463 lines
20 KiB
Python
463 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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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from __future__ import annotations
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import unittest
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from transformers import AutoTokenizer, GPTJConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow, tooslow
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers.models.gptj.modeling_tf_gptj import (
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TFGPTJForCausalLM,
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TFGPTJForQuestionAnswering,
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TFGPTJForSequenceClassification,
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TFGPTJModel,
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shape_list,
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)
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class TFGPTJModelTester:
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def __init__(self, parent):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_token_type_ids = True
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self.use_input_mask = True
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self.use_labels = True
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self.use_mc_token_ids = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.rotary_dim = 4
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self.num_hidden_layers = 2
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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self.bos_token_id = self.vocab_size - 1
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self.eos_token_id = self.vocab_size - 1
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self.pad_token_id = self.vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = GPTJConfig(
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vocab_size=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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rotary_dim=self.rotary_dim,
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return_dict=True,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPTJModel(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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inputs = [input_ids, None, input_mask] # None is the input for 'past'
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result = model(inputs)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPTJModel(config=config)
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# first forward pass
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outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
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outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
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outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
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output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
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output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
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output_from_past_slice = output_from_past[:, 0, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
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def create_and_check_gptj_model_attention_mask_past(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = TFGPTJModel(config=config)
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# create attention mask
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half_seq_length = self.seq_length // 2
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attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
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attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
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attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
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# first forward pass
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output, past_key_values = model(input_ids, attention_mask=attn_mask).to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
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vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
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condition = tf.transpose(
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tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
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)
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input_ids = tf.where(condition, random_other_next_tokens, input_ids)
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# append to next input_ids and attn_mask
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
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output_from_past_slice = output_from_past[:, 0, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12)
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def create_and_check_gptj_model_past_large_inputs(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = TFGPTJModel(config=config)
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input_ids = input_ids[:1, :]
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input_mask = input_mask[:1, :]
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token_type_ids = token_type_ids[:1, :]
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self.batch_size = 1
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# first forward pass
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outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
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next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
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output_from_no_past = model(
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next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
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)["last_hidden_state"]
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output_from_past = model(
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next_tokens,
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token_type_ids=next_token_types,
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attention_mask=next_attention_mask,
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past_key_values=past_key_values,
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)["last_hidden_state"]
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self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
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output_from_past_slice = output_from_past[:, :, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
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def create_and_check_gptj_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPTJForCausalLM(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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@require_tf
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class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
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if is_tf_available()
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else ()
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)
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all_generative_model_classes = (TFGPTJForCausalLM,) if is_tf_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": TFGPTJModel,
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"question-answering": TFGPTJForQuestionAnswering,
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"text-classification": TFGPTJForSequenceClassification,
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"text-generation": TFGPTJForCausalLM,
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"zero-shot": TFGPTJForSequenceClassification,
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}
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if is_tf_available()
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else {}
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)
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test_onnx = False
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test_pruning = False
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test_missing_keys = False
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test_head_masking = False
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if (
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pipeline_test_casse_name == "QAPipelineTests"
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and tokenizer_name is not None
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and not tokenizer_name.endswith("Fast")
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):
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# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
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# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
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# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
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return True
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return False
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def setUp(self):
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self.model_tester = TFGPTJModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_gptj_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gptj_model(*config_and_inputs)
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def test_gptj_model_past(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gptj_model_past(*config_and_inputs)
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def test_gptj_model_att_mask_past(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs)
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def test_gptj_model_past_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs)
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def test_gptj_lm_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gptj_lm_head_model(*config_and_inputs)
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@slow
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@unittest.skipIf(
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not is_tf_available() or len(tf.config.list_physical_devices("GPU")) > 0,
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"skip testing on GPU for now to avoid GPU OOM.",
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)
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def test_model_from_pretrained(self):
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model = TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
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self.assertIsNotNone(model)
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@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
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def test_resize_token_embeddings(self):
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super().test_resize_token_embeddings()
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@require_tf
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@tooslow
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# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
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class TFGPTJModelLanguageGenerationTest(unittest.TestCase):
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def test_lm_generate_gptj(self):
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model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
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input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog
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# The dog is a man's best friend. It is a loyal companion, and it is a friend
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expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] # fmt: skip
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output_ids = model.generate(input_ids, do_sample=False)
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self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
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def test_gptj_sample(self):
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
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model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
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tokenized = tokenizer("Today is a nice day and", return_tensors="tf")
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# forces the generation to happen on CPU, to avoid GPU-related quirks
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with tf.device(":/CPU:0"):
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output_ids = model.generate(**tokenized, do_sample=True, seed=[42, 0])
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output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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EXPECTED_OUTPUT_STR = "Today is a nice day and I’m going to go for a walk. I’"
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self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
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def _get_beam_search_test_objects(self):
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model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
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tokenizer.padding_side = "left"
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# Define PAD Token = EOS Token = 50256
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tokenizer.pad_token = tokenizer.eos_token
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model.config.pad_token_id = model.config.eos_token_id
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# use different length sentences to test batching
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sentences = [
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"Hello, my dog is a little",
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"Today, I",
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]
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expected_output_sentences = [
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"Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
|
||
"Today, I’m going to be talking about a topic that’",
|
||
]
|
||
return model, tokenizer, sentences, expected_output_sentences
|
||
|
||
def test_batch_beam_search(self):
|
||
# Confirms that we get the expected results with left-padded beam search
|
||
model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects()
|
||
|
||
inputs = tokenizer(sentences, return_tensors="tf", padding=True)
|
||
outputs = model.generate(**inputs, do_sample=False, num_beams=2)
|
||
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||
self.assertListEqual(expected_output_sentences, batch_out_sentence)
|
||
|
||
def test_batch_left_padding(self):
|
||
# Confirms that left-padding is working properly
|
||
model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects()
|
||
|
||
inputs = tokenizer(sentences, return_tensors="tf", padding=True)
|
||
inputs_non_padded = tokenizer(sentences[0], return_tensors="tf")
|
||
output_non_padded = model.generate(**inputs_non_padded, do_sample=False, num_beams=2)
|
||
num_paddings = (
|
||
shape_list(inputs_non_padded["input_ids"])[-1]
|
||
- tf.reduce_sum(tf.cast(inputs["attention_mask"][-1], tf.int64)).numpy()
|
||
)
|
||
inputs_padded = tokenizer(sentences[1], return_tensors="tf")
|
||
output_padded = model.generate(
|
||
**inputs_padded, do_sample=False, num_beams=2, max_length=model.config.max_length - num_paddings
|
||
)
|
||
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
||
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
||
self.assertListEqual(expected_output_sentences, [non_padded_sentence, padded_sentence])
|
||
|
||
def test_xla_beam_search(self):
|
||
# Confirms that XLA is working properly
|
||
model, tokenizer, sentences, expected_output_sentences = self._get_beam_search_test_objects()
|
||
|
||
inputs = tokenizer(sentences, return_tensors="tf", padding=True)
|
||
xla_generate = tf.function(model.generate, jit_compile=True)
|
||
outputs_xla = xla_generate(**inputs, do_sample=False, num_beams=2)
|
||
xla_sentence = tokenizer.batch_decode(outputs_xla, skip_special_tokens=True)
|
||
self.assertListEqual(expected_output_sentences, xla_sentence)
|