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switch to device agnostic device calling for test cases (#38247)

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* use device agnostic APIs in test cases

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

* fix style

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

* add one more

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

* xpu now supports integer device id, aligning to CUDA behaviors

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

* update to use device_properties

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

* fix style

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

* update comment

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

* fix comments

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>
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yao Matrix
2025-05-26 16:18:53 +08:00
committed by GitHub
parent cba279f46c
commit a5a0c7b888
39 changed files with 259 additions and 389 deletions
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19
tests/quantization/bitnet_integration/test_bitnet.py
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@@ -23,6 +23,7 @@ from transformers import (
OPTForCausalLM,
)
from transformers.testing_utils import (
backend_empty_cache,
require_accelerate,
require_torch_gpu,
slow,
@@ -56,7 +57,6 @@ class BitNetQuantConfigTest(unittest.TestCase):
@require_accelerate
class BitNetTest(unittest.TestCase):
model_name = "HF1BitLLM/Llama3-8B-1.58-100B-tokens"
device = "cuda"
# called only once for all test in this class
@classmethod
@@ -65,11 +65,11 @@ class BitNetTest(unittest.TestCase):
Load the model
"""
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(cls.model_name, device_map=cls.device)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(cls.model_name, device_map=torch_device)
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
gc.collect()
def test_replace_with_bitlinear(self):
@@ -100,7 +100,7 @@ class BitNetTest(unittest.TestCase):
"""
input_text = "What are we having for dinner?"
expected_output = "What are we having for dinner? What are we going to do for fun this weekend?"
input_ids = self.tokenizer(input_text, return_tensors="pt").to("cuda")
input_ids = self.tokenizer(input_text, return_tensors="pt").to(torch_device)
output = self.quantized_model.generate(**input_ids, max_new_tokens=11, do_sample=False)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), expected_output)
@@ -127,7 +127,7 @@ class BitNetTest(unittest.TestCase):
from transformers.integrations import BitLinear
layer = BitLinear(in_features=4, out_features=2, bias=False, dtype=torch.float32)
layer.to(self.device)
layer.to(torch_device)
input_tensor = torch.tensor([1.0, -1.0, -1.0, 1.0], dtype=torch.float32).to(torch_device)
@@ -202,9 +202,8 @@ class BitNetTest(unittest.TestCase):
class BitNetSerializationTest(unittest.TestCase):
def test_model_serialization(self):
model_name = "HF1BitLLM/Llama3-8B-1.58-100B-tokens"
device = "cuda"
quantized_model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device)
input_tensor = torch.zeros((1, 8), dtype=torch.int32, device=device)
quantized_model = AutoModelForCausalLM.from_pretrained(model_name, device_map=torch_device)
input_tensor = torch.zeros((1, 8), dtype=torch.int32, device=torch_device)
with torch.no_grad():
logits_ref = quantized_model.forward(input_tensor).logits
@@ -215,10 +214,10 @@ class BitNetSerializationTest(unittest.TestCase):
# Remove old model
del quantized_model
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
# Load and check if the logits match
model_loaded = AutoModelForCausalLM.from_pretrained("quant_model", device_map=device)
model_loaded = AutoModelForCausalLM.from_pretrained("quant_model", device_map=torch_device)
with torch.no_grad():
logits_loaded = model_loaded.forward(input_tensor).logits