[WIP] TransformModifier

examples/transform/llama3_example.py

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+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.modifiers.transform import TransformModifier
+from llmcompressor import oneshot
+
+# Select model and load it.
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Select calibration dataset.
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+# Configure the quantization algorithm to run.
+#   * quantize the weights to 4 bit with GPTQ with a group size 128
+recipe = [
+    TransformModifier(),
+    GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"]),
+]
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    pipeline="sequential",
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+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] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)

src/llmcompressor/modifiers/transform/__init__.py

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+# flake8: noqa
+
+from .transform import TransformModifier

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

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+from compressed_tensors.transform import TransformArgs, TransformConfig, TransformScheme
+
+QUIP = TransformConfig(
+    config_groups={
+        "v": TransformScheme(
+            type="random-hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["Linear"],
+                    location="input",  # non-mergable
+                    ignore="lm_head",
+                ),
+                TransformArgs(
+                    targets=["Linear"],
+                    location="weight_input",
+                    inverse=True,
+                    ignore="lm_head",
+                ),
+            ],
+            randomize=True,
+        ),
+        "u": TransformScheme(
+            type="random-hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["Linear"],
+                    location="weight_output",
+                    ignore="lm_head",
+                ),
+                TransformArgs(
+                    targets=["Linear"],
+                    location="output",  # non-mergable
+                    inverse=True,
+                    ignore="lm_head",
+                ),
+            ],
+            randomize=True,
+        ),
+    }
+)

src/llmcompressor/modifiers/transform/template/spinquant.py

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+from compressed_tensors.transform import TransformArgs, TransformConfig, TransformScheme
+
+LLAMA_SPINQUANT = TransformConfig(
+    transform_groups={
+        "R1": TransformScheme(
+            type="hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["embed_tokens", "o_proj", "down_proj"],
+                    location="weight_output",
+                ),
+                TransformArgs(
+                    targets=[
+                        "q_proj",
+                        "k_proj",
+                        "v_proj",
+                        "up_proj",
+                        "gate_proj",
+                        "lm_head",
+                    ],
+                    location="weight_input",
+                    inverse=True,
+                ),
+            ],
+        ),
+        "R2": TransformScheme(
+            type="hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["v_proj"],
+                    location="weight_output",
+                ),
+                TransformArgs(
+                    targets=["o_proj"], location="weight_input", inverse=True
+                ),
+            ],
+        ),
+        "R3": TransformScheme(
+            type="hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["self_attn"],
+                    location="k_cache",
+                ),
+                TransformArgs(
+                    targets=["self_attn"],
+                    location="q_attn",
+                ),
+            ],
+        ),
+        "R4": TransformScheme(
+            type="hadamard",
+            apply=[
+                TransformArgs(
+                    targets=["down_proj"],
+                    location="input",
+                ),
+                TransformArgs(
+                    targets=["down_proj"], location="weight_input", inverse=True
+                ),
+            ],
+        ),
+    }
+)

src/llmcompressor/modifiers/transform/transform.py

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+from typing import Dict, Optional
+
+from compressed_tensors.transform import TransformScheme, apply_transform_config
+
+from llmcompressor.core import Event, EventType, State
+from llmcompressor.modifiers import Modifier
+
+from .template.quip import QUIP
+
+
+class TransformModifier(Modifier):
+    preset_config: Optional[str] = None
+    config_groups: Optional[Dict[str, TransformScheme]] = None
+
+    # model validator to validate both preset and config groups are not provided
+
+    def on_initialize(self, state: State, **kwargs) -> bool:
+        if self.preset_config is not None:
+            # import config template and customize to model
+            pass
+
+        # config = TransformConfig(config_groups=self.config_groups)
+        config = QUIP
+
+        apply_transform_config(state.model, config)
+
+        return True
+
+    def on_start(self, state: State, event: Event, **kwargs):
+        self.started_ = True
+
+    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

tests/llmcompressor/modifiers/transform/test_correctness.py

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+import pytest
+import torch
+from compressed_tensors.transform import apply_transform_config
+from transformers import AutoModelForCausalLM
+
+from llmcompressor.modifiers.transform.template.quip import QUIP
+
+
+@pytest.mark.parametrize(
+    "dtype,exp_max,exp_mse", [
+        (torch.bfloat16, 1.1, 0.012),  # constructing and running transforms in float32 can improve to (~0.6562, ~0.0055)  # noqa: E501
+        (torch.float32, 4e-4, 2e-9)
+    ]
+)
+def test_apply_correctness(dtype, exp_max, exp_mse):
+    model = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Meta-Llama-3-8B-Instruct", device_map="cuda", torch_dtype=dtype
+    )
+
+    input = {k: v.to("cuda") for k, v in model.dummy_inputs.items()}
+    with torch.no_grad():
+        true_output = model(**input)
+
+    apply_transform_config(model, QUIP)
+    with torch.no_grad():
+        output = model(**input)
+
+    assert torch.max(true_output.logits - output.logits) <= exp_max
+    assert torch.nn.MSELoss()(output.logits, true_output.logits) <= exp_mse