465 lines
18 KiB
Python
465 lines
18 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import timeout_decorator # noqa
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from transformers import MBartConfig, is_flax_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
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from .test_generation_flax_utils import FlaxGenerationTesterMixin
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from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
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if is_flax_available():
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import os
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# The slow tests are often failing with OOM error on GPU
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# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
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# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
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os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
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import jax
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import jax.numpy as jnp
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from transformers import AutoTokenizer
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from transformers.models.mbart.modeling_flax_mbart import (
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FlaxMBartForConditionalGeneration,
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FlaxMBartForQuestionAnswering,
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FlaxMBartForSequenceClassification,
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FlaxMBartModel,
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shift_tokens_right,
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)
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def prepare_mbart_inputs_dict(
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config,
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input_ids,
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decoder_input_ids=None,
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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 = np.where(input_ids != config.pad_token_id, 1, 0)
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if decoder_attention_mask is None:
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decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
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if head_mask is None:
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head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
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if decoder_head_mask is None:
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decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
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if cross_attn_head_mask is None:
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cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
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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": decoder_attention_mask,
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}
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class FlaxMBartModelTester(unittest.TestCase):
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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=32,
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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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decoder_start_token_id=2,
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initializer_range=0.02,
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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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self.decoder_start_token_id = decoder_start_token_id
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self.initializer_range = initializer_range
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def prepare_config_and_inputs(self):
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input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
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input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
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decoder_input_ids = shift_tokens_right(input_ids, 1)
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config = MBartConfig(
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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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decoder_start_token_id=self.decoder_start_token_id,
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initializer_range=self.initializer_range,
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use_cache=False,
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)
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inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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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 check_use_cache_forward(self, model_class_name, config, inputs_dict):
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max_decoder_length = 20
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model = model_class_name(config)
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encoder_outputs = model.encode(inputs_dict["input_ids"])
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decoder_input_ids, decoder_attention_mask = (
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inputs_dict["decoder_input_ids"],
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inputs_dict["decoder_attention_mask"],
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)
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past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
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decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
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decoder_position_ids = jnp.broadcast_to(
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jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
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(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
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)
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outputs_cache = model.decode(
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decoder_input_ids[:, :-1],
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encoder_outputs,
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decoder_attention_mask=decoder_attention_mask,
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past_key_values=past_key_values,
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decoder_position_ids=decoder_position_ids,
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)
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decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model.decode(
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decoder_input_ids[:, -1:],
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encoder_outputs,
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decoder_attention_mask=decoder_attention_mask,
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past_key_values=outputs_cache.past_key_values,
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decoder_position_ids=decoder_position_ids,
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)
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outputs = model.decode(decoder_input_ids, encoder_outputs)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
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max_decoder_length = 20
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model = model_class_name(config)
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encoder_outputs = model.encode(inputs_dict["input_ids"])
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decoder_input_ids, decoder_attention_mask = (
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inputs_dict["decoder_input_ids"],
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inputs_dict["decoder_attention_mask"],
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)
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decoder_attention_mask_cache = jnp.concatenate(
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[
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decoder_attention_mask,
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jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
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],
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axis=-1,
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)
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past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
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decoder_position_ids = jnp.broadcast_to(
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jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
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(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
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)
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outputs_cache = model.decode(
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decoder_input_ids[:, :-1],
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encoder_outputs,
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decoder_attention_mask=decoder_attention_mask_cache,
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past_key_values=past_key_values,
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decoder_position_ids=decoder_position_ids,
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)
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decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model.decode(
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decoder_input_ids[:, -1:],
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encoder_outputs,
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past_key_values=outputs_cache.past_key_values,
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decoder_attention_mask=decoder_attention_mask_cache,
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decoder_position_ids=decoder_position_ids,
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)
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outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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@require_flax
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class MBartHeadTests(unittest.TestCase):
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vocab_size = 99
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def _get_config_and_data(self):
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input_ids = np.array(
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[
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[71, 82, 18, 33, 46, 91, 2],
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[68, 34, 26, 58, 30, 82, 2],
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[5, 97, 17, 39, 94, 40, 2],
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[76, 83, 94, 25, 70, 78, 2],
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[87, 59, 41, 35, 48, 66, 2],
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[55, 13, 16, 58, 5, 2, 1], # note padding
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[64, 27, 31, 51, 12, 75, 2],
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[52, 64, 86, 17, 83, 39, 2],
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[48, 61, 9, 24, 71, 82, 2],
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[26, 1, 60, 48, 22, 13, 2],
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[21, 5, 62, 28, 14, 76, 2],
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[45, 98, 37, 86, 59, 48, 2],
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[70, 70, 50, 9, 28, 0, 2],
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],
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dtype=np.int64,
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)
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batch_size = input_ids.shape[0]
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config = MBartConfig(
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vocab_size=self.vocab_size,
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d_model=24,
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encoder_layers=2,
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decoder_layers=2,
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encoder_attention_heads=2,
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decoder_attention_heads=2,
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encoder_ffn_dim=32,
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decoder_ffn_dim=32,
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max_position_embeddings=48,
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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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return config, input_ids, batch_size
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def test_sequence_classification_forward(self):
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config, input_ids, batch_size = self._get_config_and_data()
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model = FlaxMBartForSequenceClassification(config)
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outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
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expected_shape = (batch_size, config.num_labels)
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self.assertEqual(outputs["logits"].shape, expected_shape)
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def test_question_answering_forward(self):
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config, input_ids, batch_size = self._get_config_and_data()
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model = FlaxMBartForQuestionAnswering(config)
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outputs = model(input_ids=input_ids)
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self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
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self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
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# @timeout_decorator.timeout(1) # not working with the decorator so far
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def test_lm_forward(self):
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config, input_ids, batch_size = self._get_config_and_data()
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lm_model = FlaxMBartForConditionalGeneration(config)
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outputs = lm_model(input_ids=input_ids)
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expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
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self.assertEqual(outputs["logits"].shape, expected_shape)
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def test_lm_uneven_forward(self):
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config = MBartConfig(
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vocab_size=self.vocab_size,
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d_model=14,
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encoder_layers=2,
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decoder_layers=2,
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encoder_attention_heads=2,
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decoder_attention_heads=2,
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encoder_ffn_dim=8,
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decoder_ffn_dim=8,
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max_position_embeddings=48,
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)
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lm_model = FlaxMBartForConditionalGeneration(config)
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context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
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summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
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outputs = lm_model(input_ids=context, decoder_input_ids=summary)
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expected_shape = (*summary.shape, config.vocab_size)
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self.assertEqual(outputs["logits"].shape, expected_shape)
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def test_shift_tokens_right(self):
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input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
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shifted = shift_tokens_right(input_ids, 1)
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n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
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n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
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self.assertEqual(shifted.shape, input_ids.shape)
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self.assertEqual(n_pad_after, n_pad_before - 1)
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self.assertTrue(np.equal(shifted[:, 0], 2).all())
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@require_flax
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class FlaxMBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
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is_encoder_decoder = True
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all_model_classes = (
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(
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FlaxMBartModel,
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FlaxMBartForConditionalGeneration,
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FlaxMBartForSequenceClassification,
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FlaxMBartForQuestionAnswering,
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)
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if is_flax_available()
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else ()
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)
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all_generative_model_classes = (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxMBartModelTester(self)
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def test_use_cache_forward(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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self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
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def test_use_cache_forward_with_attn_mask(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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self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
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def test_encode(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def encode_jitted(input_ids, attention_mask=None, **kwargs):
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return model.encode(input_ids=input_ids, attention_mask=attention_mask)
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with self.subTest("JIT Enabled"):
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jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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def test_decode(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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model = model_class(config)
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encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
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prepared_inputs_dict = {
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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_outputs": encoder_outputs,
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}
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@jax.jit
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def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
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return model.decode(
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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encoder_outputs=encoder_outputs,
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)
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with self.subTest("JIT Enabled"):
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jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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@slow
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def test_model_from_pretrained(self):
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for model_class_name in self.all_model_classes:
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model = model_class_name.from_pretrained("facebook/mbart-large-cc25", from_pt=True)
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# FlaxMBartForSequenceClassification expects eos token in input_ids
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input_ids = np.ones((1, 1)) * model.config.eos_token_id
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outputs = model(input_ids)
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self.assertIsNotNone(outputs)
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@require_flax
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@require_sentencepiece
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@require_tokenizers
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class FlaxMBartModelIntegrationTest(unittest.TestCase):
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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]
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expected_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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]
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model_name = "facebook/mbart-large-en-ro"
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@cached_property
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def tokenizer(self):
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return AutoTokenizer.from_pretrained(self.model_name)
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@cached_property
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def model(self):
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model = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name, from_pt=True)
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return model
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def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
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generated_words = self.translate_src_text(**tokenizer_kwargs)
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self.assertListEqual(self.expected_text, generated_words)
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|
|
|
def translate_src_text(self, **tokenizer_kwargs):
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|
model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, return_tensors="np")
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|
generated_ids = self.model.generate(
|
|
model_inputs.input_ids,
|
|
attention_mask=model_inputs.attention_mask,
|
|
decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],
|
|
early_stopping=True,
|
|
num_beams=2,
|
|
).sequences
|
|
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
return generated_words
|
|
|
|
@slow
|
|
def test_batch_generation_en_ro(self):
|
|
self._assert_generated_batch_equal_expected()
|