[Transform] QuIP Modifier

examples/transform/quip_example.py

@@ -0,0 +1,43 @@
+"""
+NOTE: Models produced by this example will not be runnable in vLLM without
+the following changes: https://github.com/vllm-project/vllm/pull/22486
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.transform import QuIPModifier
+from llmcompressor.utils import dispatch_for_generation
+
+# Select model and load it.
+# NOTE: because the datafree pipeline is being used in this
+# example, you can use additional GPUs to support larger models
+MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm to run.
+#   * apply spinquant transforms to model in order to make quantization easier
+#   * quantize the weights to 4 bit with a group size 128
+recipe = [
+    QuIPModifier(transform_type="random-hadamard"),
+    QuantizationModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"]),
+]
+
+# Apply algorithms.
+oneshot(model=model, recipe=recipe, pipeline="datafree")
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=100)
+print(tokenizer.decode(output[0]))
+print("==========================================\n\n")
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-quip-w4a16"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)

src/llmcompressor/modifiers/transform/__init__.py

@@ -1,3 +1,4 @@
 # flake8: noqa
 
+from .quip import QuIPModifier
 from .spinquant import SpinQuantModifier

src/llmcompressor/modifiers/transform/quip/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .base import *

src/llmcompressor/modifiers/transform/quip/base.py

@@ -0,0 +1,155 @@
+from typing import List, Literal, Optional, Union
+
+import torch
+from compressed_tensors.transform import (
+    TransformArgs,
+    TransformConfig,
+    TransformScheme,
+    apply_transform_config,
+)
+from compressed_tensors.utils import TorchDtype
+from pydantic import Field, ValidationInfo, field_validator
+
+from llmcompressor.core import Event, EventType, State
+from llmcompressor.modifiers import Modifier
+
+__all__ = ["QuIPModifier"]
+
+
+class QuIPModifier(Modifier):
+    """
+    Implements the transforms according to
+    [QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks](https://arxiv.org/pdf/2402.04396)
+    [QuIP: 2-Bit Quantization of Large Language Models With Guarantees](https://arxiv.org/abs/2307.13304)
+
+    Transforms (rotations) are extra layers added to a model which reduce the accuracy
+    loss induced by quantization. This is achieved through "rotating" weights and
+    activations into a space with a smaller dynamic range of values, thus decreasing
+    the range of scales required for quantization.
+
+    QuIP and QuIP# apply transforms to every linear layer, two of which are fused into
+    the model weights and two of which remain as online rotations computed at runtime.
+
+    Lifecycle:
+        - on_initialize
+            - infer SpinQuantMappings & NormMappings
+            - as needed, create transform schemes for R1, R2, R3, & R4
+        - on_start
+            - normalize embeddings
+            - fuse norm layers into subsequent Linear layers
+            - apply TransformConfig
+                - fuse transforms into weights for mergeable transforms
+                - add hooks for online transforms
+        - on sequential epoch end
+        - on_end
+        - on_finalize
+
+    :param transform_type: The type of transform to apply to the model.
+        `"hadamard"` has the least performance cost but only supports sizes which are
+        powers of power of two.
+        `"random-hadamard"` has more performance cost, but supports a much larger set of
+            sizes.
+        `"random-matrix"` has the greatest performance cost, but supports any size
+    :param randomize: If true, create distinct transforms for each application
+    :param learnable: If true, attach gradients to transform weights for training
+    :param precision: Precision at which all transforms should be applied. This applies
+        to both weight fusing and online rotations
+    :param ignore: Modules to ignore when attaching transforms
+    :param transform_config: Optional transform config for overriding provided arguments
+    """  # noqa: E501
+
+    transform_type: Literal["hadamard", "random-hadamard", "random-matrix"] = Field(
+        default="random-hadamard"
+    )
+    targets: Union[List[str], str] = Field(default="str")
+    randomize: bool = Field(default=False)
+    learnable: bool = Field(default=False)
+    precision: TorchDtype = Field(default=torch.float64)
+    ignore: Union[str, List[str]] = Field(default="lm_head")
+
+    # optional override for more fine-grained control
+    # also included in recipe serialization
+    transform_config: Optional[TransformConfig] = Field(default=None, repr=False)
+
+    @field_validator("randomize", "learnable", mode="before")
+    def validate_not_implemented(cls, value, info: ValidationInfo):
+        if value:
+            raise NotImplementedError(f"{info.field_name} is not supported right now")
+        return value
+
+    def on_initialize(self, state: State, **kwargs) -> bool:
+        if self.transform_config is not None:
+            return True
+
+        self.transform_config = self._create_config()
+        return True
+
+    def on_start(self, state: State, event: Event, **kwargs):
+        self.started_ = True
+
+        apply_transform_config(state.model, self.transform_config)
+
+    def on_event(self, state: State, event: Event, **kwargs):
+        if event.type_ == EventType.CALIBRATION_EPOCH_START:
+            if not self.started_:
+                self.on_start(state, None)
+
+        elif event.type_ == EventType.SEQUENTIAL_EPOCH_END:
+            pass
+
+        elif event.type_ == EventType.CALIBRATION_EPOCH_END:
+            if not self.ended_:
+                self.on_end(state, None)
+
+    def on_end(self, state: State, event: Event, **kwargs):
+        self.ended_ = True
+
+    def on_finalize(self, state: State, **kwargs) -> bool:
+        if not self.ended_:
+            self.on_end(state, None)
+
+        return True
+
+    def _create_config(self) -> TransformConfig:
+        return TransformConfig(
+            config_groups={
+                "v": TransformScheme(
+                    type=self.transform_type,
+                    apply=[
+                        TransformArgs(
+                            targets=self.targets,
+                            location="input",  # non-mergable
+                            ignore=self.ignore,
+                        ),
+                        TransformArgs(
+                            targets=self.targets,
+                            location="weight_input",
+                            inverse=True,
+                            ignore=self.ignore,
+                        ),
+                    ],
+                    randomize=self.randomize,
+                    requires_grad=self.learnable,
+                    precision=self.precision,
+                ),
+                "u": TransformScheme(
+                    type=self.transform_type,
+                    apply=[
+                        TransformArgs(
+                            targets=self.targets,
+                            location="weight_output",
+                            ignore=self.ignore,
+                        ),
+                        TransformArgs(
+                            targets=self.targets,
+                            location="output",  # non-mergable
+                            inverse=True,
+                            ignore=self.ignore,
+                        ),
+                    ],
+                    randomize=self.randomize,
+                    requires_grad=self.learnable,
+                    precision=self.precision,
+                ),
+            }
+        )

tests/llmcompressor/modifiers/transform/test_correctness.py

@@ -5,7 +5,7 @@
 from transformers import AutoModelForCausalLM
 
 from llmcompressor.core import State
-from llmcompressor.modifiers.transform import SpinQuantModifier
+from llmcompressor.modifiers.transform import QuIPModifier, SpinQuantModifier
 from llmcompressor.transformers.sparsification.compressed_tensors_utils import (
     untie_word_embeddings,
 )
@@ -20,10 +20,10 @@
 @pytest.mark.parametrize(
     "modifier,model_dtype,precision,exp_mse",
     [
-        # (QuIPModifier, torch.bfloat16, torch.bfloat16, 5e-3),  # 0.0019
-        # (QuIPModifier, torch.bfloat16, torch.float32, 5e-3),  # 0.0022
-        # (QuIPModifier, torch.float32, torch.float32, 5e-10),  # 1.0e-10
-        # (QuIPModifier, torch.float32, torch.float64, 5e-11),  # 2.7e-11
+        (QuIPModifier, torch.bfloat16, torch.bfloat16, 5e-3),  # 0.0019
+        (QuIPModifier, torch.bfloat16, torch.float32, 5e-3),  # 0.0022
+        (QuIPModifier, torch.float32, torch.float32, 5e-10),  # 1.0e-10
+        (QuIPModifier, torch.float32, torch.float64, 5e-11),  # 2.7e-11
         (SpinQuantModifier, torch.bfloat16, torch.bfloat16, 5e-3),  # 0.0030
         (SpinQuantModifier, torch.bfloat16, torch.float32, 5e-3),  # 0.0029
         (SpinQuantModifier, torch.float32, torch.float32, 5e-4),  # 4e-4

tests/llmcompressor/modifiers/transform/test_serialization.py

@@ -1,9 +1,9 @@
 import pytest
 
-from llmcompressor.modifiers.transform import SpinQuantModifier
+from llmcompressor.modifiers.transform import QuIPModifier, SpinQuantModifier
 
 
-@pytest.mark.parametrize("modifier", [SpinQuantModifier])
+@pytest.mark.parametrize("modifier", [SpinQuantModifier, QuIPModifier])
 def test_reload(modifier):
     instance = modifier(transform_type="hadamard")
     dump = instance.model_dump()