572 lines
22 KiB
Python
572 lines
22 KiB
Python
# coding=utf-8
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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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"""Testing suite for the PyTorch Blenderbot model."""
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import tempfile
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import unittest
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from transformers import BlenderbotConfig, is_torch_available
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from transformers.testing_utils import (
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backend_empty_cache,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_torch_fp16,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotTokenizer
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from transformers.models.blenderbot.modeling_blenderbot import (
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BlenderbotDecoder,
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BlenderbotEncoder,
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BlenderbotForCausalLM,
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)
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def prepare_blenderbot_inputs_dict(
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config,
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input_ids,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_ids.ne(config.pad_token_id)
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
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if head_mask is None:
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head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
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if decoder_head_mask is None:
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decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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if cross_attn_head_mask is None:
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cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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return {
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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": attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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class BlenderbotModelTester:
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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=50,
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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).clamp(
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3,
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)
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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)
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config = self.get_config()
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inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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def get_config(self):
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return BlenderbotConfig(
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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 get_pipeline_config(self):
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config = self.get_config()
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config.max_position_embeddings = 100
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config.vocab_size = 300
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return config
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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 = BlenderbotModel(config=config).get_decoder().to(torch_device).eval()
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input_ids = inputs_dict["input_ids"]
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attention_mask = inputs_dict["attention_mask"]
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head_mask = inputs_dict["head_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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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)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -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(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = BlenderbotModel(config=config).to(torch_device).eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs.encoder_last_hidden_state
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last_hidden_state = outputs.last_hidden_state
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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 = BlenderbotEncoder.from_pretrained(tmpdirname).to(torch_device)
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encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
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0
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]
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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 = BlenderbotDecoder.from_pretrained(tmpdirname).to(torch_device)
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last_hidden_state_2 = decoder(
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input_ids=inputs_dict["decoder_input_ids"],
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attention_mask=inputs_dict["decoder_attention_mask"],
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encoder_hidden_states=encoder_last_hidden_state,
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encoder_attention_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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@require_torch
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class BlenderbotModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (BlenderbotModel, BlenderbotForConditionalGeneration) if is_torch_available() else ()
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all_generative_model_classes = (BlenderbotForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"conversational": BlenderbotForConditionalGeneration,
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"feature-extraction": BlenderbotModel,
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"summarization": BlenderbotForConditionalGeneration,
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"text-generation": BlenderbotForCausalLM,
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"text2text-generation": BlenderbotForConditionalGeneration,
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"translation": BlenderbotForConditionalGeneration,
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}
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if is_torch_available()
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else {}
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)
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is_encoder_decoder = True
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fx_compatible = True
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test_pruning = False
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test_missing_keys = False
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def setUp(self):
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self.model_tester = BlenderbotModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)
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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, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["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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@require_torch_fp16
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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 = input_ids.ne(1).to(torch_device)
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model = BlenderbotForConditionalGeneration(config).eval().to(torch_device)
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model.half()
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model.generate(input_ids, attention_mask=attention_mask)
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model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
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def assert_tensors_close(a, b, atol=1e-12, prefix=""):
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"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
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if a is None and b is None:
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return True
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try:
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if torch.allclose(a, b, atol=atol):
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return True
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raise
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except Exception:
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pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
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if a.numel() > 100:
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msg = f"tensor values are {pct_different:.1%} percent different."
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else:
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msg = f"{a} != {b}"
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if prefix:
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msg = prefix + ": " + msg
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raise AssertionError(msg)
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@unittest.skipUnless(torch_device != "cpu", "3B test too slow on CPU.")
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class Blenderbot3BIntegrationTests(unittest.TestCase):
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ckpt = "facebook/blenderbot-3B"
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@cached_property
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def tokenizer(self):
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return BlenderbotTokenizer.from_pretrained(self.ckpt)
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@slow
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def test_generation_from_short_input_same_as_parlai_3B(self):
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FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
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TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}
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backend_empty_cache(torch_device)
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model = BlenderbotForConditionalGeneration.from_pretrained(self.ckpt).half().to(torch_device)
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src_text = ["Sam"]
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model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device)
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generated_utterances = model.generate(**model_inputs, **FASTER_GEN_KWARGS)
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tgt_text = 'Sam is a great name. It means "sun" in Gaelic.'
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generated_txt = self.tokenizer.batch_decode(generated_utterances, **TOK_DECODE_KW)
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assert generated_txt[0].strip() == tgt_text
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src_text = (
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"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel"
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" like i'm going to throw up.\nand why is that?"
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)
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model_inputs = self.tokenizer([src_text], return_tensors="pt").to(torch_device)
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generated_ids = model.generate(**model_inputs, **FASTER_GEN_KWARGS)[0]
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reply = self.tokenizer.decode(generated_ids, **TOK_DECODE_KW)
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assert "I think it's because we are so worried about what people think of us." == reply.strip()
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del model
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class BlenderbotStandaloneDecoderModelTester:
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def __init__(
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self,
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parent,
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vocab_size=99,
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batch_size=13,
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d_model=16,
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decoder_seq_length=7,
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is_training=True,
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is_decoder=True,
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use_attention_mask=True,
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use_cache=False,
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use_labels=True,
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decoder_start_token_id=2,
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decoder_ffn_dim=32,
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decoder_layers=2,
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encoder_attention_heads=4,
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decoder_attention_heads=4,
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max_position_embeddings=30,
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is_encoder_decoder=False,
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encoder_no_repeat_ngram_size=0,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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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.decoder_seq_length = decoder_seq_length
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# For common tests
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self.seq_length = self.decoder_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.d_model = d_model
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self.hidden_size = d_model
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self.num_hidden_layers = decoder_layers
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self.decoder_layers = decoder_layers
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self.decoder_ffn_dim = decoder_ffn_dim
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_attention_heads = decoder_attention_heads
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self.num_attention_heads = decoder_attention_heads
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self.eos_token_id = eos_token_id
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self.bos_token_id = bos_token_id
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self.pad_token_id = pad_token_id
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self.decoder_start_token_id = decoder_start_token_id
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self.use_cache = use_cache
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self.max_position_embeddings = max_position_embeddings
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self.is_encoder_decoder = is_encoder_decoder
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self.encoder_no_repeat_ngram_size = encoder_no_repeat_ngram_size
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self.scope = None
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self.decoder_key_length = decoder_seq_length
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self.base_model_out_len = 2
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self.decoder_attention_idx = 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = BlenderbotConfig(
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vocab_size=self.vocab_size,
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d_model=self.d_model,
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decoder_layers=self.decoder_layers,
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decoder_ffn_dim=self.decoder_ffn_dim,
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encoder_attention_heads=self.encoder_attention_heads,
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decoder_attention_heads=self.decoder_attention_heads,
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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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use_cache=self.use_cache,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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max_position_embeddings=self.max_position_embeddings,
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is_encoder_decoder=self.is_encoder_decoder,
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encoder_no_repeat_ngram_size=self.encoder_no_repeat_ngram_size,
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)
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return (
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config,
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input_ids,
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attention_mask,
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lm_labels,
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)
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def create_and_check_decoder_model_past(
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self,
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config,
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input_ids,
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attention_mask,
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lm_labels,
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):
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config.use_cache = True
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model = BlenderbotDecoder(config=config).to(torch_device).eval()
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# first forward pass
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outputs = model(input_ids, use_cache=True)
|
|
outputs_use_cache_conf = model(input_ids)
|
|
outputs_no_past = model(input_ids, use_cache=False)
|
|
|
|
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
|
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
|
|
|
past_key_values = outputs["past_key_values"]
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# append to next input_ids and
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
|
|
output_from_no_past = model(next_input_ids)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# test that outputs are equal for slice
|
|
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
|
|
|
|
def create_and_check_decoder_model_attention_mask_past(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
lm_labels,
|
|
):
|
|
model = BlenderbotDecoder(config=config).to(torch_device).eval()
|
|
|
|
# create attention mask
|
|
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
|
|
|
half_seq_length = input_ids.shape[-1] // 2
|
|
attn_mask[:, half_seq_length:] = 0
|
|
|
|
# first forward pass
|
|
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
|
|
# past_key_values = model(input_ids, use_cache=True)["past_key_values"]
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# change a random masked slice from input_ids
|
|
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
|
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
|
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
|
|
|
# append to next input_ids and attn_mask
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
attn_mask = torch.cat(
|
|
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
|
|
dim=1,
|
|
)
|
|
|
|
# get two different outputs
|
|
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
|
|
"last_hidden_state"
|
|
]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# test that outputs are equal for slice
|
|
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
lm_labels,
|
|
) = config_and_inputs
|
|
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class BlenderbotStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (BlenderbotDecoder, BlenderbotForCausalLM) if is_torch_available() else ()
|
|
all_generative_model_classes = (BlenderbotForCausalLM,) if is_torch_available() else ()
|
|
test_pruning = False
|
|
is_encoder_decoder = False
|
|
|
|
def setUp(
|
|
self,
|
|
):
|
|
self.model_tester = BlenderbotStandaloneDecoderModelTester(self, is_training=False)
|
|
self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_decoder_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
|
|
|
|
def test_decoder_model_attn_mask_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
# decoder cannot keep gradients
|
|
return
|