434 lines
20 KiB
Python
434 lines
20 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The HuggingFace Inc. 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.
|
|
""" Testing suite for the PyTorch GPTNeoX model. """
|
|
|
|
|
|
import unittest
|
|
|
|
from parameterized import parameterized
|
|
|
|
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
|
|
from transformers.testing_utils import require_torch, slow, torch_device
|
|
|
|
from ...generation.test_utils import GenerationTesterMixin
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
GPTNeoXForCausalLM,
|
|
GPTNeoXForQuestionAnswering,
|
|
GPTNeoXForSequenceClassification,
|
|
GPTNeoXForTokenClassification,
|
|
GPTNeoXModel,
|
|
)
|
|
from transformers.models.gpt_neox.modeling_gpt_neox import (
|
|
GPTNeoXDynamicNTKScalingRotaryEmbedding,
|
|
GPTNeoXLinearScalingRotaryEmbedding,
|
|
GPTNeoXRotaryEmbedding,
|
|
)
|
|
|
|
|
|
class GPTNeoXModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
seq_length=7,
|
|
is_training=True,
|
|
use_input_mask=True,
|
|
use_token_type_ids=True,
|
|
use_labels=True,
|
|
vocab_size=99,
|
|
hidden_size=64,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
intermediate_size=37,
|
|
hidden_act="gelu",
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=512,
|
|
type_vocab_size=16,
|
|
type_sequence_label_size=2,
|
|
initializer_range=0.02,
|
|
num_labels=3,
|
|
num_choices=4,
|
|
scope=None,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.use_input_mask = use_input_mask
|
|
self.use_token_type_ids = use_token_type_ids
|
|
self.use_labels = use_labels
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
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 = scope
|
|
self.pad_token_id = 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_labels = None
|
|
if self.use_labels:
|
|
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, input_ids, input_mask, token_labels
|
|
|
|
def get_config(self):
|
|
return GPTNeoXConfig(
|
|
vocab_size=self.vocab_size,
|
|
hidden_size=self.hidden_size,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=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,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
type_vocab_size=self.type_vocab_size,
|
|
is_decoder=False,
|
|
initializer_range=self.initializer_range,
|
|
pad_token_id=self.pad_token_id,
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_decoder(self):
|
|
config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
|
|
|
|
config.is_decoder = True
|
|
|
|
return config, input_ids, input_mask, token_labels
|
|
|
|
def create_and_check_model(self, config, input_ids, input_mask):
|
|
model = GPTNeoXModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
_ = model(input_ids, attention_mask=input_mask)
|
|
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_model_as_decoder(self, config, input_ids, input_mask):
|
|
config.add_cross_attention = True
|
|
model = GPTNeoXModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask)
|
|
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
|
|
|
def create_and_check_for_causal_lm(self, config, input_ids, input_mask, token_labels):
|
|
model = GPTNeoXForCausalLM(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
|
|
|
def create_and_check_for_question_answering(self, config, input_ids, input_mask, token_labels):
|
|
config.num_labels = self.num_labels
|
|
model = GPTNeoXForQuestionAnswering(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask)
|
|
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
|
|
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
|
|
|
|
def create_and_check_for_sequence_classification(self, config, input_ids, input_mask, token_labels):
|
|
config.num_labels = self.num_labels
|
|
model = GPTNeoXForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
|
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
def create_and_check_for_token_classification(self, config, input_ids, input_mask, token_labels):
|
|
config.num_labels = self.num_labels
|
|
model = GPTNeoXForTokenClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_decoder_model_past_large_inputs(self, config, input_ids, input_mask):
|
|
config.is_decoder = True
|
|
model = GPTNeoXForCausalLM(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# first forward pass
|
|
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
|
|
past_key_values = outputs.past_key_values
|
|
|
|
# create hypothetical multiple next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
|
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
|
|
|
|
# append to next input_ids and
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
|
|
|
|
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True)
|
|
output_from_no_past = output_from_no_past["hidden_states"][0]
|
|
output_from_past = model(
|
|
next_tokens,
|
|
attention_mask=next_attention_mask,
|
|
past_key_values=past_key_values,
|
|
output_hidden_states=True,
|
|
)["hidden_states"][0]
|
|
|
|
# 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()
|
|
|
|
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
|
|
|
# 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 prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, input_ids, input_mask, token_labels = config_and_inputs
|
|
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
GPTNeoXModel,
|
|
GPTNeoXForCausalLM,
|
|
GPTNeoXForQuestionAnswering,
|
|
GPTNeoXForSequenceClassification,
|
|
GPTNeoXForTokenClassification,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (GPTNeoXForCausalLM,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": GPTNeoXModel,
|
|
"question-answering": GPTNeoXForQuestionAnswering,
|
|
"text-classification": GPTNeoXForSequenceClassification,
|
|
"text-generation": GPTNeoXForCausalLM,
|
|
"token-classification": GPTNeoXForTokenClassification,
|
|
"zero-shot": GPTNeoXForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
test_pruning = False
|
|
test_missing_keys = False
|
|
test_model_parallel = False
|
|
test_head_masking = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = GPTNeoXModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=GPTNeoXConfig, hidden_size=64, num_attention_heads=8)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_model(self):
|
|
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(config, input_ids, input_mask)
|
|
|
|
def test_model_as_decoder(self):
|
|
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
|
|
|
|
def test_model_as_decoder_with_default_input_mask(self):
|
|
# This regression test was failing with PyTorch < 1.3
|
|
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
|
|
input_mask = None
|
|
|
|
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
|
|
|
|
def test_decoder_model_past_large_inputs(self):
|
|
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(config, input_ids, input_mask)
|
|
|
|
def test_model_for_causal_lm(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
|
|
|
|
def test_model_for_question_answering(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
|
|
|
def test_model_for_sequence_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
|
|
|
|
def test_model_for_token_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@parameterized.expand([("linear",), ("dynamic",)])
|
|
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->GPTNeoX
|
|
def test_model_rope_scaling_from_config(self, scaling_type):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
short_input = ids_tensor([1, 10], config.vocab_size)
|
|
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
|
|
|
|
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
|
original_model = GPTNeoXModel(config)
|
|
original_model.to(torch_device)
|
|
original_model.eval()
|
|
original_short_output = original_model(short_input).last_hidden_state
|
|
original_long_output = original_model(long_input).last_hidden_state
|
|
|
|
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
|
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
|
|
scaled_model = GPTNeoXModel(config)
|
|
scaled_model.to(torch_device)
|
|
scaled_model.eval()
|
|
scaled_short_output = scaled_model(short_input).last_hidden_state
|
|
scaled_long_output = scaled_model(long_input).last_hidden_state
|
|
|
|
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
|
|
# maximum sequence length, so the outputs for the short input should match.
|
|
if scaling_type == "dynamic":
|
|
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
|
else:
|
|
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
|
|
|
# The output should be different for long inputs
|
|
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
|
|
|
|
# Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->GPTNeoX, rope_theta->rotary_emb_base
|
|
def test_model_rope_scaling(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
hidden_size = config.hidden_size
|
|
num_heads = config.num_attention_heads
|
|
head_dim = hidden_size // num_heads
|
|
scaling_factor = 10
|
|
short_input_length = 10
|
|
long_input_length = int(config.max_position_embeddings * 1.5)
|
|
|
|
# Inputs
|
|
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device
|
|
|
|
# Sanity check original RoPE
|
|
original_rope = GPTNeoXRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rotary_emb_base,
|
|
).to(torch_device)
|
|
original_cos_short, original_sin_short = original_rope(x, short_input_length)
|
|
original_cos_long, original_sin_long = original_rope(x, long_input_length)
|
|
torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :])
|
|
torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :])
|
|
|
|
# Sanity check linear RoPE scaling
|
|
# New position "x" should match original position with index "x/scaling_factor"
|
|
linear_scaling_rope = GPTNeoXLinearScalingRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rotary_emb_base,
|
|
scaling_factor=scaling_factor,
|
|
).to(torch_device)
|
|
linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
|
|
linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
|
|
torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
|
|
torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
|
|
for new_position in range(0, long_input_length, scaling_factor):
|
|
original_position = int(new_position // scaling_factor)
|
|
torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
|
|
torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])
|
|
|
|
# Sanity check Dynamic NTK RoPE scaling
|
|
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
|
|
# with scaling_factor (or that `inv_freq` decreases)
|
|
ntk_scaling_rope = GPTNeoXDynamicNTKScalingRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rotary_emb_base,
|
|
scaling_factor=scaling_factor,
|
|
).to(torch_device)
|
|
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
|
|
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
|
|
torch.testing.assert_close(ntk_cos_short, original_cos_short)
|
|
torch.testing.assert_close(ntk_sin_short, original_sin_short)
|
|
with self.assertRaises(AssertionError):
|
|
torch.testing.assert_close(ntk_cos_long, original_cos_long)
|
|
with self.assertRaises(AssertionError):
|
|
torch.testing.assert_close(ntk_sin_long, original_sin_long)
|
|
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
|
|
|
|
|
|
@require_torch
|
|
class GPTNeoXLanguageGenerationTest(unittest.TestCase):
|
|
@slow
|
|
def test_lm_generate_gptneox(self):
|
|
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
|
|
for checkpointing in [True, False]:
|
|
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
|
|
|
|
if checkpointing:
|
|
model.gradient_checkpointing_enable()
|
|
else:
|
|
model.gradient_checkpointing_disable()
|
|
model.to(torch_device)
|
|
|
|
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
|
|
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
|
|
# See: https://github.com/huggingface/transformers/pull/24193
|
|
expected_output = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
|
|
|
|
output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20)
|
|
output_str = tokenizer.batch_decode(output_ids)[0]
|
|
|
|
self.assertEqual(output_str, expected_output)
|
|
|
|
def pythia_integration_test(self):
|
|
model_name_or_path = "EleutherAI/pythia-70m"
|
|
model = GPTNeoXForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16).to(torch_device)
|
|
EXPECTED_LOGITS = torch.tensor([1069.0000, 228.7500, 1072.0000, 1072.0000, 1069.0000, 1068.0000, 1068.0000, 1071.0000, 1071.0000, 1071.0000, 1073.0000, 1070.0000, 1071.0000, 1075.0000, 1073.0000, 1075.0000, 1074.0000, 1069.0000, 1072.0000, 1071.0000, 1071.0000, 1071.0000, 1070.0000, 1069.0000, 1069.0000, 1069.0000, 1070.0000, 1075.0000, 1073.0000, 1074.0000]) # fmt: skip
|
|
input_ids = [29, 93, 303, 64, 5478, 49651, 10394, 187, 34, 12939, 875]
|
|
# alternative: tokenizer('<|im_start|>system\nA chat between')
|
|
input_ids = torch.as_tensor(input_ids)[None].to(torch_device)
|
|
outputs = model(input_ids)["logits"][:, -1][0, :30]
|
|
self.assertTrue(torch.allclose(EXPECTED_LOGITS, outputs, atol=1e-5))
|