EngineX-Cambricon/enginex-mlu370-any2any
9
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

init

Browse Source
This commit is contained in:
luopingyi
2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

387
transformers/tests/models/evolla/test_modeling_evolla.py Normal file
View File

@@ -0,0 +1,387 @@
# coding=utf-8
# Copyright 2025 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 Evolla model."""
import unittest
from functools import cached_property
from parameterized import parameterized
from transformers import BitsAndBytesConfig, EvollaConfig, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
require_bitsandbytes,
require_torch,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
ModelTesterMixin,
_config_zero_init,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EvollaForProteinText2Text, EvollaModel, EvollaProcessor
class EvollaModelTester:
def __init__(
self,
parent,
batch_size=1,
is_training=False,
text_seq_length=20,
text_vocab_size=100,
protein_seq_length=10,
protein_vocab_size=20,
hidden_size=4, # llama hidden size
intermediate_size=7, # llama intermediate size
num_hidden_layers=1, # llama hidden layers
num_attention_heads=2, # llama attention heads
num_key_value_heads=2, # llama key value heads
protein_hidden_size=8, # protein encoder hidden size
protein_num_hidden_layers=1, # protein encoder hidden layers
protein_num_attention_heads=4, # protein encoder attention heads
protein_intermediate_size=11, # protein encoder intermediate size
resampler_num_latents=7, # sequence compressor num latents
resampler_ff_mult=1, # sequence compressor ff mult
resampler_depth=2, # sequence compressor depth
resampler_dim_head=4, # sequence compressor dim head
resampler_heads=2, # sequence compressor heads
aligner_num_add_layers=1, # sequence aligner num add layers
aligner_ffn_mult=1, # sequence aligner ffn mult
use_input_mask=True,
):
self.parent = parent
self.batch_size = batch_size
self.protein_seq_length = protein_seq_length
self.protein_vocab_size = protein_vocab_size
self.text_seq_length = text_seq_length
self.text_vocab_size = text_vocab_size
self.seq_length = text_seq_length
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.protein_hidden_size = protein_hidden_size
self.protein_num_hidden_layers = protein_num_hidden_layers
self.protein_num_attention_heads = protein_num_attention_heads
self.protein_intermediate_size = protein_intermediate_size
self.resampler_num_latents = resampler_num_latents
self.resampler_ff_mult = resampler_ff_mult
self.resampler_depth = resampler_depth
self.resampler_dim_head = resampler_dim_head
self.resampler_heads = resampler_heads
self.aligner_num_add_layers = aligner_num_add_layers
self.aligner_ffn_mult = aligner_ffn_mult
self.use_input_mask = use_input_mask
self.is_training = is_training
@property
def is_encoder_decoder(self):
return False
def prepare_config_and_inputs(self, num_proteins=None):
batch_size = num_proteins if num_proteins is not None else self.batch_size
text_input_ids = ids_tensor([batch_size, self.text_seq_length], self.text_vocab_size)
protein_input_ids = ids_tensor([batch_size, self.protein_seq_length], self.protein_vocab_size)
if self.use_input_mask:
text_input_mask = random_attention_mask([batch_size, self.text_seq_length])
protein_input_mask = random_attention_mask([batch_size, self.protein_seq_length])
config = self.get_config()
return (config, text_input_ids, text_input_mask, protein_input_ids, protein_input_mask)
def get_config(self):
return EvollaConfig(
protein_encoder_config={
"vocab_size": self.protein_vocab_size,
"hidden_size": self.protein_hidden_size,
"num_hidden_layers": self.protein_num_hidden_layers,
"num_attention_heads": self.protein_num_attention_heads,
"intermediate_size": self.protein_intermediate_size,
},
vocab_size=self.text_vocab_size,
hidden_size=self.hidden_size,
intermediate_size=self.intermediate_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
aligner_ffn_mult=self.aligner_ffn_mult,
aligner_num_add_layers=self.aligner_num_add_layers,
resampler_depth=self.resampler_depth,
resampler_dim_head=self.resampler_dim_head,
resampler_heads=self.resampler_heads,
resampler_num_latents=self.resampler_num_latents,
resampler_ff_mult=self.resampler_ff_mult,
)
def create_and_check_model(
self,
config,
input_ids,
input_mask,
protein_input_ids,
protein_input_mask,
batch_size=None,
):
batch_size = batch_size if batch_size is not None else self.batch_size
model = EvollaModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
protein_input_ids=protein_input_ids,
protein_attention_mask=protein_input_mask,
)
self.parent.assertEqual(result.last_hidden_state.shape, (batch_size, input_ids.shape[1], self.hidden_size))
def create_and_check_model_gen(
self,
config,
input_ids,
input_mask,
protein_input_ids,
protein_input_mask,
):
model = EvollaForProteinText2Text(config)
model.to(torch_device)
model.eval()
model.generate(
input_ids,
attention_mask=input_mask,
protein_input_ids=protein_input_ids,
protein_attention_mask=protein_input_mask,
max_length=self.seq_length + 2,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, text_input_ids, text_input_mask, protein_input_ids, protein_input_mask) = config_and_inputs
inputs_dict = {
"input_ids": text_input_ids,
"attention_mask": text_input_mask,
"protein_input_ids": protein_input_ids,
"protein_attention_mask": protein_input_mask,
}
return config, inputs_dict
@require_torch
class EvollaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (EvollaModel, EvollaForProteinText2Text) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": EvollaModel} if is_torch_available() else {}
test_pruning = False
test_headmasking = False
test_torchscript = False
test_resize_embeddings = False
maxDiff = None
def setUp(self):
self.model_tester = EvollaModelTester(self)
self.config_tester = ConfigTester(self, config_class=EvollaConfig, hidden_size=37)
@property
def is_encoder_decoder(self):
return self.model_tester.is_encoder_decoder
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)
# XXX: EvollaForProteinText2Text has no MODEL_FOR group yet, but it should be the same
# as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing
# as super won't do it
if return_labels:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def test_model_outputs_equivalence(self):
try:
orig = self.all_model_classes
# EvollaModel.forward doesn't have labels input arg - only EvollaForProteinText2Text does
self.all_model_classes = (EvollaForProteinText2Text,) if is_torch_available() else ()
super().test_model_outputs_equivalence()
finally:
self.all_model_classes = orig
def test_config(self):
self.config_tester.run_common_tests()
def test_model_single_protein(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=1)
self.model_tester.create_and_check_model(*config_and_inputs, batch_size=1)
def test_model_multiple_proteins(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=2)
self.model_tester.create_and_check_model(*config_and_inputs, batch_size=2)
def test_generate_single_protein(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=1)
self.model_tester.create_and_check_model_gen(*config_and_inputs)
def test_generate_multiple_proteins(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=2)
self.model_tester.create_and_check_model_gen(*config_and_inputs)
def test_saprot_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
protein_information = {
"input_ids": inputs_dict["protein_input_ids"],
"attention_mask": inputs_dict["protein_attention_mask"],
}
for model_class in self.all_model_classes:
if model_class is not EvollaModel:
continue
model = model_class(config)
model.to(torch_device)
model.eval()
protein_encoder_outputs = model.protein_encoder.model(**protein_information, return_dict=True)
print(model_class, protein_encoder_outputs)
def test_protein_encoder_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
protein_information = {
"input_ids": inputs_dict["protein_input_ids"],
"attention_mask": inputs_dict["protein_attention_mask"],
}
for model_class in self.all_model_classes:
if model_class is not EvollaModel:
continue
model = model_class(config)
model.to(torch_device)
model.eval()
protein_encoder_outputs = model.protein_encoder(**protein_information, return_dict=True)
print(model_class, protein_encoder_outputs)
def test_single_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
print(outputs)
def test_initialization(self):
# we skip the latents initialization test
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
# skip latents
if name.endswith("latents"):
print(f"Skipping latents {name}")
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
@unittest.skip("Evolla requires both text and protein inputs which is currently not done in this test.")
def test_eager_matches_sdpa_inference(self):
pass
@unittest.skip("Evolla does not support eager attention implementation.")
def test_eager_padding_matches_padding_free_with_position_ids(self):
pass
@unittest.skip(
"Evolla has a separate test runner for generation tests with complex inheritance, causing this check to fail."
)
def test_generation_tester_mixin_inheritance(self):
pass
@unittest.skip("Evolla requires both text and protein inputs which is currently not done in this test.")
def test_flex_attention_with_grads(self):
pass
@require_torch
class EvollaModelIntegrationTest(TestCasePlus):
def _prepare_for_inputs(self):
aa_seq = "MLLEETLKSCPIVKRGKYHYFIHPISDGVPLVEPKLLREVATRIIKIGNFEGVNKIVTAEAMGIPLVTTLSLYTDIPYVIMRKREYKLPGEVPVFQSTGYSKGQLYLNGIEKGDKVIIIDDVISTGGTMIAIINALERAGAEIKDIICVIERGDGKKIVEEKTGYKIKTLVKIDVVDGEVVIL"
foldseek = "dvvvvqqqpfawdddppdtdgcgclapvpdpddpvvlvvllvlcvvpadpvqaqeeeeeddscpsnvvsncvvpvhyydywylddppdppkdwqwf######gitidpdqaaaheyeyeeaeqdqlrvvlsvvvrcvvrnyhhrayeyaeyhycnqvvccvvpvghyhynwywdqdpsgidtd"
question = "What is the function of this protein?"
protein_information = {
"aa_seq": aa_seq,
"foldseek": foldseek,
}
messages = [
{"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
{"role": "user", "content": question},
]
return protein_information, messages
@cached_property
def default_processor(self):
return EvollaProcessor.from_pretrained("westlake-repl/Evolla-10B-hf")
@require_bitsandbytes
@slow
def test_inference_natural_language_protein_reasoning(self):
protein_information, messages = self._prepare_for_inputs()
processor = self.default_processor
inputs = processor(
messages_list=[messages], proteins=[protein_information], return_tensors="pt", padding="longest"
).to(torch_device)
# the CI gpu is small so using quantization to fit
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype="float16",
)
model = EvollaForProteinText2Text.from_pretrained(
"westlake-repl/Evolla-10B-hf",
quantization_config=quantization_config,
device_map=torch_device,
)
generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertIn("This protein", generated_text[0])
self.assertIn("purine", generated_text[0])