SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

enable autoround cases on XPU (#38167)

Browse Source 
* enable autoround cases on XPU

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* fix style

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

---------

Signed-off-by: Matrix Yao <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-05-16 17:08:35 +08:00
committed by GitHub
parent 0f77ca72ca
commit 34c1e29cdd
2 changed files with 31 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
tests/quantization/autoround/test_auto_round.py
View File

@@ -17,11 +17,14 @@ import unittest
from transformers import AutoModelForCausalLM, AutoRoundConfig, AutoTokenizer
from transformers.testing_utils import (
backend_empty_cache,
backend_synchronize,
require_accelerate,
require_auto_round,
require_intel_extension_for_pytorch,
require_torch_accelerator,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_multi_accelerator,
slow,
torch_device,
)
@@ -33,7 +36,7 @@ if is_torch_available():
@slow
@require_torch_gpu
@require_torch_accelerator
@require_auto_round
@require_accelerate
class AutoRoundTest(unittest.TestCase):
@@ -50,8 +53,11 @@ class AutoRoundTest(unittest.TestCase):
EXPECTED_OUTPUTS.add(
"There is a girl who likes adventure, and she has been exploring the world for many years. She has visited every country in Europe and has even traveled to some of the most remote parts of Africa. She enjoys hiking through the mountains and discovering"
)
EXPECTED_OUTPUTS.add(
"There is a girl who likes adventure, and she has been exploring the world for many years. She has visited every country in Europe and has even traveled to some of the most remote parts of Africa. She has also climbed mountains and explored caves"
)
device_map = "cuda"
device_map = torch_device
# called only once for all test in this class
@classmethod
@@ -59,7 +65,7 @@ class AutoRoundTest(unittest.TestCase):
"""
Setup quantized model
"""
torch.cuda.synchronize()
backend_synchronize(torch_device)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name, device_map=cls.device_map, torch_dtype=torch.float16
@@ -67,7 +73,7 @@ class AutoRoundTest(unittest.TestCase):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
gc.collect()
def test_quantized_model(self):
@@ -128,14 +134,15 @@ class AutoRoundTest(unittest.TestCase):
)
quantized_model.save_pretrained(tmpdirname)
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map="cuda")
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=torch_device)
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
output = model.generate(**input_ids, max_new_tokens=40, do_sample=False)
self.assertIn(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
output_tokens = self.tokenizer.decode(output[0], skip_special_tokens=True)
self.assertIn(output_tokens, self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
@require_torch_multi_accelerator
def test_quantized_model_multi_gpu(self):
"""
Simple test that checks if the quantized model is working properly with multiple GPUs
@@ -159,7 +166,7 @@ class AutoRoundTest(unittest.TestCase):
quantization_config = AutoRoundConfig()
model = AutoModelForCausalLM.from_pretrained(
model_name, device_map="cuda", quantization_config=quantization_config, torch_dtype="auto"
model_name, device_map=torch_device, quantization_config=quantization_config, torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -185,6 +192,7 @@ class AutoRoundTest(unittest.TestCase):
inputs = tokenizer(text, return_tensors="pt").to(model.device)
tokenizer.decode(model.generate(**inputs, max_new_tokens=5)[0])
@require_torch_gpu
def test_mixed_bits(self):
"""
Simple test that checks if auto-round work properly with mixed bits
@@ -203,7 +211,9 @@ class AutoRoundTest(unittest.TestCase):
autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym, layer_config=layer_config)
with tempfile.TemporaryDirectory() as tmpdirname:
autoround.quantize_and_save(output_dir=tmpdirname)
model = AutoModelForCausalLM.from_pretrained(tmpdirname, torch_dtype=torch.float16, device_map="cuda")
model = AutoModelForCausalLM.from_pretrained(
tmpdirname, torch_dtype=torch.float16, device_map=torch_device
)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
tokenizer.decode(model.generate(**inputs, max_new_tokens=5)[0])