code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def test_token_dropping(self):
r"""
This test checks if the token dropping actually drops tokens.
"""
config = SwitchTransformersConfig(expert_capacity=0) # we drop everything
moe = SwitchTransformersSparseMLP(config)
dropped_token_results = moe(torch.randn(2, 3, 768))[0... |
This test checks if the token dropping actually drops tokens.
| test_token_dropping | python | huggingface/transformers | tests/models/switch_transformers/test_modeling_switch_transformers.py | https://github.com/huggingface/transformers/blob/master/tests/models/switch_transformers/test_modeling_switch_transformers.py | Apache-2.0 |
def test_small_integration_test(self):
"""
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_... |
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
>>> t5_model = t5.models.MtfModel(mod... | test_small_integration_test | python | huggingface/transformers | tests/models/t5/test_modeling_flax_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_flax_t5.py | Apache-2.0 |
def test_small_v1_1_integration_test(self):
"""
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path... |
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
>>> t5_model = t5.models.MtfMode... | test_small_v1_1_integration_test | python | huggingface/transformers | tests/models/t5/test_modeling_flax_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_flax_t5.py | Apache-2.0 |
def test_small_byt5_integration_test(self):
"""
For comparison run:
>>> import t5 # pip install t5==0.9.1
>>> path_to_byt5_small_checkpoint = '<fill_in>'
>>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
>>> vocab = t5.data.ByteV... |
For comparison run:
>>> import t5 # pip install t5==0.9.1
>>> path_to_byt5_small_checkpoint = '<fill_in>'
>>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
>>> vocab = t5.data.ByteVocabulary()
>>> score = t5_model.score(inputs=[... | test_small_byt5_integration_test | python | huggingface/transformers | tests/models/t5/test_modeling_flax_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_flax_t5.py | Apache-2.0 |
def test_export_encoder(self):
"""Test exporting T5EncoderModel to torch export format."""
if not is_torch_greater_or_equal_than_2_4:
self.skipTest("This test requires torch >= 2.4 to run.")
from transformers.integrations.executorch import Seq2SeqLMEncoderExportableModule
m... | Test exporting T5EncoderModel to torch export format. | test_export_encoder | python | huggingface/transformers | tests/models/t5/test_modeling_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_t5.py | Apache-2.0 |
def test_export_decoder(self):
"""Test exporting T5 decoder with static cache to torch export format."""
if not is_torch_greater_or_equal_than_2_4:
self.skipTest("This test requires torch >= 2.4 to run.")
from transformers import AutoModelForSeq2SeqLM, T5ForConditionalGeneration
... | Test exporting T5 decoder with static cache to torch export format. | test_export_decoder | python | huggingface/transformers | tests/models/t5/test_modeling_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_t5.py | Apache-2.0 |
def test_export_t5_summarization(self):
"""Test composing exported T5 encoder and decoder for summarization."""
if not is_torch_greater_or_equal_than_2_4:
self.skipTest("This test requires torch >= 2.4 to run.")
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5ForCon... | Test composing exported T5 encoder and decoder for summarization. | test_export_t5_summarization | python | huggingface/transformers | tests/models/t5/test_modeling_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_t5.py | Apache-2.0 |
def test_small_v1_1_integration_test(self):
"""
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_v1.1_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path... |
For comparison run:
>>> import t5 # pip install t5==0.7.1
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
>>> path_to_mtf_small_t5_v1.1_checkpoint = '<fill_in>'
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
>>> t5_model = t5.models.MtfMode... | test_small_v1_1_integration_test | python | huggingface/transformers | tests/models/t5/test_modeling_tf_t5.py | https://github.com/huggingface/transformers/blob/master/tests/models/t5/test_modeling_tf_t5.py | Apache-2.0 |
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to TvpImageProcessor,
assuming do_resize is set to True with a scalar size.
"""
if not batched:
return (int(self.pad_size["height... |
This function computes the expected height and width when providing images to TvpImageProcessor,
assuming do_resize is set to True with a scalar size.
| get_expected_values | python | huggingface/transformers | tests/models/tvp/test_image_processing_tvp.py | https://github.com/huggingface/transformers/blob/master/tests/models/tvp/test_image_processing_tvp.py | Apache-2.0 |
def test_find_longest_common_subsequence_old(self):
"""Test using the old processing functions used in the ASR pipeline, but that serves as a BC reference."""
max_source_positions = 1500
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
previous_sequence = [[51492, 406... | Test using the old processing functions used in the ASR pipeline, but that serves as a BC reference. | test_find_longest_common_subsequence_old | python | huggingface/transformers | tests/models/whisper/test_processor_whisper.py | https://github.com/huggingface/transformers/blob/master/tests/models/whisper/test_processor_whisper.py | Apache-2.0 |
def _fast_find_longest_common_sequence(sequence_left, sequence_right):
"""Old processing function used in the ASR pipeline."""
seq_len_left = len(sequence_left)
seq_len_right = len(sequence_right)
counter = [[0] * (seq_len_right + 1) for _ in range(seq_len_left + 1)]
longest = 0
for i in range(s... | Old processing function used in the ASR pipeline. | _fast_find_longest_common_sequence | python | huggingface/transformers | tests/models/whisper/test_processor_whisper.py | https://github.com/huggingface/transformers/blob/master/tests/models/whisper/test_processor_whisper.py | Apache-2.0 |
def _find_timestamp_sequence(sequences, tokenizer, feature_extractor, max_source_positions):
"""
Old processing function used in the ASR pipeline.
Computes the final sequences by merging the end of the nth sequence with the beginning of the n+1th sequence. Since
`WhisperForConditionalGeneration` produc... |
Old processing function used in the ASR pipeline.
Computes the final sequences by merging the end of the nth sequence with the beginning of the n+1th sequence. Since
`WhisperForConditionalGeneration` produces the timestamps pairwise, we filter the consecutive timestamps and only
iterate over them. We ... | _find_timestamp_sequence | python | huggingface/transformers | tests/models/whisper/test_processor_whisper.py | https://github.com/huggingface/transformers/blob/master/tests/models/whisper/test_processor_whisper.py | Apache-2.0 |
def test_create_position_ids_respects_padding_index(self):
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XLMRobert... | This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XLMRobertaXLEmbeddings.padding_idx + 1
| test_create_position_ids_respects_padding_index | python | huggingface/transformers | tests/models/xlm_roberta_xl/test_modeling_xlm_roberta_xl.py | https://github.com/huggingface/transformers/blob/master/tests/models/xlm_roberta_xl/test_modeling_xlm_roberta_xl.py | Apache-2.0 |
def test_create_position_ids_respects_padding_index(self):
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbed... | This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbeddings.padding_idx + 1
| test_create_position_ids_respects_padding_index | python | huggingface/transformers | tests/models/xmod/test_modeling_xmod.py | https://github.com/huggingface/transformers/blob/master/tests/models/xmod/test_modeling_xmod.py | Apache-2.0 |
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Zamba model outputs attention only for its attention layers
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = get... |
Overriding the test_attention_outputs test as the Zamba model outputs attention only for its attention layers
| test_attention_outputs | python | huggingface/transformers | tests/models/zamba/test_modeling_zamba.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba/test_modeling_zamba.py | Apache-2.0 |
def test_left_padding_compatibility(self):
r"""
Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
"""
import inspect
... |
Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
| test_left_padding_compatibility | python | huggingface/transformers | tests/models/zamba/test_modeling_zamba.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba/test_modeling_zamba.py | Apache-2.0 |
def test_flash_attn_2_fp32_ln(self):
r"""
Overriding the test_flash_attn_2_fp32_ln test as the Zamba model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.model_test... |
Overriding the test_flash_attn_2_fp32_ln test as the Zamba model, like Mixtral, doesn't support
right padding + use cache with FA2
| test_flash_attn_2_fp32_ln | python | huggingface/transformers | tests/models/zamba/test_modeling_zamba.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba/test_modeling_zamba.py | Apache-2.0 |
def test_past_key_values_format(self):
"""
Overwriting to pass the expected cache shapes (Zamba2 has cache shape = [batch_size, 0] for mamba layers)
"""
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
batch_size, seq_length = inputs["input_ids"].shape
... |
Overwriting to pass the expected cache shapes (Zamba2 has cache shape = [batch_size, 0] for mamba layers)
| test_past_key_values_format | python | huggingface/transformers | tests/models/zamba2/test_modeling_zamba2.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba2/test_modeling_zamba2.py | Apache-2.0 |
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Zamba2 model outputs attention only for its attention layers
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = ge... |
Overriding the test_attention_outputs test as the Zamba2 model outputs attention only for its attention layers
| test_attention_outputs | python | huggingface/transformers | tests/models/zamba2/test_modeling_zamba2.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba2/test_modeling_zamba2.py | Apache-2.0 |
def test_flash_attn_2_fp32_ln(self):
r"""
Overriding the test_flash_attn_2_fp32_ln test as the Zamba2 model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.model_tes... |
Overriding the test_flash_attn_2_fp32_ln test as the Zamba2 model, like Mixtral, doesn't support
right padding + use cache with FA2
| test_flash_attn_2_fp32_ln | python | huggingface/transformers | tests/models/zamba2/test_modeling_zamba2.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba2/test_modeling_zamba2.py | Apache-2.0 |
def test_flex_attention_with_grads(self):
"""
Overwriting as the base hidden size is big enough for compile.
Manipulation of dims causes issues due to other constraints not being satisfied anymore.
"""
for model_class in self.all_model_classes:
config, inputs_dict = s... |
Overwriting as the base hidden size is big enough for compile.
Manipulation of dims causes issues due to other constraints not being satisfied anymore.
| test_flex_attention_with_grads | python | huggingface/transformers | tests/models/zamba2/test_modeling_zamba2.py | https://github.com/huggingface/transformers/blob/master/tests/models/zamba2/test_modeling_zamba2.py | Apache-2.0 |
def test_delete_adapter(self):
"""
Enhanced test for `delete_adapter` to handle multiple adapters,
edge cases, and proper error handling.
"""
from peft import LoraConfig
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transfor... |
Enhanced test for `delete_adapter` to handle multiple adapters,
edge cases, and proper error handling.
| test_delete_adapter | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_add_adapter_with_state_dict_low_cpu_mem_usage(self):
"""
Check the usage of low_cpu_mem_usage, which is supported in PEFT >= 0.13.0
"""
from peft import LoraConfig
min_version_lcmu = "0.13.0"
is_lcmu_supported = version.parse(importlib.metadata.version("pef... |
Check the usage of low_cpu_mem_usage, which is supported in PEFT >= 0.13.0
| test_peft_add_adapter_with_state_dict_low_cpu_mem_usage | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_from_pretrained_unexpected_keys_warning(self):
"""
Test for warning when loading a PEFT checkpoint with unexpected keys.
"""
from peft import LoraConfig
logger = logging.get_logger("transformers.integrations.peft")
for model_id, peft_model_id in zip(self.t... |
Test for warning when loading a PEFT checkpoint with unexpected keys.
| test_peft_from_pretrained_unexpected_keys_warning | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_from_pretrained_missing_keys_warning(self):
"""
Test for warning when loading a PEFT checkpoint with missing keys.
"""
from peft import LoraConfig
logger = logging.get_logger("transformers.integrations.peft")
for model_id, peft_model_id in zip(self.transfo... |
Test for warning when loading a PEFT checkpoint with missing keys.
| test_peft_from_pretrained_missing_keys_warning | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_load_adapter_training_inference_mode_true(self):
"""
By default, when loading an adapter, the whole model should be in eval mode and no parameter should have
requires_grad=False.
"""
for model_id in self.peft_test_model_ids:
for transformers_class in sel... |
By default, when loading an adapter, the whole model should be in eval mode and no parameter should have
requires_grad=False.
| test_peft_load_adapter_training_inference_mode_true | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_load_adapter_training_inference_mode_false(self):
"""
When passing is_trainable=True, the LoRA modules should be in training mode and their parameters should have
requires_grad=True.
"""
for model_id in self.peft_test_model_ids:
for transformers_class in... |
When passing is_trainable=True, the LoRA modules should be in training mode and their parameters should have
requires_grad=True.
| test_peft_load_adapter_training_inference_mode_false | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_peft_pipeline_no_warning(self):
"""
Test to verify that the warning message "The model 'PeftModel' is not supported for text-generation"
does not appear when using PeftModel with text-generation pipeline.
"""
from peft import PeftModel
from transformers import p... |
Test to verify that the warning message "The model 'PeftModel' is not supported for text-generation"
does not appear when using PeftModel with text-generation pipeline.
| test_peft_pipeline_no_warning | python | huggingface/transformers | tests/peft_integration/test_peft_integration.py | https://github.com/huggingface/transformers/blob/master/tests/peft_integration/test_peft_integration.py | Apache-2.0 |
def test_input_parameter_passthrough(self):
"""Test that chunked vs non chunked versions of ASR pipelines returns the same structure for the same inputs."""
speech_recognizer = pipeline(
task="automatic-speech-recognition",
model="hf-internal-testing/tiny-random-wav2vec2",
... | Test that chunked vs non chunked versions of ASR pipelines returns the same structure for the same inputs. | test_input_parameter_passthrough | python | huggingface/transformers | tests/pipelines/test_pipelines_automatic_speech_recognition.py | https://github.com/huggingface/transformers/blob/master/tests/pipelines/test_pipelines_automatic_speech_recognition.py | Apache-2.0 |
def test_pipeline_assisted_generation(self):
"""Tests that we can run assisted generation in the pipeline"""
model = "openai/whisper-tiny"
pipe = pipeline("automatic-speech-recognition", model=model, assistant_model=model)
# We can run the pipeline
prompt = load_dataset("hf-inte... | Tests that we can run assisted generation in the pipeline | test_pipeline_assisted_generation | python | huggingface/transformers | tests/pipelines/test_pipelines_automatic_speech_recognition.py | https://github.com/huggingface/transformers/blob/master/tests/pipelines/test_pipelines_automatic_speech_recognition.py | Apache-2.0 |
def test_pipeline_with_task_parameters_no_side_effects(self):
"""
Regression test: certain pipeline flags, like `task`, modified the model configuration, causing unexpected
side-effects
"""
# This checkpoint has task-specific parameters that will modify the behavior of the pipeli... |
Regression test: certain pipeline flags, like `task`, modified the model configuration, causing unexpected
side-effects
| test_pipeline_with_task_parameters_no_side_effects | python | huggingface/transformers | tests/pipelines/test_pipelines_common.py | https://github.com/huggingface/transformers/blob/master/tests/pipelines/test_pipelines_common.py | Apache-2.0 |
def test_quantized_model_exllama(self):
"""
Simple test that checks if the quantized model is working properly with exllama backend
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
quantization_config = AwqConfig(version="exllama")
qu... |
Simple test that checks if the quantized model is working properly with exllama backend
| test_quantized_model_exllama | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_raise_save_pretrained(self):
"""
Test that `save_pretrained` is effectively blocked for fused models
"""
quantization_config = AwqConfig(bits=4, fuse_max_seq_len=128, do_fuse=True)
model = AutoModelForCausalLM.from_pretrained(
self.model_name,
qu... |
Test that `save_pretrained` is effectively blocked for fused models
| test_raise_save_pretrained | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_fused_modules_to_not_convert(self):
"""
Test if fused + modules to_not_covnert work as expected
"""
model_id = "hf-internal-testing/Mixtral-tiny-AWQ"
quantization_config = AwqConfig(bits=4, fuse_max_seq_len=128, do_fuse=True)
model = AutoModelForCausalLM.from_pr... |
Test if fused + modules to_not_covnert work as expected
| test_fused_modules_to_not_convert | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_generation_fused(self):
"""
Test generation quality for fused models - single batch case
"""
quantization_config = AwqConfig(bits=4, fuse_max_seq_len=128, do_fuse=True)
model = AutoModelForCausalLM.from_pretrained(
self.model_name,
quantization_c... |
Test generation quality for fused models - single batch case
| test_generation_fused | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_generation_fused_batched(self):
"""
Test generation quality for fused models - multi batch case
"""
quantization_config = AwqConfig(bits=4, fuse_max_seq_len=128, do_fuse=True)
model = AutoModelForCausalLM.from_pretrained(
self.model_name,
quantiz... |
Test generation quality for fused models - multi batch case
| test_generation_fused_batched | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_generation_custom_model(self):
"""
Test generation quality for fused models using custom fused map.
"""
quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=512,
modules_to_fuse={
"attention": ["q_proj", "k_proj", "v_proj"... |
Test generation quality for fused models using custom fused map.
| test_generation_custom_model | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_generation_mixtral_fused(self):
"""
Text generation test for Mixtral + AWQ + fused
"""
quantization_config = AwqConfig(bits=4, fuse_max_seq_len=1024, do_fuse=True)
model = AutoModelForCausalLM.from_pretrained(
self.mixtral_model_name,
quantization... |
Text generation test for Mixtral + AWQ + fused
| test_generation_mixtral_fused | python | huggingface/transformers | tests/quantization/autoawq/test_awq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoawq/test_awq.py | Apache-2.0 |
def test_quantized_model_multi_accelerator(self):
"""
Simple test that checks if the quantized model is working properly with multiple accelerators
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
quantization_config = AutoRoundConfig(backend=... |
Simple test that checks if the quantized model is working properly with multiple accelerators
| test_quantized_model_multi_accelerator | python | huggingface/transformers | tests/quantization/autoround/test_auto_round.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoround/test_auto_round.py | Apache-2.0 |
def test_convert_from_gptq(self):
"""
Simple test that checks if auto-round work properly with gptq format
"""
model_name = "ybelkada/opt-125m-gptq-4bit"
quantization_config = AutoRoundConfig()
model = AutoModelForCausalLM.from_pretrained(
model_name, device... |
Simple test that checks if auto-round work properly with gptq format
| test_convert_from_gptq | python | huggingface/transformers | tests/quantization/autoround/test_auto_round.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoround/test_auto_round.py | Apache-2.0 |
def test_convert_from_awq_cpu(self):
"""
Simple test that checks if auto-round work properly with awq format
"""
model_name = "casperhansen/opt-125m-awq"
quantization_config = AutoRoundConfig()
model = AutoModelForCausalLM.from_pretrained(
model_name, device... |
Simple test that checks if auto-round work properly with awq format
| test_convert_from_awq_cpu | python | huggingface/transformers | tests/quantization/autoround/test_auto_round.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoround/test_auto_round.py | Apache-2.0 |
def test_mixed_bits(self):
"""
Simple test that checks if auto-round work properly with mixed bits
"""
model_name = "facebook/opt-125m"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
... |
Simple test that checks if auto-round work properly with mixed bits
| test_mixed_bits | python | huggingface/transformers | tests/quantization/autoround/test_auto_round.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/autoround/test_auto_round.py | Apache-2.0 |
def test_packing_unpacking(self):
"""
Simple test the packing and unpacking logic
"""
from transformers.integrations import pack_weights, unpack_weights
u = torch.randint(0, 255, (256, 256), dtype=torch.uint8)
unpacked_u = unpack_weights(u, dtype=torch.bfloat16)
... |
Simple test the packing and unpacking logic
| test_packing_unpacking | python | huggingface/transformers | tests/quantization/bitnet_integration/test_bitnet.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bitnet_integration/test_bitnet.py | Apache-2.0 |
def test_weights_dtype(self):
"""
test the weights dtype after loading
"""
self_attn_q = self.quantized_model.model.layers[0].self_attn.q_proj.weight
self_attn_k = self.quantized_model.model.layers[0].self_attn.k_proj.weight
self_attn_v = self.quantized_model.model.layer... |
test the weights dtype after loading
| test_weights_dtype | python | huggingface/transformers | tests/quantization/bitnet_integration/test_bitnet.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bitnet_integration/test_bitnet.py | Apache-2.0 |
def test_replace_with_bitlinear_shape(self):
"""
test that the BitNet layer weight shapes are correct, and the weight_scale is correctly initialized to 1
"""
from transformers.integrations import replace_with_bitnet_linear
out_features = 1024
in_features = 512
... |
test that the BitNet layer weight shapes are correct, and the weight_scale is correctly initialized to 1
| test_replace_with_bitlinear_shape | python | huggingface/transformers | tests/quantization/bitnet_integration/test_bitnet.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bitnet_integration/test_bitnet.py | Apache-2.0 |
def test_generate_quality_dequantize(self):
r"""
Test that loading the model and unquantize it produce correct results
"""
bnb_config = BitsAndBytesConfig(load_in_4bit=True)
model_4bit = AutoModelForCausalLM.from_pretrained(
self.model_name, quantization_config=bnb_c... |
Test that loading the model and unquantize it produce correct results
| test_generate_quality_dequantize | python | huggingface/transformers | tests/quantization/bnb/test_4bit.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_4bit.py | Apache-2.0 |
def test_device_and_dtype_assignment(self):
r"""
Test whether attempting to change the device or cast the dtype of a model
after converting it to 4-bit precision will raise an appropriate error.
The test ensures that such operations are prohibited on 4-bit models
to prevent inval... |
Test whether attempting to change the device or cast the dtype of a model
after converting it to 4-bit precision will raise an appropriate error.
The test ensures that such operations are prohibited on 4-bit models
to prevent invalid conversions.
| test_device_and_dtype_assignment | python | huggingface/transformers | tests/quantization/bnb/test_4bit.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_4bit.py | Apache-2.0 |
def test_inference_without_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
both cases.... |
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
both cases.
| test_inference_without_keep_in_fp32 | python | huggingface/transformers | tests/quantization/bnb/test_4bit.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_4bit.py | Apache-2.0 |
def test_pipeline(self):
r"""
The aim of this test is to verify that the mixed 4bit is compatible with `pipeline` from transformers. Since
we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipeline.
"""
# s... |
The aim of this test is to verify that the mixed 4bit is compatible with `pipeline` from transformers. Since
we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipeline.
| test_pipeline | python | huggingface/transformers | tests/quantization/bnb/test_4bit.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_4bit.py | Apache-2.0 |
def test_generate_quality_dequantize(self):
r"""
Test that loading the model and dequantizing it produce correct results
"""
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model_8bit = AutoModelForCausalLM.from_pretrained(
self.model_name, quantization_config=bnb... |
Test that loading the model and dequantizing it produce correct results
| test_generate_quality_dequantize | python | huggingface/transformers | tests/quantization/bnb/test_mixed_int8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_mixed_int8.py | Apache-2.0 |
def test_device_and_dtype_assignment(self):
r"""
Test whether attempting to change the device or cast the dtype of a model
after converting it to 8-bit precision will raise an appropriate error.
The test ensures that such operations are prohibited on 8-bit models
to prevent inval... |
Test whether attempting to change the device or cast the dtype of a model
after converting it to 8-bit precision will raise an appropriate error.
The test ensures that such operations are prohibited on 8-bit models
to prevent invalid conversions.
| test_device_and_dtype_assignment | python | huggingface/transformers | tests/quantization/bnb/test_mixed_int8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_mixed_int8.py | Apache-2.0 |
def test_inference_without_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
both cases.... |
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
both cases.
| test_inference_without_keep_in_fp32 | python | huggingface/transformers | tests/quantization/bnb/test_mixed_int8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_mixed_int8.py | Apache-2.0 |
def test_inference_with_keep_in_fp32_serialized(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
a serialized model.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense... |
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
a serialized model.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
both cases.
| test_inference_with_keep_in_fp32_serialized | python | huggingface/transformers | tests/quantization/bnb/test_mixed_int8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_mixed_int8.py | Apache-2.0 |
def test_pipeline(self):
r"""
The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipeline.
"""
# s... |
The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipeline.
| test_pipeline | python | huggingface/transformers | tests/quantization/bnb/test_mixed_int8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/bnb/test_mixed_int8.py | Apache-2.0 |
def test_compressed_uncompressed_model_shapes(self):
"""
Verify that the weights of an uncompressed model and its decompressed compressed counterpart match.
Note: Weights for sparsely compressed models may differ due to packing.
"""
def _has_nested_attr(obj, attr_path):
... |
Verify that the weights of an uncompressed model and its decompressed compressed counterpart match.
Note: Weights for sparsely compressed models may differ due to packing.
| test_compressed_uncompressed_model_shapes | python | huggingface/transformers | tests/quantization/compressed_tensors_integration/test_compressed_models.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/compressed_tensors_integration/test_compressed_models.py | Apache-2.0 |
def test_outputs_match(self):
"""
Ensure that the generated outputs match between the uncompressed model
and its decompressed compressed counterpart.
"""
tokenizer = AutoTokenizer.from_pretrained(self.sparse_uncompressed_model)
input_ids = tokenizer(self.prompt, return_te... |
Ensure that the generated outputs match between the uncompressed model
and its decompressed compressed counterpart.
| test_outputs_match | python | huggingface/transformers | tests/quantization/compressed_tensors_integration/test_compressed_models.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/compressed_tensors_integration/test_compressed_models.py | Apache-2.0 |
def test_no_warnings_for_all_models(self):
"""
Confirm that loading any model using compressed tensors does not trigger
warnings about missing or unexpected keys.
"""
for model_stub in self.model_stubs:
with self.subTest(model_stub=model_stub):
with wa... |
Confirm that loading any model using compressed tensors does not trigger
warnings about missing or unexpected keys.
| test_no_warnings_for_all_models | python | huggingface/transformers | tests/quantization/compressed_tensors_integration/test_compressed_models.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/compressed_tensors_integration/test_compressed_models.py | Apache-2.0 |
def test_run_compressed_outputs_match(self):
"""Check that run_compressed=True/False output are the same"""
from transformers import AutoTokenizer
from transformers.utils.quantization_config import CompressedTensorsConfig
quantization_config = CompressedTensorsConfig(run_compressed=Fal... | Check that run_compressed=True/False output are the same | test_run_compressed_outputs_match | python | huggingface/transformers | tests/quantization/compressed_tensors_integration/test_compressed_models.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/compressed_tensors_integration/test_compressed_models.py | Apache-2.0 |
def test_quantized_model_multi_gpu(self):
"""
Simple test that checks if the quantized model is working properly with multiple GPUs
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUs
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
... |
Simple test that checks if the quantized model is working properly with multiple GPUs
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUs
| test_quantized_model_multi_gpu | python | huggingface/transformers | tests/quantization/eetq_integration/test_eetq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/eetq_integration/test_eetq.py | Apache-2.0 |
def test_quantized_model_offload(self):
"""
Simple test that checks if the quantized model returns an error when loading with cpu/disk offloaded
"""
quantization_config = FbgemmFp8Config()
with self.assertRaisesRegex(
ValueError, "You are attempting to load an FP8 mo... |
Simple test that checks if the quantized model returns an error when loading with cpu/disk offloaded
| test_quantized_model_offload | python | huggingface/transformers | tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | Apache-2.0 |
def test_save_pretrained_offload(self):
"""
Simple test that checks if the saved quantized model is working properly cpu/disk offload
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
input_ids = self.tokenizer... |
Simple test that checks if the saved quantized model is working properly cpu/disk offload
| test_save_pretrained_offload | python | huggingface/transformers | tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | Apache-2.0 |
def test_linear_with_diff_feature_size_preserves_shape(self):
"""
Test that FbgemmFp8Linear generates the correct shape when in_features != out_features.
"""
from transformers.integrations import FbgemmFp8Linear
with init_empty_weights(include_buffers=True):
linear =... |
Test that FbgemmFp8Linear generates the correct shape when in_features != out_features.
| test_linear_with_diff_feature_size_preserves_shape | python | huggingface/transformers | tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/fbgemm_fp8/test_fbgemm_fp8.py | Apache-2.0 |
def test_weight_and_weight_scale_inv(self):
"""
Simple test that checks if the weight and weight_scale_inv are working properly
"""
weight = self.quantized_model.model.layers[0].self_attn.q_proj.weight
weight_scale_inv = self.quantized_model.model.layers[0].self_attn.q_proj.weigh... |
Simple test that checks if the weight and weight_scale_inv are working properly
| test_weight_and_weight_scale_inv | python | huggingface/transformers | tests/quantization/finegrained_fp8/test_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/finegrained_fp8/test_fp8.py | Apache-2.0 |
def test_block_size(self):
"""
Simple test that checks if the block size is working properly
"""
self.assertEqual(self.quantized_model.config.quantization_config.weight_block_size, (128, 128))
quantization_config = FineGrainedFP8Config(weight_block_size=(32, 32))
quantize... |
Simple test that checks if the block size is working properly
| test_block_size | python | huggingface/transformers | tests/quantization/finegrained_fp8/test_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/finegrained_fp8/test_fp8.py | Apache-2.0 |
def test_quantized_model_multi_accelerator(self):
"""
Simple test that checks if the quantized model is working properly with multiple accelerators
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUs; or set ZE_AFFINITY_MASK=0,1 if you
have more than 2 XPUs.
"""
inp... |
Simple test that checks if the quantized model is working properly with multiple accelerators
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUs; or set ZE_AFFINITY_MASK=0,1 if you
have more than 2 XPUs.
| test_quantized_model_multi_accelerator | python | huggingface/transformers | tests/quantization/finegrained_fp8/test_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/finegrained_fp8/test_fp8.py | Apache-2.0 |
def test_linear_with_diff_feature_size_preserves_shape(self):
"""
Test that FP8Linear generates the correct shape when in_features != out_features.
"""
from transformers.integrations import FP8Linear
linear = FP8Linear(128, 256, block_size=(128, 128), device=self.device)
... |
Test that FP8Linear generates the correct shape when in_features != out_features.
| test_linear_with_diff_feature_size_preserves_shape | python | huggingface/transformers | tests/quantization/finegrained_fp8/test_fp8.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/finegrained_fp8/test_fp8.py | Apache-2.0 |
def test_dequantize(self):
"""
Test the ability to dequantize a model
"""
self.quantized_model.dequantize()
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_... |
Test the ability to dequantize a model
| test_dequantize | python | huggingface/transformers | tests/quantization/higgs/test_higgs.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/higgs/test_higgs.py | Apache-2.0 |
def test_fp16_quantized_model_multipgpu(self):
"""
Simple LLM model testing fp16 with multi-gpu
"""
quant_config = HqqConfig(nbits=8, group_size=64)
hqq_runner = HQQLLMRunner(
model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device="auto"
... |
Simple LLM model testing fp16 with multi-gpu
| test_fp16_quantized_model_multipgpu | python | huggingface/transformers | tests/quantization/hqq/test_hqq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/hqq/test_hqq.py | Apache-2.0 |
def test_fp16_quantized_model(self):
"""
Simple LLM model testing fp16 with bias
"""
quant_config = HqqConfig(nbits=8, group_size=64)
hqq_runner = HQQLLMRunner(
model_id="facebook/opt-125m", quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
... |
Simple LLM model testing fp16 with bias
| test_fp16_quantized_model | python | huggingface/transformers | tests/quantization/hqq/test_hqq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/hqq/test_hqq.py | Apache-2.0 |
def test_save_and_load_quantized_model(self):
"""
Test saving and loading a quantized model with bias
"""
import tempfile
quant_config = HqqConfig(nbits=8, group_size=64)
hqq_runner = HQQLLMRunner(
model_id="facebook/opt-125m", quant_config=quant_config, com... |
Test saving and loading a quantized model with bias
| test_save_and_load_quantized_model | python | huggingface/transformers | tests/quantization/hqq/test_hqq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/hqq/test_hqq.py | Apache-2.0 |
def test_model_serialization(self):
"""
Simple HQQ LLM save/load test
"""
quant_config = HqqConfig(nbits=4, group_size=64)
hqq_runner = HQQLLMRunner(
model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
)
inp... |
Simple HQQ LLM save/load test
| test_model_serialization | python | huggingface/transformers | tests/quantization/hqq/test_hqq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/hqq/test_hqq.py | Apache-2.0 |
def test_model_serialization_dynamic_quant_with_skip(self):
"""
Simple HQQ LLM save/load test with dynamic quant
"""
q4_config = {"nbits": 4, "group_size": 64}
q3_config = {"nbits": 3, "group_size": 64}
quant_config = HqqConfig(
dynamic_config={
... |
Simple HQQ LLM save/load test with dynamic quant
| test_model_serialization_dynamic_quant_with_skip | python | huggingface/transformers | tests/quantization/hqq/test_hqq.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/hqq/test_hqq.py | Apache-2.0 |
def test_weight_only_quantization_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly when using weight only quantization
"""
# Try with weight only quantization
quantization_config = QuantoConfig(weights="int8", activations=None)
... |
Simple test that checks if the quantized model has been converted properly when using weight only quantization
| test_weight_only_quantization_conversion | python | huggingface/transformers | tests/quantization/quanto_integration/test_quanto.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quanto_integration/test_quanto.py | Apache-2.0 |
def test_weight_and_activation_quantization_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly when using weight + activation quantization
"""
# Try with weight + activation quantization
quantization_config = QuantoConfig(weights="in... |
Simple test that checks if the quantized model has been converted properly when using weight + activation quantization
| test_weight_and_activation_quantization_conversion | python | huggingface/transformers | tests/quantization/quanto_integration/test_quanto.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quanto_integration/test_quanto.py | Apache-2.0 |
def test_conversion_with_modules_to_not_convert(self):
"""
Simple test that checks if the quantized model has been converted properly when specifying modules_to_not_convert argument
"""
# Try with weight + activatioin quantization
quantization_config = QuantoConfig(weights="int8... |
Simple test that checks if the quantized model has been converted properly when specifying modules_to_not_convert argument
| test_conversion_with_modules_to_not_convert | python | huggingface/transformers | tests/quantization/quanto_integration/test_quanto.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quanto_integration/test_quanto.py | Apache-2.0 |
def test_serialization_bin(self):
"""
Test the serialization, the loading and the inference of the quantized weights
"""
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(ValueError) as e:
self.quantized_model.save_pretrained(tmpdirname,... |
Test the serialization, the loading and the inference of the quantized weights
| test_serialization_bin | python | huggingface/transformers | tests/quantization/quanto_integration/test_quanto.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quanto_integration/test_quanto.py | Apache-2.0 |
def test_check_offload_quantized(self):
"""
We check that we have unquantized value in the cpu and in the disk
"""
from optimum.quanto import QBitsTensor, QTensor
cpu_weights = self.quantized_model.transformer.h[22].self_attention.query_key_value._hf_hook.weights_map[
... |
We check that we have unquantized value in the cpu and in the disk
| test_check_offload_quantized | python | huggingface/transformers | tests/quantization/quanto_integration/test_quanto.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quanto_integration/test_quanto.py | Apache-2.0 |
def test_device_and_dtype_assignment(self):
r"""
Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
Checks also if other models are casted correctly .
"""
# This should work
if self.device_map is None:
_ = se... |
Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
Checks also if other models are casted correctly .
| test_device_and_dtype_assignment | python | huggingface/transformers | tests/quantization/quark_integration/test_quark.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/quark_integration/test_quark.py | Apache-2.0 |
def test_json_serializable(self):
"""
Check that the config dict can be JSON serialized.
"""
quantization_config = TorchAoConfig("int4_weight_only", group_size=32, layout=TensorCoreTiledLayout())
d = quantization_config.to_dict()
self.assertIsInstance(d["quant_type_kwargs... |
Check that the config dict can be JSON serialized.
| test_json_serializable | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def test_int4wo_quant(self):
"""
Simple LLM model testing int4 weight only quantization
"""
quant_config = TorchAoConfig("int4_weight_only", **self.quant_scheme_kwargs)
# Note: we quantize the bfloat16 model on the fly to int4
quantized_model = AutoModelForCausalLM.from_... |
Simple LLM model testing int4 weight only quantization
| test_int4wo_quant | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def test_int4wo_quant_bfloat16_conversion(self):
"""
Testing the dtype of model will be modified to be bfloat16 for int4 weight only quantization
"""
quant_config = TorchAoConfig("int4_weight_only", **self.quant_scheme_kwargs)
# Note: we quantize the bfloat16 model on the fly to... |
Testing the dtype of model will be modified to be bfloat16 for int4 weight only quantization
| test_int4wo_quant_bfloat16_conversion | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def test_int4wo_offload(self):
"""
Simple test that checks if the quantized model int4 weight only is working properly with cpu/disk offload
"""
device_map_offload = {
"model.embed_tokens": 0,
"model.layers.0": 0,
"model.layers.1": 0,
"mod... |
Simple test that checks if the quantized model int4 weight only is working properly with cpu/disk offload
| test_int4wo_offload | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def test_int4wo_quant_multi_accelerator(self):
"""
Simple test that checks if the quantized model int4 weight only is working properly with multiple accelerators
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 CUDA GPUs
set ZE_AFFINITY_MASK=0,1 if you have more than 2 Intel XPUs
... |
Simple test that checks if the quantized model int4 weight only is working properly with multiple accelerators
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 CUDA GPUs
set ZE_AFFINITY_MASK=0,1 if you have more than 2 Intel XPUs
| test_int4wo_quant_multi_accelerator | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def check_serialization_expected_output(self, device, expected_output):
"""
Test if we can serialize and load/infer the model again on the same device
"""
torch_dtype = torch.bfloat16 if self.quant_scheme == "int4_weight_only" else "auto"
with tempfile.TemporaryDirectory() as tmp... |
Test if we can serialize and load/infer the model again on the same device
| check_serialization_expected_output | python | huggingface/transformers | tests/quantization/torchao_integration/test_torchao.py | https://github.com/huggingface/transformers/blob/master/tests/quantization/torchao_integration/test_torchao.py | Apache-2.0 |
def torchrun(self, script: str, is_torchrun: bool = True):
"""Run the `script` using `torchrun` command for multi-processing in a subprocess. Captures errors as necessary."""
with tempfile.NamedTemporaryFile(mode="w+", suffix=".py") as tmp:
tmp.write(script)
tmp.flush()
... | Run the `script` using `torchrun` command for multi-processing in a subprocess. Captures errors as necessary. | torchrun | python | huggingface/transformers | tests/tensor_parallel/test_tensor_parallel.py | https://github.com/huggingface/transformers/blob/master/tests/tensor_parallel/test_tensor_parallel.py | Apache-2.0 |
def test_probability_sum_error(self):
"""Test that the sum of mask_replace_prob and random_replace_prob exceeding 1 raises an error."""
tokenizer = BertTokenizer(self.vocab_file)
with self.assertRaises(ValueError):
DataCollatorForLanguageModeling(tokenizer=tokenizer, mask_replace_pro... | Test that the sum of mask_replace_prob and random_replace_prob exceeding 1 raises an error. | test_probability_sum_error | python | huggingface/transformers | tests/trainer/test_data_collator.py | https://github.com/huggingface/transformers/blob/master/tests/trainer/test_data_collator.py | Apache-2.0 |
def test_load_backbone_from_config(self):
"""
Test that load_backbone correctly loads a backbone from a backbone config.
"""
config = MaskFormerConfig(backbone_config=ResNetConfig(out_indices=(0, 2)))
backbone = load_backbone(config)
self.assertEqual(backbone.out_features... |
Test that load_backbone correctly loads a backbone from a backbone config.
| test_load_backbone_from_config | python | huggingface/transformers | tests/utils/test_backbone_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_backbone_utils.py | Apache-2.0 |
def test_load_backbone_from_checkpoint(self):
"""
Test that load_backbone correctly loads a backbone from a checkpoint.
"""
config = MaskFormerConfig(backbone="microsoft/resnet-18", backbone_config=None)
backbone = load_backbone(config)
self.assertEqual(backbone.out_indic... |
Test that load_backbone correctly loads a backbone from a checkpoint.
| test_load_backbone_from_checkpoint | python | huggingface/transformers | tests/utils/test_backbone_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_backbone_utils.py | Apache-2.0 |
def test_load_backbone_backbone_kwargs(self):
"""
Test that load_backbone correctly configures the loaded backbone with the provided kwargs.
"""
config = MaskFormerConfig(backbone="resnet18", use_timm_backbone=True, backbone_kwargs={"out_indices": (0, 1)})
backbone = load_backbon... |
Test that load_backbone correctly configures the loaded backbone with the provided kwargs.
| test_load_backbone_backbone_kwargs | python | huggingface/transformers | tests/utils/test_backbone_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_backbone_utils.py | Apache-2.0 |
def test_load_backbone_in_new_model(self):
"""
Tests that new model can be created, with its weights instantiated and pretrained backbone weights loaded.
"""
# Inherit from PreTrainedModel to ensure that the weights are initialized
class NewModel(BertPreTrainedModel):
... |
Tests that new model can be created, with its weights instantiated and pretrained backbone weights loaded.
| test_load_backbone_in_new_model | python | huggingface/transformers | tests/utils/test_backbone_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_backbone_utils.py | Apache-2.0 |
def test_dynamic_cache_retrocompatibility(self):
"""Tests that we can convert back and forth between the legacy cache format and DynamicCache"""
legacy_cache = ()
new_cache = DynamicCache()
# Creates a new cache with 10 layers in both formats
for layer_idx in range(10):
... | Tests that we can convert back and forth between the legacy cache format and DynamicCache | test_dynamic_cache_retrocompatibility | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_reorder_cache_retrocompatibility(self):
"""Tests that Cache.reorder_cache is retrocompatible with the legacy code path"""
legacy_reorder_fn = ClvpForCausalLM._reorder_cache # An example of a legacy `_reorder_cache` function
legacy_cache = ()
new_cache = DynamicCache()
... | Tests that Cache.reorder_cache is retrocompatible with the legacy code path | test_reorder_cache_retrocompatibility | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_static_cache_mha_mqa_gqa(self):
"""
Tests that static cache works with multi-head attention (MHA), grouped query attention (GQA), and multi-query
attention (MQA)
"""
def _random_kvs(config):
# shape for key and values: (batch_size, num_heads, seq_len, head_d... |
Tests that static cache works with multi-head attention (MHA), grouped query attention (GQA), and multi-query
attention (MQA)
| test_static_cache_mha_mqa_gqa | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def _skip_on_failed_cache_prerequisites(test, cache_implementation):
"""Function to skip tests on failed cache prerequisites, given a cache implementation"""
# Installed dependencies
if cache_implementation == "quantized" and not is_optimum_quanto_available():
test.skipTest("Quanto is not available"... | Function to skip tests on failed cache prerequisites, given a cache implementation | _skip_on_failed_cache_prerequisites | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_cache_batched(self, cache_implementation):
"""Sanity check: caches' `.update` function expects batched inputs"""
_skip_on_failed_cache_prerequisites(self, cache_implementation)
EXPECTED_GENERATION = ["A sequence: 1, 2, 3, 4, 5, 6, 7, 8,", "A sequence: A, B, C, D, E, F, G, H"]
... | Sanity check: caches' `.update` function expects batched inputs | test_cache_batched | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_cache_beam_search(self, cache_implementation):
"""
Sanity check: caches' `reorder_cache` is operational. We can confirm this by looking at the beam indices
(an output sequence contains multiple beam indices).
"""
_skip_on_failed_cache_prerequisites(self, cache_implementa... |
Sanity check: caches' `reorder_cache` is operational. We can confirm this by looking at the beam indices
(an output sequence contains multiple beam indices).
| test_cache_beam_search | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_cache_extra_left_padding(self, cache_implementation):
"""Tests that adding extra left-padding does not affect the generation with the cache"""
_skip_on_failed_cache_prerequisites(self, cache_implementation)
EXPECTED_GENERATION = ["The cat's whiskers are also a sign of anxiety."]
... | Tests that adding extra left-padding does not affect the generation with the cache | test_cache_extra_left_padding | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_dynamic_cache_hard(self):
"""Hard test for base cache implementation -- minor numerical fluctuations will cause this test to fail"""
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B", device_map="... | Hard test for base cache implementation -- minor numerical fluctuations will cause this test to fail | test_dynamic_cache_hard | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_static_cache_greedy_decoding_pad_left(self, attn_implementation):
"""Tests that different cache implementations work well with eager and SDPA inference"""
EXPECTED_GENERATION = [
"The best color is the one that is most suitable for the purpose.",
"We should not undermind... | Tests that different cache implementations work well with eager and SDPA inference | test_static_cache_greedy_decoding_pad_left | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
def test_offloaded_cache_uses_less_memory_than_dynamic_cache(self):
"""Tests that OffloadedCache uses less memory than the default DynamicCache"""
model_name = "microsoft/Phi-3-mini-4k-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretr... | Tests that OffloadedCache uses less memory than the default DynamicCache | test_offloaded_cache_uses_less_memory_than_dynamic_cache | python | huggingface/transformers | tests/utils/test_cache_utils.py | https://github.com/huggingface/transformers/blob/master/tests/utils/test_cache_utils.py | Apache-2.0 |
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