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#!/usr/bin/env python3
"""Run the frozen stagewise causally whitened no-KP capture screen."""
import argparse
import json
import math
import os
import subprocess
import sys
import time
import torch
import torch.nn.functional as F
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from sdil.conv import (CIFARHierarchicalFAResNet,
causal_conv_diagonal_least_squares_fit,
causal_readout_least_squares_fit,
conv_hierarchical_alignment_report,
layerwise_causal_feedback_observation)
from sdil.data import DATA_DIR, get_cifar_image_splits
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def provenance():
def run(command):
return subprocess.run(
command, cwd=ROOT, check=True, capture_output=True,
text=True).stdout.strip()
return {
"git_commit": run(["git", "rev-parse", "HEAD"]),
"git_tracked_dirty": bool(run(
["git", "status", "--porcelain", "--untracked-files=no"])),
}
def summarize_alignment(report):
values = report["teaching_negative_gradient_cosine"]
early = max(1, len(values) // 3)
ratios = report["feedback_forward_norm_ratio"]
cosines = report["feedback_forward_cosine"]
return {
"per_layer": values,
"early_third_alignment": sum(values[:early]) / early,
"all_layer_alignment": sum(values) / len(values),
"mean_feedback_forward_cosine": sum(cosines) / len(cosines),
"min_feedback_forward_norm_ratio": min(ratios),
"max_feedback_forward_norm_ratio": max(ratios),
"feedback_forward_cosine": cosines,
"feedback_forward_norm_ratio": ratios,
}
def forward_state(net):
return [value.clone() for value in (
net.W + net.gamma + net.beta + net.running_mean + net.running_var
+ net.mW + net.mgamma + net.mbeta
+ [net.W_out, net.b_out, net.mW_out, net.mb_out])]
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--device", default="cuda")
parser.add_argument("--data_dir", default=DATA_DIR)
parser.add_argument("--out", default="results/oral_a_v6_calibration/result.json")
args = parser.parse_args()
settings = {
"depth": 20, "width": 16, "seed": 0, "loader_seed": 0,
"batch_size": 128, "train_limit": 10000,
"val_examples": 5000, "split_seed": 2027,
"normalization": "batchnorm", "residual_scale": 1.0,
"feedback_scale": 1.0, "sigma": 0.01,
"perturb_seed": 5000, "events_per_stage": 20,
"readout_relative_ridge": 1e-6,
"conv_diagonal_relative_ridge": 1e-3,
"alignment_probe": 64, "calibration_augmentation": False,
}
torch.manual_seed(settings["seed"])
if str(args.device).startswith("cuda"):
if not torch.cuda.is_available():
raise RuntimeError("CUDA requested but unavailable")
torch.cuda.manual_seed_all(settings["seed"])
torch.cuda.reset_peak_memory_stats(torch.device(args.device))
train, _, _, input_shape, n_out, split = get_cifar_image_splits(
batch_size=settings["batch_size"], data_dir=args.data_dir,
device=args.device, train_limit=settings["train_limit"],
val_examples=settings["val_examples"], split_seed=settings["split_seed"],
loader_seed=settings["loader_seed"], augment_train=False)
if input_shape != (3, 32, 32) or n_out != 10:
raise AssertionError("unexpected CIFAR dimensions")
net = CIFARHierarchicalFAResNet(
depth=settings["depth"], base_width=settings["width"],
n_classes=10, device=args.device, seed=settings["seed"],
residual_scale=settings["residual_scale"],
normalization=settings["normalization"],
feedback_scale=settings["feedback_scale"])
audit_x = train.x[:settings["alignment_probe"]]
audit_y = train.y[:settings["alignment_probe"]]
fixed = summarize_alignment(
conv_hierarchical_alignment_report(net, audit_x, audit_y))
state_before = forward_state(net)
generator = torch.Generator(device=torch.device(args.device)).manual_seed(
settings["perturb_seed"])
events = 0
def collect(edge_index):
nonlocal events
observations = []
for event_index in range(settings["events_per_stage"]):
start_index = event_index * settings["batch_size"]
stop_index = start_index + settings["batch_size"]
x = train.x[start_index:stop_index]
y = train.y[start_index:stop_index]
clean = net.forward(
x, return_cache=True, training=False, update_stats=False)
signal = (torch.softmax(clean["logits"], dim=1)
- F.one_hot(y, net.n_classes).to(clean["logits"].dtype))
observation = layerwise_causal_feedback_observation(
net, x, y, clean, signal, edge_index=edge_index,
sigma=settings["sigma"], generator=generator)
# These audit tensors are not inputs to either local fit.
observation.pop("direction")
observation.pop("directional")
observations.append(observation)
events += 1
return observations
if str(args.device).startswith("cuda"):
torch.cuda.synchronize(torch.device(args.device))
start = time.time()
stages = []
readout_observations = collect(None)
readout_fit = causal_readout_least_squares_fit(
net, readout_observations,
relative_ridge=settings["readout_relative_ridge"])
stages.append({"kind": "readout", **readout_fit})
print(json.dumps(stages[-1]), flush=True)
del readout_observations
for index in reversed(range(1, len(net.Q))):
observations = collect(index)
fit = causal_conv_diagonal_least_squares_fit(
net, observations,
relative_ridge=settings["conv_diagonal_relative_ridge"])
stages.append({"kind": "convolution", **fit})
print(json.dumps(stages[-1]), flush=True)
del observations
if str(args.device).startswith("cuda"):
torch.cuda.synchronize(torch.device(args.device))
wall_seconds = time.time() - start
state_after = forward_state(net)
forward_state_max_difference = max(
float((before - after).abs().max())
for before, after in zip(state_before, state_after))
learned = summarize_alignment(
conv_hierarchical_alignment_report(net, audit_x, audit_y))
batch = settings["batch_size"]
queries = 2 * events
observations_count = events * batch
clean_forward_examples = events * batch
perturbation_forward_examples = queries * batch
# Teaching and diagonal-correlation accounting is a conservative upper
# bound: every event is charged three complete feedback traversals.
feedback_work = 3 * events * batch * net.apical_macs_per_example
work = {
"stages": len(stages), "edge_events": events,
"logical_batch_loss_queries": queries,
"per_example_causal_observations": observations_count,
"per_example_cross_entropy_terms": 2 * observations_count,
"clean_forward_examples": clean_forward_examples,
"perturbation_forward_examples": perturbation_forward_examples,
"forward_macs": ((clean_forward_examples + perturbation_forward_examples)
* net.forward_macs_per_example),
"feedback_fit_macs_conservative_estimate": feedback_work,
}
work["total_macs_conservative_estimate"] = (
work["forward_macs"] + feedback_work)
finite_values = [
fixed["early_third_alignment"], fixed["all_layer_alignment"],
learned["early_third_alignment"], learned["all_layer_alignment"],
learned["min_feedback_forward_norm_ratio"],
learned["max_feedback_forward_norm_ratio"],
]
for stage in stages:
finite_values.extend(value for value in stage.values()
if isinstance(value, float))
output = {
"schema_version": 1,
"protocol": "oral_a_v6_stagewise_whitened_causal_capture_v1",
"settings": settings, "provenance": provenance(), "split": split,
"architecture": {
"family": "CIFAR 6n+2 ResNet, option-A shortcuts",
"forward_parameters": net.n_forward_parameters,
"adaptive_feedback_parameters": net.n_fixed_feedback_parameters,
"forward_macs_per_example": net.forward_macs_per_example,
"feedback_macs_per_example": net.apical_macs_per_example,
},
"method_audit": {
"stage_order": ["readout"] + list(reversed(range(1, len(net.Q)))),
"forward_weight_reads_in_feedback_fit": 0,
"reverse_mode_learning_operations": 0,
"causal_query_normalization_state": "evaluation_running_statistics",
"ordinary_task_normalization_state": "not_run_forward_frozen",
"forward_state_max_absolute_difference": (
forward_state_max_difference),
},
"fixed_hfa": fixed, "learned_scib": learned,
"stage_fits": stages, "work": work, "wall_seconds": wall_seconds,
"finite": all(math.isfinite(value) for value in finite_values),
"test_examples_touched": 0, "validation_endpoints_observed": 0,
"hardware": {
"device": str(args.device), "torch_version": torch.__version__,
"cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
"cuda_device_name": (torch.cuda.get_device_name(torch.device(args.device))
if str(args.device).startswith("cuda") else None),
"peak_memory_allocated_bytes": (
torch.cuda.max_memory_allocated(torch.device(args.device))
if str(args.device).startswith("cuda") else None),
},
}
os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
with open(args.out, "w") as handle:
json.dump(output, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps({
"fixed_hfa": fixed, "learned_scib": learned, "work": work,
"finite": output["finite"], "wall_seconds": wall_seconds,
"out": args.out,
}, indent=2), flush=True)
if __name__ == "__main__":
main()
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