File size: 8,334 Bytes
ee93ecd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | """Tests for DelayBuffer."""
import pytest
import torch
from conftest import get_test_device
from mjlab.utils.buffers import DelayBuffer
@pytest.fixture
def device():
"""Test device fixture."""
return get_test_device()
def make_gen(seed: int, device: str) -> torch.Generator:
"""Create a seeded generator for reproducible tests."""
gen = torch.Generator(device=device)
gen.manual_seed(seed)
return gen
##
# Basic behavior.
##
def test_delay_buffer_zero_lag(device):
"""Zero lag returns current observation."""
buffer = DelayBuffer(min_lag=0, max_lag=0, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0], [2.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[1.0], [2.0]], device=device))
buffer.append(torch.tensor([[3.0], [4.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[3.0], [4.0]], device=device))
def test_delay_buffer_constant_lag(device):
"""Constant lag returns observation from N steps ago."""
buffer = DelayBuffer(min_lag=2, max_lag=2, batch_size=1, device=device)
buffer.append(torch.tensor([[1.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[1.0]], device=device))
buffer.append(torch.tensor([[2.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[1.0]], device=device))
buffer.append(torch.tensor([[3.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[1.0]], device=device))
buffer.append(torch.tensor([[4.0]], device=device))
result = buffer.compute()
assert torch.allclose(result, torch.tensor([[2.0]], device=device))
def test_value_matches_lag_per_env(device):
"""Delayed values correctly match current lags."""
B = 3
buf = DelayBuffer(
0, 2, batch_size=B, per_env=True, device=device, generator=make_gen(1234, device)
)
for t in range(6):
buf.append(torch.full((B, 1), float(t), device=device))
y = buf.compute()
lags = buf.current_lags
for e in range(B):
eff = int(min(lags[e].item(), t))
assert y[e].item() == float(t - eff)
##
# Lag sampling modes.
##
def test_delay_buffer_per_env_lags(device):
"""Per-env mode allows different lags per environment."""
buffer = DelayBuffer(min_lag=0, max_lag=3, batch_size=4, per_env=True, device=device)
for i in range(10):
buffer.append(torch.full((4, 1), float(i), device=device))
buffer.compute()
lags = buffer.current_lags
assert torch.all(lags >= 0)
assert torch.all(lags <= 3)
def test_delay_buffer_shared_lags(device):
"""Shared mode uses same lag across all environments."""
buffer = DelayBuffer(min_lag=0, max_lag=3, batch_size=4, per_env=False, device=device)
for i in range(10):
buffer.append(torch.full((4, 1), float(i), device=device))
buffer.compute()
lags = buffer.current_lags
assert torch.all(lags == lags[0])
##
# Hold probability.
##
def test_hold_prob_always_hold(device):
"""hold_prob=1.0 keeps lag frozen."""
buf = DelayBuffer(
0,
3,
batch_size=1,
device=device,
update_period=1,
hold_prob=1.0,
generator=make_gen(7, device),
)
for t in range(2):
buf.append(torch.tensor([[float(t)]], device=device))
buf.compute()
first = buf.current_lags.item()
for t in range(2, 20):
buf.append(torch.tensor([[float(t)]], device=device))
buf.compute()
assert buf.current_lags.item() == first
def test_hold_prob_never_hold(device):
"""hold_prob=0.0 allows lag to change."""
buf = DelayBuffer(
0,
3,
batch_size=1,
device=device,
update_period=1,
hold_prob=0.0,
generator=make_gen(42, device),
)
for t in range(2):
buf.append(torch.tensor([[float(t)]], device=device))
buf.compute()
prev, changed = buf.current_lags.item(), False
for t in range(2, 20):
buf.append(torch.tensor([[float(t)]], device=device))
buf.compute()
cur = buf.current_lags.item()
if cur != prev:
changed = True
break
prev = cur
assert changed
##
# Update period.
##
def test_delay_buffer_update_period(device):
"""Update period controls how often lags are resampled."""
buffer = DelayBuffer(
min_lag=0,
max_lag=3,
batch_size=1,
update_period=3,
per_env_phase=False,
device=device,
)
lag_history = []
for i in range(12):
buffer.append(torch.tensor([[float(i)]], device=device))
buffer.compute()
lag_history.append(buffer.current_lags[0].item())
assert len(lag_history) == 12
def test_update_period_changes_only_on_schedule(device):
"""Lags update only at scheduled intervals."""
buf = DelayBuffer(
0,
10, # Wider range makes value changes more likely
batch_size=1,
update_period=3,
per_env_phase=False,
device=device,
generator=make_gen(123, device),
)
# Track when lags are updated by checking step_count timing
lag_values = []
for t in range(12):
buf.append(torch.tensor([[float(t)]], device=device))
buf.compute()
lag_values.append(buf.current_lags.item())
# Verify lag stays constant between update periods
# Update happens at step 0, 3, 6, 9
# So: [0-2] same, [3-5] same, [6-8] same, [9-11] same
assert lag_values[0] == lag_values[1] == lag_values[2]
assert lag_values[3] == lag_values[4] == lag_values[5]
assert lag_values[6] == lag_values[7] == lag_values[8]
assert lag_values[9] == lag_values[10] == lag_values[11]
##
# Reset behavior.
##
def test_delay_buffer_reset_all(device):
"""Reset clears all environments."""
buffer = DelayBuffer(min_lag=1, max_lag=2, batch_size=2, device=device)
for i in range(5):
buffer.append(torch.full((2, 1), float(i), device=device))
buffer.compute()
buffer.reset()
assert torch.all(buffer.current_lags == 0)
assert torch.all(buffer._step_count == 0)
def test_delay_buffer_reset_partial(device):
"""Reset with batch_ids only resets specified environments."""
buffer = DelayBuffer(min_lag=0, max_lag=3, batch_size=4, device=device)
for _ in range(5):
buffer.append(torch.arange(4, device=device).unsqueeze(1).float())
buffer.compute()
lags_before = buffer.current_lags.clone()
buffer.reset(batch_ids=torch.tensor([0, 2], device=device))
lags_after = buffer.current_lags
assert lags_after[0] == 0
assert lags_after[2] == 0
assert lags_after[1] == lags_before[1]
assert lags_after[3] == lags_before[3]
def test_partial_reset_then_backfill(device):
"""Partial reset returns zeros until next append backfills."""
B = 3
buf = DelayBuffer(1, 2, batch_size=B, device=device, generator=make_gen(9, device))
for t in range(3):
x = torch.arange(1, B + 1, device=device).float().unsqueeze(1) * 10 + t
buf.append(x)
buf.compute()
buf.reset(batch_ids=torch.tensor([1], device=device))
y = buf.compute()
assert torch.allclose(y[1], torch.zeros_like(y[1]))
x_new = torch.tensor([[111.0], [999.0], [333.0]], device=device)
buf.append(x_new)
y2 = buf.compute()
assert torch.allclose(y2[1], torch.tensor([[999.0]], device=device))
##
# Error handling.
##
def test_delay_buffer_not_initialized(device):
"""Compute before append raises error."""
buffer = DelayBuffer(min_lag=0, max_lag=3, batch_size=1, device=device)
with pytest.raises(RuntimeError, match="Buffer not initialized"):
buffer.compute()
def test_append_wrong_batch_raises(device):
"""Appending wrong batch size raises error."""
buf = DelayBuffer(0, 1, batch_size=2, device=device)
with pytest.raises(ValueError):
buf.append(torch.zeros(3, 1, device=device))
def test_delay_buffer_validation(device):
"""Input validation catches invalid parameters."""
with pytest.raises(ValueError, match="min_lag must be >= 0"):
DelayBuffer(min_lag=-1, max_lag=3, batch_size=1, device=device)
with pytest.raises(ValueError, match="max_lag.*must be >= min_lag"):
DelayBuffer(min_lag=5, max_lag=3, batch_size=1, device=device)
with pytest.raises(ValueError, match="hold_prob must be in"):
DelayBuffer(min_lag=0, max_lag=3, batch_size=1, hold_prob=1.5, device=device)
with pytest.raises(ValueError, match="update_period must be >= 0"):
DelayBuffer(min_lag=0, max_lag=3, batch_size=1, update_period=-1, device=device)
|