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# Copyright 2024 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 OLMoE model."""
import unittest
from parameterized import parameterized
from transformers import OlmoeConfig, is_torch_available, set_seed
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers.models.gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
from transformers.testing_utils import (
require_tokenizers,
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OlmoeForCausalLM,
OlmoeModel,
)
class OlmoeModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
hidden_act="silu",
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,
pad_token_id=0,
scope=None,
num_experts_per_tok=2,
num_experts=8,
norm_topk_prob=False,
output_router_logits=False,
router_aux_loss_coef=0.001,
intermediate_size=12,
):
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.pad_token_id = pad_token_id
self.scope = scope
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
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 = torch.tril(torch.ones_like(input_ids).to(torch_device))
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)
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()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return OlmoeConfig(
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,
num_experts_per_tok=self.num_experts_per_tok,
num_experts=self.num_experts,
norm_topk_prob=self.norm_topk_prob,
output_router_logits=self.output_router_logits,
router_aux_loss_coef=self.router_aux_loss_coef,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = OlmoeModel(config=config)
model.to(torch_device)
model.eval()
result = 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 prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class OlmoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (OlmoeModel, OlmoeForCausalLM) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": OlmoeModel,
"text-generation": OlmoeForCausalLM,
}
if is_torch_available()
else {}
)
test_pruning = False
fx_compatible = False
# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
# This is because we are hitting edge cases with the causal_mask buffer
model_split_percents = [0.5, 0.7, 0.8]
def setUp(self):
self.model_tester = OlmoeModelTester(self)
self.config_tester = ConfigTester(self, config_class=OlmoeConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="OLMoE does not support head pruning.")
def test_headmasking(self):
pass
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(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 = OlmoeModel(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 = OlmoeModel(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":
torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, 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))
@require_torch
class OlmoeIntegrationTest(unittest.TestCase):
@slow
def test_model_7b_logits(self):
input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]]
model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
out = model(torch.tensor(input_ids)).logits.float()
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-1.3814, -3.4450, -2.2990, -1.9542, -2.4387, -2.7941, -2.9312, -2.8309]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([-2.3874, -2.4076, -2.4995, 4.2278, 1.4004, -0.0252, 0.4189, -2.7560, 0.3531, 1.6678, -0.7941, -1.1818, -0.2920, 0.7131, -1.4173, 1.6723, 0.5406, 0.1345, -0.1800, 0.2304, 1.2791, 0.7489, 0.6341, -0.0151, -1.3693, -1.2532, -2.3921, 0.7376, 1.6876, 0.5483]) # fmt: skip
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
@slow
def test_model_7b_greedy_generation(self):
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that \nthe speed of light is the same for all observers, no matter \nhow fast they are moving. This is a very counter-intuitive \nconcept, and it took Einstein a long time to come up with \nthe theory. The theory of relativity is based on two \npostulates"""
prompt = "Simply put, the theory of relativity states that "
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
input_ids = tokenizer.encode(prompt, return_tensors="pt")
model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
# greedy generation outputs
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
@require_tokenizers
def test_fast_special_tokens(self):
fast_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")
original_add_eos_token = fast_tokenizer.add_eos_token
fast_tokenizer.add_eos_token = False
fast = fast_tokenizer.encode("A sample test")
self.assertEqual(fast, [34, 3410, 1071])
fast_tokenizer.add_eos_token = True
fast = fast_tokenizer.encode("A sample test")
self.assertEqual(fast, [34, 3410, 1071, 50279])
fast_tokenizer.add_eos_token = original_add_eos_token
@require_tokenizers
def test_simple_encode_decode(self):
rust_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")
self.assertEqual(rust_tokenizer.encode("This is a test"), [1552, 310, 247, 1071])
self.assertEqual(rust_tokenizer.decode([1552, 310, 247, 1071], skip_special_tokens=True), "This is a test")
# bytefallback showcase
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [20025, 46549, 5225, 48561, 33656, 238, 12105]) # fmt: skip
self.assertEqual(
rust_tokenizer.decode([20025, 46549, 5225, 48561, 33656, 238, 12105], skip_special_tokens=True),
"生活的真谛是",
)
# Inner spaces showcase
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50276, 12092])
self.assertEqual(rust_tokenizer.decode([12764, 50276, 12092], skip_special_tokens=True), "Hi Hello")
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50275, 12092])
self.assertEqual(rust_tokenizer.decode([12764, 50275, 12092], skip_special_tokens=True), "Hi Hello")
self.assertEqual(rust_tokenizer.encode(""), [])
self.assertEqual(rust_tokenizer.encode(" "), [209])
self.assertEqual(rust_tokenizer.encode(" "), [50276])
self.assertEqual(rust_tokenizer.encode(" Hello"), [24387])