567 lines
23 KiB
Python
567 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2022 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 datetime
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import unittest
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from transformers import CodeGenConfig, is_torch_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import backend_manual_seed, is_flaky, 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 AutoTokenizer, CodeGenForCausalLM, CodeGenModel
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class CodeGenModelTester:
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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=256,
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hidden_size=32,
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rotary_dim=4,
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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="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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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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):
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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.rotary_dim = rotary_dim
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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 - 1
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self.pad_token_id = vocab_size - 1
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def get_large_model_config(self):
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return CodeGenConfig.from_pretrained("Salesforce/codegen-2B-mono")
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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 = self.get_config()
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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(self):
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return CodeGenConfig(
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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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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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rotary_dim=self.rotary_dim,
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)
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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_codegen_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = CodeGenModel(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_codegen_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = CodeGenModel(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_codegen_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 = CodeGenModel(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_codegen_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 = CodeGenModel(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 = CodeGenForCausalLM(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 = CodeGenForCausalLM(config)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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model.to(torch_device)
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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 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 = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
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return config, inputs_dict
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@require_torch
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class CodeGenModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (CodeGenModel, CodeGenForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (CodeGenForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": CodeGenModel, "text-generation": CodeGenForCausalLM} if is_torch_available() else {}
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)
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fx_compatible = False
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test_pruning = False
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test_missing_keys = False
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test_model_parallel = False
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test_head_masking = False
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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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return inputs_dict
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def setUp(self):
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self.model_tester = CodeGenModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CodeGenConfig, 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_codegen_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_codegen_model(*config_and_inputs)
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def test_codegen_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_codegen_model_past(*config_and_inputs)
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def test_codegen_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_codegen_model_attention_mask_past(*config_and_inputs)
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def test_codegen_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_codegen_model_past_large_inputs(*config_and_inputs)
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def test_codegen_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_lm_head_model(*config_and_inputs)
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def test_codegen_gradient_checkpointing(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_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
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@slow
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def test_batch_generation(self):
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tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
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model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
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model.to(torch_device)
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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 = ["def hellow_world():", "def greet(name):"]
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inputs = tokenizer(sentences, return_tensors="pt", padding=True)
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input_ids = inputs["input_ids"].to(torch_device)
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token_type_ids = torch.cat(
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[
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input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
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input_ids.new_full((input_ids.shape[0], 1), 500),
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],
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dim=-1,
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)
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outputs = model.generate(
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input_ids=input_ids,
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attention_mask=inputs["attention_mask"].to(torch_device),
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)
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outputs_tt = model.generate(
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input_ids=input_ids,
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attention_mask=inputs["attention_mask"].to(torch_device),
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token_type_ids=token_type_ids,
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)
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|
|
|
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
|
|
output_non_padded = model.generate(input_ids=inputs_non_padded)
|
|
|
|
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
|
|
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
|
|
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
|
|
|
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
|
|
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
|
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
|
|
|
expected_output_sentence = [
|
|
'def hellow_world():\n print("Hello World")\n\nhellow_world()',
|
|
'def greet(name):\n print(f"Hello {name}")\n\ng',
|
|
]
|
|
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
|
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
|
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Salesforce/codegen-350M-nl"
|
|
model = CodeGenModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class CodeGenModelLanguageGenerationTest(unittest.TestCase):
|
|
@cached_property
|
|
def cached_tokenizer(self):
|
|
return AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
|
|
|
|
@cached_property
|
|
def cached_model(self):
|
|
return CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
|
|
|
|
@slow
|
|
def test_lm_generate_codegen(self):
|
|
tokenizer = self.cached_tokenizer
|
|
for checkpointing in [True, False]:
|
|
model = self.cached_model
|
|
|
|
if checkpointing:
|
|
model.gradient_checkpointing_enable()
|
|
else:
|
|
model.gradient_checkpointing_disable()
|
|
model.to(torch_device)
|
|
|
|
inputs = tokenizer("def hello_world():", return_tensors="pt").to(torch_device)
|
|
expected_output = 'def hello_world():\n print("Hello World")\n\nhello_world()\n\n'
|
|
|
|
output_ids = model.generate(**inputs, do_sample=False)
|
|
output_str = tokenizer.batch_decode(output_ids)[0]
|
|
|
|
self.assertEqual(output_str, expected_output)
|
|
|
|
@slow
|
|
def test_codegen_sample(self):
|
|
tokenizer = self.cached_tokenizer
|
|
model = self.cached_model
|
|
model.to(torch_device)
|
|
|
|
torch.manual_seed(0)
|
|
backend_manual_seed(torch_device, 0)
|
|
|
|
tokenized = tokenizer("def hello_world():", return_tensors="pt", return_token_type_ids=True)
|
|
input_ids = tokenized.input_ids.to(torch_device)
|
|
output_ids = model.generate(input_ids, do_sample=True)
|
|
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
|
|
|
token_type_ids = tokenized.token_type_ids.to(torch_device)
|
|
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
|
|
output_seq_tt = model.generate(
|
|
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
|
|
)
|
|
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
|
|
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
|
|
|
|
if torch_device == "cuda":
|
|
EXPECTED_OUTPUT_STR = 'def hello_world():\n print("Hello World")\n return True\n\nresult ='
|
|
else:
|
|
EXPECTED_OUTPUT_STR = "def hello_world():\r\n print('Hello, World.')\r\n\r\n\r"
|
|
|
|
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
|
|
self.assertTrue(
|
|
all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs)))
|
|
) # token_type_ids should change output
|
|
|
|
@is_flaky(max_attempts=3, description="measure of timing is somehow flaky.")
|
|
@slow
|
|
def test_codegen_sample_max_time(self):
|
|
tokenizer = self.cached_tokenizer
|
|
model = self.cached_model
|
|
model.to(torch_device)
|
|
|
|
torch.manual_seed(0)
|
|
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
|
|
input_ids = tokenized.input_ids.to(torch_device)
|
|
|
|
MAX_TIME = 0.05
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=2 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=2 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=2 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=2 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=2 * MAX_TIME))
|