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#!/usr/bin/env python3
"""Single mechanics smoke test for the BabyAI shared-feedback path."""
from pathlib import Path
import sys
import torch
import torch.nn.functional as F
from minigrid.core.constants import COLOR_TO_IDX, OBJECT_TO_IDX, STATE_TO_IDX
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from sdil.babyai_shared import (
BabyAISharedConfig, BabyAISharedNet, build_history_index, build_vocabulary,
encode_history_visual, history_input_dim, manual_step, missions_to_bow,
select_teaching_signal,
)
from prepare_babyai_shared import generate_split
from babyai_shared_run import rollout_policy
def maximum_difference(left, right):
return max(float((a - b).abs().max()) for a, b in zip(left, right))
def main():
torch.set_num_threads(1)
data = generate_split("BabyAI-GoToObjS6-v1", range(32))
vocabulary = build_vocabulary(data["mission_text"])
missions = torch.from_numpy(missions_to_bow(
data["mission_text"], vocabulary)).to(torch.float64)
cardinalities = (
max(OBJECT_TO_IDX.values()) + 1,
max(COLOR_TO_IDX.values()) + 1,
max(STATE_TO_IDX.values()) + 1,
)
actions = torch.from_numpy(data["action"].astype("int64"))
history_steps = 4
history_indices, previous_actions, history_mask = build_history_index(
data["episode_offset"], data["action"], history_steps)
features = encode_history_visual(
data["image"][history_indices], data["direction"][history_indices],
previous_actions, history_mask, *cardinalities, 7,
device="cpu", dtype=torch.float64)
assert history_mask[:, -1].all()
assert (previous_actions[data["episode_offset"][:-1], -1] == -1).all()
config = BabyAISharedConfig(
input_dim=history_input_dim(*cardinalities, history_steps),
mission_dim=len(vocabulary), width=16, hidden_layers=2,
learning_rate=0.01, reciprocal_learning_rate=0.01,
momentum=0.0, weight_decay=0.0)
base = BabyAISharedNet(config, seed=4101, dtype=torch.float64)
clones = {name: base.clone() for name in (
"bp", "clean_kp", "raw_shared", "sdil")}
reference_logits = clones["bp"].forward_features(
features, missions)["logits"]
forward_identity_error = max(float((
reference_logits
- clones[name].forward_features(features, missions)["logits"]
).abs().max()) for name in clones if name != "bp")
assert forward_identity_error == 0.0
lesioned_logits = base.forward_features(
features, missions, context_enabled=False)["logits"]
mission_lesion_logit_change = float(
(reference_logits - lesioned_logits).abs().max())
assert mission_lesion_logit_change > 0
predictor_reports = clones["sdil"].fit_neutral_predictor(
features, missions)
assert all(row["action_observations"] == 0 for row in predictor_reports)
assert all(row["teaching_observations"] == 0 for row in predictor_reports)
state = clones["sdil"].forward_features(features, missions)
instruction = torch.randn_like(state["context"][0])
used, raw, innovation = select_teaching_signal(
clones["sdil"], 0, instruction, state["context"][0],
state["h"][1], "sdil")
raw_identity_error = float((
raw - instruction - state["context"][0]).abs().max())
innovation_identity_error = float((used - innovation).abs().max())
assert raw_identity_error < 1e-14
assert innovation_identity_error == 0.0
# The manual BP direction must exactly match autograd on the same fixed
# mission-conditioned network.
bp = base.clone()
before_w = [value.clone() for value in bp.W]
before_b = [value.clone() for value in bp.b]
manual_step(bp, features, missions, actions, "bp")
auto_w = [value.clone().requires_grad_(True) for value in before_w]
auto_b = [value.clone().requires_grad_(True) for value in before_b]
hidden = features
for layer in range(config.hidden_layers):
context = missions @ base.C[layer].t()
hidden = torch.tanh(hidden @ auto_w[layer].t()
+ auto_b[layer] + context)
logits = hidden @ auto_w[-1].t() + auto_b[-1]
F.cross_entropy(logits, actions).backward()
bp_direction_error = max(
max(float(((after - before) / config.learning_rate
+ parameter.grad).abs().max())
for after, before, parameter in zip(bp.W, before_w, auto_w)),
max(float(((after - before) / config.learning_rate
+ parameter.grad).abs().max())
for after, before, parameter in zip(bp.b, before_b, auto_b)),
)
assert bp_direction_error < 1e-12
# With a zero mission path and zero predictor, all shared KP rules coincide.
zero = base.clone()
for value in zero.C + zero.P + zero.P_bias:
value.zero_()
clean = zero.clone()
raw_net = zero.clone()
sdil = zero.clone()
manual_step(clean, features, missions, actions, "clean_kp")
manual_step(raw_net, features, missions, actions, "raw_shared")
manual_step(sdil, features, missions, actions, "sdil")
zero_context_error = max(
maximum_difference(clean.W, raw_net.W),
maximum_difference(clean.W, sdil.W),
maximum_difference(clean.Q[1:], raw_net.Q[1:]),
maximum_difference(clean.Q[1:], sdil.Q[1:]),
)
assert zero_context_error < 1e-14
# KP reciprocal parameters receive the same local correlation increment,
# without reading the updated forward tensor.
kp = base.clone()
kp_w = [value.clone() for value in kp.W]
kp_q = [None] + [value.clone() for value in kp.Q[1:]]
manual_step(kp, features, missions, actions, "clean_kp")
reciprocal_direction_error = max(float((
(kp.W[layer] - kp_w[layer]) / config.learning_rate
- (kp.Q[layer] - kp_q[layer]) / config.reciprocal_learning_rate
).abs().max()) for layer in range(1, len(kp.W)))
assert reciprocal_direction_error < 1e-12
assert all(not value.requires_grad for values in (
kp.W, kp.b, kp.Q[1:], kp.C, kp.P, kp.P_bias) for value in values)
rollout = rollout_policy(
base, "BabyAI-GoToObjS6-v1", [50_000, 50_001], vocabulary,
cardinalities, history_steps=history_steps)
assert rollout["episodes"] == 2
print({
"expert_episodes": 32,
"expert_steps": len(actions),
"forward_identity_error": forward_identity_error,
"mission_lesion_logit_change": mission_lesion_logit_change,
"raw_identity_error": raw_identity_error,
"innovation_identity_error": innovation_identity_error,
"bp_direction_error": bp_direction_error,
"zero_context_update_error": zero_context_error,
"reciprocal_direction_error": reciprocal_direction_error,
"predictor_action_observations": max(
row["action_observations"] for row in predictor_reports),
"predictor_teaching_observations": max(
row["teaching_observations"] for row in predictor_reports),
"history_rollout_episodes": rollout["episodes"],
})
if __name__ == "__main__":
main()
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