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1812 lines (1437 loc) 路 72.7 KB
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# coding=utf-8
# Copyright 2026 HuggingFace Inc.
#
# 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.
import gc
import pytest
import torch
from diffusers import (
AutoRoundConfig,
BitsAndBytesConfig,
GGUFQuantizationConfig,
NunchakuLiteQuantizationConfig,
NVIDIAModelOptConfig,
QuantoConfig,
SDNQConfig,
TorchAoConfig,
)
from diffusers.utils.import_utils import (
is_bitsandbytes_available,
is_gguf_available,
is_kernels_available,
is_nvidia_modelopt_available,
is_optimum_quanto_available,
is_torchao_available,
)
from ...testing_utils import (
assert_tensors_close,
backend_empty_cache,
backend_max_memory_allocated,
backend_reset_peak_memory_stats,
is_autoround,
is_bitsandbytes,
is_gguf,
is_modelopt,
is_quantization,
is_quanto,
is_sdnq,
is_torch_compile,
is_torchao,
require_accelerate,
require_accelerator,
require_auto_round_version_greater_or_equal,
require_bitsandbytes_version_greater,
require_gguf_version_greater_or_equal,
require_modelopt_version_greater_or_equal,
require_quanto,
require_sdnq,
require_torchao_version_greater_or_equal,
torch_device,
)
if is_nvidia_modelopt_available():
import modelopt.torch.quantization as mtq
if is_bitsandbytes_available():
import bitsandbytes as bnb
if is_optimum_quanto_available():
from optimum.quanto import QLinear
if is_gguf_available():
pass
if is_torchao_available():
import torchao.quantization as _torchao_quantization
class LoRALayer(torch.nn.Module):
"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only.
Taken from
https://github.com/huggingface/transformers/blob/566302686a71de14125717dea9a6a45b24d42b37/tests/quantization/bnb/test_4bit.py#L62C5-L78C77
"""
def __init__(self, module: torch.nn.Module, rank: int):
super().__init__()
self.module = module
self.adapter = torch.nn.Sequential(
torch.nn.Linear(module.in_features, rank, bias=False),
torch.nn.Linear(rank, module.out_features, bias=False),
)
small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
torch.nn.init.normal_(self.adapter[0].weight, std=small_std)
torch.nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def forward(self, input, *args, **kwargs):
return self.module(input, *args, **kwargs) + self.adapter(input)
@is_quantization
@require_accelerator
class QuantizationTesterMixin:
"""
Base mixin class providing common test implementations for quantization testing.
Backend-specific mixins should:
1. Implement _create_quantized_model(config_kwargs)
2. Implement _verify_if_layer_quantized(name, module, config_kwargs)
3. Define their config dict (e.g., BNB_CONFIGS, QUANTO_WEIGHT_TYPES, etc.)
4. Use @pytest.mark.parametrize to create tests that call the common test methods below
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained (e.g., {"subfolder": "transformer"})
Expected methods in test classes:
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
"""
def setup_method(self):
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
gc.collect()
backend_empty_cache(torch_device)
def _create_quantized_model(self, config_kwargs, **extra_kwargs):
"""
Create a quantized model with the given config kwargs.
Args:
config_kwargs: Quantization config parameters
**extra_kwargs: Additional kwargs to pass to from_pretrained (e.g., device_map, offload_folder)
"""
raise NotImplementedError("Subclass must implement _create_quantized_model")
def _verify_if_layer_quantized(self, name, module, config_kwargs):
raise NotImplementedError("Subclass must implement _verify_if_layer_quantized")
def _is_module_quantized(self, module):
"""
Check if a module is quantized. Returns True if quantized, False otherwise.
Default implementation tries _verify_if_layer_quantized and catches exceptions.
Subclasses can override for more efficient checking.
"""
try:
self._verify_if_layer_quantized("", module, {})
return True
except (AssertionError, AttributeError):
return False
def _load_unquantized_model(self):
kwargs = getattr(self, "pretrained_model_kwargs", {})
return self.model_class.from_pretrained(self.pretrained_model_name_or_path, **kwargs)
def _test_quantization_num_parameters(self, config_kwargs):
model = self._load_unquantized_model()
num_params = model.num_parameters()
model_quantized = self._create_quantized_model(config_kwargs)
num_params_quantized = model_quantized.num_parameters()
assert num_params == num_params_quantized, (
f"Parameter count mismatch: unquantized={num_params}, quantized={num_params_quantized}"
)
def _test_quantization_memory_footprint(self, config_kwargs, expected_memory_reduction=1.2):
model = self._load_unquantized_model()
mem = model.get_memory_footprint()
model_quantized = self._create_quantized_model(config_kwargs)
mem_quantized = model_quantized.get_memory_footprint()
ratio = mem / mem_quantized
assert ratio >= expected_memory_reduction, (
f"Memory ratio {ratio:.2f} is less than expected ({expected_memory_reduction}x). unquantized={mem}, quantized={mem_quantized}"
)
@torch.no_grad()
def _test_quantization_inference(self, config_kwargs):
model_quantized = self._create_quantized_model(config_kwargs)
model_quantized.to(torch_device)
inputs = self.get_dummy_inputs()
output = model_quantized(**inputs, return_dict=False)[0]
assert output is not None, "Model output is None"
assert not torch.isnan(output).any(), "Model output contains NaN"
def _test_quantization_dtype_assignment(self, config_kwargs):
model = self._create_quantized_model(config_kwargs)
with pytest.raises(ValueError):
model.to(torch.float16)
with pytest.raises(ValueError):
device_0 = f"{torch_device}:0"
model.to(device=device_0, dtype=torch.float16)
with pytest.raises(ValueError):
model.float()
with pytest.raises(ValueError):
model.half()
model.to(torch_device)
@torch.no_grad()
def _test_quantization_lora_inference(self, config_kwargs):
try:
from peft import LoraConfig
except ImportError:
pytest.skip("peft is not available")
from diffusers.loaders.peft import PeftAdapterMixin
if not issubclass(self.model_class, PeftAdapterMixin):
pytest.skip(f"PEFT is not supported for this model ({self.model_class.__name__})")
model = self._create_quantized_model(config_kwargs)
lora_config = LoraConfig(
r=4,
lora_alpha=4,
target_modules=["to_q", "to_k", "to_v", "to_out.0"],
init_lora_weights=False,
)
model.add_adapter(lora_config)
# Move LoRA adapter weights to device (they default to CPU)
model.to(torch_device)
inputs = self.get_dummy_inputs()
output = model(**inputs, return_dict=False)[0]
assert output is not None, "Model output is None with LoRA"
assert not torch.isnan(output).any(), "Model output contains NaN with LoRA"
@torch.no_grad()
def _test_quantization_serialization(self, config_kwargs, tmp_path, max_shard_size=None):
"""
Test that a quantized model can be saved and reloaded without changing its outputs.
Args:
config_kwargs: Quantization config parameters
tmp_path: Directory the model is serialized into
max_shard_size: When set, the checkpoint is sharded and the shard/index files are checked
"""
model = self._create_quantized_model(config_kwargs)
model.to(torch_device)
inputs = self.get_dummy_inputs()
expected_output = model(**inputs, return_dict=False)[0].detach().cpu()
save_kwargs = {"safe_serialization": True}
if max_shard_size is not None:
save_kwargs["max_shard_size"] = max_shard_size
model.save_pretrained(str(tmp_path), **save_kwargs)
del model
gc.collect()
backend_empty_cache(torch_device)
if max_shard_size is not None:
assert len(list(tmp_path.glob("*.safetensors"))) > 1, "Expected a sharded safe-serialization checkpoint."
assert any(path.name.endswith(".index.json") for path in tmp_path.iterdir()), (
"Expected an index file for sharded safe checkpoint."
)
model_loaded = self.model_class.from_pretrained(str(tmp_path), device_map=str(torch_device))
output = model_loaded(**inputs, return_dict=False)[0].detach().cpu()
assert_tensors_close(output, expected_output, rtol=1e-3, atol=1e-3)
def _test_quantization_config_serialization(self, config_kwargs):
"""
Test that the quantization config attached to a quantized model is serializable.
Args:
config_kwargs: Quantization config parameters
"""
model = self._create_quantized_model(config_kwargs)
assert "quantization_config" in model.config, "Missing quantization_config"
_ = model.config["quantization_config"].to_dict()
_ = model.config["quantization_config"].to_diff_dict()
_ = model.config["quantization_config"].to_json_string()
def _test_original_dtype(self, config_kwargs):
"""
Test that the dtype the model had before quantization is recorded on its config.
Args:
config_kwargs: Quantization config parameters
"""
model = self._create_quantized_model(config_kwargs)
assert "_pre_quantization_dtype" in model.config, "Missing _pre_quantization_dtype"
assert model.config["_pre_quantization_dtype"] in [
torch.float16,
torch.float32,
torch.bfloat16,
], f"Unexpected dtype: {model.config['_pre_quantization_dtype']}"
def _test_quantized_layers(self, config_kwargs):
model_fp = self._load_unquantized_model()
num_linear_layers = sum(1 for module in model_fp.modules() if isinstance(module, torch.nn.Linear))
model_quantized = self._create_quantized_model(config_kwargs)
num_fp32_modules = 0
if hasattr(model_quantized, "_keep_in_fp32_modules") and model_quantized._keep_in_fp32_modules:
for name, module in model_quantized.named_modules():
if isinstance(module, torch.nn.Linear):
if any(fp32_name in name for fp32_name in model_quantized._keep_in_fp32_modules):
num_fp32_modules += 1
expected_quantized_layers = num_linear_layers - num_fp32_modules
num_quantized_layers = 0
for name, module in model_quantized.named_modules():
if isinstance(module, torch.nn.Linear):
if hasattr(model_quantized, "_keep_in_fp32_modules") and model_quantized._keep_in_fp32_modules:
if any(fp32_name in name for fp32_name in model_quantized._keep_in_fp32_modules):
continue
self._verify_if_layer_quantized(name, module, config_kwargs)
num_quantized_layers += 1
assert num_quantized_layers > 0, (
f"No quantized layers found in model (expected {expected_quantized_layers} linear layers, {num_fp32_modules} kept in FP32)"
)
assert num_quantized_layers == expected_quantized_layers, (
f"Quantized layer count mismatch: expected {expected_quantized_layers}, got {num_quantized_layers} (total linear layers: {num_linear_layers}, FP32 modules: {num_fp32_modules})"
)
def _test_keep_modules_in_fp32(self, config_kwargs):
"""
Test that modules listed in the model's `_keep_in_fp32_modules` stay in FP32 after quantization.
Args:
config_kwargs: Quantization config parameters
"""
fp32_modules = getattr(self.model_class, "_keep_in_fp32_modules", None)
if not fp32_modules:
pytest.skip(f"{self.model_class.__name__} does not declare _keep_in_fp32_modules")
model = self._create_quantized_model(config_kwargs)
model.to(torch_device)
for name, module in model.named_modules():
if isinstance(module, torch.nn.Linear) and any(fp32_name in name for fp32_name in fp32_modules):
assert module.weight.dtype == torch.float32, (
f"Module {name} should be FP32 but is {module.weight.dtype}"
)
def _test_quantization_modules_to_not_convert(self, config_kwargs, modules_to_not_convert):
"""
Test that modules specified in modules_to_not_convert are not quantized.
Args:
config_kwargs: Base quantization config kwargs
modules_to_not_convert: List of module names to exclude from quantization
"""
# Create config with modules_to_not_convert
config_kwargs_with_exclusion = config_kwargs.copy()
config_kwargs_with_exclusion["modules_to_not_convert"] = modules_to_not_convert
model_with_exclusion = self._create_quantized_model(config_kwargs_with_exclusion)
# Find a module that should NOT be quantized
found_excluded = False
for name, module in model_with_exclusion.named_modules():
if isinstance(module, torch.nn.Linear):
# Check if this module is in the exclusion list
if any(excluded in name for excluded in modules_to_not_convert):
found_excluded = True
# This module should NOT be quantized
assert not self._is_module_quantized(module), (
f"Module {name} should not be quantized but was found to be quantized"
)
assert found_excluded, f"No linear layers found in excluded modules: {modules_to_not_convert}"
# Find a module that SHOULD be quantized (not in exclusion list)
found_quantized = False
for name, module in model_with_exclusion.named_modules():
if isinstance(module, torch.nn.Linear):
# Check if this module is NOT in the exclusion list
if not any(excluded in name for excluded in modules_to_not_convert):
if self._is_module_quantized(module):
found_quantized = True
break
assert found_quantized, "No quantized layers found outside of excluded modules"
# Compare memory footprint with fully quantized model
model_fully_quantized = self._create_quantized_model(config_kwargs)
mem_with_exclusion = model_with_exclusion.get_memory_footprint()
mem_fully_quantized = model_fully_quantized.get_memory_footprint()
assert mem_with_exclusion > mem_fully_quantized, (
f"Model with exclusions should be larger. With exclusion: {mem_with_exclusion}, fully quantized: {mem_fully_quantized}"
)
@torch.no_grad()
def _test_quantization_device_map(self, config_kwargs):
"""
Test that quantized models work correctly with device_map="auto".
Args:
config_kwargs: Base quantization config kwargs
"""
model = self._create_quantized_model(config_kwargs, device_map="auto")
assert hasattr(model, "hf_device_map"), "Model should have hf_device_map attribute"
assert model.hf_device_map is not None, "hf_device_map should not be None"
inputs = self.get_dummy_inputs()
output = model(**inputs, return_dict=False)[0]
assert output is not None, "Model output is None"
assert not torch.isnan(output).any(), "Model output contains NaN"
def _test_quantization_cpu_device_map(self, config_kwargs):
"""
Test that quantized models are placed on the CPU with device_map="cpu".
Args:
config_kwargs: Base quantization config kwargs
"""
model_quantized = self._create_quantized_model(config_kwargs, device_map="cpu")
assert hasattr(model_quantized, "hf_device_map"), "Model should have hf_device_map attribute"
assert model_quantized.hf_device_map is not None, "hf_device_map should not be None"
assert model_quantized.device == torch.device("cpu"), (
f"Model should be on CPU, but is on {model_quantized.device}"
)
@torch.no_grad()
def _test_dequantize(self, config_kwargs):
"""
Test that dequantize() converts quantized model back to standard linear layers.
Args:
config_kwargs: Quantization config parameters
"""
model = self._create_quantized_model(config_kwargs)
model.to(torch_device)
if not hasattr(model, "dequantize"):
pytest.skip("Model does not have dequantize method")
model.dequantize()
for name, module in model.named_modules():
if isinstance(module, torch.nn.Linear):
assert not self._is_module_quantized(module), f"Module {name} is still quantized after dequantize()"
inputs = self.get_dummy_inputs()
output = model(**inputs, return_dict=False)[0]
assert output is not None, "Model output is None after dequantization"
assert not torch.isnan(output).any(), "Model output contains NaN after dequantization"
def _test_quantization_training(self, config_kwargs):
"""
Test that quantized models can be used for training with LoRA-like adapters.
This test:
1. Freezes all model parameters
2. Casts small parameters (e.g., layernorm) to fp32 for stability
3. Adds LoRA adapters to attention layers
4. Runs forward and backward passes
5. Verifies gradients are computed correctly
Args:
config_kwargs: Quantization config parameters
"""
model = self._create_quantized_model(config_kwargs)
# Step 1: freeze all parameters
for param in model.parameters():
param.requires_grad = False
if param.ndim == 1:
# cast small parameters (e.g. layernorm) to fp32 for stability
param.data = param.data.to(torch.float32)
# Step 2: add adapters to attention layers
adapter_count = 0
for _, module in model.named_modules():
if "Attention" in repr(type(module)):
if hasattr(module, "to_k"):
module.to_k = LoRALayer(module.to_k, rank=4)
adapter_count += 1
if hasattr(module, "to_q"):
module.to_q = LoRALayer(module.to_q, rank=4)
adapter_count += 1
if hasattr(module, "to_v"):
module.to_v = LoRALayer(module.to_v, rank=4)
adapter_count += 1
if adapter_count == 0:
pytest.skip("No attention layers found in model for adapter training test")
# Step 3: run forward and backward pass
inputs = self.get_dummy_inputs()
# Use bfloat16 on XPU to avoid gradient underflow with quantized layers
autocast_dtype = torch.bfloat16 if torch_device == "xpu" else torch.float16
with torch.amp.autocast(torch_device, dtype=autocast_dtype):
out = model(**inputs, return_dict=False)[0]
out.norm().backward()
# Step 4: verify gradients are computed
for module in model.modules():
if isinstance(module, LoRALayer):
assert module.adapter[1].weight.grad is not None, "LoRA adapter gradient is None"
assert module.adapter[1].weight.grad.norm().item() > 0, "LoRA adapter gradient norm is zero"
@is_quantization
@is_bitsandbytes
@require_accelerator
@require_bitsandbytes_version_greater("0.43.2")
@require_accelerate
class BitsAndBytesConfigMixin:
"""
Base mixin providing BitsAndBytes quantization config and model creation.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained
"""
BNB_CONFIGS = {
"4bit_nf4": {
"load_in_4bit": True,
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_compute_dtype": torch.float16,
},
"4bit_fp4": {
"load_in_4bit": True,
"bnb_4bit_quant_type": "fp4",
"bnb_4bit_compute_dtype": torch.float16,
},
"8bit": {
"load_in_8bit": True,
},
}
BNB_EXPECTED_MEMORY_REDUCTIONS = {
"4bit_nf4": 3.0,
"4bit_fp4": 3.0,
"8bit": 1.5,
}
def _create_quantized_model(self, config_kwargs, **extra_kwargs):
config = BitsAndBytesConfig(**config_kwargs)
kwargs = getattr(self, "pretrained_model_kwargs", {}).copy()
kwargs["quantization_config"] = config
kwargs.update(extra_kwargs)
return self.model_class.from_pretrained(self.pretrained_model_name_or_path, **kwargs)
def _verify_if_layer_quantized(self, name, module, config_kwargs):
expected_weight_class = bnb.nn.Params4bit if config_kwargs.get("load_in_4bit") else bnb.nn.Int8Params
assert module.weight.__class__ == expected_weight_class, (
f"Layer {name} has weight type {module.weight.__class__}, expected {expected_weight_class}"
)
@is_bitsandbytes
@require_accelerator
@require_bitsandbytes_version_greater("0.43.2")
@require_accelerate
class BitsAndBytesTesterMixin(BitsAndBytesConfigMixin, QuantizationTesterMixin):
"""
Mixin class for testing BitsAndBytes quantization on models.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained (e.g., {"subfolder": "transformer"})
Expected methods to be implemented by subclasses:
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Optional class attributes:
- BNB_CONFIGS: Dict of config name -> BitsAndBytesConfig kwargs to test
Pytest mark: bitsandbytes
Use `pytest -m "not bitsandbytes"` to skip these tests
"""
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_quantization_num_parameters(self, config_name):
self._test_quantization_num_parameters(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_quantization_memory_footprint(self, config_name):
expected = BitsAndBytesConfigMixin.BNB_EXPECTED_MEMORY_REDUCTIONS.get(config_name, 1.2)
self._test_quantization_memory_footprint(
BitsAndBytesConfigMixin.BNB_CONFIGS[config_name], expected_memory_reduction=expected
)
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_quantization_inference(self, config_name):
self._test_quantization_inference(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@pytest.mark.parametrize("config_name", ["4bit_nf4"], ids=["4bit_nf4"])
def test_bnb_quantization_dtype_assignment(self, config_name):
self._test_quantization_dtype_assignment(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@pytest.mark.parametrize("config_name", ["4bit_nf4"], ids=["4bit_nf4"])
def test_bnb_quantization_lora_inference(self, config_name):
self._test_quantization_lora_inference(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@pytest.mark.parametrize("config_name", ["4bit_nf4"], ids=["4bit_nf4"])
def test_bnb_quantization_serialization(self, config_name, tmp_path):
self._test_quantization_serialization(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name], tmp_path)
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_quantized_layers(self, config_name):
self._test_quantized_layers(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_quantization_config_serialization(self, config_name):
self._test_quantization_config_serialization(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
def test_bnb_original_dtype(self):
self._test_original_dtype(BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"])
def test_bnb_keep_modules_in_fp32(self):
self._test_keep_modules_in_fp32(BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"])
def test_bnb_modules_to_not_convert(self):
"""Test that modules_to_not_convert parameter works correctly."""
modules_to_exclude = getattr(self, "modules_to_not_convert_for_test", None)
if modules_to_exclude is None:
pytest.skip("modules_to_not_convert_for_test not defined for this model")
self._test_quantization_modules_to_not_convert(
BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"], modules_to_exclude
)
@pytest.mark.parametrize("config_name", ["4bit_nf4", "8bit"], ids=["4bit_nf4", "8bit"])
def test_bnb_device_map(self, config_name):
"""Test that device_map='auto' works correctly with quantization."""
self._test_quantization_device_map(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
def test_bnb_dequantize(self):
"""Test that dequantize() works correctly."""
self._test_dequantize(BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"])
def test_bnb_training(self):
"""Test that quantized models can be used for training with adapters."""
self._test_quantization_training(BitsAndBytesConfigMixin.BNB_CONFIGS["4bit_nf4"])
@pytest.mark.parametrize(
"config_name",
list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
ids=list(BitsAndBytesConfigMixin.BNB_CONFIGS.keys()),
)
def test_bnb_cpu_device_map(self, config_name):
"""Test that device_map='cpu' works correctly with quantization."""
self._test_quantization_cpu_device_map(BitsAndBytesConfigMixin.BNB_CONFIGS[config_name])
@is_quantization
@is_quanto
@require_quanto
@require_accelerate
@require_accelerator
class QuantoConfigMixin:
"""
Base mixin providing Quanto quantization config and model creation.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained
"""
QUANTO_WEIGHT_TYPES = {
"float8": {"weights_dtype": "float8"},
"int8": {"weights_dtype": "int8"},
"int4": {"weights_dtype": "int4"},
"int2": {"weights_dtype": "int2"},
}
QUANTO_EXPECTED_MEMORY_REDUCTIONS = {
"float8": 1.5,
"int8": 1.5,
"int4": 3.0,
"int2": 7.0,
}
def _create_quantized_model(self, config_kwargs, **extra_kwargs):
config = QuantoConfig(**config_kwargs)
kwargs = getattr(self, "pretrained_model_kwargs", {}).copy()
kwargs["quantization_config"] = config
kwargs.update(extra_kwargs)
return self.model_class.from_pretrained(self.pretrained_model_name_or_path, **kwargs)
def _verify_if_layer_quantized(self, name, module, config_kwargs):
assert isinstance(module, QLinear), f"Layer {name} is not QLinear, got {type(module)}"
def _test_quantization_memory_footprint(self, config_kwargs, expected_memory_reduction=1.2):
"""Override to use max_memory_allocated for Quanto (get_memory_footprint doesn't reflect quantized _data)."""
# Measure unquantized model memory
backend_reset_peak_memory_stats(torch_device)
backend_empty_cache(torch_device)
model = self._load_unquantized_model()
model.to(torch_device)
mem = backend_max_memory_allocated(torch_device)
del model
gc.collect()
backend_empty_cache(torch_device)
# Measure quantized model memory
backend_reset_peak_memory_stats(torch_device)
model_quantized = self._create_quantized_model(config_kwargs)
model_quantized.to(torch_device)
mem_quantized = backend_max_memory_allocated(torch_device)
ratio = mem / mem_quantized
assert ratio >= expected_memory_reduction, (
f"Memory ratio {ratio:.2f} is less than expected ({expected_memory_reduction}x). unquantized={mem}, quantized={mem_quantized}"
)
@is_quanto
@require_quanto
@require_accelerate
@require_accelerator
class QuantoTesterMixin(QuantoConfigMixin, QuantizationTesterMixin):
"""
Mixin class for testing Quanto quantization on models.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained (e.g., {"subfolder": "transformer"})
Expected methods to be implemented by subclasses:
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Optional class attributes:
- QUANTO_WEIGHT_TYPES: Dict of weight_type_name -> qtype
Pytest mark: quanto
Use `pytest -m "not quanto"` to skip these tests
"""
@pytest.mark.parametrize(
"weight_type_name",
list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
ids=list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
)
def test_quanto_quantization_num_parameters(self, weight_type_name):
self._test_quantization_num_parameters(QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name])
@pytest.mark.parametrize(
"weight_type_name",
list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
ids=list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
)
def test_quanto_quantization_memory_footprint(self, weight_type_name):
expected = QuantoConfigMixin.QUANTO_EXPECTED_MEMORY_REDUCTIONS.get(weight_type_name, 1.2)
self._test_quantization_memory_footprint(
QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name], expected_memory_reduction=expected
)
@pytest.mark.parametrize(
"weight_type_name",
list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
ids=list(QuantoConfigMixin.QUANTO_WEIGHT_TYPES.keys()),
)
def test_quanto_quantization_inference(self, weight_type_name):
self._test_quantization_inference(QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name])
@pytest.mark.parametrize("weight_type_name", ["int8"], ids=["int8"])
def test_quanto_quantized_layers(self, weight_type_name):
self._test_quantized_layers(QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name])
@pytest.mark.parametrize("weight_type_name", ["int8"], ids=["int8"])
def test_quanto_quantization_lora_inference(self, weight_type_name):
self._test_quantization_lora_inference(QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name])
@pytest.mark.parametrize("weight_type_name", ["int8"], ids=["int8"])
def test_quanto_quantization_serialization(self, weight_type_name, tmp_path):
self._test_quantization_serialization(QuantoConfigMixin.QUANTO_WEIGHT_TYPES[weight_type_name], tmp_path)
def test_quanto_modules_to_not_convert(self):
"""Test that modules_to_not_convert parameter works correctly."""
modules_to_exclude = getattr(self, "modules_to_not_convert_for_test", None)
if modules_to_exclude is None:
pytest.skip("modules_to_not_convert_for_test not defined for this model")
self._test_quantization_modules_to_not_convert(
QuantoConfigMixin.QUANTO_WEIGHT_TYPES["int8"], modules_to_exclude
)
def test_quanto_device_map(self):
"""Test that device_map='auto' works correctly with quantization."""
self._test_quantization_device_map(QuantoConfigMixin.QUANTO_WEIGHT_TYPES["int8"])
def test_quanto_dequantize(self):
"""Test that dequantize() works correctly."""
self._test_dequantize(QuantoConfigMixin.QUANTO_WEIGHT_TYPES["int8"])
@is_quantization
@is_torchao
@require_accelerator
@require_torchao_version_greater_or_equal("0.7.0")
class TorchAoConfigMixin:
"""
Base mixin providing TorchAO quantization config and model creation.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained
"""
TORCHAO_QUANT_TYPES = {
"int4wo": "Int4WeightOnlyConfig",
"int8wo": "Int8WeightOnlyConfig",
"int8dq": "Int8DynamicActivationInt8WeightConfig",
}
TORCHAO_EXPECTED_MEMORY_REDUCTIONS = {
"int4wo": 1.8,
"int8wo": 1.5,
"int8dq": 1.5,
}
@staticmethod
def _get_quant_config(config_name, modules_to_not_convert=None):
config_cls = getattr(_torchao_quantization, config_name)
config_kwargs = {"version": 2}
# version=2 int4 defaults to the "plain" packing format, which routes through the
# fbgemm/mslk Int4Tensor kernels. Pin the packing format to the tinygemm
# (_convert_weight_to_int4pack) path on CUDA and plain_int32 on Intel XPU so the tests
# don't require those extra kernels to be installed.
if config_name == "Int4WeightOnlyConfig":
if torch_device == "xpu":
config_kwargs["int4_packing_format"] = "plain_int32"
elif torch_device == "cuda":
config_kwargs["int4_packing_format"] = "tile_packed_to_4d"
return TorchAoConfig(config_cls(**config_kwargs), modules_to_not_convert=modules_to_not_convert)
def _create_quantized_model(self, config_name, modules_to_not_convert=None, **extra_kwargs):
config = self._get_quant_config(config_name, modules_to_not_convert=modules_to_not_convert)
kwargs = getattr(self, "pretrained_model_kwargs", {}).copy()
kwargs["quantization_config"] = config
kwargs["device_map"] = str(torch_device)
kwargs.update(extra_kwargs)
return self.model_class.from_pretrained(self.pretrained_model_name_or_path, **kwargs)
def _verify_if_layer_quantized(self, name, module, config_kwargs):
from torchao.utils import TorchAOBaseTensor
assert isinstance(module, torch.nn.Linear), f"Layer {name} is not Linear, got {type(module)}"
assert isinstance(module.weight, TorchAOBaseTensor), (
f"Layer {name} weight is {type(module.weight)}, expected TorchAOBaseTensor"
)
# int4wo requires CUDA or XPU ops (_convert_weight_to_int4pack)
_int4wo_skip = pytest.mark.skipif(
torch_device not in ["cuda", "xpu"], reason="int4wo quantization requires CUDA or XPU"
)
# The CUDA int4 tinygemm path (Int4TilePackedTo4dTensor) does not implement aten.dequantize.
_int4wo_dequantize_skip = pytest.mark.skip(
reason="int4wo tinygemm packing (Int4TilePackedTo4dTensor) does not support dequantize"
)
@is_torchao
@require_accelerator
@require_torchao_version_greater_or_equal("0.7.0")
class TorchAoTesterMixin(TorchAoConfigMixin, QuantizationTesterMixin):
"""
Mixin class for testing TorchAO quantization on models.
Expected class attributes:
- model_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_pretrained (e.g., {"subfolder": "transformer"})
Expected methods to be implemented by subclasses:
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Optional class attributes:
- TORCHAO_QUANT_TYPES: Dict of quantization type strings to test
Pytest mark: torchao
Use `pytest -m "not torchao"` to skip these tests
"""
@pytest.mark.parametrize(
"quant_type",
[
pytest.param("int4wo", marks=_int4wo_skip),
"int8wo",
"int8dq",
],
ids=["int4wo", "int8wo", "int8dq"],
)
def test_torchao_quantization_num_parameters(self, quant_type):
self._test_quantization_num_parameters(TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type])
@pytest.mark.parametrize(
"quant_type",
[
pytest.param("int4wo", marks=_int4wo_skip),
"int8wo",
"int8dq",
],
ids=["int4wo", "int8wo", "int8dq"],
)
def test_torchao_quantization_memory_footprint(self, quant_type):
expected = TorchAoConfigMixin.TORCHAO_EXPECTED_MEMORY_REDUCTIONS.get(quant_type, 1.2)
self._test_quantization_memory_footprint(
TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type], expected_memory_reduction=expected
)
@pytest.mark.parametrize(
"quant_type",
[
pytest.param("int4wo", marks=[_int4wo_skip, _int4wo_dequantize_skip]),
"int8wo",
"int8dq",
],
ids=["int4wo", "int8wo", "int8dq"],
)
def test_torchao_quantization_inference(self, quant_type):
self._test_quantization_inference(TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type])
@pytest.mark.parametrize("quant_type", ["int8wo"], ids=["int8wo"])
def test_torchao_quantized_layers(self, quant_type):
self._test_quantized_layers(TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type])
@pytest.mark.parametrize("quant_type", ["int8wo"], ids=["int8wo"])
def test_torchao_quantization_lora_inference(self, quant_type):
self._test_quantization_lora_inference(TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type])
@pytest.mark.parametrize("quant_type", ["int8wo"], ids=["int8wo"])
@require_torchao_version_greater_or_equal("0.16.0")
def test_torchao_quantization_serialization(self, quant_type, tmp_path):
self._test_quantization_serialization(TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type], tmp_path)
@pytest.mark.parametrize("quant_type", ["int8dq"], ids=["int8dq"])
@require_torchao_version_greater_or_equal("0.16.0")
def test_torchao_quantization_sharded_serialization(self, quant_type, tmp_path):
self._test_quantization_serialization(
TorchAoConfigMixin.TORCHAO_QUANT_TYPES[quant_type], tmp_path, max_shard_size="16KB"
)
def test_torchao_modules_to_not_convert(self):
"""Test that modules_to_not_convert parameter works correctly."""
modules_to_exclude = getattr(self, "modules_to_not_convert_for_test", None)
if modules_to_exclude is None:
pytest.skip("modules_to_not_convert_for_test not defined for this model")
# TorchAoConfig takes modules_to_not_convert directly (not inside the quant_type config),
# so this can't reuse the dict-based QuantizationTesterMixin helper.