wehub-resource-sync
274 lines
11 KiB
Python
274 lines
11 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import paddle
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from paddlenlp.transformers import (
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PegasusConfig,
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PegasusDecoder,
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PegasusEncoder,
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PegasusForConditionalGeneration,
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PegasusModel,
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)
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from ..test_configuration_common import ConfigTester
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from ..test_generation_utils import GenerationTesterMixin
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from ..test_modeling_common import ModelTesterMixin, ids_tensor
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class PegasusModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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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=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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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_labels = use_labels
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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.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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# forcing a certain token to be generated, sets all other tokens to -inf
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# if however the token to be generated is already at -inf then it can lead token
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# `nan` values and thus break generation
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self.forced_bos_token_id = None
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self.forced_eos_token_id = None
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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_ids = paddle.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64"), 3)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64")
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config = self.get_config()
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attention_mask = (
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paddle.cast(input_ids == config.pad_token_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e4
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)
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decoder_attention_mask = (
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paddle.cast(decoder_input_ids == config.pad_token_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2])
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* -1e4
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)
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inputs_dict = {
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"input_ids": input_ids,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": decoder_attention_mask,
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}
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return config, inputs_dict
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def get_config(self):
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return PegasusConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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forced_bos_token_id=self.forced_bos_token_id,
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forced_eos_token_id=self.forced_eos_token_id,
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)
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = PegasusModel(config=config)
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encoder = model.get_encoder()
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decoder = model.get_decoder()
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encoder.eval()
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decoder.eval()
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input_ids = inputs_dict["input_ids"]
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decoder_input_ids = (
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paddle.zeros_like(input_ids[:, :1], dtype="int64") + PegasusModel(config).decoder_start_token_id
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)
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attention_mask = inputs_dict["attention_mask"]
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decoder_attention_mask = paddle.zeros([input_ids.shape[0], 1, 1, 1], dtype=paddle.get_default_dtype())
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encoder_output = encoder(input_ids, attention_mask)
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origin_cache = decoder.decoder.gen_cache(encoder_output)
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outputs = decoder(
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decoder_input_ids,
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decoder_attention_mask,
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encoder_output,
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attention_mask,
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cache=origin_cache,
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)
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output, cache = outputs
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size, dtype="int64")
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next_attn_mask = paddle.zeros([self.batch_size, 1, 1, 3], dtype=paddle.get_default_dtype())
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# append to next input_ids and
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next_input_ids = paddle.concat([decoder_input_ids, next_tokens], axis=-1)
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next_attention_mask = paddle.concat([decoder_attention_mask, next_attn_mask], axis=-1)
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output_from_no_past, _ = decoder(next_input_ids, next_attention_mask, encoder_output, attention_mask)
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output_from_past, _ = decoder(
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next_tokens,
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next_attention_mask,
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encoder_output,
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attention_mask,
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cache=cache,
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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], 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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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-2))
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def check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = PegasusModel(config=config)
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model.eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs[2]
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last_hidden_state = outputs[0]
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with tempfile.TemporaryDirectory() as tmpdirname:
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encoder = model.get_encoder()
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encoder.save_pretrained(tmpdirname)
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encoder = PegasusEncoder.from_pretrained(tmpdirname)
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encoder.eval()
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encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])
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self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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decoder = model.get_decoder()
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decoder.save_pretrained(tmpdirname)
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decoder = PegasusDecoder.from_pretrained(tmpdirname)
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decoder.eval()
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last_hidden_state_2 = decoder(
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decoder_input_ids=inputs_dict["decoder_input_ids"],
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decoder_attention_mask=inputs_dict["decoder_attention_mask"],
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encoder_output=encoder_last_hidden_state,
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memory_mask=inputs_dict["attention_mask"],
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)[0]
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self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
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class PegasusModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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base_model_class = PegasusModel
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all_model_classes = (PegasusModel, PegasusForConditionalGeneration)
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all_generative_model_classes = {PegasusForConditionalGeneration: (PegasusModel, "pegasus")}
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is_encoder_decoder = True
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fx_compatible = True
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test_resize_position_embeddings = False
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test_pruning = False
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test_missing_keys = False
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use_labels = False
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use_test_model_name_list = False
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use_test_inputs_embeds = False
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return_dict = False
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def setUp(self):
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self.model_tester = PegasusModelTester(self)
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self.config_tester = ConfigTester(self, config_class=PegasusConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2 = model_class.from_pretrained(tmpdirname)
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missing_keys = []
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for k in model2.state_dict().keys():
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if k not in model.state_dict().keys():
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missing_keys.append(k)
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self.assertEqual(missing_keys, [])
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def test_decoder_model_past_with_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_decoder_model_past_large_inputs(*config_and_inputs)
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def test_encoder_decoder_model_standalone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
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def test_generate_fp16(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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input_ids = input_dict["input_ids"]
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attention_mask = paddle.cast(input_ids == 1, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e4
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model = PegasusForConditionalGeneration(config=config)
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model.eval()
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with paddle.amp.auto_cast():
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model.generate(input_ids, attention_mask=attention_mask)
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model.generate(
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decode_strategy="beam_search",
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num_beams=4,
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do_sample=True,
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early_stopping=False,
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num_return_sequences=3,
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)
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