Add MLX inference support

Issue #, if available: #28

Description of changes: This PR adds MLX inference support.

Summary of changes

Update pyproject.toml withmlx dependencies.
Create chronos_mlx package which will hosts all mlx inference stuff.
- All classes from main:src/chronos/chronos.py are copy-pasted into mlx:src/chronos_mlx/chronos.py and modified to use numpy and mlx arrays instead. Note that the reason for using numpy arrays as input and output is that mlx doesn't support some operations that are required for input and output transform.
- MLX implementation of T5 is in src/chronos_mlx/t5.py. It has been adapted from ml-explore/mlx-examples with the following main modifications:
  - Added support for attention mask.
  - Added support for top_k and top_p sampling.
- src/chronos_mlx/translate.py translates weights from a torch HF model to mlx.
Add THIRD-PARTY-LICENSES.txt for third party code from mlx-examples.
Add tests and CI for mlx version.

Sample inference code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from chronos_mlx import ChronosPipeline

pipeline = ChronosPipeline.from_pretrained(
    "amazon/chronos-t5-small",
    dtype="bfloat16",
)

df = pd.read_csv(
    "https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv"
)

# context must be either a 1D tensor, a list of 1D tensors,
# or a left-padded 2D tensor with batch as the first dimension
context = df["#Passengers"].values
prediction_length = 12
forecast = pipeline.predict(
    context, prediction_length
)  # shape [num_series, num_samples, prediction_length]

# visualize the forecast
forecast_index = range(len(df), len(df) + prediction_length)
low, median, high = np.quantile(forecast[0], [0.1, 0.5, 0.9], axis=0)

plt.figure(figsize=(8, 4))
plt.plot(df["#Passengers"], color="royalblue", label="historical data")
plt.plot(forecast_index, median, color="tomato", label="median forecast")
plt.fill_between(
    forecast_index,
    low,
    high,
    color="tomato",
    alpha=0.3,
    label="80% prediction interval",
)
plt.legend()
plt.grid()
plt.show()

Benchmark

benchmark

import timeit

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from gluonts.dataset.repository import get_dataset
from gluonts.dataset.split import split
from gluonts.ev.metrics import MASE, MeanWeightedSumQuantileLoss
from gluonts.model.evaluation import evaluate_forecasts
from gluonts.model.forecast import SampleForecast
from tqdm.auto import tqdm

from chronos import ChronosPipeline as ChronosPipelineTorch
from chronos_mlx import ChronosPipeline as ChronosPipelineMLX

def benchmark_torch_model(
    pipeline: ChronosPipelineTorch,
    gluonts_dataset: str = "m4_hourly",
    batch_size: int = 32,
):
    dataset = get_dataset(gluonts_dataset)
    prediction_length = dataset.metadata.prediction_length
    _, test_template = split(dataset.test, offset=-prediction_length)
    test_data = test_template.generate_instances(prediction_length)
    test_data_input = list(test_data.input)

    start_time = timeit.default_timer()
    forecasts = []
    for idx in tqdm(range(0, len(test_data_input), batch_size)):
        batch = [
            torch.tensor(item["target"])
            for item in test_data_input[idx : idx + batch_size]
        ]
        batch_forecasts = pipeline.predict(batch, prediction_length)
        forecasts.append(batch_forecasts)
    forecasts = torch.cat(forecasts)
    end_time = timeit.default_timer()

    print(f"Inference time: {end_time-start_time:.2f}s")

    results_df = evaluate_forecasts(
        forecasts=[
            SampleForecast(fcst.numpy(), start_date=label["start"])
            for fcst, label in zip(forecasts, test_data.label)
        ],
        test_data=test_data,
        metrics=[MASE(), MeanWeightedSumQuantileLoss(np.arange(0.1, 1, 0.1))],
    )
    results_df["inference_time"] = end_time - start_time
    return results_df

def benchmark_mlx_model(
    pipeline: ChronosPipelineMLX,
    gluonts_dataset: str = "m4_hourly",
    batch_size: int = 32,
):
    dataset = get_dataset(gluonts_dataset)
    prediction_length = dataset.metadata.prediction_length
    _, test_template = split(dataset.test, offset=-prediction_length)
    test_data = test_template.generate_instances(prediction_length)
    test_data_input = list(test_data.input)

    start_time = timeit.default_timer()
    forecasts = []
    for idx in tqdm(range(0, len(test_data_input), batch_size)):
        batch = [item["target"] for item in test_data_input[idx : idx + batch_size]]
        batch_forecasts = pipeline.predict(batch, prediction_length)
        forecasts.append(batch_forecasts)
    forecasts = np.concatenate(forecasts)
    end_time = timeit.default_timer()

    print(f"Inference time: {end_time-start_time:.2f}s")

    results_df = evaluate_forecasts(
        forecasts=[
            SampleForecast(fcst, start_date=label["start"])
            for fcst, label in zip(forecasts, test_data.label)
        ],
        test_data=test_data,
        metrics=[MASE(), MeanWeightedSumQuantileLoss(np.arange(0.1, 1, 0.1))],
    )
    results_df["inference_time"] = end_time - start_time
    return results_df

def main(
    version: str = "cpu",  # cpu, mps, mlx
    dtype: str = "bfloat16",
    gluonts_dataset: str = "australian_electricity_demand",
    model_name: str = "amazon/chronos-t5-small",
    batch_size: int = 4,
):
    if version == "cpu" or version == "mps":
        pipeline = ChronosPipelineTorch.from_pretrained(
            model_name,
            device_map=version,
            torch_dtype=getattr(torch, dtype),
        )
        benchmark_fn = benchmark_torch_model
    else:
        pipeline = ChronosPipelineMLX.from_pretrained(model_name, dtype=dtype)
        benchmark_fn = benchmark_mlx_model

    result_df = benchmark_fn(
        pipeline, gluonts_dataset=gluonts_dataset, batch_size=batch_size
    )
    result_df["model"] = model_name
    return result_df

if __name__ == "__main__":
    gluonts_dataset: str = "m4_hourly"
    model_name: str = "amazon/chronos-t5-mini"
    batch_size: int = 8
    dfs = []
    for version in ["cpu", "mps", "mlx"]:
        for dtype in ["float32"]:
            try:
                df = main(
                    version=version,
                    dtype=dtype,
                    model_name=model_name,
                    gluonts_dataset=gluonts_dataset,
                    batch_size=batch_size,
                )
                df["version"] = version
                df["dtype"] = dtype
                dfs.append(df)
            except TypeError:
                pass

    result_df = pd.concat(dfs).reset_index(drop=True)
    result_df.to_csv("benchmark.csv", index=False)

    result_df["version"] = result_df["version"].map(
        {"cpu": "Torch (CPU)", "mps": "Torch (MPS)", "mlx": "MLX"}
    )
    fig = plt.figure(figsize=(8, 5))
    g = sns.barplot(
        data=result_df,
        x="dtype",
        y="inference_time",
        hue="version",
        alpha=0.6,
    )
    plt.ylabel("Inference Time (on M1 Pro)")
    plt.title(f"{model_name} inference times on {gluonts_dataset} dataset")
    plt.savefig("benchmark.png", dpi=200)

TODOs:

[x] Implement top_p sampling.
[x] Add tests.
[x] Add CI.

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amazon-science / chronos-forecasting

Add MLX inference support #41

Summary of changes

Sample inference code

Benchmark

TODOs: