629 lines
26 KiB
Python
629 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2023 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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import math
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import unittest
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from parameterized import parameterized
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from transformers import GPTBigCodeConfig, is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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GPT2TokenizerFast,
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GPTBigCodeForCausalLM,
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GPTBigCodeForSequenceClassification,
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GPTBigCodeForTokenClassification,
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GPTBigCodeModel,
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)
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from transformers.models.gpt_bigcode.modeling_gpt_bigcode import GPTBigCodeAttention
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_12
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else:
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is_torch_greater_or_equal_than_1_12 = False
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class GPTBigCodeModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="relu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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multi_query=True,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = None
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 2
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self.pad_token_id = vocab_size - 3
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self.multi_query = multi_query
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def get_large_model_config(self):
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return GPTBigCodeConfig.from_pretrained("bigcode/gpt_bigcode-santacoder")
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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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 = self.get_config(
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gradient_checkpointing=gradient_checkpointing,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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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 get_config(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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return GPTBigCodeConfig(
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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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n_inner=self.intermediate_size,
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activation_function=self.hidden_act,
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resid_pdrop=self.hidden_dropout_prob,
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attn_pdrop=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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use_cache=True,
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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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gradient_checkpointing=gradient_checkpointing,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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attention_softmax_in_fp32=False,
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scale_attention_softmax_in_fp32=False,
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multi_query=self.multi_query,
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)
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def get_pipeline_config(self):
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config = self.get_config()
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config.vocab_size = 300
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return config
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def prepare_config_and_inputs_for_decoder(self):
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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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) = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=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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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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def create_and_check_gpt_bigcode_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTBigCodeModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(input_ids, token_type_ids=token_type_ids)
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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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self.parent.assertEqual(len(result.past_key_values), config.n_layer)
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def create_and_check_gpt_bigcode_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTBigCodeModel(config=config)
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model.to(torch_device)
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model.eval()
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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 = 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 = torch.cat([input_ids, next_tokens], dim=-1)
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next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-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)[
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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 = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_gpt_bigcode_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 = GPTBigCodeModel(config=config)
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model.to(torch_device)
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model.eval()
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# create attention mask
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attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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half_seq_length = self.seq_length // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past = 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).item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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attn_mask = torch.cat(
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[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
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dim=1,
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)
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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, attention_mask=attn_mask)["last_hidden_state"]
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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[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_gpt_bigcode_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 = GPTBigCodeModel(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
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output, past = 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_token_types = ids_tensor([self.batch_size, 3], self.type_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 token_type_ids
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-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, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
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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 = 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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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTBigCodeForCausalLM(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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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_forward_and_backwards(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
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):
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model = GPTBigCodeForCausalLM(config)
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model.to(torch_device)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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result.loss.backward()
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def create_and_check_gpt_bigcode_for_sequence_classification(
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self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
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):
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config.num_labels = self.num_labels
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model = GPTBigCodeForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_gpt_bigcode_for_token_classification(
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self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
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):
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config.num_labels = self.num_labels
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model = GPTBigCodeForTokenClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_gpt_bigcode_weight_initialization(self, config, *args):
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model = GPTBigCodeModel(config)
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model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
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for key in model.state_dict().keys():
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if "c_proj" in key and "weight" in key:
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self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
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self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
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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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"head_mask": head_mask,
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}
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return config, inputs_dict
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@require_torch
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class GPTBigCodeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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|
# TODO: Update the tests to use valid pretrained models.
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all_model_classes = (
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(
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GPTBigCodeModel,
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GPTBigCodeForCausalLM,
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GPTBigCodeForSequenceClassification,
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GPTBigCodeForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (GPTBigCodeForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": GPTBigCodeModel,
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"text-classification": GPTBigCodeForSequenceClassification,
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"text-generation": GPTBigCodeForCausalLM,
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"token-classification": GPTBigCodeForTokenClassification,
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"zero-shot": GPTBigCodeForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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test_missing_keys = False
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test_pruning = False
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|
test_torchscript = False
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multi_query = True
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|
|
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# special case for DoubleHeads model
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|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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|
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return inputs_dict
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|
|
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def setUp(self):
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self.model_tester = GPTBigCodeModelTester(self, multi_query=self.multi_query)
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self.config_tester = ConfigTester(self, config_class=GPTBigCodeConfig, n_embd=37)
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|
|
|
def tearDown(self):
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import gc
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|
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|
gc.collect()
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|
|
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def test_config(self):
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|
self.config_tester.run_common_tests()
|
|
|
|
@unittest.skip("MQA models does not support retain_grad")
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|
def test_retain_grad_hidden_states_attentions(self):
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|
pass
|
|
|
|
@unittest.skip("Contrastive search not supported due to non-standard caching mechanism")
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|
def test_contrastive_generate(self):
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|
pass
|
|
|
|
@unittest.skip("Contrastive search not supported due to non-standard caching mechanism")
|
|
def test_contrastive_generate_dict_outputs_use_cache(self):
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|
pass
|
|
|
|
@unittest.skip("CPU offload seems to be broken for some reason - tiny models keep hitting corner cases")
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|
def test_cpu_offload(self):
|
|
pass
|
|
|
|
@unittest.skip("Disk offload seems to be broken for some reason - tiny models keep hitting corner cases")
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|
def test_disk_offload(self):
|
|
pass
|
|
|
|
@unittest.skip("BigCodeGPT has a non-standard KV cache format.")
|
|
def test_past_key_values_format(self):
|
|
pass
|
|
|
|
def test_gpt_bigcode_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
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|
self.model_tester.create_and_check_gpt_bigcode_model(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
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|
self.model_tester.create_and_check_gpt_bigcode_model_past(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_model_att_mask_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt_bigcode_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_model_past_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt_bigcode_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_lm_head_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_sequence_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt_bigcode_for_sequence_classification(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_token_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt_bigcode_for_token_classification(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_gradient_checkpointing(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
|
|
|
|
def test_gpt_bigcode_scale_attn_by_inverse_layer_idx(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_reorder_and_upcast_attn(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True)
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
|
|
|
|
def test_gpt_bigcode_weight_initialization(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt_bigcode_weight_initialization(*config_and_inputs)
|
|
|
|
|
|
@require_torch
|
|
class GPTBigCodeMHAModelTest(GPTBigCodeModelTest):
|
|
# `parameterized_class` breaks with mixins, so we use inheritance instead
|
|
multi_query = False
|
|
|
|
|
|
@unittest.skipIf(
|
|
not is_torch_greater_or_equal_than_1_12,
|
|
reason="`GPTBigCode` checkpoints use `PytorchGELUTanh` which requires `torch>=1.12.0`.",
|
|
)
|
|
@slow
|
|
@require_torch
|
|
class GPTBigCodeModelLanguageGenerationTest(unittest.TestCase):
|
|
def test_generate_simple(self):
|
|
model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device)
|
|
tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder")
|
|
|
|
input_ids = tokenizer("def print_hello_world():", return_tensors="pt").input_ids.to(torch_device)
|
|
|
|
output_sequence = model.generate(input_ids)
|
|
output_sentence = tokenizer.decode(output_sequence[0], skip_special_tokens=True)
|
|
|
|
expected_output = """def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_"""
|
|
self.assertEqual(output_sentence, expected_output)
|
|
|
|
def test_generate_batched(self):
|
|
tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder")
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
tokenizer.padding_side = "left"
|
|
|
|
model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device)
|
|
|
|
inputs = tokenizer(["def print_hello_world():", "def say_hello():"], return_tensors="pt", padding=True).to(
|
|
torch_device
|
|
)
|
|
outputs = model.generate(**inputs)
|
|
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
|
|
expected_output = [
|
|
'def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_',
|
|
'def say_hello():\n print("Hello, World!")\n\n\nsay_hello()',
|
|
]
|
|
self.assertListEqual(outputs, expected_output)
|
|
|
|
|
|
@require_torch
|
|
class GPTBigCodeMQATest(unittest.TestCase):
|
|
def get_attention(self, multi_query):
|
|
config = GPTBigCodeConfig.from_pretrained(
|
|
"bigcode/gpt_bigcode-santacoder",
|
|
multi_query=multi_query,
|
|
attn_pdrop=0,
|
|
resid_pdrop=0,
|
|
)
|
|
return GPTBigCodeAttention(config)
|
|
|
|
@parameterized.expand([(seed, is_train_mode) for seed in range(5) for is_train_mode in [True, False]])
|
|
def test_mqa_reduces_to_mha(self, seed, is_train_mode=True):
|
|
torch.manual_seed(seed)
|
|
|
|
# CREATE MQA AND MHA ATTENTIONS
|
|
attention_mqa = self.get_attention(True)
|
|
attention_mha = self.get_attention(False)
|
|
|
|
# ENFORCE MATCHING WEIGHTS
|
|
num_heads = attention_mqa.num_heads
|
|
embed_dim = attention_mqa.embed_dim
|
|
head_dim = attention_mqa.head_dim
|
|
|
|
with torch.no_grad():
|
|
mqa_q_weight = attention_mqa.c_attn.weight[:embed_dim, :].view(num_heads, 1, head_dim, embed_dim)
|
|
mqa_kv_weight = attention_mqa.c_attn.weight[embed_dim:, :].view(1, 2, head_dim, embed_dim)
|
|
mha_c_weight = torch.cat(
|
|
[mqa_q_weight, mqa_kv_weight.expand(num_heads, 2, head_dim, embed_dim)], dim=1
|
|
).view(3 * num_heads * head_dim, embed_dim)
|
|
|
|
mqa_q_bias = attention_mqa.c_attn.bias[:embed_dim].view(num_heads, 1, head_dim)
|
|
mqa_kv_bias = attention_mqa.c_attn.bias[embed_dim:].view(1, 2, head_dim)
|
|
mha_c_bias = torch.cat([mqa_q_bias, mqa_kv_bias.expand(num_heads, 2, head_dim)], dim=1).view(
|
|
3 * num_heads * head_dim
|
|
)
|
|
|
|
attention_mha.c_attn.weight.copy_(mha_c_weight)
|
|
attention_mha.c_attn.bias.copy_(mha_c_bias)
|
|
attention_mha.c_proj.weight.copy_(attention_mqa.c_proj.weight)
|
|
attention_mha.c_proj.bias.copy_(attention_mqa.c_proj.bias)
|
|
|
|
# PUT THE MODEL INTO THE CORRECT MODE
|
|
attention_mha.train(is_train_mode)
|
|
attention_mqa.train(is_train_mode)
|
|
|
|
# RUN AN INPUT THROUGH THE MODELS
|
|
num_tokens = 5
|
|
hidden_states = torch.randn(1, num_tokens, embed_dim)
|
|
attention_mha_result = attention_mha(hidden_states)[0]
|
|
attention_mqa_result = attention_mqa(hidden_states)[0]
|
|
|
|
# CHECK THAT ALL OUTPUTS ARE THE SAME
|
|
self.assertTrue(torch.allclose(attention_mha_result, attention_mqa_result, atol=1e-5))
|