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[tests] enable bnb tests on xpu #11001

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Merged
merged 7 commits into from
Mar 19, 2025
Merged

[tests] enable bnb tests on xpu #11001

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dada34c
enable bnb on xpu
faaany Mar 7, 2025
3e13347
add 2 more cases
faaany Mar 14, 2025
26b5916
Merge branch 'main' into enable-bnb
faaany Mar 14, 2025
7c95eeb
add missing change
faaany Mar 14, 2025
ff85a9c
add missing change
faaany Mar 14, 2025
9802231
Merge branch 'enable-bnb' of https://github.com/faaany/diffusers into…
faaany Mar 14, 2025
ba35924
add one more
faaany Mar 14, 2025
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25 changes: 18 additions & 7 deletions src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def __init__(self, quantization_config, **kwargs):
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules

def validate_environment(self, *args, **kwargs):
if not torch.cuda.is_available():
if not (torch.cuda.is_available() or torch.xpu.is_available()):
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
Expand Down Expand Up @@ -237,11 +237,15 @@ def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":

def update_device_map(self, device_map):
if device_map is None:
device_map = {"": f"cuda:{torch.cuda.current_device()}"}
if torch.xpu.is_available():
current_device = f"xpu:{torch.xpu.current_device()}"
else:
current_device = f"cuda:{torch.cuda.current_device()}"
device_map = {"": current_device}
logger.info(
"The device_map was not initialized. "
"Setting device_map to {"
": f`cuda:{torch.cuda.current_device()}`}. "
": {current_device}}. "
"If you want to use the model for inference, please set device_map ='auto' "
)
return device_map
Expand Down Expand Up @@ -311,7 +315,10 @@ def _dequantize(self, model):
logger.info(
"Model was found to be on CPU (could happen as a result of `enable_model_cpu_offload()`). So, moving it to GPU. After dequantization, will move the model back to CPU again to preserve the previous device."
)
model.to(torch.cuda.current_device())
if torch.xpu.is_available():
model.to(torch.xpu.current_device())
else:
model.to(torch.cuda.current_device())

model = dequantize_and_replace(
model, self.modules_to_not_convert, quantization_config=self.quantization_config
Expand Down Expand Up @@ -342,7 +349,7 @@ def __init__(self, quantization_config, **kwargs):
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules

def validate_environment(self, *args, **kwargs):
if not torch.cuda.is_available():
if not (torch.cuda.is_available() or torch.xpu.is_available()):
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
Expand Down Expand Up @@ -401,11 +408,15 @@ def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.update_device_map
def update_device_map(self, device_map):
if device_map is None:
device_map = {"": f"cuda:{torch.cuda.current_device()}"}
if torch.xpu.is_available():
current_device = f"xpu:{torch.xpu.current_device()}"
else:
current_device = f"cuda:{torch.cuda.current_device()}"
device_map = {"": current_device}
logger.info(
"The device_map was not initialized. "
"Setting device_map to {"
": f`cuda:{torch.cuda.current_device()}`}. "
": {current_device}}. "
"If you want to use the model for inference, please set device_map ='auto' "
)
return device_map
Expand Down
35 changes: 18 additions & 17 deletions tests/quantization/bnb/test_4bit.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
from diffusers.utils import is_accelerate_version, logging
from diffusers.utils.testing_utils import (
CaptureLogger,
backend_empty_cache,
is_bitsandbytes_available,
is_torch_available,
is_transformers_available,
Expand All @@ -34,7 +35,7 @@
require_accelerate,
require_bitsandbytes_version_greater,
require_torch,
require_torch_gpu,
require_torch_accelerator,
require_transformers_version_greater,
slow,
torch_device,
Expand Down Expand Up @@ -86,7 +87,7 @@ def forward(self, input, *args, **kwargs):
@require_bitsandbytes_version_greater("0.43.2")
@require_accelerate
@require_torch
@require_torch_gpu
@require_torch_accelerator
@slow
class Base4bitTests(unittest.TestCase):
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
Expand All @@ -102,13 +103,13 @@ class Base4bitTests(unittest.TestCase):

def get_dummy_inputs(self):
prompt_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/prompt_embeds.pt"
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/prompt_embeds.pt", torch_device
)
pooled_prompt_embeds = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/pooled_prompt_embeds.pt"
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/pooled_prompt_embeds.pt", torch_device
)
latent_model_input = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/latent_model_input.pt"
"https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/latent_model_input.pt", torch_device
)

input_dict_for_transformer = {
Expand All @@ -124,7 +125,7 @@ def get_dummy_inputs(self):
class BnB4BitBasicTests(Base4bitTests):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

# Models
self.model_fp16 = SD3Transformer2DModel.from_pretrained(
Expand All @@ -144,7 +145,7 @@ def tearDown(self):
del self.model_4bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quantization_num_parameters(self):
r"""
Expand Down Expand Up @@ -256,9 +257,9 @@ def test_device_assignment(self):
self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before)

# Move back to CUDA device
for device in [0, "cuda", "cuda:0", "call()"]:
for device in [0, f"{torch_device}", f"{torch_device}:0", "call()"]:
if device == "call()":
self.model_4bit.cuda(0)
self.model_4bit.to(f"{torch_device}:0")
else:
self.model_4bit.to(device)
self.assertEqual(self.model_4bit.device, torch.device(0))
Expand All @@ -276,7 +277,7 @@ def test_device_and_dtype_assignment(self):

with self.assertRaises(ValueError):
# Tries with a `device` and `dtype`
self.model_4bit.to(device="cuda:0", dtype=torch.float16)
self.model_4bit.to(device=f"{torch_device}:0", dtype=torch.float16)

with self.assertRaises(ValueError):
# Tries with a cast
Expand All @@ -287,7 +288,7 @@ def test_device_and_dtype_assignment(self):
self.model_4bit.half()

# This should work
self.model_4bit.to("cuda")
self.model_4bit.to(torch_device)

# Test if we did not break anything
self.model_fp16 = self.model_fp16.to(dtype=torch.float32, device=torch_device)
Expand All @@ -311,7 +312,7 @@ def test_device_and_dtype_assignment(self):
_ = self.model_fp16.float()

# Check that this does not throw an error
_ = self.model_fp16.cuda()
_ = self.model_fp16.to(torch_device)

def test_bnb_4bit_wrong_config(self):
r"""
Expand Down Expand Up @@ -402,7 +403,7 @@ def test_training(self):
class SlowBnb4BitTests(Base4bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
Expand All @@ -421,7 +422,7 @@ def tearDown(self):
del self.pipeline_4bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quality(self):
output = self.pipeline_4bit(
Expand Down Expand Up @@ -491,7 +492,7 @@ def test_moving_to_cpu_throws_warning(self):
reason="Test will pass after https://github.com/huggingface/accelerate/pull/3223 is in a release.",
strict=True,
)
def test_pipeline_cuda_placement_works_with_nf4(self):
def test_pipeline_device_placement_works_with_nf4(self):
transformer_nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
Expand Down Expand Up @@ -522,7 +523,7 @@ def test_pipeline_cuda_placement_works_with_nf4(self):
transformer=transformer_4bit,
text_encoder_3=text_encoder_3_4bit,
torch_dtype=torch.float16,
).to("cuda")
).to(torch_device)

# Check if inference works.
_ = pipeline_4bit("table", max_sequence_length=20, num_inference_steps=2)
Expand Down Expand Up @@ -685,7 +686,7 @@ def test_lora_loading(self):
class BaseBnb4BitSerializationTests(Base4bitTests):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_serialization(self, quant_type="nf4", double_quant=True, safe_serialization=True):
r"""
Expand Down
29 changes: 15 additions & 14 deletions tests/quantization/bnb/test_mixed_int8.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
from diffusers.utils import is_accelerate_version
from diffusers.utils.testing_utils import (
CaptureLogger,
backend_empty_cache,
is_bitsandbytes_available,
is_torch_available,
is_transformers_available,
Expand All @@ -40,7 +41,7 @@
require_bitsandbytes_version_greater,
require_peft_version_greater,
require_torch,
require_torch_gpu,
require_torch_accelerator,
require_transformers_version_greater,
slow,
torch_device,
Expand Down Expand Up @@ -92,7 +93,7 @@ def forward(self, input, *args, **kwargs):
@require_bitsandbytes_version_greater("0.43.2")
@require_accelerate
@require_torch
@require_torch_gpu
@require_torch_accelerator
@slow
class Base8bitTests(unittest.TestCase):
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
Expand Down Expand Up @@ -130,7 +131,7 @@ def get_dummy_inputs(self):
class BnB8bitBasicTests(Base8bitTests):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

# Models
self.model_fp16 = SD3Transformer2DModel.from_pretrained(
Expand All @@ -146,7 +147,7 @@ def tearDown(self):
del self.model_8bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quantization_num_parameters(self):
r"""
Expand Down Expand Up @@ -274,7 +275,7 @@ def test_device_and_dtype_assignment(self):

with self.assertRaises(ValueError):
# Tries with a `device`
self.model_8bit.to(torch.device("cuda:0"))
self.model_8bit.to(torch.device(f"{torch_device}:0"))

with self.assertRaises(ValueError):
# Tries with a `device`
Expand Down Expand Up @@ -312,7 +313,7 @@ def test_device_and_dtype_assignment(self):
class Bnb8bitDeviceTests(Base8bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True)
self.model_8bit = SanaTransformer2DModel.from_pretrained(
Expand All @@ -326,7 +327,7 @@ def tearDown(self):
del self.model_8bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_buffers_device_assignment(self):
for buffer_name, buffer in self.model_8bit.named_buffers():
Expand All @@ -340,7 +341,7 @@ def test_buffers_device_assignment(self):
class BnB8bitTrainingTests(Base8bitTests):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True)
self.model_8bit = SD3Transformer2DModel.from_pretrained(
Expand Down Expand Up @@ -384,7 +385,7 @@ def test_training(self):
class SlowBnb8bitTests(Base8bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True)
model_8bit = SD3Transformer2DModel.from_pretrained(
Expand All @@ -399,7 +400,7 @@ def tearDown(self):
del self.pipeline_8bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quality(self):
output = self.pipeline_8bit(
Expand Down Expand Up @@ -611,7 +612,7 @@ def get_dummy_tensor_inputs(device=None, seed: int = 0):
class SlowBnb8bitFluxTests(Base8bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

model_id = "hf-internal-testing/flux.1-dev-int8-pkg"
t5_8bit = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder_2")
Expand All @@ -628,7 +629,7 @@ def tearDown(self):
del self.pipeline_8bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quality(self):
# keep the resolution and max tokens to a lower number for faster execution.
Expand Down Expand Up @@ -675,7 +676,7 @@ def test_lora_loading(self):
class BaseBnb8bitSerializationTests(Base8bitTests):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
Expand All @@ -688,7 +689,7 @@ def tearDown(self):
del self.model_0

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_serialization(self):
r"""
Expand Down