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test_predictor.py
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test_predictor.py
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# Copyright (c) 2022 PaddlePaddle Authors. 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.
from __future__ import annotations
import os
import unittest
import paddle
from parameterized import parameterized_class
from paddlenlp.experimental.transformers import QWenForQWenVLInferenceModel
from paddlenlp.transformers import ( # ChatGLMForCausalLM,
AutoConfig,
AutoTokenizer,
BloomForCausalLM,
ChatGLMForCausalLM,
ChatGLMv2ForCausalLM,
LlamaForCausalLM,
QWenForCausalLM,
)
from paddlenlp.utils.downloader import (
COMMUNITY_MODEL_PREFIX,
get_path_from_url_with_filelock,
url_file_exists,
)
from tests.testing_utils import GPUsTesting, require_gpu
from .testing_utils import LLMTest, argv_context_guard, load_test_config
@parameterized_class(
["model_name_or_path", "model_class"],
[
["__internal_testing__/tiny-random-llama", LlamaForCausalLM],
["__internal_testing__/tiny-fused-bloom", BloomForCausalLM],
["__internal_testing__/tiny-fused-chatglm", ChatGLMForCausalLM],
["__internal_testing__/tiny-fused-chatglm2", ChatGLMv2ForCausalLM],
["__internal_testing__/tiny-fused-qwen-inference5.2", QWenForCausalLM],
],
)
class PredictorTest(LLMTest, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/predictor.yaml"
model_name_or_path: str = None
model_class = None
def setUp(self) -> None:
super().setUp()
paddle.set_default_dtype("float32")
self.model_class.from_pretrained(self.model_name_or_path, dtype="float16").save_pretrained(self.output_dir)
AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
def test_predictor(self):
self.run_predictor({"inference_model": True, "src_length": 512, "max_length": 48})
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor({"inference_model": False, "src_length": 512, "max_length": 48})
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
# compare the generation result of inference & dygraph model
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.25)
if self.model_name_or_path == "__internal_testing__/tiny-fused-chatglm":
self.assertGreaterEqual(count / len(result_0), 0.3)
else:
self.assertGreaterEqual(count / len(result_0), 0.4)
def test_flash_attention(self):
self.run_predictor(
{"inference_model": False, "use_flash_attention": False, "src_length": 512, "max_length": 48}
)
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor(
{"inference_model": False, "use_flash_attention": True, "src_length": 512, "max_length": 48}
)
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
# compare the generation result of dygraph & flash attention model
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
if self.model_name_or_path == "__internal_testing__/tiny-random-llama":
min_length = 5
else:
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
if self.model_name_or_path == "__internal_testing__/tiny-random-llama":
self.assertGreaterEqual(count / len(result_0), 0.2)
else:
self.assertEqual(full_match / len(result_0), 1.0)
def test_wint8(self):
self.run_predictor(
{"inference_model": True, "quant_type": "weight_only_int8", "src_length": 512, "max_length": 48}
)
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor({"inference_model": False, "src_length": 512, "max_length": 48})
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.1)
if self.model_name_or_path == "__internal_testing__/tiny-fused-chatglm":
self.assertGreaterEqual(count / len(result_0), 0.3)
else:
self.assertGreaterEqual(count / len(result_0), 0.4)
@parameterized_class(
["model_name_or_path", "model_class"],
[["__internal_testing__/tiny-random-llama", LlamaForCausalLM]],
)
class PredictorPrecacheTest(LLMTest, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/predictor.yaml"
model_name_or_path: str = None
model_class = None
def setUp(self) -> None:
super().setUp()
AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
self.download_precache_files()
def download_precache_files(self):
files = [
"prefix_config.json",
"config.json",
"model_state.pdparams",
"pre_caches.npy",
"prefix_model_state.pdparams",
]
for file in files:
file_url = os.path.join(COMMUNITY_MODEL_PREFIX, self.model_name_or_path, file)
if not url_file_exists(file_url):
continue
get_path_from_url_with_filelock(file_url, root_dir=self.output_dir)
def test_predictor(self):
self.run_predictor(
{
"inference_model": True,
"export_precache": True,
"prefix_path": self.output_dir,
"src_length": 512,
"max_length": 48,
}
)
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor(
{
"inference_model": False,
"export_precache": True,
"prefix_path": self.output_dir,
"src_length": 512,
"max_length": 48,
}
)
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
# compare the generation result of inference & dygraph model
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.6)
self.assertGreaterEqual(count / len(result_0), 0.8)
@parameterized_class(
["model_name_or_path", "model_class"],
[
["__internal_testing__/tiny-fused-llama-inference5.2", LlamaForCausalLM],
["__internal_testing__/tiny-fused-bloom", BloomForCausalLM],
],
)
class BlockAttnPredictorTest(LLMTest, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/predictor.yaml"
model_name_or_path: str = None
model_class = None
def setUp(self) -> None:
super().setUp()
paddle.set_default_dtype("float32")
self.model_class.from_pretrained(self.model_name_or_path, dtype="float16").save_pretrained(self.output_dir)
AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
def test_blha(self):
self.run_predictor({"inference_model": True, "block_attn": True, "src_length": 512, "max_length": 48})
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor({"inference_model": False, "src_length": 512, "max_length": 48})
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
# compare the generation result of inference & dygraph model
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.3)
if self.model_name_or_path == "__internal_testing__/tiny-fused-chatglm":
self.assertGreaterEqual(count / len(result_0), 0.3)
else:
self.assertGreaterEqual(count / len(result_0), 0.4)
def test_wint8(self):
self.run_predictor(
{
"inference_model": True,
"quant_type": "weight_only_int8",
"block_attn": True,
"src_length": 512,
"max_length": 48,
}
)
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor(
{"inference_model": True, "quant_type": "weight_only_int8", "src_length": 512, "max_length": 48}
)
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.4)
if self.model_name_or_path == "__internal_testing__/tiny-fused-chatglm":
self.assertGreaterEqual(count / len(result_0), 0.3)
else:
self.assertGreaterEqual(count / len(result_0), 0.4)
def test_cachekv_int8(self):
self.run_predictor(
{
"inference_model": True,
"block_attn": True,
"cachekv_int8_type": "dynamic",
"src_length": 512,
"max_length": 48,
}
)
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor({"inference_model": True, "block_attn": True, "src_length": 512, "max_length": 48})
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
print(f"result_0 {result_0}, result_1 {result_1}")
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(count / len(result_0), 0.1)
@parameterized_class(
["model_name_or_path", "model_class"],
[
["__internal_testing__/tiny-random-llama", LlamaForCausalLM],
],
)
class GPUsPredictorTest(LLMTest, GPUsTesting, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/predictor.yaml"
model_name_or_path: str = None
model_class = None
def setUp(self) -> None:
super().setUp()
self.model_class.from_pretrained(self.model_name_or_path, dtype="float16").save_pretrained(self.output_dir)
AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
@require_gpu(2)
def test_predictor(self):
self.init_dist_env()
self.run_predictor({"inference_model": True, "src_length": 512, "max_length": 48})
result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
self.run_predictor({"inference_model": False, "src_length": 512, "max_length": 48})
result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
# compare the generation result of inference & dygraph model
assert len(result_0) == len(result_1)
count, full_match = 0, 0
for inference_item, no_inference_item in zip(result_0, result_1):
min_length = min(len(inference_item), len(no_inference_item))
count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
self.assertGreaterEqual(full_match / len(result_0), 0.25)
self.assertGreaterEqual(count / len(result_0), 0.4)
class QWenVLTest(LLMTest, unittest.TestCase):
config_path: str = "./tests/fixtures/llm/predictor.yaml"
model_name_or_path: str = "__internal_testing__/tiny-fused-qwen"
model_class = QWenForCausalLM
def setUp(self) -> None:
super().setUp()
paddle.set_default_dtype("float32")
self.model_class.from_pretrained(self.model_name_or_path, dtype="float16").save_pretrained(self.output_dir)
AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
def test_forward(self):
self.disable_static()
config = AutoConfig.from_pretrained(self.output_dir)
config.quant_type = ""
paddle.set_default_dtype("float16")
# need to use dtype guard
model = QWenForQWenVLInferenceModel.from_pretrained(self.output_dir, config=config, dtype="float16")
batch = 1
seq = 31
max_len = 50
dtype = "float16"
input_ids = paddle.randint(0, 100, [batch, seq], dtype="int64")
image_features = paddle.randn([batch, 16, config.hidden_size], dtype="float16")
tgt_generation_mask = paddle.full([batch, 1, 1, max_len], 1, dtype=dtype)
img_pos = paddle.to_tensor([[0, 4, 21]], dtype="int64")
attention_mask = paddle.full([batch, 1, max_len, max_len], 0, dtype=dtype)
attention_mask[:, 0, :seq, :seq] = paddle.tril(paddle.ones(shape=(seq, seq), dtype=dtype))
position_ids = paddle.full([batch, seq], 0, dtype="int64")
for i in range(batch):
position_ids[i, :] = paddle.to_tensor([i for i in range(seq)], dtype="int64")
inputs = [
input_ids, # input_ids
image_features, # image_features
img_pos, # img_pos
attention_mask, # attention_mask
position_ids, # position_ids
paddle.full([batch, 1], 1.0, dtype="float32"), # penalty_score
paddle.full([batch, 1], 0.0, dtype="float32"), # frequency_score,
paddle.full([batch, 1], 0.0, dtype="float32"), # presence_score,
paddle.full([batch, 1], 1, dtype="int64"), # min_length,
paddle.full([batch, 1], max_len - seq, dtype="int64"), # max_length,
paddle.full([batch, 1], 1.0, dtype="float32"), # temperature,
paddle.full([batch, 1], 0.0, dtype="float32"), # top_p,
paddle.full([1], 151643, dtype="int64"), # eos_token_id,
paddle.full([batch, 1], seq, dtype="int32"), # seq_len_encoder,
paddle.full([batch, 1], seq, dtype="int32"), # seq_len_decoder,
paddle.full([batch, 1], 0, dtype="int64"), # step_idx,
paddle.full([batch, 1], False, dtype="bool"), # stop_flags,
paddle.full([batch, 1], -123, dtype="int64"), # tgt_ids can be be initialized arbitrarily
paddle.full([batch, 1], seq - 1, dtype="int64"), # tgt_pos,
tgt_generation_mask, # tgt_generation_mask,
paddle.full([batch, max_len], -100, dtype="int64"), # pre_ids, can be initialized arbitrarily
paddle.full([1], batch, dtype="int64"), # stop_nums, be batch
]
for i in range(config.num_hidden_layers):
tmp = paddle.rand(shape=[2, batch, 1, max_len, 64], dtype=dtype)
inputs.append(tmp)
model.eval()
model.generate_text_with_image_features(
input_ids=inputs[0],
image_features=inputs[1],
img_pos=inputs[2],
attention_mask=inputs[3],
position_ids=inputs[4],
penalty_score=inputs[5],
frequency_score=inputs[6],
presence_score=inputs[7],
min_length=inputs[8],
max_length=inputs[9],
temperature=inputs[10],
top_p=inputs[11],
eos_token_id=inputs[12],
seq_len_encoder=inputs[13],
seq_len_decoder=inputs[14],
step_idx=inputs[15],
stop_flags=inputs[16],
tgt_ids=inputs[17],
tgt_pos=inputs[18],
tgt_generation_mask=inputs[19],
pre_ids=inputs[20],
stop_nums=inputs[21],
cache_kvs=inputs[22:],
)
def test_export(self):
self.disable_static()
config = load_test_config(self.config_path, "inference-to-static")
config["model_name_or_path"] = self.model_name_or_path
config["output_path"] = self.output_dir
config["dtype"] = "float16"
config["inference_model"] = True
config["model_prefix"] = "qwen"
config["model_type"] = "qwen-img2txt"
with argv_context_guard(config):
from predict.export_model import main
main()