368 lines
15 KiB
Python
368 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2024 Mistral AI and The HuggingFace Inc. 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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"""Testing suite for the TF 2.0 Mistral model."""
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import unittest
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import numpy as np
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from transformers import AutoTokenizer, MistralConfig, is_tf_available
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from transformers.testing_utils import (
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require_tf,
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slow,
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)
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from ...generation.test_tf_utils import TFGenerationIntegrationTests
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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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if is_tf_available():
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import tensorflow as tf
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from transformers.models.mistral.modeling_tf_mistral import (
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TFMistralForCausalLM,
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TFMistralForSequenceClassification,
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TFMistralModel,
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)
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class TFMistralModelTester:
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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_input_mask = True
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self.use_token_type_ids = False
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 2
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self.num_attention_heads = 4
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self.num_key_value_heads = 2
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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.pad_token_id = 0
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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], self.vocab_size)
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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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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 = MistralConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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num_key_value_heads=self.num_key_value_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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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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)
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return (
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config,
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input_ids,
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token_type_ids,
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input_mask,
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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_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFMistralModel(config=config)
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result = model(input_ids, attention_mask=input_mask)
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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_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFMistralModel(config)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask)
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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_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = TFMistralForCausalLM(config=config)
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = TFMistralForCausalLM(config=config)
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple 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_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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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_mask], axis=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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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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token_type_ids,
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input_mask,
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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 = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_tf
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class TFMistralModelTest(TFModelTesterMixin, TFGenerationIntegrationTests, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TFMistralModel, TFMistralForCausalLM, TFMistralForSequenceClassification) if is_tf_available() else ()
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)
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all_generative_model_classes = (TFMistralForCausalLM,) if is_tf_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": TFMistralModel,
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"text-classification": TFMistralForSequenceClassification,
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"text-generation": TFMistralForCausalLM,
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"zero-shot": TFMistralForSequenceClassification,
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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 (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
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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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return True
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def setUp(self):
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self.model_tester = TFMistralModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MistralConfig, hidden_size=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_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_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_Mistral_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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input_ids = input_dict["input_ids"]
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attention_mask = tf.not_equal(input_ids, 1)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = TFMistralForSequenceClassification(config)
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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def test_Mistral_sequence_classification_model_for_single_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "single_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = tf.not_equal(input_ids, 1)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = TFMistralForSequenceClassification(config)
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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def test_Mistral_sequence_classification_model_for_multi_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "multi_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = tf.not_equal(input_ids, 1)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = TFMistralForSequenceClassification(config)
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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@unittest.skip("Mistral buffers include complex numbers, which breaks this test")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
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def test_past_key_values_format(self):
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pass
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@unittest.skip("Vocab resizing is not supported")
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def test_save_load_after_resize_token_embeddings(self):
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pass
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@require_tf
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class TFMistralIntegrationTest(unittest.TestCase):
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@slow
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def test_model_7b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = TFMistralForCausalLM.from_pretrained(
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"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
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)
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input_ids = tf.constant([input_ids])
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out = model(input_ids).logits
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# Expected mean on dim = -1
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EXPECTED_MEAN = tf.constant(
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[[-1.281e-04, -2.869e-04, -9.989e-05, -8.995e-05, 2.494e-04, -3.083e-04, -2.672e-04, -1.239e-04]]
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)
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tf.debugging.assert_near(tf.reduce_mean(out, axis=-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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EXPECTED_SLICE = tf.constant([0.1033, 0.1493, -0.0041, -0.0021, -0.1686, 0.0356, 0.0812, 0.2218, -0.1257, 0.1920, 0.0929, 0.1181, 0.0111, 0.0395, -0.0064, 0.1712, -0.0751, 0.0625, -0.2409, 0.1541, -0.1271, -0.2296, -0.0099, -0.0160, 0.0311, -0.0824, -0.1518, 0.0722, 0.0187, 0.0484]) # fmt: skip
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tf.debugging.assert_near(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
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@slow
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def test_model_7b_generation(self):
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EXPECTED_TEXT_COMPLETION = """My favourite condiment is Werk a EgyadjustPrintfigiousPDFPHPct guns Ein motor conceti barSequ内 infrastructure millretval"""
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM", use_fast=False)
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model = TFMistralForCausalLM.from_pretrained(
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"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
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)
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input_ids = tokenizer.encode(prompt, return_tensors="tf")
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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