First chronos-2 commit

README.md

@@ -1,5 +1,185 @@
 ---
 license: other
-license_name: embedl-models-community-licence-1.0
+license_name: embedl-models-community-licence-v1.0
 license_link: https://github.com/embedl/embedl-models/blob/main/LICENSE
+base_model: amazon/chronos-2
+quantized_from: amazon/chronos-2
+tags:
+- time-series
+- time-series-forecasting
+- chronos
+- chronos-2
+- int8
+- tensorrt
+- quantization
+- edge
+- jetson
+- orin
+library_name: onnx
+pipeline_tag: time-series-forecasting
+gated: true
+extra_gated_heading: Acknowledge Embedl Models Community Licence v1.0
+extra_gated_description: |
+  By requesting access you agree to the Embedl Models Community
+  Licence v1.0 (no redistribution as a hosted service) and to the
+  upstream chronos-2 license terms.
+extra_gated_button_content: Request access
 ---
+
+<p align="center">
+  <a href="https://embedl.com">
+    <img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/embedl_banner.svg" alt="Embedl" width="240">
+  </a>
+</p>
+
+# Embedl Chronos-2 (Quantized for TensorRT)
+
+Deployable INT8-quantized version of
+[`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2),
+optimized with
+[embedl-deploy](https://github.com/embedl/embedl-deploy) for
+low-latency NVIDIA TensorRT inference on edge GPUs. Two
+static-context variants ship: **ctx=512** for short-history
+forecasting and **ctx=2048** for long-history use cases.
+
+## Highlights
+
+- **Per-tensor INT8** activations + **per-channel INT8** weights via
+  embedl-deploy's PTQ flow on top of TensorRT's fused MHA kernel.
+  No QAT or distillation needed.
+- **Drop-in replacement** for `amazon/chronos-2` inference: same
+  `(context, group_ids) → quantile_preds` signature; 21-quantile
+  output grid (0.01..0.99 step 0.05).
+- **Validated** on the [GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval)
+  benchmark across 125 task configurations. See Accuracy below.
+- **Two ctx variants** so you can pick the latency/history-window
+  trade-off that fits your deployment.
+
+## Quick Start
+
+```bash
+pip install onnxruntime-gpu numpy
+python infer_trt.py --ctx 512    # 1.2× faster than FP16 on Orin
+python infer_trt.py --ctx 2048   # 1.3× faster than FP16 on Orin
+```
+
+The `infer_trt.py` helper script creates a synthetic seasonal context
+for demonstration; replace it with your own series of the right
+length.
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `embedl_chronos_2_ctx512_int8.onnx` | INT8 ONNX with Q/DQ — ctx=512, 1024-step horizon. |
+| `embedl_chronos_2_ctx2048_int8.onnx` | INT8 ONNX with Q/DQ — ctx=2048, 1024-step horizon. |
+| `infer_trt.py` | ONNX Runtime / TensorRT inference example. |
+
+Both artifacts emit a `(1, 21, 1024)` quantile tensor (21 quantile
+levels × 64 output patches × 16 steps-per-patch = 1024 horizon
+steps). Slice the median (`preds[0, 10]`) for a point forecast and
+clip to your needed prediction length.
+
+## Performance
+
+Latency measured with TensorRT + `trtexec`, GPU compute time only
+(`--noDataTransfers`), CUDA Graph + Spin Wait enabled, clocks locked
+(`nvpmodel -m 0 && jetson_clocks` on Jetson).
+
+### Jetson Orin Nano Super
+
+#### ctx=512
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/chronos-2-quantized-trt/chronos-2-quantized-trt__orin-mountain-view__latency_ctx512.svg" alt="Chronos-2 INT8 latency, ctx=512" width="640">
+</p>
+
+| Build | Mean latency (ms) |
+|---|---|
+| TensorRT FP16 | **2.977** |
+| TensorRT `--best` | 2.974 |
+| **embedl INT8** | **2.432** |
+| Speedup (FP16 → embedl INT8) | **1.22×** |
+
+#### ctx=2048
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/chronos-2-quantized-trt/chronos-2-quantized-trt__orin-mountain-view__latency_ctx2048.svg" alt="Chronos-2 INT8 latency, ctx=2048" width="640">
+</p>
+
+| Build | Mean latency (ms) |
+|---|---|
+| TensorRT FP16 | **4.482** |
+| TensorRT `--best` | 4.482 |
+| **embedl INT8** | **3.482** |
+| Speedup (FP16 → embedl INT8) | **1.29×** |
+
+## Accuracy
+
+Evaluated on the
+[GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval)
+benchmark — 125 task configurations spanning 50 datasets ×
+{short, medium, long} horizons. Aggregate WQL (weighted quantile
+loss, lower is better) reported using the
+[TIME-paper normalization](https://arxiv.org/html/2602.12147v2):
+geomean of per-task ratio against the Seasonal-Naive baseline.
+
+| Metric | FP32 baseline | **embedl INT8 ctx=512** | **embedl INT8 ctx=2048** |
+|---|---|---|---|
+| Geomean WQL / Seasonal-Naive | 0.549 | **0.634** | **0.618** |
+| Geomean WQL / FP32 | 1.000 | **1.156×** | **1.126×** |
+| Median WQL / FP32 | 1.000 | 1.074× | 1.045× |
+| Cells within 10 % of FP32 | — | 71 / 125 (57 %) | 79 / 125 (63 %) |
+| Cells within 20 % of FP32 | — | 96 / 125 (77 %) | 98 / 125 (78 %) |
+| Cells beating FP32 | — | 14 / 125 | 19 / 125 |
+
+**How to read the headline number.** Geomean WQL/S-Naive 0.634
+(ctx=512) and 0.618 (ctx=2048) means the INT8 model retains the
+bulk of `chronos-2`'s skill margin over the no-model Seasonal-Naive
+baseline. The FP32 model sits at 0.549 by the same convention; the
+INT8 versions are 15-16 % closer to S-Naive but still convincingly
+beat it on the geomean.
+
+**Where the regression concentrates.** Worst-case cells are
+out-of-distribution low-frequency series (`us_births/M`,
+`m4_hourly/{medium,long}`) and high-frequency long-horizon
+forecasts (`solar/10T/{medium,long}`). The full per-task CSVs
+ship with the artifacts; check them before deploying to a domain
+that resembles those outliers.
+
+## How This Was Built
+
+1. **Trace.** `torch.export` on `amazon/chronos-2` at static
+   ctx={512, 2048}, `num_output_patches=64` (1024-step horizon).
+2. **Transform.** embedl-deploy's TensorRT pattern set recomposes
+   chronos-2's manual SDPA + RoPE into fused INT8-ready modules.
+3. **Quantize (PTQ).** Per-tensor symmetric INT8 activations +
+   per-channel INT8 weights. Calibrated on a 1327-window cohort
+   drawn from 28 diverse GIFT-Eval **training** splits (no eval-set
+   leakage).
+4. **Export.** ONNX opset 20 + onnxslim + onnxsim simplification,
+   with Bool→Float casts inserted before Einsum nodes for TRT 10.x
+   compatibility.
+
+## Creating Your Own Optimized Models
+
+This artifact was produced with
+[embedl-deploy](https://github.com/embedl/embedl-deploy), Embedl's
+open-source PyTorch → TensorRT deployment library. The same workflow
+applies to your own models — see
+[the documentation](https://github.com/embedl/embedl-deploy#readme)
+for installation and usage.
+
+## License
+
+| Component | License |
+|---|---|
+| Optimized model artifacts (this repo) | [Embedl Models Community Licence v1.0](https://github.com/embedl/embedl-models/blob/main/LICENSE) — no redistribution as a hosted service |
+| Upstream architecture and weights | [Amazon Chronos-2 License](https://huggingface.co/amazon/chronos-2/blob/main/LICENSE) |
+
+## Contact
+
+We offer engineering support for on-prem/edge deployments and partner
+co-marketing opportunities. Reach out at
+[contact@embedl.com](mailto:contact@embedl.com), or open an issue on
+[GitHub](https://github.com/embedl/embedl-deploy).

chronos_2_fp32_gifteval.csv

@@ -0,0 +1,126 @@
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embedl_chronos_2_ctx2048_int8.onnx

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embedl_chronos_2_ctx512_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ab07da37234417f1449ecee1364863597b57db34e93908ea2f942c6b4d6c427
+size 479671206

infer_trt.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Run inference with embedl-deploy's INT8 chronos-2 on TensorRT.
+
+Reads a context series, runs the model, prints the median forecast.
+Uses ONNX Runtime's TensorrtExecutionProvider; falls back to CUDA / CPU
+if TRT isn't available.
+
+Usage::
+
+    pip install onnxruntime-gpu numpy
+    python infer_trt.py --ctx 512   # or --ctx 2048
+
+The script generates a synthetic seasonal context for demonstration;
+swap in your own series of the right length.
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+
+# Native output grid emitted by the model: 21 quantiles spaced 0.01..0.99
+# step 0.05; the index of the median (q=0.5) is element 10.
+QUANTILE_LEVELS = [round(0.01 + 0.05 * i, 2) for i in range(21)]
+MEDIAN_IDX = 10
+NUM_OUTPUT_PATCHES = 64       # baked into the ONNX
+OUTPUT_PATCH_SIZE = 16        # baked into the ONNX
+MODEL_HORIZON = NUM_OUTPUT_PATCHES * OUTPUT_PATCH_SIZE  # 1024 steps
+
+
+def _make_session(onnx_path: Path) -> ort.InferenceSession:
+    providers = [
+        ("TensorrtExecutionProvider", {"trt_int8_enable": True}),
+        "CUDAExecutionProvider",
+        "CPUExecutionProvider",
+    ]
+    return ort.InferenceSession(str(onnx_path), providers=providers)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ctx", type=int, choices=(512, 2048), default=512,
+        help="Static context length of the artifact to use.",
+    )
+    parser.add_argument(
+        "--horizon", type=int, default=48,
+        help="How many steps of the median forecast to print "
+        f"(capped at MODEL_HORIZON={MODEL_HORIZON}).",
+    )
+    args = parser.parse_args()
+    if args.horizon > MODEL_HORIZON:
+        sys.exit(f"--horizon must be <= {MODEL_HORIZON}")
+
+    onnx_path = Path(__file__).with_name(
+        f"embedl_chronos_2_ctx{args.ctx}_int8.onnx"
+    )
+    if not onnx_path.exists():
+        sys.exit(f"Missing {onnx_path}; run `huggingface-cli download` first.")
+
+    # Synthetic seasonal context for demonstration.
+    t = np.arange(args.ctx, dtype=np.float32)
+    context = (
+        10.0 + 5.0 * np.sin(2 * np.pi * t / 24)
+        + 2.0 * np.sin(2 * np.pi * t / 168)
+        + 0.3 * np.random.RandomState(0).standard_normal(args.ctx).astype(np.float32)
+    ).reshape(1, args.ctx).astype(np.float32)
+    group_ids = np.zeros((1,), dtype=np.int64)
+
+    session = _make_session(onnx_path)
+    print(f"Providers in use: {session.get_providers()}")
+    preds = session.run(
+        None,
+        {"context": context, "group_ids": group_ids},
+    )[0]
+    # preds shape: (1, 21, 1024)
+    median = preds[0, MEDIAN_IDX, : args.horizon]
+    print(f"Median forecast (first {args.horizon} steps):")
+    np.set_printoptions(precision=3, suppress=True, linewidth=120)
+    print(median)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

snaive_gifteval.csv

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