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fc102e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | #!/usr/bin/env python3
"""Standalone GPU HTM micro-canary for HYDRA/Feather.
This intentionally bypasses the full language-model forward path and exercises
only the HTMLayer CUDA path that failed in the H200 optimal-strict canary. It
prints JSON lines so HF job logs can be parsed mechanically.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
import traceback
from pathlib import Path
from typing import Any
import torch
def ensure_repo_on_path() -> None:
"""Make overlay package imports work from both /app/scripts and repo-root runs."""
candidates = [
Path('/workspace/feather'),
Path(__file__).resolve().parents[1] if len(Path(__file__).resolve().parents) > 1 else None,
]
for candidate in candidates:
if candidate and (candidate / 'subsystems' / 'htm.py').exists():
candidate_s = str(candidate)
if candidate_s not in sys.path:
sys.path.insert(0, candidate_s)
return
def build_htm_env(mode: str) -> dict[str, str]:
"""Return env overrides for the requested HTM diagnostic mode."""
if mode not in {"batched-fused", "fused", "cuda"}:
raise ValueError(f"unknown mode: {mode}")
return {
"HYDRA_FORCE_HTM_CPU": "0",
"HYDRA_HTM_FUSED": "1" if mode in {"batched-fused", "fused"} else "0",
"HYDRA_HTM_BATCHED_FUSED": "1" if mode == "batched-fused" else "0",
# Strict only for batched-fused: the goal is to catch missing batched
# entrypoints loudly. The other modes are deliberate diagnostic bisection
# modes and should be allowed to exercise narrower paths.
"HYDRA_STRICT_OPTIMAL_COMPONENTS": "1" if mode == "batched-fused" else "0",
}
def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--mode", choices=["batched-fused", "fused", "cuda"], default="batched-fused")
parser.add_argument("--batch", type=int, default=int(os.environ.get("HYDRA_BATCH_SIZE", "4")))
parser.add_argument("--seq", type=int, default=int(os.environ.get("HYDRA_HTM_MICRO_SEQ", os.environ.get("HYDRA_MAX_SEQ_LEN", "512"))))
parser.add_argument("--input-bits", type=int, default=int(os.environ.get("HYDRA_HTM_INPUT_BITS", "16384")))
parser.add_argument("--n-columns", type=int, default=int(os.environ.get("HYDRA_HTM_COLUMNS", "2048")))
parser.add_argument("--cells-per-column", type=int, default=int(os.environ.get("HYDRA_HTM_CELLS_PER_COLUMN", "32")))
parser.add_argument("--active-bits", type=int, default=int(os.environ.get("HYDRA_HTM_ACTIVE_BITS", "256")))
parser.add_argument("--seed", type=int, default=1234)
parser.add_argument("--learn", action="store_true")
parser.add_argument("--sync-each", action="store_true", help="use HTMLayer.forward instead of forward_async/forward_await")
parser.add_argument("--dry-run", action="store_true")
return parser.parse_args(argv)
def emit(event: str, **payload: Any) -> None:
print(json.dumps({"event": event, **payload}, sort_keys=True), flush=True)
def make_sparse_sdr(*, batch: int, seq: int, input_bits: int, active_bits: int, device: str, seed: int):
import torch
if active_bits <= 0 or active_bits > input_bits:
raise ValueError("active_bits must be in [1, input_bits]")
gen = torch.Generator(device="cpu")
gen.manual_seed(seed)
sdr = torch.zeros((batch, seq, input_bits), dtype=torch.uint8, device="cpu")
for b in range(batch):
for t in range(seq):
idx = torch.randperm(input_bits, generator=gen)[:active_bits]
sdr[b, t, idx] = 1
return sdr.to(device, non_blocking=False)
def _plan_payload(args: argparse.Namespace, env: dict[str, str]) -> dict[str, Any]:
return {
"mode": args.mode,
"shape": {"batch": args.batch, "seq": args.seq, "input_bits": args.input_bits},
"htm": {"n_columns": args.n_columns, "cells_per_column": args.cells_per_column, "active_bits": args.active_bits},
"learn": bool(args.learn),
"sync_each": bool(args.sync_each),
"env": env,
}
def main(argv: list[str] | None = None) -> int:
args = parse_args(argv)
env = build_htm_env(args.mode)
os.environ.update(env)
emit("plan", **_plan_payload(args, env))
if args.dry_run:
return 0
import torch
ensure_repo_on_path()
from subsystems.htm import HTMLayer
emit(
"cuda_state",
torch_cuda_available=torch.cuda.is_available(),
device_count=torch.cuda.device_count() if torch.cuda.is_available() else 0,
device_name=torch.cuda.get_device_name(0) if torch.cuda.is_available() else None,
)
if not torch.cuda.is_available():
raise RuntimeError("CUDA is required for HTM GPU micro-canary")
device = "cuda"
sdr = make_sparse_sdr(
batch=args.batch,
seq=args.seq,
input_bits=args.input_bits,
active_bits=args.active_bits,
device=device,
seed=args.seed,
)
emit("sdr_ready", dtype=str(sdr.dtype), shape=list(sdr.shape), active_total=int(sdr.sum().item()))
layer = HTMLayer(
input_bits=args.input_bits,
n_columns=args.n_columns,
cells_per_column=args.cells_per_column,
batch_size=args.batch,
seed=args.seed,
learn=args.learn,
use_gpu=True,
reset_each_forward=True,
).to(device)
if args.learn:
layer.train()
else:
layer.eval()
emit("layer_ready", use_gpu=bool(getattr(layer, "_use_gpu", False)), region_count=len(getattr(layer, "_regions", [])))
start = time.perf_counter()
if args.sync_each:
out = layer(sdr)
else:
handle = layer.forward_async(sdr)
emit("forward_submitted", handle_keys=sorted(handle.keys()))
out = layer.forward_await(handle)
torch.cuda.synchronize()
elapsed_ms = (time.perf_counter() - start) * 1000.0
emit("success", elapsed_ms=round(elapsed_ms, 3), output_shape=list(out.shape), output_dtype=str(out.dtype))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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