mlc-ai--mlc-llm/python/mlc_llm/interface/calibrate.py

"""Python entrypoint for calibration."""

import asyncio
import json
import random
from collections.abc import Mapping
from typing import List, Optional, Tuple  # noqa: UP035

import numpy as np
import tqdm.asyncio
import tvm
from tvm.contrib import tvmjs

from mlc_llm.serve.engine import AsyncMLCEngine, EngineConfig
from mlc_llm.tokenizers import Tokenizer


class CalibrationObserver:
    """A singleton class to observe the calibration parameters.""" ""

    instance: "CalibrationObserver" = None

    params: Mapping[str, tvm.runtime.Tensor] = {}

    @staticmethod
    def get():
        """Get the singleton instance of the class.""" ""
        if CalibrationObserver.instance is None:
            CalibrationObserver.instance = CalibrationObserver()
        return CalibrationObserver.instance

    @tvm.register_global_func("mlc_llm.calibration_observer")
    @staticmethod
    def callback(
        name: str,
        mode: str,
        value: "tvm.runtime.Tensor",
        out_value: "tvm.runtime.Tensor",
    ):
        """The callback function to update the saved calibration parameters."""
        instance = CalibrationObserver.get()
        if mode == "max":
            reducer = np.maximum
        else:
            raise NotImplementedError(f"Unsupported calibration mode: {mode}")
        if name in instance.params:
            instance.params[name] = reducer(instance.params[name], value.numpy())
        else:
            instance.params[name] = value.numpy()
        out_value.copyfrom(instance.params[name])

    def save_params(self, output: str):
        """Save the calibration parameters to the given output directory."""
        tvmjs.dump_tensor_cache(
            self.params,
            output,
            encode_format="f32-to-bf16",
            meta_data=None,
            show_progress=False,
            update_if_exists=True,
        )


def sample_requests(
    dataset_path: str,
    num_requests: int,
    tokenizer: Tokenizer,
) -> List[Tuple[str, int, int]]:  # noqa: UP006
    """Sample the requests from the given dataset."""
    # Load the dataset.
    with open(dataset_path, encoding="utf-8") as f:
        dataset = json.load(f)

    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
    dataset = [
        (data["conversations"][0]["value"], data["conversations"][1]["value"]) for data in dataset
    ]
    prompts = [prompt for prompt, _ in dataset]
    prompt_token_ids = tokenizer.encode_batch(prompts)
    completions = [completion for _, completion in dataset]
    completion_token_ids = tokenizer.encode_batch(completions)
    tokenized_dataset: List[Tuple[str, List[int], int]] = []  # noqa: UP006
    for i in range(len(dataset)):
        output_len = len(completion_token_ids[i])
        tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))

    # Filter out too long sequences.
    filtered_dataset: List[Tuple[str, int, int]] = []  # noqa: UP006
    for prompt, token_ids, output_len in tokenized_dataset:
        prompt_len = len(token_ids)
        if prompt_len < 4 or output_len < 4:
            # Prune too short sequences.
            continue
        if prompt_len > 1024 or prompt_len + output_len > 2048:
            # Prune too long sequences.
            continue
        filtered_dataset.append((prompt, prompt_len, output_len))

    # Sample the requests.
    sampled_requests = random.sample(filtered_dataset, num_requests)
    return sampled_requests


async def send_calibration_requests(
    async_engine: AsyncMLCEngine,
    sampled_requests: List[Tuple[str, int, int]],  # noqa: UP006
    max_concurrent_requests: int,
) -> None:
    """Send the calibration requests to the engine."""
    tasks = []

    semaphore = asyncio.Semaphore(max_concurrent_requests)

    async def generate_task(request_idx):
        async with semaphore:
            prompt, _, output_len = sampled_requests[request_idx]
            await async_engine.chat.completions.create(
                messages=[{"role": "user", "content": prompt}],
                max_tokens=output_len,
                request_id=str(request_idx),
            )

    for i in range(len(sampled_requests)):
        task = asyncio.create_task(generate_task(i))
        tasks.append(task)
    await tqdm.asyncio.tqdm.gather(*tasks)


def calibrate(
    model: str,
    device: str,
    model_lib: Optional[str],
    dataset: str,
    output: str,
    num_calibration_samples: int,
    *,
    seed: int,
    max_num_sequence: Optional[int] = None,
    max_total_sequence_length: Optional[int] = None,
    prefill_chunk_size: Optional[int] = None,
    max_history_size: Optional[int] = None,
    gpu_memory_utilization: Optional[float] = None,
) -> None:
    """Calibrate the quantized model using the given dataset."""
    random.seed(seed)
    async_engine = AsyncMLCEngine(
        model=model,
        device=device,
        model_lib=model_lib,
        mode="server",
        engine_config=EngineConfig(
            max_num_sequence=max_history_size,
            max_total_sequence_length=max_total_sequence_length,
            prefill_chunk_size=prefill_chunk_size,
            max_history_size=max_history_size,
            gpu_memory_utilization=gpu_memory_utilization,
        ),
    )
    sampled_requests = sample_requests(dataset, num_calibration_samples, async_engine.tokenizer)
    asyncio.run(
        send_calibration_requests(
            async_engine,
            sampled_requests,
            max_concurrent_requests=max_num_sequence or 32,
        )
    )
    async_engine.terminate()

    calibrator = CalibrationObserver.get()
    calibrator.save_params(output)