Merge branch 'main' into cache-non-lora-outputs

Author: sayakpaul

docs/source/en/quantization/torchao.md

@@ -11,69 +11,96 @@ specific language governing permissions and limitations under the License. -->
 
 # torchao
 
-[TorchAO](https://github.com/pytorch/ao) is an architecture optimization library for PyTorch. It provides high-performance dtypes, optimization techniques, and kernels for inference and training, featuring composability with native PyTorch features like [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html), FullyShardedDataParallel (FSDP), and more.
+[torchao](https://github.com/pytorch/ao) provides high-performance dtypes and optimizations based on quantization and sparsity for inference and training PyTorch models. It is supported for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers.
 
-Before you begin, make sure you have Pytorch 2.5+ and TorchAO installed.
+Make sure Pytorch 2.5+ and torchao are installed with the command below.
 
 ```bash
-pip install -U torch torchao
+uv pip install -U torch torchao
 ```
 
+Each quantization dtype is available as a separate instance of a [AOBaseConfig](https://docs.pytorch.org/ao/main/api_ref_quantization.html#inference-apis-for-quantize) class. This provides more flexible configuration options by exposing more available arguments.
 
-Quantize a model by passing [`TorchAoConfig`] to [`~ModelMixin.from_pretrained`] (you can also load pre-quantized models). This works for any model in any modality, as long as it supports loading with [Accelerate](https://hf.co/docs/accelerate/index) and contains `torch.nn.Linear` layers.
+Pass the `AOBaseConfig` of a quantization dtype, like [Int4WeightOnlyConfig](https://docs.pytorch.org/ao/main/generated/torchao.quantization.Int4WeightOnlyConfig) to [`TorchAoConfig`] in [`~ModelMixin.from_pretrained`].
 
-The example below only quantizes the weights to int8.
-
-```python
+```py
 import torch
-from diffusers import FluxPipeline, AutoModel, TorchAoConfig
-
-model_id = "black-forest-labs/FLUX.1-dev"
-dtype = torch.bfloat16
+from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig
+from torchao.quantization import Int8WeightOnlyConfig
 
-quantization_config = TorchAoConfig("int8wo")
-transformer = AutoModel.from_pretrained(
-    model_id,
-    subfolder="transformer",
-    quantization_config=quantization_config,
-    torch_dtype=dtype,
+pipeline_quant_config = PipelineQuantizationConfig(
+    quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig(group_size=128)))}
 )
-pipe = FluxPipeline.from_pretrained(
-    model_id,
-    transformer=transformer,
-    torch_dtype=dtype,
+pipeline = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    quantzation_config=pipeline_quant_config,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda"
 )
-pipe.to("cuda")
+```
 
-# Without quantization: ~31.447 GB
-# With quantization: ~20.40 GB
-print(f"Pipeline memory usage: {torch.cuda.max_memory_reserved() / 1024**3:.3f} GB")
+For simple use cases, you could also provide a string identifier in [`TorchAo`] as shown below.
 
-prompt = "A cat holding a sign that says hello world"
-image = pipe(
-    prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512
-).images[0]
-image.save("output.png")
+```py
+import torch
+from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig
+
+pipeline_quant_config = PipelineQuantizationConfig(
+    quant_mapping={"transformer": TorchAoConfig("int8wo")}
+)
+pipeline = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    quantzation_config=pipeline_quant_config,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda"
+)
 ```
 
-TorchAO is fully compatible with [torch.compile](../optimization/fp16#torchcompile), setting it apart from other quantization methods. This makes it easy to speed up inference with just one line of code.
+## torch.compile
+
+torchao supports [torch.compile](../optimization/fp16#torchcompile) which can speed up inference with one line of code.
 
 ```python
-# In the above code, add the following after initializing the transformer
-transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True)
+import torch
+from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig
+from torchao.quantization import Int4WeightOnlyConfig
+
+pipeline_quant_config = PipelineQuantizationConfig(
+    quant_mapping={"transformer": TorchAoConfig(Int4WeightOnlyConfig(group_size=128)))}
+)
+pipeline = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    quantzation_config=pipeline_quant_config,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda"
+)
+
+pipeline.transformer.compile(transformer, mode="max-autotune", fullgraph=True)
 ```
 
-For speed and memory benchmarks on Flux and CogVideoX, please refer to the table [here](https://github.com/huggingface/diffusers/pull/10009#issue-2688781450). You can also find some torchao [benchmarks](https://github.com/pytorch/ao/tree/main/torchao/quantization#benchmarks) numbers for various hardware.
+Refer to this [table](https://github.com/huggingface/diffusers/pull/10009#issue-2688781450) for inference speed and memory usage benchmarks with Flux and CogVideoX. More benchmarks on various hardware are also available in the torchao [repository](https://github.com/pytorch/ao/tree/main/torchao/quantization#benchmarks).
 
 > [!TIP]
 > The FP8 post-training quantization schemes in torchao are effective for GPUs with compute capability of at least 8.9 (RTX-4090, Hopper, etc.). FP8 often provides the best speed, memory, and quality trade-off when generating images and videos. We recommend combining FP8 and torch.compile if your GPU is compatible.
 
-torchao also supports an automatic quantization API through [autoquant](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md#autoquantization). Autoquantization determines the best quantization strategy applicable to a model by comparing the performance of each technique on chosen input types and shapes. Currently, this can be used directly on the underlying modeling components. Diffusers will also expose an autoquant configuration option in the future.
+## autoquant
+
+torchao provides [autoquant](https://docs.pytorch.org/ao/stable/generated/torchao.quantization.autoquant.html#torchao.quantization.autoquant) an automatic quantization API. Autoquantization chooses the best quantization strategy by comparing the performance of each strategy on chosen input types and shapes. This is only supported in Diffusers for individual models at the moment.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+from torchao.quantization import autoquant
+
+# Load the pipeline
+pipeline = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=torch.bfloat16,
+    device_map="cuda"
+)
 
-The `TorchAoConfig` class accepts three parameters:
-- `quant_type`: A string value mentioning one of the quantization types below.
-- `modules_to_not_convert`: A list of module full/partial module names for which quantization should not be performed. For example, to not perform any quantization of the [`FluxTransformer2DModel`]'s first block, one would specify: `modules_to_not_convert=["single_transformer_blocks.0"]`.
-- `kwargs`: A dict of keyword arguments to pass to the underlying quantization method which will be invoked based on `quant_type`.
+transformer = autoquant(pipeline.transformer)
+```
 
 ## Supported quantization types
 

src/diffusers/quantizers/quantization_config.py

@@ -21,19 +21,20 @@
 """
 
 import copy
+import dataclasses
 import importlib.metadata
 import inspect
 import json
 import os
 import warnings
-from dataclasses import dataclass
+from dataclasses import dataclass, is_dataclass
 from enum import Enum
 from functools import partial
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from packaging import version
 
-from ..utils import is_torch_available, is_torchao_available, logging
+from ..utils import is_torch_available, is_torchao_available, is_torchao_version, logging
 
 
 if is_torch_available():
@@ -443,7 +444,7 @@ class TorchAoConfig(QuantizationConfigMixin):
     """This is a config class for torchao quantization/sparsity techniques.
 
     Args:
-        quant_type (`str`):
+        quant_type (Union[`str`, AOBaseConfig]):
             The type of quantization we want to use, currently supporting:
                 - **Integer quantization:**
                     - Full function names: `int4_weight_only`, `int8_dynamic_activation_int4_weight`,
@@ -465,6 +466,7 @@ class TorchAoConfig(QuantizationConfigMixin):
                 - **Unsigned Integer quantization:**
                     - Full function names: `uintx_weight_only`
                     - Shorthands: `uint1wo`, `uint2wo`, `uint3wo`, `uint4wo`, `uint5wo`, `uint6wo`, `uint7wo`
+                - An AOBaseConfig instance: for more advanced configuration options.
         modules_to_not_convert (`List[str]`, *optional*, default to `None`):
             The list of modules to not quantize, useful for quantizing models that explicitly require to have some
             modules left in their original precision.
@@ -478,6 +480,12 @@ class TorchAoConfig(QuantizationConfigMixin):
         ```python
         from diffusers import FluxTransformer2DModel, TorchAoConfig
 
+        # AOBaseConfig-based configuration
+        from torchao.quantization import Int8WeightOnlyConfig
+
+        quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
+
+        # String-based config
         quantization_config = TorchAoConfig("int8wo")
         transformer = FluxTransformer2DModel.from_pretrained(
             "black-forest-labs/Flux.1-Dev",
@@ -490,7 +498,7 @@ class TorchAoConfig(QuantizationConfigMixin):
 
     def __init__(
         self,
-        quant_type: str,
+        quant_type: Union[str, "AOBaseConfig"],  # noqa: F821
         modules_to_not_convert: Optional[List[str]] = None,
         **kwargs,
     ) -> None:
@@ -504,34 +512,103 @@ def __init__(
         else:
             self.quant_type_kwargs = kwargs
 
-        TORCHAO_QUANT_TYPE_METHODS = self._get_torchao_quant_type_to_method()
-        if self.quant_type not in TORCHAO_QUANT_TYPE_METHODS.keys():
-            is_floating_quant_type = self.quant_type.startswith("float") or self.quant_type.startswith("fp")
-            if is_floating_quant_type and not self._is_xpu_or_cuda_capability_atleast_8_9():
+        self.post_init()
+
+    def post_init(self):
+        if not isinstance(self.quant_type, str):
+            if is_torchao_version("<=", "0.9.0"):
                 raise ValueError(
-                    f"Requested quantization type: {self.quant_type} is not supported on GPUs with CUDA capability <= 8.9. You "
-                    f"can check the CUDA capability of your GPU using `torch.cuda.get_device_capability()`."
+                    f"torchao <= 0.9.0 only supports string quant_type, got {type(self.quant_type).__name__}. "
+                    f"Upgrade to torchao > 0.9.0 to use AOBaseConfig."
                 )
 
-            raise ValueError(
-                f"Requested quantization type: {self.quant_type} is not supported or is an incorrect `quant_type` name. If you think the "
-                f"provided quantization type should be supported, please open an issue at https://github.com/huggingface/diffusers/issues."
-            )
+            from torchao.quantization.quant_api import AOBaseConfig
 
-        method = TORCHAO_QUANT_TYPE_METHODS[self.quant_type]
-        signature = inspect.signature(method)
-        all_kwargs = {
-            param.name
-            for param in signature.parameters.values()
-            if param.kind in [inspect.Parameter.KEYWORD_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD]
-        }
-        unsupported_kwargs = list(self.quant_type_kwargs.keys() - all_kwargs)
+            if not isinstance(self.quant_type, AOBaseConfig):
+                raise TypeError(f"quant_type must be a AOBaseConfig instance, got {type(self.quant_type).__name__}")
 
-        if len(unsupported_kwargs) > 0:
-            raise ValueError(
-                f'The quantization method "{quant_type}" does not support the following keyword arguments: '
-                f"{unsupported_kwargs}. The following keywords arguments are supported: {all_kwargs}."
-            )
+        elif isinstance(self.quant_type, str):
+            TORCHAO_QUANT_TYPE_METHODS = self._get_torchao_quant_type_to_method()
+
+            if self.quant_type not in TORCHAO_QUANT_TYPE_METHODS.keys():
+                is_floating_quant_type = self.quant_type.startswith("float") or self.quant_type.startswith("fp")
+                if is_floating_quant_type and not self._is_xpu_or_cuda_capability_atleast_8_9():
+                    raise ValueError(
+                        f"Requested quantization type: {self.quant_type} is not supported on GPUs with CUDA capability <= 8.9. You "
+                        f"can check the CUDA capability of your GPU using `torch.cuda.get_device_capability()`."
+                    )
+
+                raise ValueError(
+                    f"Requested quantization type: {self.quant_type} is not supported or is an incorrect `quant_type` name. If you think the "
+                    f"provided quantization type should be supported, please open an issue at https://github.com/huggingface/diffusers/issues."
+                )
+
+            method = TORCHAO_QUANT_TYPE_METHODS[self.quant_type]
+            signature = inspect.signature(method)
+            all_kwargs = {
+                param.name
+                for param in signature.parameters.values()
+                if param.kind in [inspect.Parameter.KEYWORD_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD]
+            }
+            unsupported_kwargs = list(self.quant_type_kwargs.keys() - all_kwargs)
+
+            if len(unsupported_kwargs) > 0:
+                raise ValueError(
+                    f'The quantization method "{self.quant_type}" does not support the following keyword arguments: '
+                    f"{unsupported_kwargs}. The following keywords arguments are supported: {all_kwargs}."
+                )
+
+    def to_dict(self):
+        """Convert configuration to a dictionary."""
+        d = super().to_dict()
+
+        if isinstance(self.quant_type, str):
+            # Handle layout serialization if present
+            if "quant_type_kwargs" in d and "layout" in d["quant_type_kwargs"]:
+                if is_dataclass(d["quant_type_kwargs"]["layout"]):
+                    d["quant_type_kwargs"]["layout"] = [
+                        d["quant_type_kwargs"]["layout"].__class__.__name__,
+                        dataclasses.asdict(d["quant_type_kwargs"]["layout"]),
+                    ]
+                if isinstance(d["quant_type_kwargs"]["layout"], list):
+                    assert len(d["quant_type_kwargs"]["layout"]) == 2, "layout saves layout name and layout kwargs"
+                    assert isinstance(d["quant_type_kwargs"]["layout"][0], str), "layout name must be a string"
+                    assert isinstance(d["quant_type_kwargs"]["layout"][1], dict), "layout kwargs must be a dict"
+                else:
+                    raise ValueError("layout must be a list")
+        else:
+            # Handle AOBaseConfig serialization
+            from torchao.core.config import config_to_dict
+
+            # For now we assume there is 1 config per Transformer, however in the future
+            # We may want to support a config per fqn.
+            d["quant_type"] = {"default": config_to_dict(self.quant_type)}
+
+        return d
+
+    @classmethod
+    def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
+        """Create configuration from a dictionary."""
+        if not is_torchao_version(">", "0.9.0"):
+            raise NotImplementedError("TorchAoConfig requires torchao > 0.9.0 for construction from dict")
+        config_dict = config_dict.copy()
+        quant_type = config_dict.pop("quant_type")
+
+        if isinstance(quant_type, str):
+            return cls(quant_type=quant_type, **config_dict)
+        # Check if we only have one key which is "default"
+        # In the future we may update this
+        assert len(quant_type) == 1 and "default" in quant_type, (
+            "Expected only one key 'default' in quant_type dictionary"
+        )
+        quant_type = quant_type["default"]
+
+        # Deserialize quant_type if needed
+        from torchao.core.config import config_from_dict
+
+        quant_type = config_from_dict(quant_type)
+
+        return cls(quant_type=quant_type, **config_dict)
 
     @classmethod
     def _get_torchao_quant_type_to_method(cls):
@@ -681,8 +758,38 @@ def _is_xpu_or_cuda_capability_atleast_8_9() -> bool:
             raise RuntimeError("TorchAO requires a CUDA compatible GPU or Intel XPU and installation of PyTorch.")
 
     def get_apply_tensor_subclass(self):
-        TORCHAO_QUANT_TYPE_METHODS = self._get_torchao_quant_type_to_method()
-        return TORCHAO_QUANT_TYPE_METHODS[self.quant_type](**self.quant_type_kwargs)
+        """Create the appropriate quantization method based on configuration."""
+        if not isinstance(self.quant_type, str):
+            return self.quant_type
+        else:
+            methods = self._get_torchao_quant_type_to_method()
+            quant_type_kwargs = self.quant_type_kwargs.copy()
+            if (
+                not torch.cuda.is_available()
+                and is_torchao_available()
+                and self.quant_type == "int4_weight_only"
+                and version.parse(importlib.metadata.version("torchao")) >= version.parse("0.8.0")
+                and quant_type_kwargs.get("layout", None) is None
+            ):
+                if torch.xpu.is_available():
+                    if version.parse(importlib.metadata.version("torchao")) >= version.parse(
+                        "0.11.0"
+                    ) and version.parse(importlib.metadata.version("torch")) > version.parse("2.7.9"):
+                        from torchao.dtypes import Int4XPULayout
+                        from torchao.quantization.quant_primitives import ZeroPointDomain
+
+                        quant_type_kwargs["layout"] = Int4XPULayout()
+                        quant_type_kwargs["zero_point_domain"] = ZeroPointDomain.INT
+                    else:
+                        raise ValueError(
+                            "TorchAoConfig requires torchao >= 0.11.0 and torch >= 2.8.0 for XPU support. Please upgrade the version or use run on CPU with the cpu version pytorch."
+                        )
+                else:
+                    from torchao.dtypes import Int4CPULayout
+
+                    quant_type_kwargs["layout"] = Int4CPULayout()
+
+            return methods[self.quant_type](**quant_type_kwargs)
 
     def __repr__(self):
         r"""