404 lines
15 KiB
Python
404 lines
15 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import timeout_decorator # noqa
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from transformers import OPTConfig, is_flax_available
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from transformers.testing_utils import require_flax, require_sentencepiece, slow
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from ...generation.test_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 FlaxOPTForCausalLM, FlaxOPTModel, GPT2Tokenizer
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def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None):
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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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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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}
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@require_flax
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class FlaxOPTModelTester:
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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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embed_dim=16,
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word_embed_proj_dim=16,
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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.embed_dim = embed_dim
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self.word_embed_proj_dim = word_embed_proj_dim
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self.initializer_range = initializer_range
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self.is_encoder_decoder = False
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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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config = OPTConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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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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embed_dim=self.embed_dim,
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is_encoder_decoder=False,
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word_embed_proj_dim=self.word_embed_proj_dim,
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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_opt_inputs_dict(config, 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_length = 20
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model = model_class_name(config)
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input_ids = inputs_dict["input_ids"]
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attention_mask = inputs_dict["attention_mask"]
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past_key_values = model.init_cache(input_ids.shape[0], max_length)
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attention_mask = jnp.ones((input_ids.shape[0], max_length), dtype="i4")
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :],
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(input_ids.shape[0], input_ids.shape[-1] - 1),
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)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model(
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input_ids[:, -1:],
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attention_mask=attention_mask,
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past_key_values=outputs_cache.past_key_values,
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position_ids=position_ids,
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)
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outputs = model(input_ids)
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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_length = 20
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model = model_class_name(config)
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input_ids, attention_mask = (
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inputs_dict["input_ids"],
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inputs_dict["attention_mask"],
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)
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attention_mask_cache = jnp.concatenate(
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[
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attention_mask,
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jnp.zeros((attention_mask.shape[0], max_length - 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(input_ids.shape[0], max_length)
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :],
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(input_ids.shape[0], input_ids.shape[-1] - 1),
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)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask_cache,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model(
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input_ids[:, -1:],
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past_key_values=outputs_cache.past_key_values,
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attention_mask=attention_mask_cache,
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position_ids=position_ids,
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)
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outputs = model(input_ids, attention_mask=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 FlaxOPTModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
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all_model_classes = (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = () if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxOPTModelTester(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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@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/opt-125m")
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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_sentencepiece
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@require_flax
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class FlaxOPTModelIntegrationTests(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = FlaxOPTModel.from_pretrained("facebook/opt-350m")
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input_ids = jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids=input_ids).last_hidden_state
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expected_shape = (1, 11, 512)
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self.assertEqual(output.shape, expected_shape)
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expected_slice = jnp.array(
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[[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]
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)
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self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=4e-2))
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@require_flax
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@slow
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class FlaxOPTEmbeddingsTest(unittest.TestCase):
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def setUp(self):
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super().setUp()
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self.path_model = "facebook/opt-350m"
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def test_logits(self):
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model = FlaxOPTForCausalLM.from_pretrained(self.path_model)
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tokenizer = GPT2Tokenizer.from_pretrained(self.path_model)
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prompts = [
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"Today is a beautiful day and I want to",
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"In the city of",
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"Paris is the capital of France and",
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"Computers and mobile phones have taken",
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]
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# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
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inputs = tokenizer(prompts, return_tensors="jax", padding=True, add_special_tokens=False)
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logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1)
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logits_meta = jnp.array(
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[
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[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
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[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
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[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
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[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
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]
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)
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self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2))
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model = jax.jit(model)
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logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1)
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self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2))
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@require_flax
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@slow
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class FlaxOPTGenerationTest(unittest.TestCase):
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@property
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def prompts(self):
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return [
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"Today is a beautiful day and I want",
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"In the city of",
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"Paris is the capital of France and",
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"Computers and mobile phones have taken",
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]
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def test_generation_pre_attn_layer_norm(self):
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model_id = "facebook/opt-125m"
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EXPECTED_OUTPUTS = [
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"Today is a beautiful day and I want to",
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"In the city of New York, the city",
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"Paris is the capital of France and the capital",
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"Computers and mobile phones have taken over the",
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]
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predicted_outputs = []
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model = FlaxOPTForCausalLM.from_pretrained(model_id)
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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for prompt in self.prompts:
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input_ids = tokenizer(prompt, return_tensors="jax").input_ids
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generated_ids = model.generate(input_ids, max_length=10)
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generated_ids = generated_ids[0]
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generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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predicted_outputs += generated_string
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self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
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def test_generation_post_attn_layer_norm(self):
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model_id = "facebook/opt-350m"
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EXPECTED_OUTPUTS = [
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"Today is a beautiful day and I want to",
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"In the city of San Francisco, the city",
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"Paris is the capital of France and the capital",
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"Computers and mobile phones have taken over the",
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]
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predicted_outputs = []
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model = FlaxOPTForCausalLM.from_pretrained(model_id)
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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for prompt in self.prompts:
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input_ids = tokenizer(prompt, return_tensors="jax").input_ids
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generated_ids = model.generate(input_ids, max_length=10)
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generated_ids = generated_ids[0]
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generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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predicted_outputs += generated_string
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self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
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def test_jitted_batch_generation(self):
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model_id = "facebook/opt-125m"
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EXPECTED_OUTPUTS = [
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"Today is a beautiful day and I want to thank",
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"In the city of Rome Canaver Canaver Canaver Canaver",
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]
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model = FlaxOPTForCausalLM.from_pretrained(model_id)
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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inputs = tokenizer(
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[
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"Today is a beautiful day and I want to",
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"In the city of",
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],
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return_tensors="jax",
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padding=True,
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)
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jit_generate = jax.jit(model.generate)
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output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences
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output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
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self.assertIsNotNone(output_string, EXPECTED_OUTPUTS)
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def test_batch_generation(self):
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model_id = "facebook/opt-350m"
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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model = FlaxOPTForCausalLM.from_pretrained(model_id)
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tokenizer.padding_side = "left"
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# use different length sentences to test batching
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sentences = [
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"Hello, my dog is a little",
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"Today, I",
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]
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inputs = tokenizer(sentences, return_tensors="jax", padding=True)
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input_ids = inputs["input_ids"]
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outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], trace=False)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="jax").input_ids
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output_non_padded = model.generate(input_ids=inputs_non_padded)
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num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
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inputs_padded = tokenizer(sentences[1], return_tensors="jax").input_ids
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output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
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batch_out_sentence = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)
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non_padded_sentence = tokenizer.decode(output_non_padded[0][0], skip_special_tokens=True)
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padded_sentence = tokenizer.decode(output_padded[0][0], skip_special_tokens=True)
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expected_output_sentence = [
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"Hello, my dog is a little bit of a dork.\nI'm a little bit",
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"Today, I was in the middle of a conversation with a friend about the",
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]
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self.assertListEqual(expected_output_sentence, batch_out_sentence)
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self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
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