Remove the PyTorch vs ONNX comparison

On the Space, ONNX Runtime took 5.6 s per request against 1.2 s for
PyTorch (identical answers), so the demo stays on PyTorch. The export
also added about a minute to each start.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

app.py

@@ -28,8 +28,6 @@ os.environ.setdefault("LAYA_MODELS", "typed")
 os.environ.setdefault("LAYA_DEVICE", "cpu")
 # About 1 s per request on the Space's CPU: wait for a pause in typing before calling the model.
 os.environ.setdefault("LAYA_DEBOUNCE", "700")
-# Temporary: compare PyTorch and ONNX on the Space's hardware (see GET /bench).
-os.environ.setdefault("LAYA_BENCH", "1")
 
 import gradio as gr  # noqa: E402
 import uvicorn  # noqa: E402

onnx_backend.py

@@ -1,110 +0,0 @@
-"""ONNX Runtime engine for the demo: the original laya checkpoint exported to ONNX at startup.
-
-The export follows laya's scripts/export_onnx.py (Apache 2.0). The resulting file is not on the
-Hub, so it is rebuilt on each start (about a minute on CPU) and kept in LAYA_ONNX_DIR.
-"""
-import os
-import statistics
-import subprocess
-import sys
-import time
-
-ONNX_DIR = os.environ.get("LAYA_ONNX_DIR", os.path.join(os.path.expanduser("~"), ".cache", "laya-onnx"))
-
-
-def onnx_path_for(model_id):
-    return os.path.join(ONNX_DIR, model_id.replace("/", "--"), "model.onnx")
-
-
-def export(model_id, path):
-    """Trace the PyTorch model with variable batch and sequence sizes and save it as ONNX."""
-    import torch
-    from laya.agent import Agent
-
-    agent = Agent(model_id, compile=False, device="cpu")
-    inputs = (
-        torch.randint(0, 100, (1, 16), dtype=torch.long),  # input_ids
-        torch.ones((1, 16), dtype=torch.long),  # attention_mask
-        torch.tensor([[1, 5]], dtype=torch.long),  # marker_pos
-        torch.tensor([[True, True]], dtype=torch.bool),  # marker_mask
-        torch.tensor([0], dtype=torch.long),  # qtype
-    )
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    torch.onnx.export(
-        agent.model, inputs, path,
-        export_params=True, opset_version=18, do_constant_folding=True,
-        input_names=["input_ids", "attention_mask", "marker_pos", "marker_mask", "qtype"],
-        output_names=["logits", "act_logits"],
-        dynamic_axes={
-            "input_ids": {0: "batch_size", 1: "seq_len"},
-            "attention_mask": {0: "batch_size", 1: "seq_len"},
-            "marker_pos": {0: "batch_size", 1: "num_markers"},
-            "marker_mask": {0: "batch_size", 1: "num_markers"},
-            "qtype": {0: "batch_size"},
-            "logits": {0: "batch_size", 1: "num_markers"},
-            "act_logits": {0: "batch_size"},
-        },
-    )
-
-
-def load(model_id):
-    from laya.onnx_agent import ONNXAgent
-
-    path = onnx_path_for(model_id)
-    if not os.path.exists(path):
-        start = time.perf_counter()
-        # A separate process: on ZeroGPU hardware the `spaces` package, already imported here,
-        # replaces torch's CUDA with an emulation that makes the export fail.
-        subprocess.run([sys.executable, os.path.abspath(__file__), model_id, path], check=True)
-        print(f"ONNX export of {model_id} took {time.perf_counter() - start:.0f} s", flush=True)
-    return ONNXAgent(model_id, onnx_path=path)
-
-
-# Texts of increasing length for the comparison; answers must not depend on the engine.
-BENCH_TEXTS = [
-    "Félicitations ! Vous avez gagné un iPhone 17. Cliquez ici pour récupérer votre cadeau.",
-    "Bonjour, je suis CFO d'une scale-up de 120 personnes. Nous devons remplacer notre outil de "
-    "reporting avant la clôture de décembre et nous avons prévu une enveloppe de 40k€.",
-    "Bonjour, je suis directrice générale d'un groupe de distribution de 1 200 salariés. Notre "
-    "plateforme e-commerce plante à chaque pic de ventes et nous perdons du chiffre d'affaires. "
-    "Le conseil a approuvé un budget de 250k€ pour la refondre, je suis seule décisionnaire et la "
-    "mise en ligne doit se faire avant le Black Friday. Pouvez-vous démarrer dans deux semaines ?",
-]
-
-
-def _probabilities(answers):
-    values = []
-    for key in sorted(answers):
-        answer = answers[key]
-        values += [answer["noul"]] if answer["type"] == "noul" else list(answer["probabilities"].values())
-    return values
-
-
-def compare(engines, cases, repeats=3):
-    """Run every case on every text with each engine; report median times and the largest gap."""
-    times = {name: [] for name in engines}
-    worst_gap = 0.0
-    for case in cases.values():
-        questions = case["questions"]()
-        for agent in engines.values():
-            agent.predict("warmup", questions)
-        for text in BENCH_TEXTS:
-            results = {}
-            for name, agent in engines.items():
-                for _ in range(repeats):
-                    start = time.perf_counter()
-                    results[name] = _probabilities(agent.predict(text, questions)["answers"])
-                    times[name].append(time.perf_counter() - start)
-            first, *others = results.values()
-            for other in others:
-                worst_gap = max(worst_gap, max(abs(a - b) for a, b in zip(first, other)))
-    return {
-        "median_ms": {name: round(statistics.median(t) * 1000) for name, t in times.items()},
-        "max_probability_gap": round(worst_gap, 4),
-        "cpu_count": os.cpu_count(),
-        "requests_per_engine": len(times[next(iter(times))]),
-    }
-
-
-if __name__ == "__main__":
-    export(sys.argv[1], sys.argv[2])

requirements.txt

@@ -5,7 +5,3 @@ laya==0.3.20
 fastapi
 uvicorn
 spaces
-# Temporary ONNX comparison (onnx_backend.py).
-onnx
-onnxruntime
-onnxscript