to `_set_default_torch_dtype` original_set_default_torch_dtype = MistralForCausalLM._set_default_torch_dtype def debug(*args, **kwargs): # call the method as usual, than raise a RuntimeError original_set_default_torch_dtype(*args, **kwargs) raise RuntimeError with mock.patch( "transformers.models.mistral.modeling_mistral.MistralForCausalLM._set_default_torch_dtype", side_effect=debug, ): with self.assertRaises(RuntimeError): config.torch_dtype = torch.float16 _ = AutoModelForCausalLM.from_config( config, ) # default should still be float32 assert torch.get_default_dtype() == torch.float32 torch.set_default_dtype(old_dtype) def test_unknown_quantization_config(self): with tempfile.TemporaryDirectory() as tmpdir: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) config.quantization_config = {"quant_method": "unknown"} model.save_pretrained(tmpdir) with self.assertLogs("transformers", level="WARNING") as cm: BertModel.from_pretrained(tmpdir) self.assertEqual(len(cm.records), 1) self.assertTrue(cm.records[0].message.startswith("Unknown quantization type, got")) @parameterized.expand([("Qwen/Qwen2.5-3B-Instruct", 10), ("meta-llama/Llama-2-7b-chat-hf", 10)]) @slow @require_read_token @require_torch_gpu def test_loading_is_fast_on_gpu(self, model_id: str, max_loading_time: float): """ This test is used to avoid regression on https://github.com/huggingface/transformers/pull/36380. 10s should be more than enough for both models, and allows for some margin as loading time are quite unstable. Before #36380, it used to take more than 40s, so 10s is still reasonable. Note that we run this test in a subprocess, to ensure that cuda is not already initialized/warmed-up. """ # First download the weights if not already on disk _ = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16) script_to_run = textwrap.dedent( """ import torch import time import argparse from transformers import AutoModelForCausalLM parser = argparse.ArgumentParser() parser.add_argument("model_id", type=str) parser.add_argument("max_loading_time", type=float) args = parser.parse_args() device = torch.device("cuda:0") torch.cuda.synchronize(device) t0 = time.time() model = AutoModelForCausalLM.from_pretrained(args.model_id, torch_dtype=torch.float16, device_map=device) torch.cuda.synchronize(device) dt = time.time() - t0 # Assert loading is faster (it should be more than enough in both cases) if dt > args.max_loading_time: raise ValueError(f"Loading took {dt:.2f}s! It should not take more than {args.max_loading_time}s") # Ensure everything is correctly loaded on gpu bad_device_params = {k for k, v in model.named_parameters() if v.device != device} if len(bad_device_params) > 0: raise ValueError(f"The following parameters are not on GPU: {bad_device_params}") """ ) with tempfile.NamedTemporaryFile(mode="w+", suffix=".py") as tmp: tmp.write(script_to_run) tmp.flush() tmp.seek(0) cmd = f"python {tmp.name} {model_id} {max_loading_time}".split() try: # We cannot use a timeout of `max_loading_time` as cuda initialization can take up to 15-20s _ = subprocess.run(cmd, capture_output=True, env=self.get_env(), text=True, check=True, timeout=60) except subprocess.CalledProcessError as e: raise Exception(f"The following error was captured: {e.stderr}") @slow @require_torch class ModelOnTheFlyConversionTester(unittest.TestCase): @classmethod def setUpClass(cls): cls.user = "huggingface-hub-ci" cls.token = os.getenv("HUGGINGFACE_PRODUCTION_USER_TOKEN", None) if cls.token is None: raise ValueError("Cannot run tests as secret isn't setup.") cls.api = HfApi(token=cls.token) def setUp(self) -> None: self.repo_name = f"{self.user}/test-model-on-the-fly-{uuid.uuid4()}" def tearDown(self) -> None: self.api.delete_repo(self.repo_name) def test_safetensors_on_the_fly_conversion(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False) converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, "SFconvertbot") self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_conversion_private(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, private=True) converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name, token=self.token) discussion = next(discussions) self.assertEqual(discussion.author, self.user) self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_conversion_gated(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False) headers = {"Authorization": f"Bearer {self.token}"} requests.put( f"https://huggingface.co/api/models/{self.repo_name}/settings", json={"gated": "auto"}, headers=headers ) converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, "SFconvertbot") self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_sharded_conversion(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, max_shard_size="200kb") converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, "SFconvertbot") self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_sharded_conversion_private(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub( self.repo_name, token=self.token, safe_serialization=False, max_shard_size="200kb", private=True ) converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, self.user) self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_sharded_conversion_gated(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, max_shard_size="200kb", safe_serialization=False) headers = {"Authorization": f"Bearer {self.token}"} requests.put( f"https://huggingface.co/api/models/{self.repo_name}/settings", json={"gated": "auto"}, headers=headers ) converted_model = BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) with self.subTest("Initial and converted models are equal"): for p1, p2 in zip(initial_model.parameters(), converted_model.parameters()): self.assertTrue(torch.equal(p1, p2)) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, "SFconvertbot") self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") @unittest.skip(reason="Edge case, should work once the Space is updated`") def test_safetensors_on_the_fly_wrong_user_opened_pr(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, private=True) BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) # This should have opened a PR with the user's account with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) discussion = next(discussions) self.assertEqual(discussion.author, self.user) self.assertEqual(discussion.title, "Adding `safetensors` variant of this model") # We now switch the repo visibility to public self.api.update_repo_settings(self.repo_name, private=False) # We once again call from_pretrained, which should call the bot to open a PR BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token) with self.subTest("PR was open with the safetensors account"): discussions = self.api.get_repo_discussions(self.repo_name) bot_opened_pr = None bot_opened_pr_title = None for discussion in discussions: if discussion.author == "SFconvertbot": bot_opened_pr = True bot_opened_pr_title = discussion.title self.assertTrue(bot_opened_pr) self.assertEqual(bot_opened_pr_title, "Adding `safetensors` variant of this model") def test_safetensors_on_the_fly_specific_revision(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) # Push a model on `main` initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False) # Push a model on a given revision initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False, revision="new-branch") # Try to convert the model on that revision should raise with self.assertRaises(EnvironmentError): BertModel.from_pretrained(self.repo_name, use_safetensors=True, token=self.token, revision="new-branch") def test_absence_of_safetensors_triggers_conversion(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) # Push a model on `main` initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False) # Download the model that doesn't have safetensors BertModel.from_pretrained(self.repo_name, token=self.token) for thread in threading.enumerate(): if thread.name == "Thread-autoconversion": thread.join(timeout=10) discussions = self.api.get_repo_discussions(self.repo_name) bot_opened_pr = None bot_opened_pr_title = None for discussion in discussions: if discussion.author == "SFconvertbot": bot_opened_pr = True bot_opened_pr_title = discussion.title self.assertTrue(bot_opened_pr) self.assertEqual(bot_opened_pr_title, "Adding `safetensors` variant of this model") @mock.patch("transformers.safetensors_conversion.spawn_conversion") def test_absence_of_safetensors_triggers_conversion_failed(self, spawn_conversion_mock): spawn_conversion_mock.side_effect = HTTPError() config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) initial_model = BertModel(config) # Push a model on `main` initial_model.push_to_hub(self.repo_name, token=self.token, safe_serialization=False) # The auto conversion is mocked to always raise; ensure that it doesn't raise in the main thread BertModel.from_pretrained(self.repo_name, token=self.token) @require_torch @is_staging_test class ModelPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = TOKEN HfFolder.save_token(TOKEN) @unittest.skip(reason="This test is flaky") def test_push_to_hub(self): with TemporaryHubRepo(token=self._token) as tmp_repo: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub(tmp_repo.repo_id, token=self._token) new_model = BertModel.from_pretrained(tmp_repo.repo_id) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) @unittest.skip(reason="This test is flaky") def test_push_to_hub_via_save_pretrained(self): with TemporaryHubRepo(token=self._token) as tmp_repo: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token) new_model = BertModel.from_pretrained(tmp_repo.repo_id) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_with_description(self): with TemporaryHubRepo(token=self._token) as tmp_repo: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) COMMIT_DESCRIPTION = """ The commit description supports markdown synthax see: ```python >>> form transformers import AutoConfig >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased") ``` """ commit_details = model.push_to_hub( tmp_repo.repo_id, use_auth_token=self._token, create_pr=True, commit_description=COMMIT_DESCRIPTION ) self.assertEqual(commit_details.commit_description, COMMIT_DESCRIPTION) @unittest.skip(reason="This test is flaky") def test_push_to_hub_in_organization(self): with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) model.push_to_hub(tmp_repo.repo_id, token=self._token) new_model = BertModel.from_pretrained(tmp_repo.repo_id) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) @unittest.skip(reason="This test is flaky") def test_push_to_hub_in_organization_via_save_pretrained(self): with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo: config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = BertModel(config) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, push_to_hub=True, token=self._token, repo_id=tmp_repo.repo_id) new_model = BertModel.from_pretrained(tmp_repo.repo_id) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_push_to_hub_dynamic_model(self): with TemporaryHubRepo(token=self._token) as tmp_repo: CustomConfig.register_for_auto_class() CustomModel.register_for_auto_class() config = CustomConfig(hidden_size=32) model = CustomModel(config) model.push_to_hub(tmp_repo.repo_id, token=self._token) # checks self.assertDictEqual( config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"}, ) new_model = AutoModel.from_pretrained(tmp_repo.repo_id, trust_remote_code=True) # Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module self.assertEqual(new_model.__class__.__name__, "CustomModel") for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) config = AutoConfig.from_pretrained(tmp_repo.repo_id, trust_remote_code=True) new_model = AutoModel.from_config(config, trust_remote_code=True) self.assertEqual(new_model.__class__.__name__, "CustomModel") def test_push_to_hub_with_tags(self): with TemporaryHubRepo(token=self._token) as tmp_repo: from huggingface_hub import ModelCard new_tags = ["tag-1", "tag-2"] CustomConfig.register_for_auto_class() CustomModel.register_for_auto_class() config = CustomConfig(hidden_size=32) model = CustomModel(config) self.assertTrue(model.model_tags is None) model.add_model_tags(new_tags) self.assertTrue(model.model_tags == new_tags) model.push_to_hub(tmp_repo.repo_id, token=self._token) loaded_model_card = ModelCard.load(tmp_repo.repo_id) self.assertEqual(loaded_model_card.data.tags, new_tags) @require_torch class AttentionMaskTester(unittest.TestCase): def check_non_causal(self, bsz, q_len, kv_len, mask_2d, mask_4d): mask_indices = (mask_2d != 1)[:, None].broadcast_to((bsz, q_len, kv_len)) mask_4d_values = mask_4d[:, 0][mask_indices] is_inf = mask_4d_values == -float("inf") is_min = mask_4d_values == torch.finfo(mask_4d.dtype).min assert torch.logical_or(is_inf, is_min).all() def check_to_4d(self, mask_converter, q_len, kv_len, additional_mask=None, bsz=3): mask_2d = torch.ones((bsz, kv_len), device=torch_device, dtype=torch.long) if additional_mask is not None: for bsz_idx, seq_idx in additional_mask: mask_2d[bsz_idx, seq_idx] = 0 mask_4d = mask_converter.to_4d(mask_2d, query_length=q_len, key_value_length=kv_len, dtype=torch.float32) assert mask_4d.shape == (bsz, 1, q_len, kv_len) # make sure there are no overflows assert mask_4d.min() != float("-inf") context = mask_converter.sliding_window if mask_converter.is_causal and context is None: # k * (k+1) / 2 tokens are masked in triangualar masks num_tokens_masked = bsz * (q_len * (q_len - 1) // 2) if 0 not in mask_2d: assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked if 0 in mask_2d: # at least causal mask + maybe more assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d) elif not mask_converter.is_causal and context is None: if 0 not in mask_2d: assert (mask_4d != 0).sum().cpu().item() == 0 if 0 in mask_2d: self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d) elif mask_converter.is_causal and context is not None: # k * (k+1) / 2 tokens are masked in triangualar masks num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len) num_tokens_masked = bsz * num_tokens_masked if 0 not in mask_2d: assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked if 0 in mask_2d: # at least causal mask + maybe more assert (mask_4d != 0).sum().cpu().item() >= num_tokens_masked self.check_non_causal(bsz, q_len, kv_len, mask_2d, mask_4d) def check_to_causal(self, mask_converter, q_len, kv_len, bsz=3): mask_4d = mask_converter.to_causal_4d( bsz, query_length=q_len, key_value_length=kv_len, device=torch_device, dtype=torch.float32 ) if q_len == 1 and mask_converter.sliding_window is None: # no causal mask if q_len is 1 assert mask_4d is None return context = mask_converter.sliding_window if mask_converter.is_causal and context is None: # k * (k+1) / 2 tokens are masked in triangualar masks num_tokens_masked = bsz * (q_len * (q_len - 1) // 2) assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked elif not mask_converter.is_causal and context is None: assert (mask_4d != 0).sum().cpu().item() == 0 elif mask_converter.is_causal and context is not None: # k * (k+1) / 2 tokens are masked in triangualar masks num_tokens_masked = (q_len * (q_len - 1) // 2) + self.compute_num_context_mask(kv_len, context, q_len) num_tokens_masked = bsz * num_tokens_masked assert (mask_4d != 0).sum().cpu().item() == num_tokens_masked def compute_num_context_mask(self, kv_len, context, q_len): # This function computes the # of attention tokens that are added for # the sliding window c_mask_len = kv_len - context - 1 num_mask_triangle = c_mask_len * (c_mask_len + 1) // 2 cut_mask_len = max(c_mask_len - q_len, 0) num_cut_mask = cut_mask_len * (cut_mask_len + 1) // 2 return num_mask_triangle - num_cut_mask def test_2d_to_4d_causal(self): mask_converter = AttentionMaskConverter(is_causal=True) # auto-regressive use case self.check_to_4d(mask_converter, q_len=1, kv_len=7) # special auto-regressive case self.check_to_4d(mask_converter, q_len=3, kv_len=7) # non auto-regressive case self.check_to_4d(mask_converter, q_len=7, kv_len=7) # same with extra attention masks self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) # check that the mask does not overflow on causal masked tokens self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 0), (1, 0), (1, 1)]) def test_2d_to_4d(self): mask_converter = AttentionMaskConverter(is_causal=False) # non auto-regressive case self.check_to_4d(mask_converter, q_len=7, kv_len=7) # same with extra attention masks self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) def test_2d_to_4d_causal_sliding(self): mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=5) # auto-regressive use case self.check_to_4d(mask_converter, q_len=1, kv_len=7) # special auto-regressive case self.check_to_4d(mask_converter, q_len=3, kv_len=7) # non auto-regressive case self.check_to_4d(mask_converter, q_len=7, kv_len=7) # same with extra attention masks self.check_to_4d(mask_converter, q_len=1, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) self.check_to_4d(mask_converter, q_len=3, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) self.check_to_4d(mask_converter, q_len=7, kv_len=7, additional_mask=[(0, 2), (1, 3), (2, 0)]) def test_causal_mask(self): mask_converter = AttentionMaskConverter(is_causal=True) # auto-regressive use case self.check_to_causal(mask_converter, q_len=1, kv_len=7) # special auto-regressive case self.check_to_causal(mask_converter, q_len=3, kv_len=7) # non auto-regressive case self.check_to_causal(mask_converter, q_len=7, kv_len=7) def test_causal_mask_sliding(self): mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=3) # auto-regressive use case self.check_to_causal(mask_converter, q_len=1, kv_len=7) # special auto-regressive case self.check_to_causal(mask_converter, q_len=3, kv_len=7) # non auto-regressive case self.check_to_causal(mask_converter, q_len=7, kv_len=7) def test_torch_compile_fullgraph(self): model = Prepare4dCausalAttentionMaskModel() inputs_embeds = torch.rand([1, 3, 32]) res_non_compiled = model(inputs_embeds) compiled_model = torch.compile(model, fullgraph=True) res_compiled = compiled_model(inputs_embeds) self.assertTrue(torch.equal(res_non_compiled, res_compiled)) model = Create4dCausalAttentionMaskModel() inputs_embeds = torch.rand(2, 4, 16) res_non_compiled = model(inputs_embeds) compiled_model = torch.compile(model, fullgraph=True) res_compiled = compiled_model(inputs_embeds) self.assertTrue(torch.equal(res_non_compiled, res_compiled)) model = Prepare4dAttentionMaskModel() mask = torch.ones(2, 4) mask[0, :2] = 0 inputs_embeds = torch.rand(2, 4, 16) res_non_compiled = model(mask, inputs_embeds) compiled_model = torch.compile(model, fullgraph=True) res_compiled = compiled_model(mask, inputs_embeds) self.assertTrue(torch.equal(res_non_compiled, res_compiled)) @require_torch @slow def test_unmask_unattended_left_padding(self): attention_mask = torch.Tensor([[0, 0, 1], [1, 1, 1], [0, 1, 1]]).to(torch.int64) expanded_mask = torch.Tensor( [ [[[0, 0, 0], [0, 0, 0], [0, 0, 1]]], [[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], [[[0, 0, 0], [0, 1, 0], [0, 1, 1]]], ] ).to(torch.int64) reference_output = torch.Tensor( [ [[[1, 1, 1], [1, 1, 1], [0, 0, 1]]], [[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], [[[1, 1, 1], [0, 1, 0], [0, 1, 1]]], ] ).to(torch.int64) result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=1) self.assertTrue(torch.equal(result, reference_output)) attention_mask = torch.Tensor([[0, 0, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 1]]).to(torch.int64) attn_mask_converter = AttentionMaskConverter(is_causal=True) past_key_values_length = 0 key_value_length = attention_mask.shape[-1] + past_key_values_length expanded_mask = attn_mask_converter.to_4d( attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32 ) result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0) min_inf = torch.finfo(torch.float32).min reference_output = torch.Tensor( [ [ [ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [min_inf, min_inf, 0, min_inf, min_inf], [min_inf, min_inf, 0, 0, min_inf], [min_inf, min_inf, 0, 0, 0], ] ], [ [ [0, min_inf, min_inf, min_inf, min_inf], [0, 0, min_inf, min_inf, min_inf], [0, 0, 0, min_inf, min_inf], [0, 0, 0, 0, min_inf], [0, 0, 0, 0, 0], ] ], [ [ [0, 0, 0, 0, 0], [min_inf, 0, min_inf, min_inf, min_inf], [min_inf, 0, 0, min_inf, min_inf], [min_inf, 0, 0, 0, min_inf], [min_inf, 0, 0, 0, 0], ] ], ] ) self.assertTrue(torch.equal(reference_output, result)) @require_torch @slow def test_unmask_unattended_right_padding(self): attention_mask = torch.Tensor([[1, 1, 1, 0], [1, 1, 1, 1], [1, 1, 0, 0]]).to(torch.int64) attn_mask_converter = AttentionMaskConverter(is_causal=True) past_key_values_length = 0 key_value_length = attention_mask.shape[-1] + past_key_values_length expanded_mask = attn_mask_converter.to_4d( attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32 ) result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0) self.assertTrue(torch.equal(expanded_mask, result)) @require_torch @slow def test_unmask_unattended_random_mask(self): attention_mask = torch.Tensor([[1, 0, 1, 0], [1, 0, 1, 1], [1, 1, 0, 1]]).to(torch.int64) attn_mask_converter = AttentionMaskConverter(is_causal=True) past_key_values_length = 0 key_value_length = attention_mask.shape[-1] + past_key_values_length expanded_mask = attn_mask_converter.to_4d( attention_mask, attention_mask.shape[-1], key_value_length=key_value_length, dtype=torch.float32 ) result = AttentionMaskConverter._unmask_unattended(expanded_mask, attention_mask, unmasked_value=0) self.assertTrue(torch.equal(expanded_mask, result)) @require_torch class TestAttentionImplementation(unittest.TestCase): def test_error_no_sdpa_available(self): with self.assertRaises(ValueError) as cm: _ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="sdpa") self.assertTrue( "does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention" in str(cm.exception) ) _ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel") def test_error_no_flash_available(self): with self.assertRaises(ValueError) as cm: _ = AutoModel.from_pretrained( "hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="flash_attention_2" ) self.assertTrue("does not support Flash Attention 2.0" in str(cm.exception)) def test_error_no_flash_available_with_config(self): with self.assertRaises(ValueError) as cm: config = AutoConfig.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel") _ = AutoModel.from_pretrained( "hf-tiny-model-private/tiny-random-MCTCTModel", config=config, attn_implementation="flash_attention_2" ) self.assertTrue("does not support Flash Attention 2.0" in str(cm.exception)) def test_error_wrong_attn_implementation(self): with self.assertRaises(ValueError) as cm: _ = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation="foo") self.assertTrue('The only possible arguments are `attn_implementation="eager"' in str(cm.exception)) def test_not_available_flash(self): if is_flash_attn_2_available(): self.skipTest(reason="Please uninstall flash-attn package to run test_not_available_flash") if is_torch_npu_available(): self.skipTest( reason="FlashAttention2 is supported on Ascend NPU without using package `flash-attn`, ignore this test case." ) with self.assertRaises(ImportError) as cm: _ = AutoModel.from_pretrained( "hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="flash_attention_2" ) self.assertTrue("the package flash_attn seems to be not installed" in str(cm.exception)) def test_not_available_flash_with_config(self): if is_flash_attn_2_available(): self.skipTest(reason="Please uninstall flash-attn package to run test_not_available_flash") if is_torch_npu_available(): self.skipTest( reason="FlashAttention2 is supported on Ascend NPU without using package `flash-attn`, ignore this test case." ) config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeModel") with self.assertRaises(ImportError) as cm: _ = AutoModel.from_pretrained( "hf-internal-testing/tiny-random-GPTBigCodeModel", config=config, attn_implementation="flash_attention_2", ) self.assertTrue("the package flash_attn seems to be not installed" in str(cm.exception)) def test_not_available_sdpa(self): if is_torch_sdpa_available(): self.skipTest(reason="This test requires torch<=2.0") with self.assertRaises(ImportError) as cm: _ = AutoModel.from_pretrained( "hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="sdpa" ) self.assertTrue("PyTorch SDPA requirements in Transformers are not met" in str(cm.exception)) @require_torch class TestTensorSharing(TestCasePlus): def test_disjoint(self): main = torch.zeros(10) a = main[:5] b = main[5:] state_dict = {"a": a, "b": b} shared_names, disjoint_names = _find_disjoint([{"a", "b"}], state_dict) self.assertEqual(shared_names, []) self.assertEqual(disjoint_names, ["a", "b"]) a = main[::2] b = main[1::2] state_dict = {"a": a, "b": b} shared_names, disjoint_names = _find_disjoint([{"a", "b"}], state_dict) self.assertEqual(shared_names, [{"a", "b"}]) self.assertEqual(disjoint_names, []) def test_identical(self): a = torch.zeros(10) b = a state_dict = {"a": a, "b": b} shared_names, identical_names = _find_identical([{"a", "b"}], state_dict) self.assertEqual(shared_names, []) self.assertEqual(identical_names, [{"a", "b"}]) b = a[:5] state_dict = {"a": a, "b": b} shared_names, identical_names = _find_identical([{"a", "b"}], state_dict) self.assertEqual(shared_names, [{"a", "b"}]) self.assertEqual(identical_names, [])