Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
0
tests/models/gptj/__init__.py
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0
tests/models/gptj/__init__.py
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328
tests/models/gptj/test_modeling_flax_gptj.py
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328
tests/models/gptj/test_modeling_flax_gptj.py
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@@ -0,0 +1,328 @@
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# 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 tempfile
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import unittest
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import numpy as np
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import transformers
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from transformers import GPT2Tokenizer, GPTJConfig, is_flax_available, is_torch_available
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from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
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from ...generation.test_generation_flax_utils import FlaxGenerationTesterMixin
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from ...test_modeling_flax_common import FlaxModelTesterMixin, 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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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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
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if is_torch_available():
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import torch
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class FlaxGPTJModelTester:
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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_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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rotary_dim=4,
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num_hidden_layers=4,
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num_attention_heads=4,
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intermediate_size=37,
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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=512,
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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_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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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.rotary_dim = rotary_dim
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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.initializer_range = initializer_range
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self.scope = None
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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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 = GPTJConfig(
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vocab_size=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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n_positions=self.max_position_embeddings,
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use_cache=False,
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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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rotary_dim=self.rotary_dim,
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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 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_flax
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class FlaxGPTJModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxGPTJModelTester(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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@tooslow
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def test_batch_generation(self):
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2", pad_token="<|endoftext|>", 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 = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gptj-6B")
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model.do_sample = False
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model.config.pad_token_id = model.config.eos_token_id
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jit_generate = jax.jit(model.generate)
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output_sequences = jit_generate(
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inputs["input_ids"], attention_mask=inputs["attention_mask"], pad_token_id=tokenizer.pad_token_id
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).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 text.\n\nI'm trying to get the text of the",
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"Hey, I'm a little late to the party. I'm going to",
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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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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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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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@tooslow
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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("EleutherAI/gptj-6B")
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
|
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569
tests/models/gptj/test_modeling_gptj.py
Normal file
569
tests/models/gptj/test_modeling_gptj.py
Normal file
@@ -0,0 +1,569 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import datetime
|
||||
import unittest
|
||||
|
||||
from transformers import GPTJConfig, is_torch_available
|
||||
from transformers.testing_utils import require_torch, slow, tooslow, torch_device
|
||||
|
||||
from ...generation.test_generation_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
AutoTokenizer,
|
||||
GPTJForCausalLM,
|
||||
GPTJForQuestionAnswering,
|
||||
GPTJForSequenceClassification,
|
||||
GPTJModel,
|
||||
)
|
||||
|
||||
|
||||
class GPTJModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=14,
|
||||
seq_length=7,
|
||||
is_training=True,
|
||||
use_token_type_ids=True,
|
||||
use_input_mask=True,
|
||||
use_labels=True,
|
||||
use_mc_token_ids=True,
|
||||
vocab_size=99,
|
||||
hidden_size=32,
|
||||
rotary_dim=4,
|
||||
num_hidden_layers=5,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=37,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout_prob=0.0,
|
||||
attention_probs_dropout_prob=0.0,
|
||||
max_position_embeddings=512,
|
||||
type_vocab_size=16,
|
||||
type_sequence_label_size=2,
|
||||
initializer_range=0.02,
|
||||
num_labels=3,
|
||||
num_choices=4,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.use_token_type_ids = use_token_type_ids
|
||||
self.use_input_mask = use_input_mask
|
||||
self.use_labels = use_labels
|
||||
self.use_mc_token_ids = use_mc_token_ids
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.rotary_dim = rotary_dim
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.intermediate_size = intermediate_size
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.type_sequence_label_size = type_sequence_label_size
|
||||
self.initializer_range = initializer_range
|
||||
self.num_labels = num_labels
|
||||
self.num_choices = num_choices
|
||||
self.scope = None
|
||||
self.bos_token_id = vocab_size - 1
|
||||
self.eos_token_id = vocab_size - 1
|
||||
self.pad_token_id = vocab_size - 1
|
||||
|
||||
def get_large_model_config(self):
|
||||
return GPTJConfig.from_pretrained("EleutherAI/gpt-j-6B")
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
||||
|
||||
mc_token_ids = None
|
||||
if self.use_mc_token_ids:
|
||||
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
|
||||
|
||||
sequence_labels = None
|
||||
token_labels = None
|
||||
choice_labels = None
|
||||
if self.use_labels:
|
||||
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
)
|
||||
|
||||
def get_config(self):
|
||||
return GPTJConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
n_embd=self.hidden_size,
|
||||
n_layer=self.num_hidden_layers,
|
||||
n_head=self.num_attention_heads,
|
||||
intermediate_size=self.intermediate_size,
|
||||
hidden_act=self.hidden_act,
|
||||
hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
||||
n_positions=self.max_position_embeddings,
|
||||
type_vocab_size=self.type_vocab_size,
|
||||
initializer_range=self.initializer_range,
|
||||
use_cache=True,
|
||||
bos_token_id=self.bos_token_id,
|
||||
eos_token_id=self.eos_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
rotary_dim=self.rotary_dim,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_decoder(self):
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = self.prepare_config_and_inputs()
|
||||
|
||||
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
|
||||
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)
|
||||
|
||||
def create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = GPTJModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
|
||||
result = model(input_ids, token_type_ids=token_type_ids)
|
||||
result = model(input_ids)
|
||||
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
|
||||
|
||||
def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = GPTJModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
|
||||
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
|
||||
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
|
||||
|
||||
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
||||
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
||||
|
||||
output, past = outputs.to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
|
||||
|
||||
# append to next input_ids and token_type_ids
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
|
||||
"last_hidden_state"
|
||||
]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_and_check_gptj_model_attention_mask_past(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
||||
):
|
||||
model = GPTJModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# create attention mask
|
||||
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
||||
half_seq_length = self.seq_length // 2
|
||||
attn_mask[:, half_seq_length:] = 0
|
||||
|
||||
# first forward pass
|
||||
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
# change a random masked slice from input_ids
|
||||
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
||||
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
||||
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
||||
|
||||
# append to next input_ids and attn_mask
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
attn_mask = torch.cat(
|
||||
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
|
||||
dim=1,
|
||||
)
|
||||
|
||||
# get two different outputs
|
||||
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_and_check_gptj_model_past_large_inputs(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
||||
):
|
||||
model = GPTJModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
|
||||
|
||||
output, past = outputs.to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
|
||||
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
|
||||
|
||||
# append to next input_ids and token_type_ids
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
|
||||
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
|
||||
)["last_hidden_state"]
|
||||
output_from_past = model(
|
||||
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
|
||||
)["last_hidden_state"]
|
||||
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = GPTJForCausalLM(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
|
||||
self.parent.assertEqual(result.loss.shape, ())
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def create_and_check_forward_and_backwards(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
|
||||
):
|
||||
model = GPTJForCausalLM(config)
|
||||
if gradient_checkpointing:
|
||||
model.gradient_checkpointing_enable()
|
||||
model.to(torch_device)
|
||||
|
||||
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
|
||||
self.parent.assertEqual(result.loss.shape, ())
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
result.loss.backward()
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
|
||||
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
|
||||
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
|
||||
all_model_classes = (
|
||||
(GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, GPTJForQuestionAnswering)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
all_generative_model_classes = (GPTJForCausalLM,) if is_torch_available() else ()
|
||||
fx_compatible = True
|
||||
test_pruning = False
|
||||
test_missing_keys = False
|
||||
test_model_parallel = False
|
||||
test_head_masking = False
|
||||
|
||||
# special case for DoubleHeads model
|
||||
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
||||
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
||||
return inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = GPTJModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_gptj_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_past(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_past(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_att_mask_past(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_past_large_inputs(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_gptj_lm_head_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
|
||||
|
||||
def test_gptj_gradient_checkpointing(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
|
||||
|
||||
@tooslow
|
||||
def test_batch_generation(self):
|
||||
# Marked as @tooslow due to GPU OOM
|
||||
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16)
|
||||
model.to(torch_device)
|
||||
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
|
||||
|
||||
tokenizer.padding_side = "left"
|
||||
|
||||
# Define PAD Token = EOS Token = 50256
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
model.config.pad_token_id = model.config.eos_token_id
|
||||
|
||||
# use different length sentences to test batching
|
||||
sentences = [
|
||||
"Hello, my dog is a little",
|
||||
"Today, I",
|
||||
]
|
||||
|
||||
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
||||
input_ids = inputs["input_ids"].to(torch_device)
|
||||
token_type_ids = torch.cat(
|
||||
[
|
||||
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
|
||||
input_ids.new_full((input_ids.shape[0], 1), 500),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
|
||||
outputs = model.generate(
|
||||
input_ids=input_ids,
|
||||
attention_mask=inputs["attention_mask"].to(torch_device),
|
||||
)
|
||||
|
||||
outputs_tt = model.generate(
|
||||
input_ids=input_ids,
|
||||
attention_mask=inputs["attention_mask"].to(torch_device),
|
||||
token_type_ids=token_type_ids,
|
||||
)
|
||||
|
||||
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
|
||||
output_non_padded = model.generate(input_ids=inputs_non_padded)
|
||||
|
||||
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
|
||||
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
|
||||
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
||||
|
||||
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||||
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
|
||||
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
||||
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
||||
|
||||
expected_output_sentence = [
|
||||
"Hello, my dog is a little over a year old and has been diagnosed with a heart murmur",
|
||||
"Today, I’m going to talk about the most important thing in the",
|
||||
]
|
||||
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
||||
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
||||
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = GPTJModel.from_pretrained(model_name, revision="float16", torch_dtype=torch.float16)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
@require_torch
|
||||
class GPTJModelLanguageGenerationTest(unittest.TestCase):
|
||||
@tooslow
|
||||
def test_lm_generate_gptj(self):
|
||||
# Marked as @tooslow due to GPU OOM
|
||||
for checkpointing in [True, False]:
|
||||
model = GPTJForCausalLM.from_pretrained(
|
||||
"EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16
|
||||
)
|
||||
if checkpointing:
|
||||
model.gradient_checkpointing_enable()
|
||||
else:
|
||||
model.gradient_checkpointing_disable()
|
||||
model.to(torch_device)
|
||||
input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog
|
||||
# fmt: off
|
||||
# The dog is a man's best friend. It is a loyal companion, and it is a friend
|
||||
expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
|
||||
# fmt: on
|
||||
output_ids = model.generate(input_ids, do_sample=False)
|
||||
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|
||||
|
||||
@tooslow
|
||||
def test_gptj_sample(self):
|
||||
# Marked as @tooslow due to GPU OOM (issue #13676)
|
||||
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
|
||||
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16)
|
||||
model.to(torch_device)
|
||||
|
||||
torch.manual_seed(0)
|
||||
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
|
||||
input_ids = tokenized.input_ids.to(torch_device)
|
||||
output_ids = model.generate(input_ids, do_sample=True)
|
||||
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
||||
|
||||
token_type_ids = tokenized.token_type_ids.to(torch_device)
|
||||
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
|
||||
output_seq_tt = model.generate(
|
||||
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
|
||||
)
|
||||
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
|
||||
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
|
||||
|
||||
if torch_device == "cuda":
|
||||
EXPECTED_OUTPUT_STR = (
|
||||
"Today is a nice day and I've already been enjoying it. I walked to work with my wife"
|
||||
)
|
||||
else:
|
||||
EXPECTED_OUTPUT_STR = "Today is a nice day and one of those days that feels a bit more alive. I am ready"
|
||||
|
||||
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
|
||||
self.assertTrue(
|
||||
all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))])
|
||||
) # token_type_ids should change output
|
||||
|
||||
@slow
|
||||
def test_gptj_sample_max_time(self):
|
||||
tokenizer = AutoTokenizer.from_pretrained("anton-l/gpt-j-tiny-random")
|
||||
model = GPTJForCausalLM.from_pretrained("anton-l/gpt-j-tiny-random")
|
||||
model.to(torch_device)
|
||||
|
||||
torch.manual_seed(0)
|
||||
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
|
||||
input_ids = tokenized.input_ids.to(torch_device)
|
||||
|
||||
MAX_TIME = 0.5
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
495
tests/models/gptj/test_modeling_tf_gptj.py
Normal file
495
tests/models/gptj/test_modeling_tf_gptj.py
Normal file
@@ -0,0 +1,495 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import datetime
|
||||
import unittest
|
||||
|
||||
from transformers import AutoTokenizer, GPTJConfig, is_tf_available
|
||||
from transformers.testing_utils import require_tf, slow, tooslow
|
||||
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
|
||||
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
|
||||
|
||||
|
||||
if is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
from transformers.models.gptj.modeling_tf_gptj import (
|
||||
TFGPTJForCausalLM,
|
||||
TFGPTJForQuestionAnswering,
|
||||
TFGPTJForSequenceClassification,
|
||||
TFGPTJModel,
|
||||
shape_list,
|
||||
)
|
||||
|
||||
|
||||
class TFGPTJModelTester:
|
||||
def __init__(self, parent):
|
||||
self.parent = parent
|
||||
self.batch_size = 13
|
||||
self.seq_length = 7
|
||||
self.is_training = True
|
||||
self.use_token_type_ids = True
|
||||
self.use_input_mask = True
|
||||
self.use_labels = True
|
||||
self.use_mc_token_ids = True
|
||||
self.vocab_size = 99
|
||||
self.hidden_size = 32
|
||||
self.num_hidden_layers = 5
|
||||
self.num_attention_heads = 4
|
||||
self.intermediate_size = 37
|
||||
self.hidden_act = "gelu"
|
||||
self.hidden_dropout_prob = 0.1
|
||||
self.attention_probs_dropout_prob = 0.1
|
||||
self.max_position_embeddings = 512
|
||||
self.type_vocab_size = 16
|
||||
self.type_sequence_label_size = 2
|
||||
self.initializer_range = 0.02
|
||||
self.num_labels = 3
|
||||
self.num_choices = 4
|
||||
self.scope = None
|
||||
self.bos_token_id = self.vocab_size - 1
|
||||
self.eos_token_id = self.vocab_size - 1
|
||||
self.pad_token_id = self.vocab_size - 1
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
||||
|
||||
mc_token_ids = None
|
||||
if self.use_mc_token_ids:
|
||||
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
|
||||
|
||||
sequence_labels = None
|
||||
token_labels = None
|
||||
choice_labels = None
|
||||
if self.use_labels:
|
||||
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = GPTJConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
n_embd=self.hidden_size,
|
||||
n_layer=self.num_hidden_layers,
|
||||
n_head=self.num_attention_heads,
|
||||
intermediate_size=self.intermediate_size,
|
||||
hidden_act=self.hidden_act,
|
||||
hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
||||
n_positions=self.max_position_embeddings,
|
||||
type_vocab_size=self.type_vocab_size,
|
||||
initializer_range=self.initializer_range,
|
||||
bos_token_id=self.bos_token_id,
|
||||
eos_token_id=self.eos_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
return_dict=True,
|
||||
)
|
||||
|
||||
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
)
|
||||
|
||||
def create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = TFGPTJModel(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
result = model(inputs)
|
||||
|
||||
inputs = [input_ids, None, input_mask] # None is the input for 'past'
|
||||
result = model(inputs)
|
||||
|
||||
result = model(input_ids)
|
||||
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
|
||||
def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = TFGPTJModel(config=config)
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
|
||||
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
|
||||
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
|
||||
|
||||
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
||||
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
||||
|
||||
output, past = outputs.to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
|
||||
|
||||
# append to next input_ids and token_type_ids
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
|
||||
|
||||
def create_and_check_gptj_model_attention_mask_past(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
||||
):
|
||||
model = TFGPTJModel(config=config)
|
||||
|
||||
# create attention mask
|
||||
half_seq_length = self.seq_length // 2
|
||||
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
|
||||
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
|
||||
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
|
||||
|
||||
# first forward pass
|
||||
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
# change a random masked slice from input_ids
|
||||
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
|
||||
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
|
||||
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
|
||||
condition = tf.transpose(
|
||||
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
|
||||
)
|
||||
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
|
||||
|
||||
# append to next input_ids and attn_mask
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1)
|
||||
|
||||
# get two different outputs
|
||||
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, past=past, attention_mask=attn_mask)["last_hidden_state"]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12)
|
||||
|
||||
def create_and_check_gptj_model_past_large_inputs(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
||||
):
|
||||
model = TFGPTJModel(config=config)
|
||||
|
||||
input_ids = input_ids[:1, :]
|
||||
input_mask = input_mask[:1, :]
|
||||
token_type_ids = token_type_ids[:1, :]
|
||||
self.batch_size = 1
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, use_cache=True)
|
||||
|
||||
output, past = outputs.to_tuple()
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
|
||||
next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size)
|
||||
|
||||
# append to next input_ids and token_type_ids
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
|
||||
next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
|
||||
)["last_hidden_state"]
|
||||
output_from_past = model(
|
||||
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past=past
|
||||
)["last_hidden_state"]
|
||||
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
|
||||
|
||||
def create_and_check_gptj_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = TFGPTJForCausalLM(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
|
||||
inputs_dict = {
|
||||
"input_ids": input_ids,
|
||||
"token_type_ids": token_type_ids,
|
||||
"attention_mask": input_mask,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase):
|
||||
|
||||
all_model_classes = (
|
||||
(TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
|
||||
if is_tf_available()
|
||||
else ()
|
||||
)
|
||||
|
||||
all_generative_model_classes = (TFGPTJForCausalLM,) if is_tf_available() else ()
|
||||
test_onnx = False
|
||||
test_pruning = False
|
||||
test_missing_keys = False
|
||||
test_head_masking = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFGPTJModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_gptj_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_past(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_past(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_att_mask_past(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs)
|
||||
|
||||
def test_gptj_model_past_large_inputs(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_gptj_lm_head_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_gptj_lm_head_model(*config_and_inputs)
|
||||
|
||||
def test_model_common_attributes(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
|
||||
|
||||
if model_class in self.all_generative_model_classes:
|
||||
x = model.get_output_embeddings()
|
||||
assert isinstance(x, tf.keras.layers.Layer)
|
||||
name = model.get_bias()
|
||||
assert name is None
|
||||
else:
|
||||
x = model.get_output_embeddings()
|
||||
assert x is None
|
||||
name = model.get_bias()
|
||||
assert name is None
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(
|
||||
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) > 0,
|
||||
"skip testing on GPU for now to avoid GPU OOM.",
|
||||
)
|
||||
def test_model_from_pretrained(self):
|
||||
model = TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
|
||||
def test_resize_token_embeddings(self):
|
||||
super().test_resize_token_embeddings()
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFGPTJModelLanguageGenerationTest(unittest.TestCase):
|
||||
@tooslow
|
||||
def test_lm_generate_gptj(self):
|
||||
# Marked as @tooslow due to GPU OOM
|
||||
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
|
||||
input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog
|
||||
# fmt: off
|
||||
# The dog is a man's best friend. It is a loyal companion, and it is a friend
|
||||
expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
|
||||
# fmt: on
|
||||
output_ids = model.generate(input_ids, do_sample=False)
|
||||
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
|
||||
|
||||
@tooslow
|
||||
def test_gptj_sample(self):
|
||||
# Marked as @tooslow due to GPU OOM (issue #13676)
|
||||
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
|
||||
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
|
||||
|
||||
tf.random.set_seed(0)
|
||||
tokenized = tokenizer("Today is a nice day and", return_tensors="tf", return_token_type_ids=True)
|
||||
input_ids, token_type_ids = tokenized.input_ids, tokenized.token_type_ids
|
||||
output_ids = model.generate(input_ids, do_sample=True)
|
||||
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
||||
|
||||
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
|
||||
output_seq_tt = model.generate(
|
||||
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
|
||||
)
|
||||
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
|
||||
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
|
||||
|
||||
EXPECTED_OUTPUT_STR = "Today is a nice day and I am taking an hour to sit in the hammock and just enjoy"
|
||||
|
||||
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
|
||||
self.assertTrue(
|
||||
all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))])
|
||||
) # token_type_ids should change output
|
||||
|
||||
@slow
|
||||
@unittest.skip(reason="TF generate currently has no time-based stopping criteria")
|
||||
def test_gptj_sample_max_time(self):
|
||||
tokenizer = AutoTokenizer.from_pretrained("anton-l/gpt-j-tiny-random")
|
||||
model = TFGPTJForCausalLM.from_pretrained("anton-l/gpt-j-tiny-random", from_pt=True)
|
||||
|
||||
input_ids = tokenizer("Today is a nice day and", return_tensors="tf", return_token_type_ids=True).input_ids
|
||||
|
||||
MAX_TIME = 0.5
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
||||
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
start = datetime.datetime.now()
|
||||
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
|
||||
duration = datetime.datetime.now() - start
|
||||
self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
||||
|
||||
@tooslow
|
||||
def test_batch_generation(self):
|
||||
# Marked as @tooslow due to GPU OOM
|
||||
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
|
||||
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
|
||||
|
||||
tokenizer.padding_side = "left"
|
||||
|
||||
# Define PAD Token = EOS Token = 50256
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
model.config.pad_token_id = model.config.eos_token_id
|
||||
|
||||
# use different length sentences to test batching
|
||||
sentences = [
|
||||
"Hello, my dog is a little",
|
||||
"Today, I",
|
||||
]
|
||||
|
||||
inputs = tokenizer(sentences, return_tensors="tf", padding=True)
|
||||
input_ids = inputs["input_ids"]
|
||||
token_type_ids = tf.concat(
|
||||
[
|
||||
tf.zeros((input_ids.shape[0], input_ids.shape[1] - 1), dtype=tf.int64),
|
||||
500 * tf.ones((input_ids.shape[0], 1), dtype=tf.int64),
|
||||
],
|
||||
axis=-1,
|
||||
)
|
||||
|
||||
outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"])
|
||||
outputs_tt = model.generate(
|
||||
input_ids=input_ids,
|
||||
attention_mask=inputs["attention_mask"],
|
||||
token_type_ids=token_type_ids,
|
||||
)
|
||||
|
||||
inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
|
||||
output_non_padded = model.generate(input_ids=inputs_non_padded)
|
||||
|
||||
num_paddings = (
|
||||
shape_list(inputs_non_padded)[-1] - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1], tf.int64)).numpy()
|
||||
)
|
||||
inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
|
||||
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
||||
|
||||
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||||
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
|
||||
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
||||
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
||||
|
||||
expected_output_sentence = [
|
||||
"Hello, my dog is a little over a year old and has been diagnosed with a heart murmur",
|
||||
"Today, I’m going to share with you a few of my favorite",
|
||||
]
|
||||
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
||||
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
||||
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
||||
Reference in New Issue
Block a user