wehub-resource-sync
567 lines
21 KiB
Python
567 lines
21 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import math
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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 GPTTokenizer, OPTConfig, OPTForCausalLM, OPTModel
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from tests.testing_utils import PaddleNLPModelTest, require_package, slow
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from tests.transformers.test_generation_utils import GenerationTesterMixin
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from tests.transformers.test_modeling_common import ( # GenerationD2STestMixin,
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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)
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OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"facebook/opt-125m",
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]
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class OPTModelTester:
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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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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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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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normalize_before=True,
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word_embed_proj_dim=32,
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num_labels=3,
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num_choices=4,
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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_input_mask = use_input_mask
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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.normalize_before = normalize_before
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self.word_embed_proj_dim = word_embed_proj_dim
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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 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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# construct input_mask filling with 0 and -1e4
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# left padding: [[-1e4, -1e4, -1e4, 0, 0], [-1e4, -1e4, -1e4, 0, 0], ...]
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input_mask = []
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for _ in range(self.batch_size):
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pad_length = random.randint(0, self.seq_length)
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input_mask.append([0] * (self.seq_length - pad_length) + [1] * pad_length)
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input_mask = paddle.to_tensor(input_mask, 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.parent.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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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 OPTConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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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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normalize_before=self.normalize_before,
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word_embed_proj_dim=self.word_embed_proj_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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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 = paddle.cast(
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ids_tensor([self.batch_size, self.seq_length], vocab_size=2), dtype="int64"
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)
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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_opt_model(self, config, input_ids, input_mask, *args):
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model = OPTModel(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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result = model(input_ids, use_cache=True, return_dict=self.parent.return_dict)
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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.num_hidden_layers)
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def create_and_check_opt_model_past(self, config, input_ids, input_mask, *args):
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model = OPTModel(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_use_cache_conf = model(input_ids, use_cache=True, return_dict=self.parent.return_dict)
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model(input_ids, use_cache=False, return_dict=self.parent.return_dict)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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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)
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if self.parent.return_dict:
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output_from_no_past = output_from_no_past[0]
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past_key_values_length = paddle.shape(past[0].k)[2]
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attention_mask = paddle.ones(shape=[next_tokens.shape[0], 1 + past_key_values_length])
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output_from_past = model(
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next_tokens, use_cache=True, attention_mask=attention_mask, cache=past, 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]).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_opt_model_past_large_inputs(self, config, input_ids, input_mask, *args):
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model = OPTModel(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 = paddle.ones_like(next_tokens, dtype=paddle.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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)
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if self.parent.return_dict:
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output_from_no_past = output_from_no_past[0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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cache=past,
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use_cache=True,
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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_opt_for_causal_lm(self, config, input_ids, input_mask, *args):
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model = OPTForCausalLM(config)
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model.eval()
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result = model(
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input_ids,
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use_cache=True,
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labels=input_ids if self.parent.use_labels else None,
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return_dict=self.parent.return_dict,
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)
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if self.parent.use_labels:
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self.parent.assertIsInstance(result[0].item(), float)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])
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else:
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self.parent.assertEqual(result[0].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 = OPTForCausalLM(config)
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if self.parent.use_labels:
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loss, logits = model(input_ids, labels=input_ids, return_dict=self.parent.return_dict)
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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 create_and_check_opt_weight_initialization(self, config, *args):
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model = OPTModel(config)
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model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
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for key in model.state_dict().keys():
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if "out_proj" in key and "weight" in key:
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self.parent.assertLessEqual(abs((paddle.std(model.state_dict()[key]) - model_std).numpy()), 0.02)
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self.parent.assertLessEqual(abs((paddle.mean(model.state_dict()[key]) - 0.0).numpy()), 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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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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}
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return config, inputs_dict
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def create_and_check_model_cache(self, config, input_ids, input_mask, *args):
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model = OPTModel(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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past_key_values = outputs.past_key_values if self.parent.return_dict else outputs[1]
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size, dtype="int64")
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# all next mask is ones
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next_mask = paddle.ones_like(next_tokens, dtype="int64")
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# append to next input_ids and
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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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outputs = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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output_hidden_states=True,
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return_dict=self.parent.return_dict,
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)
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output_from_no_past = outputs[1][0]
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outputs = model(
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next_tokens,
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attention_mask=next_attention_mask,
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cache=past_key_values,
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output_hidden_states=True,
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return_dict=self.parent.return_dict,
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)
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output_from_past = outputs[1][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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@parameterized_class(
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("return_dict", "use_labels"),
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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 OPTModelTest(ModelTesterMixin, GenerationTesterMixin, PaddleNLPModelTest):
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base_model_class = OPTModel
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use_labels = False
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return_dict = False
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use_test_inputs_embeds = True
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all_model_classes = [
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OPTModel,
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]
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all_generative_model_classes = {OPTForCausalLM: (OPTModel, "opt")}
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test_missing_keys = False
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test_model_parallel = True
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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 = OPTModelTester(self)
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random.seed(128)
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np.random.seed(128)
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paddle.seed(128)
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def test_opt_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_opt_model(*config_and_inputs)
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def test_opt_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_opt_model_past(*config_and_inputs)
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def test_opt_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_opt_model_past_large_inputs(*config_and_inputs)
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def test_opt_causal_lm_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_opt_for_causal_lm(*config_and_inputs)
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def test_opt_weight_initialization(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_opt_weight_initialization(*config_and_inputs)
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def test_for_model_cache(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_cache(*config_and_inputs)
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@slow
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def test_batch_generation(self):
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model = OPTForCausalLM.from_pretrained("facebook/opt-1.3b")
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model.eval()
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tokenizer = GPTTokenizer.from_pretrained("facebook/opt-1.3b")
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tokenizer.padding_side = "left"
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# use different length sentences to test batching
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sentences = [
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"my dog is",
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"Today, I",
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]
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inputs = tokenizer(sentences, return_tensors="pd", padding=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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decode_strategy="greedy_search",
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use_cache=True,
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)
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batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="pd")["input_ids"]
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output_non_padded, _ = model.generate(
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input_ids=inputs_non_padded, use_cache=True, decode_strategy="greedy_search"
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)
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non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
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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, use_cache=True, decode_strategy="greedy_search")
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padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
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expected_output_sentence = [
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" a rescue and she's the best dog ever. she's a little bitch but she's the best",
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" am going to share with you a few of my favorite recipes.\nI have been cooking for a",
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]
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self.assertListEqual(expected_output_sentence, batch_out_sentence)
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self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
|
|
|
def _get_input_ids_and_config(self):
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|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
input_ids = inputs_dict[self.input_name]
|
|
|
|
max_batch_size = 2
|
|
sequence_length = input_ids.shape[-1] // 2
|
|
input_ids = input_ids[:max_batch_size, :sequence_length]
|
|
|
|
attention_mask = paddle.ones_like(input_ids, dtype=paddle.int64)
|
|
|
|
# generate max 3 tokens
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|
max_length = 3
|
|
|
|
if config.eos_token_id or config.pad_token_id:
|
|
config["pad_token_id"] = config["eos_token_id"]
|
|
|
|
return config, input_ids, attention_mask, max_length
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
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|
for model_name in OPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = OPTModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class OPTCompatibilityTest(unittest.TestCase):
|
|
test_model_id = "hf-internal-testing/tiny-random-OPTModel"
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_model_config_mapping(self):
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
import transformers
|
|
|
|
torch_model_class = getattr(transformers, "OPTModel")
|
|
torch_model = torch_model_class.from_pretrained(OPTCompatibilityTest.test_model_id)
|
|
torch_model.eval()
|
|
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 3. forward the paddle model
|
|
from paddlenlp import transformers
|
|
|
|
paddle_model_class = getattr(transformers, "OPTModel")
|
|
paddle_model = paddle_model_class.from_pretrained(OPTCompatibilityTest.test_model_id, from_hf_hub=True)
|
|
paddle_model.eval()
|
|
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids), return_dict=False)[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
atol=1e-4,
|
|
)
|
|
)
|
|
|
|
|
|
class OPTModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_no_attention(self):
|
|
model = OPTModel.from_pretrained("facebook/opt-1.3b")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
with paddle.no_grad():
|
|
output = model(input_ids)
|
|
|
|
expected_shape = [1, 11, 2048]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[0.81907797, -1.08688772, 1.26071370],
|
|
[0.96454084, -0.42267877, 1.70609033],
|
|
[0.78616256, -0.27438506, 0.74083930],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_with_attention(self):
|
|
model = OPTModel.from_pretrained("facebook/opt-1.3b")
|
|
model.eval()
|
|
|
|
input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
|
|
with paddle.no_grad():
|
|
output = model(input_ids, attention_mask=attention_mask)
|
|
expected_shape = [1, 11, 2048]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[0.15988758, -0.21016182, -0.28532112],
|
|
[-0.18293847, -0.35511413, 0.56858277],
|
|
[0.39969346, -0.33906624, -0.43125907],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
|
|
# class OPTGenerationD2STest(GenerationD2STestMixin, unittest.TestCase):
|
|
# internal_testing_model = "__internal_testing__/tiny-random-opt"
|
|
# TokenizerClass = GPTTokenizer
|