347 lines
15 KiB
Python
347 lines
15 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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import tempfile
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import unittest
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import transformers
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from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
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from transformers.testing_utils import is_pt_flax_cross_test, 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, floats_tensor, ids_tensor, random_attention_mask
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if is_flax_available():
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import jax
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import jax.numpy as jnp
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import numpy as np
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from transformers.modeling_flax_pytorch_utils import (
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convert_pytorch_state_dict_to_flax,
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load_flax_weights_in_pytorch_model,
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)
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from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel
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if is_torch_available():
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import torch
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@require_flax
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class FlaxXGLMModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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d_model=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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ffn_dim=37,
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activation_function="gelu",
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activation_dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = d_model
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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.ffn_dim = ffn_dim
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self.activation_function = activation_function
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self.activation_dropout = activation_dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = None
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self.bos_token_id = 0
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self.eos_token_id = 2
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self.pad_token_id = 1
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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], self.vocab_size), 3, self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = XGLMConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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num_layers=self.num_hidden_layers,
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attention_heads=self.num_attention_heads,
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ffn_dim=self.ffn_dim,
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activation_function=self.activation_function,
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activation_dropout=self.activation_dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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use_cache=True,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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)
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return (config, input_ids, input_mask)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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def prepare_config_and_inputs_for_decoder(self):
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config, input_ids, attention_mask = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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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encoder_hidden_states,
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encoder_attention_mask,
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)
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def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
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attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (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, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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attention_mask_cache = jnp.concatenate(
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[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
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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_decoder_length)
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (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_sentencepiece
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@require_flax
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class FlaxXGLMModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = (FlaxXGLMForCausalLM,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxXGLMModelTester(self)
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def test_use_cache_forward(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
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def test_use_cache_forward_with_attn_mask(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward_with_attn_mask(
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model_class_name, config, input_ids, attention_mask
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)
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@slow
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def test_batch_generation(self):
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tokenizer = XGLMTokenizer.from_pretrained("XGLM", padding_side="left")
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inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True)
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model = FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
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model.config.num_beams = 1
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model.config.do_sample = False
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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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expected_string = [
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"Hello this is a long string of questions, but I'm not sure if I'm",
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"Hey, I'm a newbie to the forum and I'",
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]
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self.assertListEqual(output_string, expected_string)
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# overwrite from common since `attention_mask` in combination
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# with `causal_mask` behaves slighly differently
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@is_pt_flax_cross_test
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def test_equivalence_pt_to_flax(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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# prepare inputs
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
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# load corresponding PyTorch class
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pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
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pt_model_class = getattr(transformers, pt_model_class_name)
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batch_size, seq_length = pt_inputs["input_ids"].shape
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rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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pt_inputs["attention_mask"][batch_idx, :start_index] = 0
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pt_inputs["attention_mask"][batch_idx, start_index:] = 1
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prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
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prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
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pt_model = pt_model_class(config).eval()
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# Flax models don't use the `use_cache` option and cache is not returned as a default.
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# So we disable `use_cache` here for PyTorch model.
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pt_model.config.use_cache = False
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fx_model = model_class(config, dtype=jnp.float32)
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fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
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fx_model.params = fx_state
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with torch.no_grad():
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pt_outputs = pt_model(**pt_inputs).to_tuple()
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fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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for fx_output, pt_output in zip(fx_outputs, pt_outputs):
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self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
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with tempfile.TemporaryDirectory() as tmpdirname:
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pt_model.save_pretrained(tmpdirname)
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fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
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fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
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self.assertEqual(
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len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
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)
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for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
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self.assert_almost_equals(fx_output_loaded[:, -1], pt_output[:, -1].numpy(), 4e-2)
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# overwrite from common since `attention_mask` in combination
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# with `causal_mask` behaves slighly differently
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@is_pt_flax_cross_test
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def test_equivalence_flax_to_pt(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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# prepare inputs
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
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# load corresponding PyTorch class
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pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
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pt_model_class = getattr(transformers, pt_model_class_name)
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pt_model = pt_model_class(config).eval()
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pt_model.config.use_cache = False
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fx_model = model_class(config, dtype=jnp.float32)
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pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
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batch_size, seq_length = pt_inputs["input_ids"].shape
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rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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pt_inputs["attention_mask"][batch_idx, :start_index] = 0
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pt_inputs["attention_mask"][batch_idx, start_index:] = 1
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prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
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prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
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# make sure weights are tied in PyTorch
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pt_model.tie_weights()
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with torch.no_grad():
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pt_outputs = pt_model(**pt_inputs).to_tuple()
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fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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for fx_output, pt_output in zip(fx_outputs, pt_outputs):
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self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
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with tempfile.TemporaryDirectory() as tmpdirname:
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fx_model.save_pretrained(tmpdirname)
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pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
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with torch.no_grad():
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pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
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self.assertEqual(
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len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
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)
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for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded):
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self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
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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/xglm-564M")
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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