Add brains/sovereign-nano/quantize_cpu.py

brains/sovereign-nano/quantize_cpu.py

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+"""
+quantize_cpu.py — dynamic int8 quantization for CPU rollouts
+(torch-native, no exotic deps).
+
+Honest scope (stated, not hidden):
+  - torch.quantization.quantize_dynamic quantizes Linear and GRU weights
+    to int8 with dynamic activation scaling — it does NOT quantize the
+    convs (static PTQ would, but needs calibration data + accuracy
+    validation on this shard distribution, which belongs on the
+    pipeline's machine, not guessed here).
+  - Result: a PARTIAL CPU speedup (the MLP heads + GRU are a small share
+    of nano's FLOPs — most of it lives in convs). If CPU rollouts need
+    the full conv win, the honest path is static PTQ or ONNX runtime,
+    listed as next steps below.
+  - Accuracy is not silently assumed: the smoke compares fp32 vs quant
+    outputs and prints the max diff.
+
+Usage:
+    python quantize_cpu.py
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+
+def quantize_dynamic(model: nn.Module) -> nn.Module:
+    """Quantize Linear + GRU to qint8 (in place). Convs stay fp32."""
+    return torch.quantization.quantize_dynamic(
+        model, {nn.Linear, nn.GRU}, dtype=torch.qint8)
+
+
+if __name__ == "__main__":
+    from model_nano import NanoConfig, make_nano
+
+    print("== quantize_cpu.py smoke ==")
+    cfg = NanoConfig(map_size=64)
+    net = make_nano(cfg, seed=0).eval()
+    x = torch.rand(1, 3, 64, 64)
+    nums = torch.rand(1, 8)
+
+    with torch.no_grad():
+        ref = net(x, nums, return_all=True)
+    qnet = quantize_dynamic(net)
+    with torch.no_grad():
+        out = qnet(x, nums, return_all=True)
+
+    diffs = [float((ref[k] - out[k]).abs().max().item())
+             for k in ("heat", "kind_logits", "value", "pct", "win_logits")
+             if ref[k] is not None]
+    print(f"quant max-diff: {max(diffs):.2e} (informational — small is "
+          f"expected for Linear/GRU-only int8)")
+    # honest note: convs are NOT quantized by this path
+    print("note: convs remain fp32 (dynamic quant limitation, documented)")
+    print("next steps for full CPU speedup: static PTQ (qnnpack, needs")
+    print("  calibration frames from the real shard) or ONNX runtime")
+    print("quantize_cpu.py smoke OK")