# 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 from unittest.mock import patch import httpx import pytest from huggingface_hub import HfApi, snapshot_download, split_torch_state_dict_into_shards from parameterized import parameterized from pytest import mark from transformers import ( AutoConfig, AutoModel, AutoModelForImageClassification, AutoModelForSequenceClassification, BartConfig, BartForConditionalGeneration, BartModel, CLIPTextModelWithProjection, DynamicCache, GPT2Config, GPT2LMHeadModel, LlavaConfig, LlavaForConditionalGeneration, MistralConfig, MistralForCausalLM, OPTConfig, OPTForCausalLM, OwlViTForObjectDetection, PreTrainedConfig, T5Config, T5ForConditionalGeneration, is_torch_available, logging, ) from transformers.modeling_flash_attention_utils import is_flash_attn_available from transformers.models.mistral.modeling_mistral import MistralModel from transformers.testing_utils import ( TOKEN, CaptureLogger, LoggingLevel, TemporaryHubRepo, TestCasePlus, force_serialization_as_bin_files, hub_retry, is_staging_test, require_accelerate, require_non_hpu, require_torch, require_torch_accelerator, require_torch_gpu, require_torch_multi_accelerator, 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 ( PACKAGE_DISTRIBUTION_MAPPING, is_flash_attn_2_available, is_flash_attn_3_available, is_flash_attn_4_available, is_kernels_available, is_torch_npu_available, ) from ..test_modeling_common import compare_state_dicts sys.path.append(str(Path(__file__).parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig if is_torch_available(): import torch from safetensors.torch import load_file from safetensors.torch import save_file as safe_save_file from test_module.custom_modeling import CustomModel from torch import nn import transformers.initialization as init from transformers import ( AutoModelForCausalLM, AutoTokenizer, BertConfig, BertModel, CLIPTextModel, GenerationMixin, LlamaConfig, LlamaForCausalLM, MixtralConfig, MixtralModel, MusicgenConfig, MusicgenForConditionalGeneration, PreTrainedModel, T5Config, T5ForConditionalGeneration, ) from transformers.conversion_mapping import MergeModulelist, WeightConverter, get_model_conversion_mapping from transformers.modeling_utils import ( FLASH_ATTN_KERNEL_FALLBACK, _find_disjoint, _find_identical, get_total_byte_count, ) # 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) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithUnexpectedKeys(PreTrainedModel): base_model_prefix = "base" config_class = PreTrainedConfig _keys_to_ignore_on_load_unexpected = [r"^mtp.*"] def __init__(self, config): super().__init__(config) self.linear = nn.Linear(50, 50) self.linear_2 = nn.Linear(50, 50) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithMissingKeys(PreTrainedModel): base_model_prefix = "base" config_class = PreTrainedConfig _keys_to_ignore_on_load_missing = [r"^linear"] def __init__(self, config): super().__init__(config) self.linear = nn.Linear(50, 50) self.linear_2 = nn.Linear(50, 50) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithTiedWeights(PreTrainedModel): config_class = PreTrainedConfig _tied_weights_keys = {"linear_2.weight": "linear.weight"} def __init__(self, config): super().__init__(config) self.linear = nn.Linear(5, 5) self.linear_2 = nn.Linear(5, 5) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithMultipleTiedWeights(PreTrainedModel): config_class = PreTrainedConfig _tied_weights_keys = {"linear_2.weight": "linear.weight", "linear_3.weight": "linear.weight"} def __init__(self, config): super().__init__(config) self.linear = nn.Linear(5, 5) self.linear_2 = nn.Linear(5, 5) self.linear_3 = nn.Linear(5, 5) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) class BaseModelWithMultipleMixedTiedWeights(PreTrainedModel): config_class = PreTrainedConfig # Here the tied keys both refer to `linear.weight`, but they are inconsistent in the mapping, i.e. they # are provided as a "circular" dependency _tied_weights_keys = {"linear_2.weight": "linear.weight", "linear_3.weight": "linear_2.weight"} def __init__(self, config): super().__init__(config) self.linear = nn.Linear(5, 5) self.linear_2 = nn.Linear(5, 5) self.linear_3 = nn.Linear(5, 5) self.post_init() def forward(self, x): return self.linear_2(self.linear(x)) 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) self.post_init() def forward(self, x): return self.linear2(self.linear(self.base(x))) class ModelWithDirectParam(PreTrainedModel): base_model_prefix = "base" config_class = PreTrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) # direct params and submodules is helpful for testing offloading logic self.weight = nn.Parameter(torch.rand((5, 5))) self.base = BaseModel(config) self.post_init() def forward(self, x): return self.base(x @ self.weight.T) class ModelWithDirectParamSubmodule(PreTrainedModel): base_model_prefix = "base" config_class = PreTrainedConfig def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.submodule = ModelWithDirectParam(config) # needed so model can have at least one module on accelerator self.linear = nn.Linear(5, 5) self.post_init() def forward(self, x): return self.linear(self.submodule(x)) class ModelWithHeadAndTiedWeights(PreTrainedModel): base_model_prefix = "base" config_class = PreTrainedConfig _tied_weights_keys = {"decoder.weight": "base.linear.weight"} def _init_weights(self, module): pass def __init__(self, config): super().__init__(config) self.base = BaseModel(config) self.decoder = nn.Linear(5, 5) self.post_init() def forward(self, x): return self.decoder(self.base(x)) class VerySimpleLayer(nn.Module): def __init__(self): super().__init__() self.simple = nn.Linear(2, 2) def forward(self, x): return self.simple(x) class DummyLanguageModel(PreTrainedModel): _keep_in_fp32_modules = ["linear"] _no_split_modules = ["VerySimpleLayer"] def __init__(self, config): super().__init__(config) self.linear = nn.Linear(2, 2) self.layers = nn.ModuleList((VerySimpleLayer(), VerySimpleLayer())) self.post_init() def forward(self, x): return self.linear(self.layers[1](self.layers[0](x))) class DummyVisionModel(PreTrainedModel): _keep_in_fp32_modules_strict = ["simple"] def __init__(self, config): super().__init__(config) self.simple = nn.Linear(2, 2) self.post_init() def forward(self, x): return self.simple(x) class MultimodalModel(PreTrainedModel): _keep_in_fp32_modules = ["head"] def __init__(self, config): super().__init__(config) self.language_model = DummyLanguageModel(config) self.vision_model = DummyVisionModel(config) self.head = nn.Linear(2, 2) self.post_init() def forward(self, x): return self.head(self.language_model(self.vision_model(x))) class TestOffline(unittest.TestCase): def test_offline(self): with tempfile.TemporaryDirectory() as tmpdir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": tmpdir}): # First offline load should fail with patch("huggingface_hub.constants.HF_HUB_OFFLINE", True): with pytest.raises(OSError): AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) # Enable online mode for download with patch("huggingface_hub.constants.HF_HUB_OFFLINE", False): snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) # Load again in offline mode - should work now with patch("huggingface_hub.constants.HF_HUB_OFFLINE", True): AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) def test_local_files_only(self): with tempfile.TemporaryDirectory() as tmpdir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": tmpdir}): # Empty cache => fail to load from cache with pytest.raises(OSError): AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, cache_dir=tmpdir, local_files_only=True ) # Populate cache snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir) # Load again from cache => success AutoModelForImageClassification.from_pretrained( TINY_IMAGE_CLASSIF, cache_dir=tmpdir, local_files_only=True ) # Need to be serializable, which means they cannot be in a test class method 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() 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() @require_torch def test_get_total_byte_count_does_not_require_process_group(self): model = BaseModel(PreTrainedConfig()) model._tp_plan = {"linear.weight": "rowwise"} accelerator_device_map = {"linear.weight": torch.device("cpu")} with ( patch("transformers.modeling_utils.torch.distributed.is_available", return_value=True), patch("transformers.modeling_utils.torch.distributed.is_initialized", return_value=False), patch("transformers.modeling_utils.torch.distributed.get_world_size") as mock_world_size, ): total_byte_count = get_total_byte_count(model, accelerator_device_map, None) mock_world_size.assert_not_called() self.assertIn(torch.device("cpu"), total_byte_count) self.assertGreater(total_byte_count[torch.device("cpu")], 0) 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 httpx.ConnectError("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_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`. " "This may be expected if you are loading a checkpoint that shares a subset" 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_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.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, dtype=torch.float16) self.assertEqual(model.dtype, torch.float16) # the resolved dtype is kept on the config so it matches the actual weights (see #46512) self.assertEqual(model.config.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, dtype=torch.int64) def test_model_from_config_dtype_str(self): # test that from_pretrained works with dtype being strings like "float32" for PyTorch backend model = AutoModel.from_pretrained(TINY_T5, dtype="float32") self.assertEqual(model.dtype, torch.float32) self.assertIsInstance(model.config.dtype, torch.dtype) model = AutoModel.from_pretrained(TINY_T5, dtype="float16") self.assertEqual(model.dtype, torch.float16) self.assertIsInstance(model.config.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, dtype="int64") def test_model_from_config_dtype_composite(self): """ Test that from_pretrained works with dtype being as a dict per each sub-config in composite config Tiny-Llava has saved auto dtype as `torch.float32` for all modules. Note, this is a deprecated feature and we fallback to main dtype in all cases below. This test checks if the dtype fallback works correctly. """ # Load without dtype specified model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA) self.assertEqual(model.model.language_model.dtype, torch.float32) self.assertEqual(model.model.vision_tower.dtype, torch.float32) self.assertIsInstance(model.config.dtype, torch.dtype) # should be able to set dtype as a simple string and the model loads it correctly model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, dtype="float32") self.assertEqual(model.model.language_model.dtype, torch.float32) self.assertEqual(model.model.vision_tower.dtype, torch.float32) self.assertIsInstance(model.config.dtype, torch.dtype) model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, dtype=torch.float16) self.assertEqual(model.model.language_model.dtype, torch.float16) self.assertEqual(model.model.vision_tower.dtype, torch.float16) self.assertIsInstance(model.config.dtype, torch.dtype) # should be able to accept dtype as a dict for each sub-config model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, dtype={"text_config": "float32", "vision_config": "float16", "": "bfloat16"} ) self.assertEqual(model.model.language_model.dtype, torch.bfloat16) self.assertEqual(model.model.vision_tower.dtype, torch.bfloat16) self.assertEqual(model.model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16) self.assertIsInstance(model.config.dtype, torch.dtype) # should be able to accept the values as torch.dtype (not str) model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, dtype={"text_config": torch.float32, "vision_config": torch.float16, "": torch.bfloat16} ) self.assertEqual(model.model.language_model.dtype, torch.bfloat16) self.assertEqual(model.model.vision_tower.dtype, torch.bfloat16) self.assertEqual(model.model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16) self.assertIsInstance(model.config.dtype, torch.dtype) # should be able to accept the values in configs directly and pass it to `from_pretrained` config = copy.deepcopy(model.config) config.text_config.dtype = torch.float32 config.vision_config.dtype = torch.bfloat16 config.dtype = torch.float16 model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, config=config, dtype="auto") self.assertEqual(model.model.language_model.dtype, torch.float16) self.assertEqual(model.model.vision_tower.dtype, torch.float16) self.assertEqual(model.model.multi_modal_projector.linear_1.weight.dtype, torch.float16) self.assertIsInstance(model.config.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, dtype="auto") self.assertEqual( model.model.language_model.dtype, torch.float16 ) # remember config says float32 for text_config self.assertEqual(model.model.vision_tower.dtype, torch.float16) self.assertEqual(model.model.multi_modal_projector.linear_1.weight.dtype, torch.float32) self.assertIsInstance(model.config.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, dtype="int64") model = LlavaForConditionalGeneration.from_pretrained( TINY_LLAVA, dtype={"text_config": "float32", "vision_config": "int64", "": "float16"} ) # Check that `from_config` also works and uses the same dtype for all modules config = AutoConfig.from_pretrained(TINY_LLAVA) config.text_config.dtype = torch.float16 config.dtype = torch.float32 model = LlavaForConditionalGeneration._from_config(config) self.assertEqual(model.model.language_model.dtype, torch.float32) self.assertEqual(model.model.vision_tower.dtype, torch.float32) self.assertEqual(model.dtype, torch.float32) def test_model_from_pretrained_dtype(self): # test that the model can be instantiated with dtype of either # 1. explicit from_pretrained's dtype argument # 2. via autodiscovery by looking at model weights (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_dtype(model_path): file = f"{model_path}/config.json" with open(file, encoding="utf-8") as f: s = json.load(f) s.pop("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 dtype="auto" via `config.dtype` model = T5ForConditionalGeneration.from_pretrained(model_path, dtype="auto") self.assertEqual(model.dtype, torch.float32) # 2. test dtype="auto" via auto-derivation # now remove the dtype entry from config.json and try "auto" again which should # perform auto-derivation from weights remove_dtype(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, 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, 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 dtype="auto" via `config.dtype` model = T5ForConditionalGeneration.from_pretrained(model_path, dtype="auto") self.assertEqual(model.config.dtype, torch.float16) self.assertEqual(model.dtype, torch.float16) # tests `config.dtype` saving with open(f"{model_path}/config.json") as f: config_dict = json.load(f) self.assertEqual(config_dict["dtype"], "float16") # 2. test dtype="auto" via auto-derivation # now same with using config info remove_dtype(model_path) model = T5ForConditionalGeneration.from_pretrained(model_path, dtype="auto") self.assertEqual(model.dtype, torch.float16) # 3. now retest that AutoModel behaves the same wrt dtype="auto" as T5ForConditionalGeneration model = AutoModel.from_pretrained(model_path, dtype="auto") self.assertEqual(model.dtype, torch.float16) # test fp16 save_pretrained, loaded with the explicit fp16 model = T5ForConditionalGeneration.from_pretrained(model_path, 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 dtype entry model = AutoModel.from_pretrained(TINY_T5, dtype="auto") # test that the config object didn't get polluted with dtype="auto" # there was a bug that after this call we ended up with config.dtype=="auto" self.assertNotEqual(model.config.dtype, "auto") # now test the outcome self.assertEqual(model.dtype, torch.float32) model = AutoModel.from_pretrained(TINY_T5, 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, 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", 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", "sdpa"] if is_flash_attn_available(): attn_implementation_available.append("flash_attention_2") if is_flash_attn_3_available(): attn_implementation_available.append("flash_attention_3") if is_flash_attn_4_available(): attn_implementation_available.append("flash_attention_4") 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_implementation and from_config # 2. explicit from_config's attn_implementation argument with a config argument # 3. config created with explicit attn_implementation and from_config overriding with explicit attn_implementation argument attn_implementation_available = ["eager", "sdpa"] if is_flash_attn_available(): attn_implementation_available.append("flash_attention_2") if is_flash_attn_3_available(): attn_implementation_available.append("flash_attention_3") if is_flash_attn_4_available(): attn_implementation_available.append("flash_attention_4") 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) 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, 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") model = AutoModelForCausalLM.from_config(config=config, attn_implementation=requested_attn_implementation) self.assertEqual(model.config._attn_implementation, requested_attn_implementation) def test_checkpoint_sharding_local(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) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".safetensors"): 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, SAFE_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, SAFE_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: 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 = load_file(shard_file) self.assertEqual(len(state_dict), 1) # Check the index and the shard files found match with open(index_file, 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(".safetensors")} 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_transformers_weights_config_field_rejects_path_traversal(self): config = BertConfig( vocab_size=16, hidden_size=8, num_hidden_layers=1, num_attention_heads=1, intermediate_size=8, max_position_embeddings=8, ) with tempfile.TemporaryDirectory() as tmp_dir: repo = os.path.join(tmp_dir, "repo") outside_dir = os.path.join(tmp_dir, "outside") BertModel(config).save_pretrained(repo) BertModel(config).save_pretrained(outside_dir) config_file = os.path.join(repo, "config.json") with open(config_file, encoding="utf-8") as f: repo_config = json.load(f) for bad in [ os.path.join("..", "outside", "model.safetensors"), os.path.join(outside_dir, "model.safetensors"), ]: repo_config["transformers_weights"] = bad with open(config_file, "w", encoding="utf-8") as f: json.dump(repo_config, f) with self.assertRaises(ValueError): BertModel.from_pretrained(repo) repo_config["transformers_weights"] = "model.safetensors" with open(config_file, "w", encoding="utf-8") as f: json.dump(repo_config, f) self.assertIsInstance(BertModel.from_pretrained(repo), BertModel) def test_transformers_weights_allows_symlinked_snapshot_file(self): if os.name == "nt": self.skipTest("creating symlinks requires privileges on Windows") config = BertConfig( vocab_size=16, hidden_size=8, num_hidden_layers=1, num_attention_heads=1, intermediate_size=8, max_position_embeddings=8, ) with tempfile.TemporaryDirectory() as tmp_dir: blobs = os.path.join(tmp_dir, "blobs") snapshot = os.path.join(tmp_dir, "snapshot") os.makedirs(blobs) os.makedirs(snapshot) BertModel(config).save_pretrained(blobs, safe_serialization=True) config_dict = config.to_dict() config_dict["transformers_weights"] = "model.safetensors" with open(os.path.join(snapshot, "config.json"), "w", encoding="utf-8") as f: json.dump(config_dict, f) os.symlink(os.path.join(blobs, "model.safetensors"), os.path.join(snapshot, "model.safetensors")) self.assertIsInstance(BertModel.from_pretrained(snapshot), BertModel) 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(self): model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, variant="v2") 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) def test_checkpoint_variant_local_sharded(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") 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") 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 in safetensors 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: # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): model.save_pretrained(tmp_dir, max_shard_size="50kB") 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 _ = 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: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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", use_safetensors=False, ) self.assertIsNotNone(model) def test_checkpoint_variant_hub_sharded(self): with tempfile.TemporaryDirectory() as tmp_dir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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", use_safetensors=False, ) self.assertIsNotNone(model) def test_checkpoint_variant_hub_safe(self): with tempfile.TemporaryDirectory() as tmp_dir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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) def test_checkpoint_variant_hub_sharded_safe(self): with tempfile.TemporaryDirectory() as tmp_dir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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(self): with tempfile.TemporaryDirectory() as tmp_dir: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": tmp_dir}): model = BertModel.from_pretrained( "hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2", use_safetensors=False, ) weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"]) model.save_pretrained(tmp_dir, variant="v2") # saving will create a variant checkpoint self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name))) model.save_pretrained(tmp_dir) # saving shouldn't delete variant checkpoints weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"]) 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, SAFE_WEIGHTS_NAME))) self.assertIsNotNone(model) @require_non_hpu @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_accelerate @mark.accelerate_tests @require_torch_accelerator def test_disk_onload_dtype(self): from accelerate.utils import align_module_device with tempfile.TemporaryDirectory() as tmp_dir: # load model with full disk offloading model = AutoModelForCausalLM.from_pretrained( "inference-optimization/DSV4-tiny-empty", device_map="auto", max_memory={}, offload_folder=tmp_dir, offload_buffers=True, ) # note that `model.dtype` and the dtype of `tid2eid` differ offloaded = model.model.layers[0].mlp.gate.tid2eid self.assertEqual(offloaded.dtype, torch.int64) self.assertEqual(model.dtype, torch.bfloat16) # the dtype used to load from disk should match index = model.model.layers[0].mlp.gate._hf_hook.weights_map.dataset.index weights_info = index["model.layers.0.mlp.gate.tid2eid"] weights_info_type = getattr(torch, weights_info["dtype"]) self.assertEqual(weights_info_type, offloaded.dtype) self.assertEqual(weights_info_type, torch.int64) # the onloaded dtype should be the weight dtype, not the model dtype with align_module_device(model.model.layers[0].mlp.gate): self.assertEqual(model.model.layers[0].mlp.gate.tid2eid.dtype, offloaded.dtype) self.assertEqual(model.model.layers[0].mlp.gate.tid2eid.dtype, torch.int64) @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) 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 = httpx.HTTPStatusError( "failed", request=mock.Mock(), response=mock.Mock() ) 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("httpx.Client.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_accelerate @mark.accelerate_tests @require_torch_accelerator def test_save_offloaded_model_with_direct_params(self): from accelerate import dispatch_model device_map = {"submodule": "cpu", "linear": f"{torch_device}:0"} model = ModelWithDirectParamSubmodule(PreTrainedConfig()) dispatch_model(model, device_map) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) @require_accelerate @mark.accelerate_tests @require_torch_accelerator @unittest.skip("TODO @cyrilvallez when saving") def test_save_offloaded_model_dynamic_tied_weights_keys(self): from accelerate import dispatch_model device_map = {"base": f"{torch_device}:0", "linear": "cpu", "linear2": "cpu"} model = ModelWithHead(PreTrainedConfig()) dispatch_model(model, device_map) transform_a = torch.nn.Linear(1, 1, bias=False) transform_a._dynamic_tied_weights_keys = ["weight"] transform_b = torch.nn.Linear(1, 1, bias=False) transform_b._dynamic_tied_weights_keys = ["weight"] model.linear.register_module("transform_a", transform_a) model.linear.register_module("transform_b", transform_b) model.linear2.register_module("transform_a", transform_a) model.linear2.register_module("transform_b", transform_b) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) 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: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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: # TODO: only necessary for read-only cache systems; replace with a shared helper with unittest.mock.patch.dict(os.environ, {"HF_XET_CACHE": 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") 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 model.safetensors, or pytorch_model.bin" in str(missing_model_file_error.exception), msg=missing_model_file_error.exception, ) 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) # 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) 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) 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, 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) 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) @unittest.skip("This now just works by defaults :) no complicated load from task blah blah") 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) # 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(tie_word_embeddings=True)) 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.assertSetEqual(load_info["missing_keys"], set()) self.assertIs(new_model.linear.weight, new_model.linear_2.weight) # With head model = BaseModel(PreTrainedConfig(tie_word_embeddings=True)) 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.assertSetEqual(load_info["missing_keys"], {"decoder.bias"}) def test_tied_weights_can_load_symmetrically(self): """Test that we can correctly load and tie weights even though the wrong key was saved.""" model = BaseModelWithTiedWeights(PreTrainedConfig(tie_word_embeddings=True)) # Just to be sure it's actually tied self.assertIs(model.linear.weight, model.linear_2.weight, msg="Weights are not tied!") with tempfile.TemporaryDirectory() as tmp_dir: # Save the config with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write(json.dumps(model.config.to_dict())) state_dict = model.state_dict() # Save using the wrong key state_dict.pop("linear.weight") safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) # Assert no missing keys self.assertSetEqual(load_info["missing_keys"], set(), msg=f"{load_info['missing_keys']} are missing!") # It's still the same weight self.assertIs(new_model.linear.weight, new_model.linear_2.weight, msg="Weights are not tied!") # Make sure both state dict are the same compare_state_dicts(model.state_dict(), new_model.state_dict()) def test_tied_weights_can_load_symmetrically_multiple_keys(self): """Test that we can correctly load and tie weights even though the wrong key was saved, when we have more than 1 target to the same source.""" # First class is consistent in how they provide the source, second is not -> make sure it works in both cases for model_class in [BaseModelWithMultipleTiedWeights, BaseModelWithMultipleMixedTiedWeights]: with self.subTest(model_class.__name__): model = model_class(PreTrainedConfig(tie_word_embeddings=True)) # Just to be sure it's actually tied self.assertIs(model.linear.weight, model.linear_2.weight, msg="Weights are not tied!") self.assertIs(model.linear.weight, model.linear_3.weight, msg="Weights are not tied!") with tempfile.TemporaryDirectory() as tmp_dir: # Save the config with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write(json.dumps(model.config.to_dict())) state_dict = model.state_dict() # Keep only 1 of the 3 tied keys, but not the source (which is `linear.weight`) state_dict.pop("linear.weight") state_dict.pop("linear_3.weight") safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) new_model, load_info = BaseModelWithMultipleTiedWeights.from_pretrained( tmp_dir, output_loading_info=True ) # Assert no missing keys self.assertSetEqual( load_info["missing_keys"], set(), msg=f"{load_info['missing_keys']} are missing!" ) # It's still the same weight self.assertIs(new_model.linear.weight, new_model.linear_2.weight, msg="Weights are not tied!") self.assertIs(new_model.linear.weight, new_model.linear_3.weight, msg="Weights are not tied!") # Make sure both state dict are the same compare_state_dicts(model.state_dict(), new_model.state_dict()) # Now, do the same but try to keep `linear_2.weight` in the saved key instead of `linear_3.weight` # to make sure it does not matter state_dict = model.state_dict() # Keep only 1 of the 3 tied keys, but not the source (which is `linear.weight`) state_dict.pop("linear.weight") state_dict.pop("linear_2.weight") safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) new_model, load_info = BaseModelWithMultipleTiedWeights.from_pretrained( tmp_dir, output_loading_info=True ) # Assert no missing keys self.assertSetEqual( load_info["missing_keys"], set(), msg=f"{load_info['missing_keys']} are missing!" ) # It's still the same weight self.assertIs(new_model.linear.weight, new_model.linear_2.weight, msg="Weights are not tied!") self.assertIs(new_model.linear.weight, new_model.linear_3.weight, msg="Weights are not tied!") # Make sure both state dict are the same compare_state_dicts(model.state_dict(), new_model.state_dict()) def test_tied_weights_are_not_tied_if_both_present_but_different(self): """Test that if both the source and target of tied weights are present and different, we do NOT tie them, and instead raise a warning""" model = BaseModelWithTiedWeights(PreTrainedConfig(tie_word_embeddings=True)) # Just to be sure it's actually tied self.assertIs(model.linear.weight, model.linear_2.weight, msg="Weights are not tied!") with tempfile.TemporaryDirectory() as tmp_dir: # Save the config with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write(json.dumps(model.config.to_dict())) state_dict = model.state_dict() # Clone every param to make sure nothing is tied -> we save everything state_dict = {k: v.clone() for k, v in state_dict.items()} # Make sure the target tied weights has a different value than the source state_dict["linear_2.weight"] = state_dict["linear_2.weight"] + 2 safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) # We should have raised a warning here saying that we will NOT tie the weights self.assertIn("both are present in the checkpoints with different values, so we will NOT tie them", cl.out) # Assert no missing keys self.assertSetEqual(load_info["missing_keys"], set(), msg=f"{load_info['missing_keys']} are missing!") # It should not be the same weight anymore self.assertIsNot( new_model.linear.weight, new_model.linear_2.weight, msg="Weights are tied but they should not!" ) def test_tied_weights_are_tied_if_both_present_and_similar(self): """Test that if both the source and target of tied weights are present but have same values, we tie them""" model = BaseModelWithTiedWeights(PreTrainedConfig(tie_word_embeddings=True)) # Just to be sure it's actually tied self.assertIs(model.linear.weight, model.linear_2.weight, msg="Weights are not tied!") with tempfile.TemporaryDirectory() as tmp_dir: # Save the config with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write(json.dumps(model.config.to_dict())) state_dict = model.state_dict() # Clone every param to make sure nothing is tied -> we save everything state_dict = {k: v.clone() for k, v in state_dict.items()} safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) # Assert no missing keys self.assertSetEqual(load_info["missing_keys"], set(), msg=f"{load_info['missing_keys']} are missing!") # It should still be the same weight self.assertIs( new_model.linear.weight, new_model.linear_2.weight, msg="Weights are NOT tied but they should be!" ) # Make sure both state dict are the same compare_state_dicts(model.state_dict(), new_model.state_dict()) def test_tied_weights_are_missing_if_both_absent(self): """Test that if both the source and target of tied weights are absent, we do tie them, but they are missing""" model = BaseModelWithTiedWeights(PreTrainedConfig(tie_word_embeddings=True)) # Just to be sure it's actually tied self.assertIs(model.linear.weight, model.linear_2.weight, msg="Weights are not tied!") with tempfile.TemporaryDirectory() as tmp_dir: # Save the config with open(os.path.join(tmp_dir, "config.json"), "w") as f: f.write(json.dumps(model.config.to_dict())) state_dict = model.state_dict() # Remove both from the state dict state_dict.pop("linear.weight") state_dict.pop("linear_2.weight") safe_save_file(state_dict, os.path.join(tmp_dir, "model.safetensors")) logger = logging.get_logger("transformers.modeling_utils") with CaptureLogger(logger) as cl: new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True) # We should have raised a warning here saying that we will NOT tie the weights self.assertIn( "This checkpoint seem corrupted. The tied weights mapping for this model specifies to tie", cl.out ) # Assert both are in the missing keys self.assertSetEqual(load_info["missing_keys"], {"linear.weight", "linear_2.weight"}) # They should still be tied though self.assertIs(new_model.linear.weight, new_model.linear_2.weight, msg="Weights are not tied!") def test_tied_weights_are_always_tied_from_config(self): """Test that if a model is initialized from config it's always tied, and that the context `no_tie_weights` works as expected""" config = LlamaConfig(num_hidden_layers=2, hidden_size=32, intermediate_size=16, tie_word_embeddings=True) # Make sure they are tied if called with `_from_config` and directly model = LlamaForCausalLM._from_config(copy.deepcopy(config)) self.assertTrue(model.lm_head.weight is model.model.embed_tokens.weight) model = LlamaForCausalLM(copy.deepcopy(config)) self.assertTrue(model.lm_head.weight is model.model.embed_tokens.weight) # Also when using a meta device explicitly (as it skips e.g. weight init automatically) with torch.device("meta"): model = LlamaForCausalLM._from_config(copy.deepcopy(config)) self.assertTrue(model.lm_head.weight is model.model.embed_tokens.weight) model = LlamaForCausalLM(copy.deepcopy(config)) self.assertTrue(model.lm_head.weight is model.model.embed_tokens.weight) # Make sure the context works as expected with init.no_tie_weights(): model = LlamaForCausalLM._from_config(copy.deepcopy(config)) self.assertTrue(model.lm_head.weight is not model.model.embed_tokens.weight) def test_unexpected_keys_warnings(self): model = ModelWithHead(PreTrainedConfig(tie_word_embeddings=True)) 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"]), {"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) # Will be colored if terminal is interactive expected_output = "added_key | UNEXPECTED" if sys.stdout.isatty() else "added_key | UNEXPECTED" self.assertIn(expected_output, 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(pad_token_id=None, bos_token_id=None, eos_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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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(pad_token_id=0, bos_token_id=0, eos_token_id=None) 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) 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(pad_token_id=0, bos_token_id=None, eos_token_id=None) 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, dtype=torch.float16, ) 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) 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) 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_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, 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_saving_model_config_with_generation_params(self): """ Calling `model.save_pretrained` with generation parameters should raise a `ValueError` """ model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") self.assertTrue(model.generation_config.repetition_penalty is None) self.assertFalse(hasattr(model.config, "repetition_penalty")) # If the user attempts to save a custom generation parameter, we raise an Error model.config.repetition_penalty = 3.0 with self.assertRaises(ValueError): with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) 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) 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_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 - Legacy: models with a custom `prepare_inputs_for_generation` can generate (it was assumed # they inherited `GenerationMixin`). Deprecated in v4.45 and removed in v4.51. 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.assertFalse(can_generate) def test_save_and_load_config_with_custom_generation(self): """ Tests that saving and loading a config with a custom generation kwarg is not possible """ model = T5ForConditionalGeneration.from_pretrained(TINY_T5) self.assertTrue(model.generation_config.num_beams is None) self.assertTrue(model.generation_config.early_stopping is None) self.assertFalse(hasattr(model.config, "num_beams")) self.assertFalse(hasattr(model.config, "early_stopping")) # Sanity check: We can run `generate` with the model without any warnings random_ids = torch.randint(0, 100, (1, 5)) with warnings.catch_warnings(record=True) as w: model.generate(random_ids, max_new_tokens=3) self.assertTrue(len(w) == 0) # When we save the model and config has generation-related parameter, # we will throw an error, nudging user to save attributes in the generation_config model.config.num_beams = 5 model.config.early_stopping = True self.assertTrue(model.generation_config.num_beams is None) # default value with tempfile.TemporaryDirectory() as tmp_dir: with self.assertRaises(ValueError): model.save_pretrained(tmp_dir) 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)) @unittest.skip("Skipping flaky test") 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(1, 2, num_virtual_tokens, 8), torch.randn(1, 2, num_virtual_tokens, 8)), (torch.randn(1, 2, num_virtual_tokens, 8), torch.randn(1, 2, num_virtual_tokens, 8)), ] past_key_values = DynamicCache(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 # NOTE: after #41900, we need to pass the correct attention mask size model_inputs["attention_mask"] = model_inputs["attention_mask"][:, :-num_virtual_tokens] model_outputs = model(**model_inputs, use_cache=False) self.assertIsNone(model_outputs.past_key_values) self.assertTrue(model.training) def test_restore_default_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 `torch.set_default_dtype` original_set_default_dtype = torch.set_default_dtype def debug(*args, **kwargs): # call the method as usual, than raise a RuntimeError original_set_default_dtype(*args, **kwargs) raise RuntimeError with patch("torch.set_default_dtype", new=debug): with self.assertRaises(RuntimeError): _ = AutoModelForCausalLM.from_pretrained(TINY_MISTRAL, device_map="auto", dtype=torch.float16) # default should still be float32 self.assertTrue(torch.get_default_dtype() == torch.float32) torch.set_default_dtype(old_dtype) def test_restore_default_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 `torch.set_default_dtype` original_set_default_dtype = torch.set_default_dtype def debug(*args, **kwargs): # call the method as usual, than raise a RuntimeError original_set_default_dtype(*args, **kwargs) raise RuntimeError with patch("torch.set_default_dtype", new=debug): with self.assertRaises(RuntimeError): config.dtype = torch.float16 _ = AutoModelForCausalLM.from_config(config) # default should still be float32 self.assertTrue(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_torch_accelerator 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, dtype=torch.float16) script_to_run = textwrap.dedent( """ import torch import time import argparse from transformers import AutoModelForCausalLM from transformers.utils import is_torch_accelerator_available parser = argparse.ArgumentParser() parser.add_argument("model_id", type=str) parser.add_argument("max_loading_time", type=float) args = parser.parse_args() device_type = torch.accelerator.current_accelerator().type if is_torch_accelerator_available() else "cuda" device = torch.device(f"{device_type}:0") torch_accelerator_module = getattr(torch, device_type, torch.cuda) torch_accelerator_module.synchronize(device) t0 = time.time() model = AutoModelForCausalLM.from_pretrained(args.model_id, dtype=torch.float16, device_map=device) torch_accelerator_module.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 accelerator 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 accelerator: {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}") def test_explicit_transformers_weights(self): """ Transformers supports loading from repos where the weights file is explicitly set in the config. When loading a config file, transformers will see whether `transformers_weights` is defined in the config. If so, it will load from that file. Here, we ensure that the correct file is loaded. """ model = BertModel.from_pretrained("hf-internal-testing/explicit_transformers_weight_in_config") self.assertEqual(model.num_parameters(), 87929) def test_explicit_transformers_weights_index(self): """ Transformers supports loading from repos where the weights file is explicitly set in the config. When loading a config file, transformers will see whether `transformers_weights` is defined in the config. If so, it will load from that file. Here, we ensure that the correct file is loaded, given the file is an index of multiple weights. """ model = BertModel.from_pretrained("hf-internal-testing/explicit_transformers_weight_in_config_sharded") self.assertEqual(model.num_parameters(), 87929) def test_explicit_transformers_weights_save_and_reload(self): """ Transformers supports loading from repos where the weights file is explicitly set in the config. When loading a config file, transformers will see whether `transformers_weights` is defined in the config. If so, it will load from that file. When saving the model, we should be careful not to safe the `transformers_weights` attribute in the config; otherwise, transformers will try to load from that file whereas it should simply load from the default file. We test that for a non-sharded repo. """ model = BertModel.from_pretrained("hf-internal-testing/explicit_transformers_weight_in_config") explicit_transformers_weights = model.config.transformers_weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # The config should not have a mention of transformers_weights with open(os.path.join(tmpdirname, "config.json")) as f: config = json.loads(f.read()) self.assertFalse("transformers_weights" in config) # The serialized weights should be in model.safetensors and not the transformers_weights self.assertTrue(explicit_transformers_weights not in os.listdir(tmpdirname)) self.assertTrue("model.safetensors" in os.listdir(tmpdirname)) def test_explicit_transformers_weights_index_save_and_reload(self): """ Transformers supports loading from repos where the weights file is explicitly set in the config. When loading a config file, transformers will see whether `transformers_weights` is defined in the config. If so, it will load from that file. When saving the model, we should be careful not to safe the `transformers_weights` attribute in the config; otherwise, transformers will try to load from that file whereas it should simply load from the default file. We test that for a sharded repo. """ model = BertModel.from_pretrained("hf-internal-testing/explicit_transformers_weight_in_config_sharded") explicit_transformers_weights = model.config.transformers_weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, max_shard_size="100kb") # The config should not have a mention of transformers_weights with open(os.path.join(tmpdirname, "config.json")) as f: config = json.loads(f.read()) self.assertFalse("transformers_weights" in config) # The serialized weights should be in model.safetensors and not the transformers_weights self.assertTrue(explicit_transformers_weights not in os.listdir(tmpdirname)) self.assertTrue("model.safetensors.index.json" in os.listdir(tmpdirname)) def test_config_class_attribute(self): # custom configs class MyConfigA(PreTrainedConfig): pass class MyConfigB(PreTrainedConfig): pass class MyConfigC(PreTrainedConfig): pass # custom models class MyModelA(PreTrainedModel): config: dict config_class = MyConfigA class MyModelB(MyModelA): config: MyConfigB class MyModelC(MyModelA): config_class = MyConfigC class MyModelD(MyModelA): pass # child config_class > child 'config:' > parent config_class > parent 'config:' self.assertIs(MyModelA.config_class, MyConfigA) self.assertIs(MyModelB.config_class, MyConfigB) self.assertIs(MyModelC.config_class, MyConfigC) self.assertIs(MyModelD.config_class, MyConfigA) def test_ignore_missing_key_works(self): """Test that if a parameter (not buffer) is specified in `_keys_to_ignore_on_load_missing` and is actually missing from the checkpoint, it will still be moved to cpu and initialized""" temp = tempfile.TemporaryDirectory() # Create dummy model model = BaseModelWithMissingKeys(PreTrainedConfig()) # Save the config model.config.save_pretrained(temp.name) # Get the state dict to save state_dict = model.state_dict() # Remove the layer that we should ignore if missing del state_dict["linear.weight"], state_dict["linear.bias"] # Save the state dict as a single shard safe_save_file(state_dict, Path(temp.name) / "model.safetensors", metadata={"format": "pt"}) # Try loading back, with the missing key not present in the state_dict model = BaseModelWithMissingKeys.from_pretrained(temp.name) # Make sure the skipped missing key is not still on meta device! for k, v in model.state_dict().items(): self.assertTrue(v.device.type == "cpu", f"{k} is not on cpu!") def test_device_map_works_with_unexpected_keys(self): """Test that if a parameter is specified in `_keys_to_ignore_on_load_unexpected` and is actually present in the checkpoint, it will correctly be removed from the weights we load, especially those we use if the device map has offloading""" temp = tempfile.TemporaryDirectory() # Create dummy model model = BaseModelWithUnexpectedKeys(PreTrainedConfig()) # Save the config model.config.save_pretrained(temp.name) # Get the state dict to save state_dict = model.state_dict() # Add a layer that is in the "_keys_to_ignore_on_load_unexpected" list to ignore state_dict["mtp"] = torch.randn(12, 12) # Save the state dict as a single shard safe_save_file(state_dict, Path(temp.name) / "model.safetensors", metadata={"format": "pt"}) # Load the model with entire shards placed on disk in order to trigger `get_disk_only_shard_files`. # Unexpected keys (mtp) should be removed from the state dict, therefore this should not error out. BaseModelWithUnexpectedKeys.from_pretrained(temp.name, device_map={"linear": "cpu", "linear_2": "disk"}) def test_device_map_works_with_unexpected_keys_sharded(self): """Test that if a parameter is specified in `_keys_to_ignore_on_load_unexpected` and is actually present in the checkpoint, it will correctly be removed from the weights we load, especially those we use if the device map has offloading""" temp = tempfile.TemporaryDirectory() # Create dummy model model = BaseModelWithUnexpectedKeys(PreTrainedConfig()) # Save the config model.config.save_pretrained(temp.name) # Get the state dict to save state_dict = model.state_dict() # Add a layer that is in the "_keys_to_ignore_on_load_unexpected" list to ignore state_dict["mtp"] = torch.randn(50, 50) # Split the state dict in shards, save the index and the shards shards = split_torch_state_dict_into_shards(state_dict, max_shard_size="1kb") index = { "metadata": {"total_parameters": model.num_parameters(), **shards.metadata}, "weight_map": shards.tensor_to_filename, } with open(Path(temp.name) / SAFE_WEIGHTS_INDEX_NAME, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) # Save each shard filename_to_tensors = shards.filename_to_tensors.items() for shard_file, tensors in filename_to_tensors: shard = {} for tensor in tensors: shard[tensor] = state_dict[tensor].contiguous() safe_save_file(shard, Path(temp.name) / shard_file, metadata={"format": "pt"}) # Load the model with entire shards placed on disk in order to trigger `get_disk_only_shard_files`. # Unexpected keys (mtp) should be removed from the state dict, therefore this should not error out. BaseModelWithUnexpectedKeys.from_pretrained(temp.name, device_map={"linear": "cpu", "linear_2": "disk"}) def test_loading_respect_env_variable_for_threading(self): """Test that we can correctly control threading during loading""" model = BaseModel(PreTrainedConfig()) # Monkey patch Thread.__init__ to add a counter of launched threads original_init = threading.Thread.__init__ counter = 0 def tracking_init(self, *args, **kwargs): nonlocal counter counter += 1 original_init(self, *args, **kwargs) threading.Thread.__init__ = tracking_init with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # Use threading os.environ["HF_DEACTIVATE_ASYNC_LOAD"] = "0" before = counter _ = BaseModel.from_pretrained(tmpdirname) after = counter self.assertTrue(after - before > 0, "Loading should have spawned new threads!") # Deactivate threading os.environ["HF_DEACTIVATE_ASYNC_LOAD"] = "1" before = counter _ = BaseModel.from_pretrained(tmpdirname) after = counter self.assertTrue(after == before, "It looks like loading did spawn new threads, but it should not have!") # Reverse monkey patch threading.Thread.__init__ = original_init def test_error_in_weight_conversion_is_raised(self): """Test that errors in `ConversionOps` are correctly re-raised after loading.""" small_config = MixtralConfig(num_hidden_layers=2, hidden_size=32, intermediate_size=32, num_attention_heads=8) model = MixtralModel(small_config) weight_conversions = get_model_conversion_mapping(model) converters = [conversion for conversion in weight_conversions if isinstance(conversion, WeightConverter)] # Just a safeguard self.assertTrue( any(isinstance(ops, MergeModulelist) for converter in converters for ops in converter.operations), "The test is useless without conversions on the model", ) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # Now try to reload while mocking the WeightConversion to raise with patch.object(MergeModulelist, "convert", side_effect=Exception("failed")): # It should raise the proper error with self.assertRaisesRegex( RuntimeError, "We encountered some issues during automatic conversion of the weights." ): _ = MixtralModel.from_pretrained(tmpdirname) def test_composite_model_inherit_properties(self): model = MultimodalModel(PreTrainedConfig()) # Make sure the top level inherited properties from its child language and vision models self.assertEqual(model._no_split_modules, {"VerySimpleLayer"}) # language model self.assertEqual(model._keep_in_fp32_modules, {"linear", "head"}) # language model + composite model self.assertEqual(model._keep_in_fp32_modules_strict, {"simple"}) # vision model @parameterized.expand([("sdpa",), ("flash_attention_2",)]) def test_decoder_only_model_can_be_used_as_encoder(self, attn_implementation: str): """Test that most well-behaved decoder models can be used as encoders through the `is_causal` kwarg/config. Note that it's enough to test it on Llama, as the entry points are all through general code (masking_utils.py + `capture_outputs` decorator). This makes it easier as the model need to use both the mask API from masking_utils.py and the decorator as mentionned above, and we don't know what models follow that standard exactly (so we cannot make it easily a common model test).""" if attn_implementation == "flash_attention_2" and not is_flash_attn_2_available(): self.skipTest("FA2 not available") from transformers import LlamaConfig, LlamaModel from transformers.masking_utils import create_bidirectional_mask config = LlamaConfig( num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=1, head_dim=16, hidden_size=32, intermediate_size=64, vocab_size=100, attn_implementation=attn_implementation, ) model = LlamaModel(copy.deepcopy(config)).to(device=torch_device, dtype=torch.bfloat16) # Create inputs, making sure we use padding to verify that mask creation accounts for it correctly input_ids = torch.randint(5, 95, (2, 17), device=torch_device) attention_mask = torch.ones_like(input_ids, device=torch_device) attention_mask[1, 0:3] = 0 # The original `create_causal_mask` used in modeling_llama forward more kwargs than `create_bidirectional_mask`, # so we need this one instead to absorb them def create_bidirectional_mask_with_kwargs( config, inputs_embeds, attention_mask, encoder_hidden_states=None, or_mask_function=None, and_mask_function=None, **kwargs, ): return create_bidirectional_mask( config, inputs_embeds, attention_mask, encoder_hidden_states, or_mask_function, and_mask_function ) # Explicitly monkey patch the mask creation function + forward the is_causal kwarg to get the expected result # from the model behaving as encoder instead of decoder with patch( "transformers.models.llama.modeling_llama.create_causal_mask", new=create_bidirectional_mask_with_kwargs ): reference = model(input_ids, attention_mask=attention_mask, is_causal=False).last_hidden_state without_kwarg = model(input_ids, attention_mask=attention_mask).last_hidden_state # Here, since we have padding, the mask created should never be None. Since the mask is never None, the sdpa # backend will always use `is_causal=False`, so both should be strictly equivalent if attn_implementation == "sdpa": torch.testing.assert_close(reference, without_kwarg) # But FA2 relies solely on the `is_causal` kwarg to decide how to dispatch, as it will use varlen since we # have padding, so both won't be equivalent at all else: # Everything should be different (we only test the maximum of the diff to avoid flakyness) self.assertTrue(torch.abs(reference - without_kwarg).max() >= 1e-1) # Now if we simply forward the kwarg with the usual mask function, it should still work the exact same with_kwarg_only = model(input_ids, attention_mask=attention_mask, is_causal=False).last_hidden_state torch.testing.assert_close(reference, with_kwarg_only) # Now, if we use the usual forward, the model should behave normally as a decoder, and output should be # completely different as_decoder = model(input_ids, attention_mask=attention_mask).last_hidden_state # Everything should be different (we only test the maximum of the diff to avoid flakyness) self.assertTrue(torch.abs(reference - as_decoder).max() >= 1e-1) # It should also work with it in the config model.config.is_causal = False with_config_only = model(input_ids, attention_mask=attention_mask).last_hidden_state torch.testing.assert_close(reference, with_config_only) def test_linear_attention_models_can_use_accelerate_hooks(self): """ Test that linear attention models (here only tested on lfm2 as it has small checkpoints) can use device_map and offloading correctly (the conv module inside the Mixer are not used through `forward`, so accelerate hooks have to be forced) """ model_id = "LiquidAI/LFM2.5-230M" input_text = "Hello, who are you?" tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer.apply_chat_template( [{"role": "user", "content": input_text}], tokenize=True, return_tensors="pt", add_generation_prompt=True, ) model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16) out = model.generate(**inputs.to(model.device), max_new_tokens=20, do_sample=False) output_text1 = tokenizer.batch_decode(out[:, inputs["input_ids"].shape[1] :])[0] # This will offload to disk model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.float16, device_map="auto", max_memory={"cpu": "200MiB", "disk": "12GiB"} ) # Make sure it was offloaded self.assertTrue("cpu" in model.hf_device_map.values()) self.assertTrue("disk" in model.hf_device_map.values()) # Make sure we will not crash when reaching a disk offloaded mixer with linear attention out = model.generate(**inputs.to(model.device), max_new_tokens=20, do_sample=False) output_text2 = tokenizer.batch_decode(out[:, inputs["input_ids"].shape[1] :])[0] EXPECTED_TEXT = ( "I’m an AI language model created to help answer questions, provide information, and assist with a wide" ) # Make sure they are equal, and equal to the ref self.assertEqual(output_text1, output_text2) self.assertEqual(output_text1, EXPECTED_TEXT) @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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token) 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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, 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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token) self.api.update_repo_settings(self.repo_name, gated="auto") 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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, 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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, 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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, max_shard_size="200kb") headers = {"Authorization": f"Bearer {self.token}"} httpx.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) # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, 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` # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token) # Push a model on a given revision # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token, 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` # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token) # 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 = httpx.HTTPError("failed") 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` # Since we don't support saving with bins files anymore, but still support loading we use this context # to easily create the bins files and try to load them with force_serialization_as_bin_files(): initial_model.push_to_hub(self.repo_name, token=self.token) # 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 @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, create_pr=True, token=self._token, 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 TestAttentionImplementation(unittest.TestCase): @unittest.skip("Just a bit annoying") 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") # TODO (ydshieh): use another model @unittest.skip("model deleted") 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)) # TODO (ydshieh): use another model @unittest.skip("model deleted") 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)) # TODO (ydshieh): use another model @unittest.skip("model deleted") 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_registered_experts_implementation_is_valid(self): from transformers.integrations.moe import ALL_EXPERTS_FUNCTIONS def custom_experts_forward(*args, **kwargs): pass experts_implementation = "custom_experts" model = BaseModel(PreTrainedConfig()) with patch.dict(ALL_EXPERTS_FUNCTIONS._global_mapping, {}, clear=False): ALL_EXPERTS_FUNCTIONS.register(experts_implementation, custom_experts_forward) self.assertEqual(model.get_correct_experts_implementation(experts_implementation), experts_implementation) 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." ) if is_kernels_available(): self.skipTest(reason="Please uninstall `kernels` package to run `test_not_available_flash`") with self.assertRaises(ImportError) as cm: _ = AutoModel.from_pretrained( "hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="flash_attention_2" ) self.assertTrue("the package for FlashAttention2 doesn't seem to be installed." in str(cm.exception)) @parameterized.expand( [ # (key head_dim, value head_dim, use_mask, GQA kept, expected backend): GQA is kept only for matched # head_dim <= 256 without a mask, else `repeat_kv` runs so a fused kernel still serves it. (256, 256, False, True, "flash"), # matched <= 256: GQA kept (512, 512, False, False, "efficient"), # head_dim > 256: repeat_kv (192, 128, False, False, "efficient"), # mismatched (MLA): repeat_kv (256, 256, True, False, "efficient"), # mask disables GQA ] ) @require_torch_gpu def test_gqa_in_sdpa_uses_a_fused_kernel( self, key_head_dim, value_head_dim, use_mask, keeps_gqa, expected_backend ): # Restricting SDPA to the expected backend (which excludes `math`) proves each case stays on a fused kernel # rather than silently falling back to math: the call must succeed. from torch.nn.attention import SDPBackend, sdpa_kernel from transformers.integrations.sdpa_attention import sdpa_attention_forward, use_gqa_in_sdpa backends = {"flash": SDPBackend.FLASH_ATTENTION, "efficient": SDPBackend.EFFICIENT_ATTENTION} module = torch.nn.Module() module.num_key_value_groups = 4 # 8 query heads, 2 key/value heads query = torch.randn(1, 8, 16, key_head_dim, device=torch_device, dtype=torch.float16) key = torch.randn(1, 2, 16, key_head_dim, device=torch_device, dtype=torch.float16) value = torch.randn(1, 2, 16, value_head_dim, device=torch_device, dtype=torch.float16) attention_mask = torch.zeros(1, 1, 16, 16, device=torch_device, dtype=torch.float16) if use_mask else None self.assertEqual(use_gqa_in_sdpa(attention_mask, key, value), keeps_gqa) with sdpa_kernel([backends[expected_backend]]): attn_output, _ = sdpa_attention_forward(module, query, key, value, attention_mask=attention_mask) self.assertEqual(attn_output.shape, (1, 16, 8, value_head_dim)) def test_flash_attn_available_no_keyerror_when_missing_from_distribution_map(self): # Regression test for https://github.com/huggingface/transformers/issues/45520. # When flash_attn is importable but not present in PACKAGE_DISTRIBUTION_MAPPING # (e.g. installed via a non-standard wheel), the availability checks must not raise # a KeyError; they should simply return False. stripped_map = { k: v for k, v in PACKAGE_DISTRIBUTION_MAPPING.items() if k not in ("flash_attn", "flash_attn_interface") } with patch("transformers.utils.import_utils.PACKAGE_DISTRIBUTION_MAPPING", stripped_map): with patch("transformers.modeling_flash_attention_utils.PACKAGE_DISTRIBUTION_MAPPING", stripped_map): self.assertFalse(is_flash_attn_2_available()) self.assertFalse(is_flash_attn_3_available()) self.assertFalse(is_flash_attn_4_available()) 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." ) if is_kernels_available(): self.skipTest(reason="Please uninstall `kernels` package to run `test_not_available_flash_with_config`") 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 for FlashAttention2 doesn't seem to be installed." in str(cm.exception)) def test_kernels_fallback(self): if not is_kernels_available(): self.skipTest(reason="Please install `kernels` package to run `test_kernels_fallback`") if is_flash_attn_2_available(): self.skipTest(reason="Please uninstall flash-attn package to run test_kernels_fallback") if is_torch_npu_available(): self.skipTest( reason="FlashAttention2 is supported on Ascend NPU without using package `flash-attn`, ignore this test case." ) logger = logging.get_logger("transformers.modeling_utils") with LoggingLevel(logging.WARNING): with CaptureLogger(logger) as cl: _ = AutoModel.from_pretrained( "hf-internal-testing/tiny-random-GPTBigCodeModel", attn_implementation="flash_attention_2" ) self.assertTrue( f"You do not have `flash_attn` installed, using `{FLASH_ATTN_KERNEL_FALLBACK['flash_attention_2']}` from the `kernels` library instead!" in cl.out ) # TODO (ydshieh): use another model @unittest.skip("model deleted") def test_not_available_kernels(self): if is_kernels_available(): self.skipTest(reason="Please uninstall `kernels` package to run `test_not_available_kernels`") with self.assertRaises(ImportError) as cm: _ = AutoModel.from_pretrained( "hf-tiny-model-private/tiny-random-MCTCTModel", attn_implementation=FLASH_ATTN_KERNEL_FALLBACK["flash_attention_2"], ) self.assertTrue("`kernels` is either not installed or uses an incompatible version." in str(cm.exception)) def test_attention_and_experts_modules_can_be_used_standalone(self): """Test that both Attention and Expert modules can be used on their own, instantiated from a config without the respective `_xxx_implementation` attr set. Also checks that it correctly raises a warning""" from transformers.models.mixtral.configuration_mixtral import MixtralConfig from transformers.models.mixtral.modeling_mixtral import ( MixtralAttention, MixtralExperts, MixtralRotaryEmbedding, ) hidden_size = 32 seq_len = 10 config = MixtralConfig(hidden_size=32, intermediate_size=16, num_hidden_layers=2) experts_module = MixtralExperts(config) attn_module = MixtralAttention(config, layer_idx=0) hidden_states = torch.randn(1, seq_len, hidden_size) # Try the Attention (check it works + raises the warning) dummy_ids = torch.arange(seq_len).unsqueeze(0) dummy_embeddings = MixtralRotaryEmbedding(config)(hidden_states, dummy_ids) with CaptureLogger(logging.get_logger("transformers.modeling_utils")) as cl: _ = attn_module(hidden_states, dummy_embeddings, None) self.assertIn( "You tried to access the `AttentionInterface` with a `config._attn_implementation` set to `None`.", cl.out ) # With a wrong _attn_implementation, it should raise a proper exception attn_module.config._attn_implementation = "foobar" with self.assertRaisesRegex(KeyError, "`foobar` is not a valid attention implementation registered"): _ = attn_module(hidden_states, dummy_embeddings, None) # Try the Experts (check it works + raises the warning) hidden_states = hidden_states.reshape(-1, hidden_size) dummy_scores = torch.randn(seq_len, config.num_experts_per_tok) dummy_indices = torch.randint(0, config.num_local_experts, (seq_len, config.num_experts_per_tok)) with CaptureLogger(logging.get_logger("transformers.integrations.moe")) as cl: _ = experts_module(hidden_states, dummy_indices, dummy_scores) self.assertIn( "You tried to access the `ExpertsInterface` with a `config._experts_implementation` set to `None`.", cl.out ) # With a wrong _experts_implementation, it should raise a proper exception experts_module.config._experts_implementation = "foobar" with self.assertRaisesRegex(KeyError, "`foobar` is not a valid experts implementation registered"): _ = experts_module(hidden_states, dummy_indices, dummy_scores) def test_can_set_attn_returns_false_when_module_missing(self): # Simulate the "module cleared from sys.modules" case (test cleanup, REPL). from transformers.models.llama.modeling_llama import LlamaModel # The method _can_set_attn_implementation caches the result on a succesful call, so we need to clear the cache if hasattr(LlamaModel, "_can_set_attn_implementation_cached_value"): delattr(LlamaModel, "_can_set_attn_implementation_cached_value") original = sys.modules.pop(LlamaModel.__module__) try: self.assertFalse(LlamaModel._can_set_attn_implementation()) self.assertFalse(LlamaModel._can_set_experts_implementation()) finally: sys.modules[LlamaModel.__module__] = original def test_can_set_attn_skips_torch_dynamic_wrappers(self): # Simulate FSDP2's `FSDP`: a dynamic subclass whose __module__ lives under torch.* from transformers.models.llama.modeling_llama import LlamaModel FSDPLlamaModel = type("FSDPLlamaModel", (LlamaModel,), {}) FSDPLlamaModel.__module__ = "torch.distributed.fsdp._fully_shard._fsdp_state" # The MRO walk should skip past FSDPLlamaModel to LlamaModel and find the underlying answer. self.assertTrue(FSDPLlamaModel._can_set_attn_implementation()) def test_can_set_attn_modern_vs_legacy(self): # Modern interface model: True. Legacy model (T5 doesn't use ALL_ATTENTION_FUNCTIONS): False. from transformers.models.llama.modeling_llama import LlamaModel from transformers.models.t5.modeling_t5 import T5Model self.assertTrue(LlamaModel._can_set_attn_implementation()) self.assertFalse(T5Model._can_set_attn_implementation()) def test_can_set_attn_legacy_edge_cases(self): # FSMT: bare `class Attention(nn.Module):` -- tightened regex catches this case. from transformers.models.fsmt.modeling_fsmt import FSMTModel self.assertFalse(FSMTModel._can_set_attn_implementation()) # SLANet: `class SLANetAttentionGRUCell(nn.Module):` -- "Attention" not at end of class name. from transformers.models.slanet.modeling_slanet import SLANetBackbone self.assertFalse(SLANetBackbone._can_set_attn_implementation()) # ShieldGemma2: output dataclass with "Attention" in the name but no `nn.Module` parent. # No actual Attention class -> assume True (multimodal model or inherits from elsewhere). from transformers.models.shieldgemma2.modeling_shieldgemma2 import ShieldGemma2ForImageClassification self.assertTrue(ShieldGemma2ForImageClassification._can_set_attn_implementation()) def test_can_set_experts_moe_vs_dense(self): # MoE model with @use_experts_implementation: True. Non-MoE model: False. from transformers.models.llama.modeling_llama import LlamaModel from transformers.models.mixtral.modeling_mixtral import MixtralModel self.assertTrue(MixtralModel._can_set_experts_implementation()) self.assertFalse(LlamaModel._can_set_experts_implementation()) @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, []) @require_torch class TestSaveAndLoadModelWithExtraState(TestCasePlus): """ This test checks that a model can be saved and loaded that uses the torch extra state API. https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.get_extra_state. Currently, only tensor-valued extra_states are supported. """ def test_save_and_load_model_with_tensor_extra_state(self): class MyConfig(PreTrainedConfig): def __init__(self, **kwargs): super().__init__(**kwargs) class MyModule(torch.nn.Module): def __init__(self): super().__init__() self.some_counter = 0 self.linear = torch.nn.Linear(320, 320) def get_extra_state(self): return torch.tensor(self.some_counter) def set_extra_state(self, state): self.some_counter = state.item() class MyModel(PreTrainedModel): config_class = MyConfig def __init__(self, config: MyConfig): super().__init__(config) self.my_layer = MyModule() self.post_init() def forward(self, hidden_states, attention_mask): return self.my_layer(hidden_states, attention_mask) config = MyConfig() model = MyModel(config) model.my_layer.some_counter = 42 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) del model model, loading_info = MyModel.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(model.my_layer.some_counter, 42) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 0) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) @mark.xfail(reason="save and from_pretrained currently only supports tensor extra_state") def test_save_and_load_model_with_dict_extra_state(self): class MyConfig(PreTrainedConfig): def __init__(self, **kwargs): super().__init__(**kwargs) class MyModule(torch.nn.Module): def __init__(self): super().__init__() self.some_counter = 0 self.linear = torch.nn.Linear(320, 320) def get_extra_state(self): return {"some_counter": self.some_counter} def set_extra_state(self, state): self.some_counter = state["some_counter"] class MyModel(PreTrainedModel): config_class = MyConfig def __init__(self, config: MyConfig): super().__init__(config) self.my_layer = MyModule() def forward(self, hidden_states, attention_mask): return self.my_layer(hidden_states, attention_mask) config = MyConfig() model = MyModel(config) model.my_layer.some_counter = 42 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) del model model, loading_info = MyModel.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(model.my_layer.some_counter, 42) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 0) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) class TestGetDecoder(unittest.TestCase): def test_causal_lm_get_decoder_returns_underlying_model(self): cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) model = MistralForCausalLM(cfg) dec = model.get_decoder() assert dec is model.model, f"Expected get_decoder() to return model.model, got {type(dec)}" def test_seq2seq_get_decoder_still_returns_decoder_module(self): cfg = BartConfig( vocab_size=128, d_model=32, encoder_layers=2, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, encoder_ffn_dim=64, decoder_ffn_dim=64, ) model = BartForConditionalGeneration(cfg) dec = model.get_decoder() assert dec is model.model.decoder, "Seq2seq get_decoder() should return the decoder submodule" def test_base_model_returns_self(self): """Test that base transformer models (no decoder/model attributes) return self.""" cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) base_model = MistralModel(cfg) dec = base_model.get_decoder() assert dec is base_model, f"Base model get_decoder() should return self, got {type(dec)}" def test_explicit_decoder_attribute_opt(self): """Test models with explicit decoder attribute (OPT style).""" cfg = OPTConfig( vocab_size=128, hidden_size=32, ffn_dim=64, num_hidden_layers=2, num_attention_heads=4, max_position_embeddings=512, ) model = OPTForCausalLM(cfg) dec = model.get_decoder() assert dec is model.model.decoder, f"OPT get_decoder() should return model.decoder, got {type(dec)}" def test_explicit_decoder_attribute_t5(self): """Test encoder-decoder models with explicit decoder attribute.""" cfg = T5Config( vocab_size=128, d_model=32, d_ff=64, num_layers=2, num_heads=4, ) model = T5ForConditionalGeneration(cfg) dec = model.get_decoder() assert dec is model.decoder, f"T5 get_decoder() should return decoder attribute, got {type(dec)}" def test_same_type_recursion_prevention(self): """Test that same-type recursion is prevented (see issue #40815).""" cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) model = MistralForCausalLM(cfg) assert type(model) is not type(model.model), "Types should be different to prevent recursion" dec = model.get_decoder() assert dec is model.model, f"Should return model.model without infinite recursion, got {type(dec)}" inner_dec = model.model.get_decoder() assert inner_dec is model.model, f"Inner model should return itself, got {type(inner_dec)}" def test_nested_wrapper_recursion(self): """Test models that don't have model/decoder attributes return self.""" cfg = GPT2Config( vocab_size=128, n_embd=32, n_layer=2, n_head=4, n_positions=512, ) model = GPT2LMHeadModel(cfg) dec = model.get_decoder() assert dec is model.transformer, f"GPT2 get_decoder() should return self (fallback), got {type(dec)}" def test_model_without_get_decoder(self): """Test edge case where model has model attribute but no get_decoder method.""" class MockInnerModel: """Mock model without get_decoder method.""" pass class MockWrapperModel: """Mock wrapper with model attribute but inner has no get_decoder.""" def __init__(self): self.model = MockInnerModel() def get_decoder(self): if hasattr(self, "decoder"): return self.decoder if hasattr(self, "model"): inner = self.model if hasattr(inner, "get_decoder") and type(inner) is not type(self): return inner.get_decoder() return inner return self wrapper = MockWrapperModel() dec = wrapper.get_decoder() assert dec is wrapper.model, f"Should return inner model when no get_decoder, got {type(dec)}" def test_vision_language_model(self): """Test vision-language models like LLaVA that delegate to language_model.""" text_config = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) vision_config = { "hidden_size": 32, "intermediate_size": 64, "num_hidden_layers": 2, "num_attention_heads": 4, "num_channels": 3, "image_size": 224, "patch_size": 16, } cfg = LlavaConfig( text_config=text_config.to_dict(), vision_config=vision_config, vocab_size=128, ) model = LlavaForConditionalGeneration(cfg) dec = model.get_decoder() assert dec is model.model.language_model, f"LLaVA get_decoder() should return language_model, got {type(dec)}" class TestGetEncoder(unittest.TestCase): def test_seq2seq_lm_get_encoder_returns_encoder(self): cfg = BartConfig( vocab_size=128, d_model=32, encoder_layers=2, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, encoder_ffn_dim=64, decoder_ffn_dim=64, ) model = BartForConditionalGeneration(cfg) encoder = model.get_encoder() assert encoder is model.model.encoder, ( f"Expected get_encoder() to return model.model.encoder, got {type(encoder)}" ) def test_base_model_returns_encoder(self): cfg = BartConfig( vocab_size=128, d_model=32, encoder_layers=2, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, encoder_ffn_dim=64, decoder_ffn_dim=64, ) model = BartModel(cfg) encoder = model.get_encoder() assert encoder is model.encoder, f"Expected get_encoder() to return model.encoder, got {type(encoder)}" def test_decoder_only_model_returns_self(self): """Test that decoder-only models (no encoder) return self.""" cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) model = MistralForCausalLM(cfg) encoder = model.get_encoder() assert encoder is model, f"Base model get_encoder() should return self, got {type(encoder)}" def test_when_encoder_has_different_name(self): """Test models with non-standard name for encoder modular (Musicgen has `self.model.text_encoder`).""" cfg = MusicgenConfig( text_encoder={ "model_type": "t5", "vocab_size": 99, "d_model": 32, "d_ff": 37, "num_layers": 2, "num_heads": 2, }, audio_encoder={ "model_type": "encodec", "hidden_size": 99, "compress": 1, "num_filters": 2, "codebook_size": 32, "codebook_dim": 32, }, decoder={ "vocab_size": 99, "ffn_dim": 32, "num_attention_heads": 2, "hidden_size": 32, "num_hidden_layers": 2, }, ) model = MusicgenForConditionalGeneration(cfg) encoder = model.get_encoder() assert encoder is model.text_encoder, ( f"MusicgenForConditionalGeneration get_encoder() should return model.model.text_encoder, got {type(encoder)}" ) def test_audio_encoder(self): """Test models with multiple modality encoders (Musicgen has `self.model.audio_encoder`).""" cfg = MusicgenConfig( text_encoder={ "model_type": "t5", "vocab_size": 99, "d_model": 32, "d_ff": 37, "num_layers": 2, "num_heads": 2, }, audio_encoder={ "model_type": "encodec", "hidden_size": 99, "compress": 1, "num_filters": 2, "codebook_size": 32, "codebook_dim": 32, }, decoder={ "vocab_size": 99, "ffn_dim": 32, "num_attention_heads": 2, "hidden_size": 32, "num_hidden_layers": 2, }, ) model = MusicgenForConditionalGeneration(cfg) encoder = model.get_encoder(modality="audio") assert encoder is model.audio_encoder, ( f"MusicgenForConditionalGeneration get_encoder(modality='audio') should return model.model.audio_encoder, got {type(encoder)}" ) def test_non_existant_modality_throws_error(self): """Test that an error is thrown when a rquested modality does not exist.""" cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) model = MistralModel(cfg) with self.assertRaises(ValueError): _ = model.get_encoder(modality="3d") def test_encoder_return_self_when_modality_not_found(self): """Test that `self` is returned if the model has no encoder for requested modality.""" cfg = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) model = MistralModel(cfg) encoder = model.get_encoder(modality="image") assert encoder is model, f"Mistral get_encoder(modality='image') should return self, got {type(encoder)}" def test_model_without_get_encoder(self): """Test edge case where model has model attribute but no get_encoder method.""" class MockInnerModel: """Mock model without get_encoder method.""" pass class MockWrapperModel: """Mock wrapper with model attribute but inner has no get_encoder.""" def __init__(self): self.model = MockInnerModel() def get_encoder(self): if hasattr(self, "encoder"): return self.encoder if hasattr(self, "model"): inner = self.model if hasattr(inner, "get_encoder") and type(inner) is not type(self): return inner.get_encoder() return inner return self wrapper = MockWrapperModel() encoder = wrapper.get_encoder() assert encoder is wrapper.model, f"Should return inner model when no get_encoder, got {type(encoder)}" def test_vision_language_model(self): """Test vision-language models like LLaVA can find the modality encoder ("image").""" text_config = MistralConfig( vocab_size=128, hidden_size=32, intermediate_size=64, num_hidden_layers=2, num_attention_heads=4, ) vision_config = { "hidden_size": 32, "intermediate_size": 64, "num_hidden_layers": 2, "num_attention_heads": 4, "num_channels": 3, "image_size": 224, "patch_size": 16, } cfg = LlavaConfig( text_config=text_config.to_dict(), vision_config=vision_config, vocab_size=128, ) model = LlavaForConditionalGeneration(cfg) image_encoder = model.get_encoder(modality="image") assert image_encoder is model.model.vision_tower, ( f"LLaVA get_encoder(modality='image') should return vision_tower, got {type(image_encoder)}" ) @require_torch class DisableMmapLoadingTest(unittest.TestCase): """Tests for the `disable_mmap` kwarg in `load_state_dict` and the `_is_on_hf_mount` helper.""" def _fake_open_factory(self, proc_mounts_contents): """Return a patched `open` that serves `proc_mounts_contents` for `/proc/mounts` and defers otherwise.""" import builtins real_open = builtins.open def fake_open(path, *args, **kwargs): if path == "/proc/mounts": import io return io.StringIO(proc_mounts_contents) return real_open(path, *args, **kwargs) return fake_open def test_is_on_hf_mount_linux_match(self): from transformers.modeling_utils import _is_on_hf_mount mounts = ( "proc /proc proc rw,nosuid,nodev,noexec,relatime 0 0\n" "hf-mount /data fuse.hf-mount rw,nosuid,nodev,relatime,user_id=0 0 0\n" ) with patch("sys.platform", "linux"), patch("builtins.open", self._fake_open_factory(mounts)): self.assertTrue(_is_on_hf_mount("/data/model.safetensors")) def test_is_on_hf_mount_no_match(self): from transformers.modeling_utils import _is_on_hf_mount mounts = "proc /proc proc rw,nosuid,nodev,noexec,relatime 0 0\n/dev/nvme0n1p1 /data ext4 rw,relatime 0 0\n" with patch("sys.platform", "linux"), patch("builtins.open", self._fake_open_factory(mounts)): self.assertFalse(_is_on_hf_mount("/data/model.safetensors")) def test_is_on_hf_mount_non_linux(self): from transformers.modeling_utils import _is_on_hf_mount with patch("sys.platform", "darwin"): self.assertFalse(_is_on_hf_mount("/data/model.safetensors")) def test_load_state_dict_disable_mmap_explicit(self): import torch from safetensors.torch import save_file as safe_save_file from transformers.modeling_utils import load_state_dict state_dict = { "weight": torch.arange(12, dtype=torch.float32).reshape(3, 4), "bias": torch.tensor([1.0, 2.0, 3.0]), } with tempfile.TemporaryDirectory() as tmpdir: ckpt_path = os.path.join(tmpdir, "model.safetensors") safe_save_file(state_dict, ckpt_path) loaded_mmap = load_state_dict(ckpt_path, disable_mmap=False) loaded_no_mmap = load_state_dict(ckpt_path, disable_mmap=True) self.assertEqual(set(loaded_mmap.keys()), set(loaded_no_mmap.keys())) for k in loaded_mmap: torch.testing.assert_close(loaded_mmap[k], loaded_no_mmap[k]) @require_torch class RemoteAndCustomCodeModelTests(unittest.TestCase): def test_remote_code_registration(self): """ Test that remote code is correctly registered with auto classes, and correctly sets its auto class, so that it's detected by `is_remote_code()` """ model = AutoModel.from_pretrained("hf-internal-testing/dummy_remote_code", trust_remote_code=True) # Those 2 checks are equivalent, but keep both to make sure implementation of `is_remote_code` will not diverge self.assertTrue(model.is_remote_code()) self.assertFalse(model.__class__.__module__.startswith("transformers.")) def test_remote_code_registration_with_model_type_collision(self): """ Test that remote code is correctly registered with auto classes, and correctly sets its auto class, so that it's detected by `is_remote_code()`, even if the `model_type` registered in the custom model is the same as an existing one in the library. In this case, we should also be able to load the native model if `trust_remote_code=False` after registration. """ # This remote model uses "llama" as model_type, same to our llama inside the lib model_name = "hf-internal-testing/dummy_remote_code_collision_model_type" custom_model = AutoModel.from_pretrained(model_name, trust_remote_code=True) # Those 2 checks are equivalent, but keep both to make sure implementation of `is_remote_code` will not diverge self.assertTrue(custom_model.is_remote_code()) self.assertFalse(custom_model.__class__.__module__.startswith("transformers.")) # Check the config as well self.assertFalse(custom_model.config.__class__.__module__.startswith("transformers.")) # Check that if we do NOT use `trust_remote_code=True`, then the registration of the same model type before it will not force # a model type of the wrong type, i.e. we use the native LlamaModel native_model = AutoModel.from_pretrained("hf-internal-testing/tiny-llama") # Those 2 checks are equivalent, but keep both to make sure implementation of `is_remote_code` will not diverge self.assertFalse(native_model.is_remote_code()) self.assertTrue(native_model.__class__.__module__.startswith("transformers.")) # Check the config as well self.assertTrue(native_model.config.__class__.__module__.startswith("transformers.")) def test_remote_code_registration_with_config_and_model_type_collision(self): """ Test that remote code is correctly registered with auto classes, and correctly sets its auto class, so that it's detected by `is_remote_code()`, even if the config class AND the `model_type` used in the custom model are the same as an existing model in the library. In this case, we should also be able to load the native model if `trust_remote_code=False` after registration. """ model_name = "hf-internal-testing/dummy_remote_code_collision_config_and_model_type" custom_model = AutoModel.from_pretrained(model_name, trust_remote_code=True) # Those 2 checks are equivalent, but keep both to make sure implementation of `is_remote_code` will not diverge self.assertTrue(custom_model.is_remote_code()) self.assertFalse(custom_model.__class__.__module__.startswith("transformers.")) # Here, the config class is the original llama config even with the remote model self.assertTrue(custom_model.config.__class__.__module__.startswith("transformers.")) # Check that if we do NOT use `trust_remote_code=True`, then the registration of the same model type with the # same name and same config class before it will not force a model type of the wrong type, i.e. we use the native LlamaModel native_model = AutoModel.from_pretrained("hf-internal-testing/tiny-llama") # Those 2 checks are equivalent, but keep both to make sure implementation of `is_remote_code` will not diverge self.assertFalse(native_model.is_remote_code()) self.assertTrue(native_model.__class__.__module__.startswith("transformers.")) # Check config as well self.assertTrue(native_model.config.__class__.__module__.startswith("transformers.")) def test_custom_code_is_detected(self): """Test that classes defined in the current context (user module, notebook, ...) will be detected as custom code""" class MyNewModel(PreTrainedModel): def __init__(self, config): super().__init__(config) self.foo = nn.Linear(2, 2) self.post_init() def forward(self, x): return self.foo(x) # This is a type created in the current session - it should be custom code, but not remote code model = MyNewModel(PreTrainedConfig()) self.assertTrue(model.is_custom_code()) self.assertFalse(model.is_remote_code())