start higgs

Author: sayakpaul

src/diffusers/quantizers/auto.py

@@ -24,6 +24,7 @@
 from .quantization_config import (
     BitsAndBytesConfig,
     GGUFQuantizationConfig,
+    HiggsConfig,
     QuantizationConfigMixin,
     QuantizationMethod,
     QuantoConfig,
@@ -39,6 +40,7 @@
     "gguf": GGUFQuantizer,
     "quanto": QuantoQuantizer,
     "torchao": TorchAoHfQuantizer,
+    "higgs": 1,
 }
 
 AUTO_QUANTIZATION_CONFIG_MAPPING = {
@@ -47,6 +49,7 @@
     "gguf": GGUFQuantizationConfig,
     "quanto": QuantoConfig,
     "torchao": TorchAoConfig,
+    "higgs": HiggsConfig,
 }
 
 

src/diffusers/quantizers/higgs/__init__.py

@@ -0,0 +1,205 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""
+Adapted from
+https://github.com/huggingface/transformers/blob/d3d835d4fc145e5062d2153ac23ccd4b3e2c2cbd/src/transformers/quantizers/quantizer_higgs.py
+"""
+
+from typing import TYPE_CHECKING, Any, Optional
+
+from ...utils import get_module_from_name
+from ..base import DiffusersQuantizer
+
+
+if TYPE_CHECKING:
+    from ...models.modeling_utils import ModelMixin
+
+from ...utils import is_accelerate_available, is_torch_available, logging
+from ...utils.logging import tqdm
+
+
+if is_torch_available():
+    import torch
+
+logger = logging.get_logger(__name__)
+
+
+class HiggsHfQuantizer(DiffusersQuantizer):
+    """
+    Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of
+    full-precision models.
+    """
+
+    requires_calibration = False
+    requires_parameters_quantization = True
+    required_packages = ["flute-kernel", "fast_hadamard_transform"]
+
+    def __init__(self, quantization_config, **kwargs):
+        super().__init__(quantization_config, **kwargs)
+        self.quantization_config = quantization_config
+
+    def validate_environment(self, device_map, **kwargs):
+        if not torch.cuda.is_available():
+            raise NotImplementedError("HIGGS quantization is only supported on GPU. Please use a different quantizer.")
+
+        if not is_accelerate_available():
+            raise ImportError("Using `higgs` quantization requires Accelerate: `pip install accelerate`")
+
+        # TODO: enable this.
+        # if not is_flute_available():
+        #     raise ImportError("Using `higgs` quantization requires FLUTE: `pip install flute-kernel>=0.3.0`")
+
+        # if not is_hadamard_available():
+        #     raise ImportError(
+        #         "Using `higgs` quantization requires fast_hadamard_transform: `pip install fast_hadamard_transform`"
+        #     )
+
+        if device_map is None:
+            raise ValueError(
+                "You are attempting to load a HIGGS model without setting device_map."
+                " Please set device_map comprised of 'cuda' devices."
+            )
+        elif isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
+            raise ValueError(
+                "You are attempting to load a HIGGS model with a device_map that contains a CPU or disk device."
+                " This is not supported. Please remove the CPU or disk device from the device_map."
+            )
+
+    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
+        if torch_dtype is None:
+            logger.info("`torch_dtype` is None. Setting `torch_dtype=torch.float16` for FLUTE compatibility.")
+            torch_dtype = torch.float16
+        elif torch_dtype != torch.float16 and torch_dtype != torch.bfloat16:
+            raise ValueError(
+                f"Invalid `torch_dtype` {torch_dtype}. HIGGS quantization only supports `torch_dtype=torch.float16` or `torch_dtype=torch.bfloat16`."
+            )
+
+        return torch_dtype
+
+    def create_quantized_param(
+        self,
+        model: "ModelMixin",
+        param_value: "torch.Tensor",
+        param_name: str,
+        target_device: "torch.device",
+        state_dict: dict[str, Any],
+        unexpected_keys: Optional[list[str]] = None,
+    ):
+        from .utils import quantize_with_higgs
+
+        """
+        Quantizes weights into weight and weight_scale
+        """
+        flute_dict = quantize_with_higgs(
+            param_value.to(target_device),
+            self.quantization_config.bits,
+            self.quantization_config.p,
+            self.quantization_config.group_size,
+            self.quantization_config.hadamard_size,
+        )
+        del param_value
+
+        module, _ = get_module_from_name(model, param_name)
+        module_name = ".".join(param_name.split(".")[:-1])
+        for key, value in flute_dict.items():
+            if key in module._parameters:
+                module._parameters[key] = torch.nn.Parameter(value, requires_grad=False)
+            elif key in module._buffers:
+                module._buffers[key] = torch.nn.Buffer(value)
+            elif key == "tune_metadata":
+                module.tune_metadata = value
+                self.quantization_config.tune_metadata[module_name] = value.to_dict()
+            else:
+                raise ValueError(f"Unexpected key {key} in module {module}")
+
+        if unexpected_keys is not None and param_name in unexpected_keys:
+            unexpected_keys.remove(param_name)
+
+    def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
+        from .utils import HiggsLinear
+
+        higgs_names = {name for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
+
+        def should_update(key: str) -> bool:
+            if key.endswith(".weight") or key.endswith(".bias"):
+                return False
+            full_key = f"{prefix}.{key}"
+            return any(name in key or name in full_key for name in higgs_names)
+
+        return [key for key in missing_keys if not should_update(key)]
+
+    @property
+    def is_trainable(self):
+        return False
+
+    def is_serializable(self):
+        return True
+
+    def check_quantized_param(
+        self,
+        model: "ModelMixin",
+        param_value: "torch.Tensor",
+        param_name: str,
+        state_dict: dict[str, Any],
+        **kwargs,
+    ) -> bool:
+        from .utils import HiggsLinear
+
+        module, tensor_name = get_module_from_name(model, param_name)
+        if isinstance(module, HiggsLinear) and tensor_name == "weight" and param_value.dtype != torch.int16:
+            # Only quantize weights of HiggsLinear modules that are not already quantized
+            return True
+        else:
+            return False
+
+    def _process_model_before_weight_loading(
+        self,
+        model: "ModelMixin",
+        **kwargs,
+    ):
+        from .utils import replace_with_higgs_linear
+
+        replace_with_higgs_linear(model, quantization_config=self.quantization_config)
+        model.config.quantization_config = self.quantization_config
+
+    def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
+        from flute.tune import TuneMetaData, maybe_tune_and_repack
+        from flute.utils import make_workspace_streamk
+
+        from .utils import HiggsLinear
+
+        flute_workspaces = {}
+        flute_modules = {name: module for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
+        for name, module in tqdm(flute_modules.items(), desc="Repacking HIGGS modules", leave=False):
+            # Every HiggsLinear needs a "workspace": a buffer for the unpacking operation.
+            # This buffer needs to be on the same device as the weights, but can be reused across modules otherwise.
+            if module.weight.device not in flute_workspaces:
+                flute_workspaces[module.weight.device] = make_workspace_streamk(device=module.weight.device)
+            module.workspace = flute_workspaces[module.weight.device]
+
+            # FLUTE weights are packed in a way that is optimized for a specific number of SMs (GPU streaming multiprocessors).
+            # If the model is loaded on a different device than the one it was saved on, we need to repack the weights.
+            module.tune_metadata = TuneMetaData.from_dict(self.quantization_config.tune_metadata[name])
+            module.weight.data, module.tune_metadata = maybe_tune_and_repack(
+                weight=module.weight.data,
+                scales=module.scales.data,
+                metadata=module.tune_metadata,
+            )
+            self.quantization_config.tune_metadata[name] = module.tune_metadata.to_dict()
+
+    def _dequantize(self, model):
+        from .utils import dequantize_higgs
+
+        model = dequantize_higgs(model)
+        return model