chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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 random
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import unittest
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import numpy as np
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import paddle
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from parameterized import parameterized_class
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from paddlenlp.transformers import (
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CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
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AutoTokenizer,
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CodeGenConfig,
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CodeGenForCausalLM,
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CodeGenModel,
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)
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from ...testing_utils import slow
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from ..test_generation_utils import GenerationTesterMixin
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from ..test_modeling_common import (
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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class CodeGenModelTester:
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test_model_name_list = False
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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_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=5,
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num_attention_heads=4,
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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_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.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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paddle.seed(128)
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np.random.seed(128)
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random.seed(128)
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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, dtype="int64")
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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], dtype="int64")
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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, dtype="int64")
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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, dtype="int64")
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels, dtype="int64")
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choice_labels = ids_tensor([self.batch_size], self.num_choices, dtype="int64")
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config = self.get_config()
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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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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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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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n_ctx=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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rotary_dim=self.rotary_dim,
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)
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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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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, dtype="int64")
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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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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, *args):
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model = CodeGenModel(config)
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model.eval()
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result = model(input_ids, use_cache=True, return_dict=self.parent.return_dict)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(len(result[1]), config["n_layer"])
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def create_and_check_codegen_model_past(self, config, input_ids, input_mask, *args):
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model = CodeGenModel(config)
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model.eval()
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# first forward pass
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outputs = model(input_ids, use_cache=True, return_dict=self.parent.return_dict)
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outputs_no_past = model(input_ids, use_cache=False, return_dict=self.parent.return_dict)
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past = outputs[:2]
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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"], dtype="int64")
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# append to next input_ids
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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output_from_no_past = model(next_input_ids, return_dict=self.parent.return_dict)[0]
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output_from_past = model(next_tokens, cache=past, return_dict=self.parent.return_dict)[0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").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(paddle.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(self, config, input_ids, input_mask, *args):
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model = CodeGenModel(config)
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model.eval()
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# create attention mask
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attn_mask = paddle.ones(input_ids.shape, dtype="int64")
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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, use_cache=True, return_dict=self.parent.return_dict)[
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:2
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]
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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"], dtype="int64")
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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, dtype="int64").item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config["vocab_size"], dtype="int64").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 = paddle.concat([input_ids, next_tokens], axis=-1)
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attn_mask = paddle.concat(
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[attn_mask, paddle.ones((attn_mask.shape[0], 1), dtype="int64")],
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axis=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, return_dict=self.parent.return_dict)[0]
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output_from_past = model(
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next_tokens, cache=past, attention_mask=attn_mask, return_dict=self.parent.return_dict
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)[0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").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(paddle.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(self, config, input_ids, input_mask, *args):
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model = CodeGenModel(config)
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model.eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.parent.return_dict)
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output, past = outputs[:2]
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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"], dtype="int64")
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2, dtype="int64")
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# append to next input_ids
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1)
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output_from_no_past = model(
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next_input_ids, attention_mask=next_attention_mask, return_dict=self.parent.return_dict
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)[0]
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output_from_past = model(
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next_tokens, attention_mask=next_attention_mask, cache=past, return_dict=self.parent.return_dict
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)[0]
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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], dtype="int64").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(paddle.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, *args):
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model = CodeGenForCausalLM(config)
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outputs = model(
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input_ids, labels=input_ids if self.parent.use_labels else None, return_dict=self.parent.return_dict
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)
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if self.parent.use_labels:
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loss, logits = outputs[:2]
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self.parent.assertEqual(loss.shape, [1])
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else:
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logits = outputs[0]
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self.parent.assertEqual(logits.shape, [self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, *args):
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model = CodeGenForCausalLM(config)
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loss, logits = model(input_ids, return_dict=self.parent.return_dict, labels=input_ids)[:2]
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self.parent.assertEqual(loss.shape, [1])
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self.parent.assertEqual(logits.shape, [self.batch_size, self.seq_length, self.vocab_size])
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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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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}
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return config, inputs_dict
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@parameterized_class(
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("return_dict",),
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[
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[False, False],
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[False, True],
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[True, False],
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[True, True],
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],
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)
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class CodeGenModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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base_model_class = CodeGenModel
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all_model_classes = (CodeGenModel, CodeGenForCausalLM)
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all_generative_model_classes = {CodeGenForCausalLM: (CodeGenModel, "transformer")}
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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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use_test_model_name_list = False
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return_dict = False
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use_labels = False
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use_test_inputs_embeds = True
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# attention mask issue
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def _get_input_ids_and_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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input_ids = inputs_dict[self.input_name]
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attention_mask = paddle.zeros_like(input_ids, dtype=paddle.float32)
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max_batch_size = 2
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sequence_length = input_ids.shape[-1] // 2
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input_ids = input_ids[:max_batch_size, :sequence_length]
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attention_mask = attention_mask[:max_batch_size, :sequence_length].unsqueeze([1, 2])
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# generate max 3 tokens
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max_length = 3
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if config.get("eos_token_id", None) is not None and config.get("pad_token_id", None) is None:
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# hack to allow generate for models such as GPT2 as is done in `generate()`
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config["pad_token_id"] = config["eos_token_id"]
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return config, input_ids, attention_mask, max_length
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# special case for DoubleHeads model
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def _prepare_for_class(self, inputs_dict, model_class):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class)
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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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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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@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.eval()
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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.transformer.config["pad_token_id"] = model.transformer.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="pd", padding=True, return_attention_mask=True)
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input_ids = inputs["input_ids"]
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outputs, _ = model.generate(
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input_ids=input_ids,
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attention_mask=inputs["attention_mask"],
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)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="pd")["input_ids"]
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output_non_padded, _ = model.generate(input_ids=inputs_non_padded)
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inputs_padded = tokenizer(sentences[1], return_tensors="pd")["input_ids"]
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output_padded, _ = model.generate(input_ids=inputs_padded)
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# batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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||||
|
||||
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 = [
|
||||
'\n print("Hello World")\n\nhellow_world()\n\n#',
|
||||
'\n print(f"Hello {name}")\n\ngreet("Rolf")\n',
|
||||
]
|
||||
# self.assertEqual(str(expected_output_sentence), str(batch_out_sentence))
|
||||
self.assertEqual(str(expected_output_sentence), str([non_padded_sentence, padded_sentence]))
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = CodeGenModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
@unittest.skip("Not implemented")
|
||||
def test_model_name_list(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_auto_tokenizer(self):
|
||||
for model_name in CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST:
|
||||
AutoTokenizer.from_pretrained(model_name) # assign a tokenizer but never use
|
||||
|
||||
|
||||
class CodeGenModelLanguageGenerationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_lm_generate_codegen(self):
|
||||
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
|
||||
model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
|
||||
model.eval()
|
||||
|
||||
inputs = tokenizer(
|
||||
"def hello_world():", return_tensors="pd", return_attention_mask=True, return_token_type_ids=False
|
||||
)
|
||||
expected_output = '\n print("Hello World")\n\nhello_world()\n\n#'
|
||||
|
||||
output_ids, _ = model.generate(**inputs, decode_strategy="sampling", top_k=1)
|
||||
output_str = tokenizer.batch_decode(output_ids)[0]
|
||||
|
||||
self.assertEqual(output_str, expected_output)
|
||||
|
||||
@slow
|
||||
def test_codegen_sample(self):
|
||||
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
|
||||
model = CodeGenForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
|
||||
model.eval()
|
||||
|
||||
tokenized = tokenizer(
|
||||
"def hello_world():", return_tensors="pd", return_token_type_ids=True, return_attention_mask=True
|
||||
)
|
||||
input_ids = tokenized["input_ids"]
|
||||
output_ids, _ = model.generate(input_ids, decode_strategy="sampling", top_k=1)
|
||||
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
||||
|
||||
token_type_ids = tokenized.token_type_ids
|
||||
output_seq, _ = model.generate(
|
||||
input_ids=input_ids, decode_strategy="sampling", top_k=1, num_return_sequences=5
|
||||
)
|
||||
output_seq_tt, _ = model.generate(
|
||||
input_ids=input_ids,
|
||||
token_type_ids=token_type_ids,
|
||||
decode_strategy="sampling",
|
||||
top_k=1,
|
||||
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)
|
||||
|
||||
EXPECTED_OUTPUT_STR = '\n print("Hello World")\n\nhello_world()\n\n#'
|
||||
|
||||
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
|
||||
Reference in New Issue
Block a user