1449 lines
64 KiB
Python
1449 lines
64 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import inspect
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import json
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import os
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import random
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import tempfile
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import unittest
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from importlib import import_module
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from typing import List, Tuple
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from huggingface_hub import delete_repo, login
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from requests.exceptions import HTTPError
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from transformers import is_tf_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import tooslow # noqa: F401
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from transformers.testing_utils import (
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PASS,
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USER,
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CaptureLogger,
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_tf_gpu_memory_limit,
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is_pt_tf_cross_test,
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is_staging_test,
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require_tf,
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require_tf2onnx,
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slow,
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)
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from transformers.utils import logging
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if is_tf_available():
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import numpy as np
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import tensorflow as tf
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from transformers import (
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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BertConfig,
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TFAutoModel,
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TFAutoModelForSequenceClassification,
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TFBertModel,
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TFSharedEmbeddings,
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tf_top_k_top_p_filtering,
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)
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from transformers.generation_tf_utils import (
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TFBeamSampleDecoderOnlyOutput,
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TFBeamSampleEncoderDecoderOutput,
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TFBeamSearchDecoderOnlyOutput,
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TFBeamSearchEncoderDecoderOutput,
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TFGreedySearchDecoderOnlyOutput,
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TFGreedySearchEncoderDecoderOutput,
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TFSampleDecoderOnlyOutput,
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TFSampleEncoderDecoderOutput,
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)
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if _tf_gpu_memory_limit is not None:
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gpus = tf.config.list_physical_devices("GPU")
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for gpu in gpus:
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# Restrict TensorFlow to only allocate x GB of memory on the GPUs
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try:
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tf.config.set_logical_device_configuration(
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gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
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)
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logical_gpus = tf.config.list_logical_devices("GPU")
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print("Logical GPUs", logical_gpus)
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except RuntimeError as e:
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# Virtual devices must be set before GPUs have been initialized
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print(e)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key:
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setattr(configs_no_init, key, 0.0)
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return configs_no_init
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@require_tf
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class TFModelTesterMixin:
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model_tester = None
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all_model_classes = ()
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all_generative_model_classes = ()
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test_mismatched_shapes = True
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test_resize_embeddings = True
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test_head_masking = True
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is_encoder_decoder = False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
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inputs_dict = copy.deepcopy(inputs_dict)
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if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict = {
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k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
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if isinstance(v, tf.Tensor) and v.ndim > 0
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else v
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for k, v in inputs_dict.items()
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}
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if return_labels:
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if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
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elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
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inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
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elif model_class in [
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*get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
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*get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
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]:
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inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
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elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
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inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
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elif model_class in [
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*get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
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*get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
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*get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
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*get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
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*get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
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]:
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inputs_dict["labels"] = tf.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
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)
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return inputs_dict
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def test_initialization(self):
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pass
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def test_save_load(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname, saved_model=False)
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model = model_class.from_pretrained(tmpdirname)
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after_outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assert_outputs_same(after_outputs, outputs)
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def test_save_load_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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model_config = model.get_config()
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# make sure that returned config is jsonifiable, which is required by keras
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json.dumps(model_config)
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new_model = model_class.from_config(model.get_config())
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# make sure it also accepts a normal config
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_ = model_class.from_config(model.config)
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_ = new_model(self._prepare_for_class(inputs_dict, model_class)) # Build model
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new_model.set_weights(model.get_weights())
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after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class))
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self.assert_outputs_same(after_outputs, outputs)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.call)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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if model.config.is_encoder_decoder:
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expected_arg_names = [
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"input_ids",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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expected_arg_names.extend(
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["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else []
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)
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# Necessary to handle BART with newly added cross_attn_head_mask
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expected_arg_names.extend(
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["cross_attn_head_mask", "encoder_outputs"]
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if "cross_attn_head_mask" in arg_names
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else ["encoder_outputs"]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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else:
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expected_arg_names = ["input_ids"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_onnx_compliancy(self):
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if not self.test_onnx:
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return
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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INTERNAL_OPS = [
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"Assert",
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"AssignVariableOp",
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"EmptyTensorList",
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"ReadVariableOp",
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"ResourceGather",
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"TruncatedNormal",
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"VarHandleOp",
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"VarIsInitializedOp",
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]
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onnx_ops = []
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with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f:
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onnx_opsets = json.load(f)["opsets"]
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for i in range(1, self.onnx_min_opset + 1):
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onnx_ops.extend(onnx_opsets[str(i)])
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for model_class in self.all_model_classes:
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model_op_names = set()
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with tf.Graph().as_default() as g:
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model = model_class(config)
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model(model.dummy_inputs)
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for op in g.get_operations():
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model_op_names.add(op.node_def.op)
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model_op_names = sorted(model_op_names)
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incompatible_ops = []
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for op in model_op_names:
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if op not in onnx_ops and op not in INTERNAL_OPS:
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incompatible_ops.append(op)
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self.assertEqual(len(incompatible_ops), 0, incompatible_ops)
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@require_tf2onnx
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@slow
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def test_onnx_runtime_optimize(self):
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if not self.test_onnx:
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return
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import onnxruntime
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import tf2onnx
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model(model.dummy_inputs)
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onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)
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onnxruntime.InferenceSession(onnx_model_proto.SerializeToString())
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def test_keras_save_load(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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tf_main_layer_classes = set(
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module_member
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for model_class in self.all_model_classes
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for module in (import_module(model_class.__module__),)
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for module_member_name in dir(module)
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if module_member_name.endswith("MainLayer")
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# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
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and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
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for module_member in (getattr(module, module_member_name),)
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if isinstance(module_member, type)
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and tf.keras.layers.Layer in module_member.__bases__
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and getattr(module_member, "_keras_serializable", False)
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)
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for main_layer_class in tf_main_layer_classes:
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# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
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if "T5" in main_layer_class.__name__:
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# Take the same values than in TFT5ModelTester for this shared layer
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shared = TFSharedEmbeddings(99, 32, name="shared")
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config.use_cache = inputs_dict.pop("use_cache", None)
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main_layer = main_layer_class(config, embed_tokens=shared)
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else:
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main_layer = main_layer_class(config)
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symbolic_inputs = {
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name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
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}
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model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
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outputs = model(inputs_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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filepath = os.path.join(tmpdirname, "keras_model.h5")
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model.save(filepath)
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if "T5" in main_layer_class.__name__:
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model = tf.keras.models.load_model(
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filepath,
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custom_objects={
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main_layer_class.__name__: main_layer_class,
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"TFSharedEmbeddings": TFSharedEmbeddings,
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},
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)
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else:
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model = tf.keras.models.load_model(
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filepath, custom_objects={main_layer_class.__name__: main_layer_class}
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)
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assert isinstance(model, tf.keras.Model)
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after_outputs = model(inputs_dict)
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self.assert_outputs_same(after_outputs, outputs)
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def assert_outputs_same(self, after_outputs, outputs):
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# Make sure we don't have nans
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if isinstance(after_outputs, tf.Tensor):
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out_1 = after_outputs.numpy()
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elif isinstance(after_outputs, dict):
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out_1 = after_outputs[list(after_outputs.keys())[0]].numpy()
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else:
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out_1 = after_outputs[0].numpy()
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out_2 = outputs[0].numpy()
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self.assertEqual(out_1.shape, out_2.shape)
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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@is_pt_tf_cross_test
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def test_pt_tf_model_equivalence(self):
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import torch
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import transformers
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
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pt_model_class = getattr(transformers, pt_model_class_name)
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config.output_hidden_states = True
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tf_model = model_class(config)
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pt_model = pt_model_class(config)
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# Check we can load pt model in tf and vice-versa with model => model functions
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tf_model = transformers.load_pytorch_model_in_tf2_model(
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tf_model, pt_model, tf_inputs=self._prepare_for_class(inputs_dict, model_class)
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)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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pt_inputs_dict = {}
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for name, key in self._prepare_for_class(inputs_dict, model_class).items():
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if type(key) == bool:
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pt_inputs_dict[name] = key
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elif name == "input_values":
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
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elif name == "pixel_values":
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
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else:
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
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# need to rename encoder-decoder "inputs" for PyTorch
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if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
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with torch.no_grad():
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pto = pt_model(**pt_inputs_dict)
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tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
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tf_hidden_states = tfo[0].numpy()
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pt_hidden_states = pto[0].numpy()
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tf_nans = np.copy(np.isnan(tf_hidden_states))
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pt_nans = np.copy(np.isnan(pt_hidden_states))
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pt_hidden_states[tf_nans] = 0
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tf_hidden_states[tf_nans] = 0
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pt_hidden_states[pt_nans] = 0
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tf_hidden_states[pt_nans] = 0
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max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
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self.assertLessEqual(max_diff, 4e-2)
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# Check we can load pt model in tf and vice-versa with checkpoint => model functions
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with tempfile.TemporaryDirectory() as tmpdirname:
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pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
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torch.save(pt_model.state_dict(), pt_checkpoint_path)
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tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
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tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
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tf_model.save_weights(tf_checkpoint_path)
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pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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pt_inputs_dict = {}
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for name, key in self._prepare_for_class(inputs_dict, model_class).items():
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if type(key) == bool:
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key = np.array(key, dtype=bool)
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pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
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elif name == "input_values":
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
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elif name == "pixel_values":
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
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else:
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pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
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# need to rename encoder-decoder "inputs" for PyTorch
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if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
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pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
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with torch.no_grad():
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pto = pt_model(**pt_inputs_dict)
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tfo = tf_model(self._prepare_for_class(inputs_dict, model_class))
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tfo = tfo[0].numpy()
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pto = pto[0].numpy()
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tf_nans = np.copy(np.isnan(tfo))
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pt_nans = np.copy(np.isnan(pto))
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pto[tf_nans] = 0
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tfo[tf_nans] = 0
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pto[pt_nans] = 0
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tfo[pt_nans] = 0
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max_diff = np.amax(np.abs(tfo - pto))
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self.assertLessEqual(max_diff, 4e-2)
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|
|
def test_compile_tf_model(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
max_input = getattr(self.model_tester, "max_position_embeddings", 512)
|
|
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
|
|
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
|
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
|
|
|
|
for model_class in self.all_model_classes:
|
|
if self.is_encoder_decoder:
|
|
inputs = {
|
|
"decoder_input_ids": tf.keras.Input(
|
|
batch_shape=(2, max_input),
|
|
name="decoder_input_ids",
|
|
dtype="int32",
|
|
),
|
|
"input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
|
|
}
|
|
# TODO: A better way to handle vision models
|
|
elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification", "TFCLIPVisionModel"]:
|
|
inputs = tf.keras.Input(
|
|
batch_shape=(
|
|
3,
|
|
self.model_tester.num_channels,
|
|
self.model_tester.image_size,
|
|
self.model_tester.image_size,
|
|
),
|
|
name="pixel_values",
|
|
dtype="float32",
|
|
)
|
|
elif model_class.__name__ in ["TFCLIPModel"]:
|
|
inputs = {
|
|
"input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
|
|
"pixel_values": tf.keras.Input(
|
|
batch_shape=(
|
|
3,
|
|
self.model_tester.vision_model_tester.num_channels,
|
|
self.model_tester.vision_model_tester.image_size,
|
|
self.model_tester.vision_model_tester.image_size,
|
|
),
|
|
name="pixel_values",
|
|
dtype="float32",
|
|
),
|
|
}
|
|
elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
|
|
inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
|
|
else:
|
|
inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")
|
|
|
|
# Prepare our model
|
|
model = model_class(config)
|
|
model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving.
|
|
# Let's load it from the disk to be sure we can use pretrained weights
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname, saved_model=False)
|
|
model = model_class.from_pretrained(tmpdirname)
|
|
|
|
outputs_dict = model(inputs)
|
|
hidden_states = outputs_dict[0]
|
|
|
|
# Add a dense layer on top to test integration with other keras modules
|
|
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
|
|
|
|
# Compile extended model
|
|
extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
|
|
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
|
|
|
|
def test_keyword_and_dict_args(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs_dict = model(inputs)
|
|
|
|
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
|
|
input_ids = inputs_keywords.pop("input_ids", None)
|
|
if input_ids is None:
|
|
input_ids = inputs_keywords.pop("pixel_values", None)
|
|
outputs_keywords = model(input_ids, **inputs_keywords)
|
|
output_dict = outputs_dict[0].numpy()
|
|
output_keywords = outputs_keywords[0].numpy()
|
|
|
|
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
|
|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
|
|
decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
|
|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
|
|
def check_decoder_attentions_output(outputs):
|
|
out_len = len(outputs)
|
|
self.assertEqual(min(out_len % 2, out_len % 5), 0) # differentiation due to newly added cross_attentions
|
|
decoder_attentions = outputs.decoder_attentions
|
|
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(decoder_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
|
)
|
|
|
|
def check_encoder_attentions_output(outputs):
|
|
attentions = [
|
|
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
|
|
]
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["use_cache"] = False
|
|
config.output_hidden_states = False
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
out_len = len(outputs)
|
|
self.assertEqual(config.output_hidden_states, False)
|
|
check_encoder_attentions_output(outputs)
|
|
|
|
if self.is_encoder_decoder:
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
self.assertEqual(config.output_hidden_states, False)
|
|
check_decoder_attentions_output(outputs)
|
|
|
|
# Check that output attentions can also be changed via the config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
self.assertEqual(config.output_hidden_states, False)
|
|
check_encoder_attentions_output(outputs)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = True
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
|
|
self.assertEqual(model.config.output_hidden_states, True)
|
|
check_encoder_attentions_output(outputs)
|
|
|
|
def test_headmasking(self):
|
|
if not self.test_head_masking:
|
|
return
|
|
|
|
random.Random().seed(42)
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
random.Random().seed()
|
|
|
|
inputs_dict["output_attentions"] = True
|
|
config.output_hidden_states = True
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
|
|
# Prepare head_mask
|
|
def prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
|
|
if i == 0:
|
|
return tf.concat(
|
|
(tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0
|
|
)
|
|
elif i == num_hidden_layers - 1:
|
|
return tf.concat(
|
|
(tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0
|
|
)
|
|
else:
|
|
return tf.ones(attention_heads, dtype=tf.float32)
|
|
|
|
head_mask = tf.stack(
|
|
[
|
|
prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
|
|
for i in range(config.num_hidden_layers)
|
|
],
|
|
0,
|
|
)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
|
|
inputs["head_mask"] = head_mask
|
|
if model.config.is_encoder_decoder:
|
|
signature = inspect.signature(model.call)
|
|
arg_names = [*signature.parameters.keys()]
|
|
if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model
|
|
inputs["decoder_head_mask"] = head_mask
|
|
if "cross_attn_head_mask" in arg_names:
|
|
inputs["cross_attn_head_mask"] = head_mask
|
|
|
|
outputs = model(**inputs, return_dict=True)
|
|
|
|
def check_attentions_validity(attentions):
|
|
# Remove Nan
|
|
for t in attentions:
|
|
self.assertLess(
|
|
(tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy()
|
|
) # Check we don't have more than 25% nans (arbitrary)
|
|
|
|
attentions = [
|
|
tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions
|
|
] # remove them (the test is less complete)
|
|
|
|
self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0)
|
|
self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0)
|
|
if len(attentions) > 2: # encoder-decodere models have only 2 layers in each modules
|
|
self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0)
|
|
self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0)
|
|
self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0)
|
|
|
|
if model.config.is_encoder_decoder:
|
|
check_attentions_validity(outputs.encoder_attentions)
|
|
check_attentions_validity(outputs.decoder_attentions)
|
|
if "cross_attn_head_mask" in arg_names:
|
|
check_attentions_validity(outputs.cross_attentions)
|
|
else:
|
|
check_attentions_validity(outputs.attentions)
|
|
|
|
def test_hidden_states_output(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def check_hidden_states_output(config, inputs_dict, model_class):
|
|
model = model_class(config)
|
|
outputs = model(self._prepare_for_class(inputs_dict, model_class))
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
|
)
|
|
|
|
if model.config.is_encoder_decoder:
|
|
encoder_hidden_states = outputs.encoder_hidden_states
|
|
decoder_hidden_states = outputs.decoder_hidden_states
|
|
|
|
self.assertEqual(config.output_attentions, False)
|
|
self.assertEqual(len(encoder_hidden_states), expected_num_layers)
|
|
self.assertListEqual(
|
|
list(encoder_hidden_states[0].shape[-2:]),
|
|
[self.model_tester.seq_length, self.model_tester.hidden_size],
|
|
)
|
|
self.assertEqual(len(decoder_hidden_states), expected_num_layers)
|
|
self.assertListEqual(
|
|
list(decoder_hidden_states[0].shape[-2:]),
|
|
[self.model_tester.seq_length, self.model_tester.hidden_size],
|
|
)
|
|
else:
|
|
hidden_states = outputs.hidden_states
|
|
self.assertEqual(config.output_attentions, False)
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[self.model_tester.seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(config, inputs_dict, model_class)
|
|
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
check_hidden_states_output(config, inputs_dict, model_class)
|
|
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
list_lm_models = (
|
|
get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING)
|
|
+ get_values(TF_MODEL_FOR_MASKED_LM_MAPPING)
|
|
+ get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
|
|
)
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
|
|
|
|
if model_class in list_lm_models:
|
|
x = model.get_output_embeddings()
|
|
assert isinstance(x, tf.keras.layers.Layer)
|
|
name = model.get_bias()
|
|
assert isinstance(name, dict)
|
|
for k, v in name.items():
|
|
assert isinstance(v, tf.Variable)
|
|
else:
|
|
x = model.get_output_embeddings()
|
|
assert x is None
|
|
name = model.get_bias()
|
|
assert name is None
|
|
|
|
def test_determinism(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
first, second = (
|
|
model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
|
|
model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
|
|
)
|
|
out_1 = first.numpy()
|
|
out_2 = second.numpy()
|
|
out_1 = out_1[~np.isnan(out_1)]
|
|
out_2 = out_2[~np.isnan(out_2)]
|
|
max_diff = np.amax(np.abs(out_1 - out_2))
|
|
self.assertLessEqual(max_diff, 1e-5)
|
|
|
|
def test_model_outputs_equivalence(self):
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
|
|
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
|
|
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
|
|
|
|
def recursive_check(tuple_object, dict_object):
|
|
if isinstance(tuple_object, (List, Tuple)):
|
|
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
|
|
recursive_check(tuple_iterable_value, dict_iterable_value)
|
|
elif tuple_object is None:
|
|
return
|
|
else:
|
|
self.assertTrue(
|
|
all(tf.equal(tuple_object, dict_object)),
|
|
msg=f"Tuple and dict output are not equal. Difference: {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}",
|
|
)
|
|
|
|
recursive_check(tuple_output, dict_output)
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs)
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs)
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
|
|
|
|
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
check_equivalence(
|
|
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
|
|
)
|
|
|
|
def test_inputs_embeds(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
|
|
inputs = copy.deepcopy(inputs_dict)
|
|
|
|
if not self.is_encoder_decoder:
|
|
input_ids = inputs["input_ids"]
|
|
del inputs["input_ids"]
|
|
else:
|
|
encoder_input_ids = inputs["input_ids"]
|
|
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
|
|
del inputs["input_ids"]
|
|
inputs.pop("decoder_input_ids", None)
|
|
|
|
if not self.is_encoder_decoder:
|
|
inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids)
|
|
else:
|
|
inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids)
|
|
inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids)
|
|
|
|
inputs = self._prepare_for_class(inputs, model_class)
|
|
|
|
model(inputs)
|
|
|
|
def test_numpy_arrays_inputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def prepare_numpy_arrays(inputs_dict):
|
|
inputs_np_dict = {}
|
|
for k, v in inputs_dict.items():
|
|
if tf.is_tensor(v):
|
|
inputs_np_dict[k] = v.numpy()
|
|
else:
|
|
inputs_np_dict[k] = np.array(k)
|
|
|
|
return inputs_np_dict
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
inputs_np = prepare_numpy_arrays(inputs)
|
|
|
|
output_for_dict_input = model(inputs_np)
|
|
output_for_kw_input = model(**inputs_np)
|
|
self.assert_outputs_same(output_for_dict_input, output_for_kw_input)
|
|
|
|
def test_resize_token_embeddings(self):
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
def _get_word_embedding_weight(model, embedding_layer):
|
|
embeds = getattr(embedding_layer, "weight", None)
|
|
if embeds is not None:
|
|
return embeds
|
|
|
|
embeds = getattr(embedding_layer, "decoder", None)
|
|
if embeds is not None:
|
|
return embeds
|
|
|
|
model(model.dummy_inputs)
|
|
|
|
embeds = getattr(embedding_layer, "weight", None)
|
|
if embeds is not None:
|
|
return embeds
|
|
|
|
embeds = getattr(embedding_layer, "decoder", None)
|
|
if embeds is not None:
|
|
return embeds
|
|
|
|
return None
|
|
|
|
for model_class in self.all_model_classes:
|
|
for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
|
|
# build the embeddings
|
|
model = model_class(config=config)
|
|
old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
|
|
old_bias = model.get_bias()
|
|
old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
|
|
# reshape the embeddings
|
|
model.resize_token_embeddings(size)
|
|
new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
|
|
new_bias = model.get_bias()
|
|
new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
|
|
|
|
# check that the resized embeddings size matches the desired size.
|
|
assert_size = size if size is not None else config.vocab_size
|
|
self.assertEqual(new_input_embeddings.shape[0], assert_size)
|
|
|
|
# check that weights remain the same after resizing
|
|
models_equal = True
|
|
for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
|
|
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
|
|
models_equal = False
|
|
self.assertTrue(models_equal)
|
|
|
|
if old_bias is not None and new_bias is not None:
|
|
for old_weight, new_weight in zip(old_bias.values(), new_bias.values()):
|
|
self.assertEqual(new_weight.shape[0], assert_size)
|
|
|
|
models_equal = True
|
|
for p1, p2 in zip(old_weight.value(), new_weight.value()):
|
|
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
|
|
models_equal = False
|
|
self.assertTrue(models_equal)
|
|
|
|
if old_output_embeddings is not None and new_output_embeddings is not None:
|
|
self.assertEqual(new_output_embeddings.shape[0], assert_size)
|
|
self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1])
|
|
|
|
models_equal = True
|
|
for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
|
|
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
|
|
models_equal = False
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_lm_head_model_random_no_beam_search_generate(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = inputs_dict.get("input_ids", None)
|
|
|
|
# iterate over all generative models
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config)
|
|
|
|
if config.bos_token_id is None:
|
|
# if bos token id is not defined mobel needs input_ids
|
|
with self.assertRaises(AssertionError):
|
|
model.generate(do_sample=True, max_length=5)
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True))
|
|
else:
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(do_sample=True, max_length=5))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# generating multiple sequences when no beam search generation
|
|
# is not allowed as it would always generate the same sequences
|
|
model.generate(input_ids, do_sample=False, num_return_sequences=2)
|
|
|
|
# num_return_sequences > 1, sample
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2))
|
|
|
|
# check bad words tokens language generation
|
|
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
|
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
|
|
output_tokens = model.generate(
|
|
input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
|
|
)
|
|
# only count generated tokens
|
|
generated_ids = output_tokens[:, input_ids.shape[-1] :]
|
|
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
|
|
|
|
def test_lm_head_model_no_beam_search_generate_dict_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = inputs_dict.get("input_ids", None)
|
|
|
|
# iterate over all generative models
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config)
|
|
output_greedy = model.generate(
|
|
input_ids,
|
|
do_sample=False,
|
|
output_scores=True,
|
|
output_hidden_states=True,
|
|
output_attentions=True,
|
|
return_dict_in_generate=True,
|
|
)
|
|
output_sample = model.generate(
|
|
input_ids,
|
|
do_sample=True,
|
|
output_scores=True,
|
|
output_hidden_states=True,
|
|
output_attentions=True,
|
|
return_dict_in_generate=True,
|
|
)
|
|
|
|
if model.config.is_encoder_decoder:
|
|
self.assertIsInstance(output_greedy, TFGreedySearchEncoderDecoderOutput)
|
|
self.assertIsInstance(output_sample, TFSampleEncoderDecoderOutput)
|
|
else:
|
|
self.assertIsInstance(output_greedy, TFGreedySearchDecoderOnlyOutput)
|
|
self.assertIsInstance(output_sample, TFSampleDecoderOnlyOutput)
|
|
|
|
def test_lm_head_model_random_beam_search_generate(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = inputs_dict.get("input_ids", None)
|
|
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config)
|
|
|
|
if config.bos_token_id is None:
|
|
# if bos token id is not defined mobel needs input_ids, num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2))
|
|
else:
|
|
# num_return_sequences = 1
|
|
self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# generating more sequences than having beams leads is not possible
|
|
model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
|
|
|
|
# num_return_sequences > 1, sample
|
|
self._check_generated_ids(
|
|
model.generate(
|
|
input_ids,
|
|
do_sample=True,
|
|
num_beams=2,
|
|
num_return_sequences=2,
|
|
)
|
|
)
|
|
# num_return_sequences > 1, greedy
|
|
self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2))
|
|
|
|
# check bad words tokens language generation
|
|
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
|
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
|
|
output_tokens = model.generate(
|
|
input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
|
|
)
|
|
# only count generated tokens
|
|
generated_ids = output_tokens[:, input_ids.shape[-1] :]
|
|
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
|
|
|
|
def test_lm_head_model_beam_search_generate_dict_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
input_ids = inputs_dict.get("input_ids", None)
|
|
|
|
# iterate over all generative models
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config)
|
|
output_beam_search = model.generate(
|
|
input_ids,
|
|
num_beams=2,
|
|
do_sample=False,
|
|
output_scores=True,
|
|
output_hidden_states=True,
|
|
output_attentions=True,
|
|
return_dict_in_generate=True,
|
|
)
|
|
output_beam_sample = model.generate(
|
|
input_ids,
|
|
num_beams=2,
|
|
do_sample=True,
|
|
output_scores=True,
|
|
output_hidden_states=True,
|
|
output_attentions=True,
|
|
return_dict_in_generate=True,
|
|
)
|
|
|
|
if model.config.is_encoder_decoder:
|
|
self.assertIsInstance(output_beam_search, TFBeamSearchEncoderDecoderOutput)
|
|
self.assertIsInstance(output_beam_sample, TFBeamSampleEncoderDecoderOutput)
|
|
else:
|
|
self.assertIsInstance(output_beam_search, TFBeamSearchDecoderOnlyOutput)
|
|
self.assertIsInstance(output_beam_sample, TFBeamSampleDecoderOnlyOutput)
|
|
|
|
def test_loss_computation(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
if getattr(model, "hf_compute_loss", None):
|
|
# The number of elements in the loss should be the same as the number of elements in the label
|
|
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
|
|
added_label = prepared_for_class[
|
|
sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
|
|
]
|
|
loss_size = tf.size(added_label)
|
|
|
|
if model.__class__ in get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING):
|
|
# if loss is causal lm loss, labels are shift, so that one label per batch
|
|
# is cut
|
|
loss_size = loss_size - self.model_tester.batch_size
|
|
|
|
# Test that model correctly compute the loss with kwargs
|
|
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
|
|
input_name = "input_ids" if "input_ids" in prepared_for_class else "pixel_values"
|
|
input_ids = prepared_for_class.pop(input_name)
|
|
|
|
loss = model(input_ids, **prepared_for_class)[0]
|
|
self.assertEqual(loss.shape, [loss_size])
|
|
|
|
# Test that model correctly compute the loss with a dict
|
|
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
|
|
loss = model(prepared_for_class)[0]
|
|
self.assertEqual(loss.shape, [loss_size])
|
|
|
|
# Test that model correctly compute the loss with a tuple
|
|
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
|
|
|
|
# Get keys that were added with the _prepare_for_class function
|
|
label_keys = prepared_for_class.keys() - inputs_dict.keys()
|
|
signature = inspect.signature(model.call).parameters
|
|
signature_names = list(signature.keys())
|
|
|
|
# Create a dictionary holding the location of the tensors in the tuple
|
|
tuple_index_mapping = {0: input_name}
|
|
for label_key in label_keys:
|
|
label_key_index = signature_names.index(label_key)
|
|
tuple_index_mapping[label_key_index] = label_key
|
|
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
|
|
# Initialize a list with their default values, update the values and convert to a tuple
|
|
list_input = []
|
|
|
|
for name in signature_names:
|
|
if name != "kwargs":
|
|
list_input.append(signature[name].default)
|
|
|
|
for index, value in sorted_tuple_index_mapping:
|
|
list_input[index] = prepared_for_class[value]
|
|
|
|
tuple_input = tuple(list_input)
|
|
|
|
# Send to model
|
|
loss = model(tuple_input[:-1])[0]
|
|
|
|
self.assertEqual(loss.shape, [loss_size])
|
|
|
|
def test_generate_with_headmasking(self):
|
|
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_generative_model_classes:
|
|
model = model_class(config)
|
|
|
|
# We want to test only encoder-decoder models
|
|
if not config.is_encoder_decoder:
|
|
continue
|
|
|
|
head_masking = {
|
|
"head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)),
|
|
"decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
|
|
"cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
|
|
}
|
|
|
|
signature = inspect.signature(model.call)
|
|
if set(head_masking.keys()) < set([*signature.parameters.keys()]):
|
|
continue
|
|
|
|
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
|
|
out = model.generate(
|
|
inputs_dict["input_ids"],
|
|
num_beams=1,
|
|
max_length=inputs_dict["input_ids"] + 5,
|
|
output_attentions=True,
|
|
return_dict_in_generate=True,
|
|
**{name: mask},
|
|
)
|
|
# We check the state of decoder_attentions and cross_attentions just from the last step
|
|
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
|
|
self.assertEqual(sum([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0)
|
|
|
|
def test_load_with_mismatched_shapes(self):
|
|
if not self.test_mismatched_shapes:
|
|
return
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
|
|
continue
|
|
|
|
with self.subTest(msg=f"Testing {model_class}"):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = model_class(config)
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
_ = model(**inputs)
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
# Fails when we don't set ignore_mismatched_sizes=True
|
|
with self.assertRaises(ValueError):
|
|
new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
|
|
with self.assertRaises(ValueError):
|
|
new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10)
|
|
|
|
logger = logging.get_logger("transformers.modeling_tf_utils")
|
|
with CaptureLogger(logger) as cl:
|
|
new_model = TFAutoModelForSequenceClassification.from_pretrained(
|
|
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
|
|
)
|
|
self.assertIn("the shapes did not match", cl.out)
|
|
|
|
logits = new_model(**inputs).logits
|
|
self.assertEqual(logits.shape[1], 42)
|
|
|
|
with CaptureLogger(logger) as cl:
|
|
new_model_without_prefix = TFAutoModel.from_pretrained(
|
|
tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
|
|
)
|
|
self.assertIn("the shapes did not match", cl.out)
|
|
|
|
# Although Tf models always have a prefix pointing to `MainLayer`,
|
|
# we still add this "without prefix" test to keep a consistency between tf and pt tests.
|
|
input_ids = ids_tensor((2, 8), 10)
|
|
if self.is_encoder_decoder:
|
|
new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
|
|
else:
|
|
new_model_without_prefix(input_ids)
|
|
|
|
def test_model_main_input_name(self):
|
|
for model_class in self.all_model_classes:
|
|
model_signature = inspect.signature(getattr(model_class, "call"))
|
|
# The main input is the name of the argument after `self`
|
|
observed_main_input_name = list(model_signature.parameters.keys())[1]
|
|
self.assertEqual(model_class.main_input_name, observed_main_input_name)
|
|
|
|
def _generate_random_bad_tokens(self, num_bad_tokens, model):
|
|
# special tokens cannot be bad tokens
|
|
special_tokens = []
|
|
if model.config.bos_token_id is not None:
|
|
special_tokens.append(model.config.bos_token_id)
|
|
if model.config.pad_token_id is not None:
|
|
special_tokens.append(model.config.pad_token_id)
|
|
if model.config.eos_token_id is not None:
|
|
special_tokens.append(model.config.eos_token_id)
|
|
|
|
# create random bad tokens that are not special tokens
|
|
bad_tokens = []
|
|
while len(bad_tokens) < num_bad_tokens:
|
|
token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0]
|
|
if token not in special_tokens:
|
|
bad_tokens.append(token)
|
|
return bad_tokens
|
|
|
|
def _check_generated_ids(self, output_ids):
|
|
for token_id in output_ids[0].numpy().tolist():
|
|
self.assertGreaterEqual(token_id, 0)
|
|
self.assertLess(token_id, self.model_tester.vocab_size)
|
|
|
|
def _check_match_tokens(self, generated_ids, bad_words_ids):
|
|
# for all bad word tokens
|
|
for bad_word_ids in bad_words_ids:
|
|
# for all slices in batch
|
|
for generated_ids_slice in generated_ids:
|
|
# for all word idx
|
|
for i in range(len(bad_word_ids), len(generated_ids_slice)):
|
|
# if tokens match
|
|
if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
|
|
return True
|
|
return False
|
|
|
|
|
|
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
|
|
"""Creates a random int32 tensor of the shape within the vocab size."""
|
|
if rng is None:
|
|
rng = random.Random()
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.randint(0, vocab_size - 1))
|
|
|
|
output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32)
|
|
|
|
return output
|
|
|
|
|
|
def random_attention_mask(shape, rng=None, name=None, dtype=None):
|
|
attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype)
|
|
# make sure that at least one token is attended to for each batch
|
|
attn_mask = tf.concat([tf.constant(value=1, shape=(shape[0], 1), dtype=dtype), attn_mask[:, 1:]], axis=1)
|
|
return attn_mask
|
|
|
|
|
|
def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
|
|
"""Creates a random float32 tensor"""
|
|
if rng is None:
|
|
rng = random.Random()
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.random() * scale)
|
|
|
|
return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape)
|
|
|
|
|
|
@require_tf
|
|
class UtilsFunctionsTest(unittest.TestCase):
|
|
|
|
# tests whether the top_k_top_p_filtering function behaves as expected
|
|
def test_top_k_top_p_filtering(self):
|
|
logits = tf.convert_to_tensor(
|
|
[
|
|
[
|
|
8.2220991, # 3rd highest value; idx. 0
|
|
-0.5620044,
|
|
5.23229752,
|
|
4.0386393,
|
|
-6.8798378,
|
|
-0.54785802,
|
|
-3.2012153,
|
|
2.92777176,
|
|
1.88171953,
|
|
7.35341276, # 5th highest value; idx. 9
|
|
8.43207833, # 2nd highest value; idx. 10
|
|
-9.85711836,
|
|
-5.96209236,
|
|
-1.13039161,
|
|
-7.1115294,
|
|
-0.8369633,
|
|
-5.3186408,
|
|
7.06427407,
|
|
0.81369344,
|
|
-0.82023817,
|
|
-5.9179796,
|
|
0.58813443,
|
|
-6.99778438,
|
|
4.71551189,
|
|
-0.18771637,
|
|
7.44020759, # 4th highest value; idx. 25
|
|
9.38450987, # 1st highest value; idx. 26
|
|
2.12662941,
|
|
-9.32562038,
|
|
2.35652522,
|
|
], # cummulative prob of 5 highest values <= 0.6
|
|
[
|
|
0.58425518,
|
|
4.53139238,
|
|
-5.57510464,
|
|
-6.28030699,
|
|
-7.19529503,
|
|
-4.02122551,
|
|
1.39337037,
|
|
-6.06707057,
|
|
1.59480517,
|
|
-9.643119,
|
|
0.03907799,
|
|
0.67231762,
|
|
-8.88206726,
|
|
6.27115922, # 4th highest value; idx. 13
|
|
2.28520723,
|
|
4.82767506,
|
|
4.30421368,
|
|
8.8275313, # 2nd highest value; idx. 17
|
|
5.44029958, # 5th highest value; idx. 18
|
|
-4.4735794,
|
|
7.38579536, # 3rd highest value; idx. 20
|
|
-2.91051663,
|
|
2.61946077,
|
|
-2.5674762,
|
|
-9.48959302,
|
|
-4.02922645,
|
|
-1.35416918,
|
|
9.67702323, # 1st highest value; idx. 27
|
|
-5.89478553,
|
|
1.85370467,
|
|
], # cummulative prob of 5 highest values <= 0.6
|
|
],
|
|
dtype=tf.float32,
|
|
)
|
|
|
|
non_inf_expected_idx = tf.convert_to_tensor(
|
|
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
|
|
dtype=tf.int32,
|
|
) # expected non filtered idx as noted above
|
|
|
|
non_inf_expected_output = tf.convert_to_tensor(
|
|
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
|
|
dtype=tf.float32,
|
|
) # expected non filtered values as noted above
|
|
|
|
output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
|
|
|
|
non_inf_output = output[output != -float("inf")]
|
|
non_inf_idx = tf.cast(
|
|
tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
|
|
dtype=tf.int32,
|
|
)
|
|
|
|
tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
|
|
tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
|
|
|
|
|
|
@require_tf
|
|
@is_staging_test
|
|
class TFModelPushToHubTester(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls._token = login(username=USER, password=PASS)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
try:
|
|
delete_repo(token=cls._token, name="test-model-tf")
|
|
except HTTPError:
|
|
pass
|
|
|
|
try:
|
|
delete_repo(token=cls._token, name="test-model-tf-org", organization="valid_org")
|
|
except HTTPError:
|
|
pass
|
|
|
|
def test_push_to_hub(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = TFBertModel(config)
|
|
# Make sure model is properly initialized
|
|
_ = model(model.dummy_inputs)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(os.path.join(tmp_dir, "test-model-tf"), push_to_hub=True, use_auth_token=self._token)
|
|
|
|
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
|
|
models_equal = True
|
|
for p1, p2 in zip(model.weights, new_model.weights):
|
|
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
|
|
models_equal = False
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_push_to_hub_with_model_card(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = TFBertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.push_to_hub(os.path.join(tmp_dir, "test-model-tf"))
|
|
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "test-model-card-tf", "README.md")))
|
|
|
|
def test_push_to_hub_in_organization(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = TFBertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(
|
|
os.path.join(tmp_dir, "test-model-tf-org"),
|
|
push_to_hub=True,
|
|
use_auth_token=self._token,
|
|
organization="valid_org",
|
|
)
|
|
|
|
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
|
|
models_equal = True
|
|
for p1, p2 in zip(model.weights, new_model.weights):
|
|
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
|
|
models_equal = False
|
|
self.assertTrue(models_equal)
|