File size: 7,344 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 | """Tests for CircularBuffer."""
import pytest
import torch
from conftest import get_test_device
from mjlab.utils.buffers import CircularBuffer
@pytest.fixture
def device():
"""Test device fixture."""
return get_test_device()
def test_circular_buffer_basic_append(device):
"""Basic append and chronological retrieval (oldest -> newest)."""
buffer = CircularBuffer(max_len=3, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device))
buffer.append(torch.tensor([[5.0, 6.0], [7.0, 8.0]], device=device))
buffer.append(torch.tensor([[9.0, 10.0], [11.0, 12.0]], device=device))
result = buffer.buffer
assert result.shape == (2, 3, 2)
# Oldest to newest.
assert torch.allclose(
result[0], torch.tensor([[1.0, 2.0], [5.0, 6.0], [9.0, 10.0]], device=device)
)
assert torch.allclose(
result[1], torch.tensor([[3.0, 4.0], [7.0, 8.0], [11.0, 12.0]], device=device)
)
def test_circular_buffer_overwrite(device):
"""Overwrites oldest once capacity reached."""
buffer = CircularBuffer(max_len=2, batch_size=1, device=device)
buffer.append(torch.tensor([[1.0]], device=device))
buffer.append(torch.tensor([[2.0]], device=device))
buffer.append(torch.tensor([[3.0]], device=device)) # Overwrites first.
result = buffer.buffer
assert result.shape == (1, 2, 1)
assert torch.allclose(result[0], torch.tensor([[2.0], [3.0]], device=device))
def test_circular_buffer_reset_single_batch(device):
"""Reset clears values and counters for specified batch rows."""
buffer = CircularBuffer(max_len=2, batch_size=3, device=device)
buffer.append(torch.tensor([[1.0], [2.0], [3.0]], device=device))
buffer.append(torch.tensor([[4.0], [5.0], [6.0]], device=device))
# Reset only batch index 1.
buffer.reset(batch_ids=torch.tensor([1], device=device))
result = buffer.buffer
# Oldest-to-newest for each batch row.
assert result[0, 0, 0] == 1.0
assert result[1, 0, 0] == 0.0 # Reset backfilled to zeros for that row.
assert result[2, 0, 0] == 3.0
# current_length reflects reset: rows 0 and 2 had 2 pushes, row 1 is 0.
cl = buffer.current_length
assert torch.equal(cl.cpu(), torch.tensor([2, 0, 2]))
def test_circular_buffer_first_append_fills(device):
"""First append back-fills whole history for each batch row."""
buffer = CircularBuffer(max_len=3, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0], [2.0]], device=device))
result = buffer.buffer
assert torch.allclose(result[0], torch.tensor([[1.0], [1.0], [1.0]], device=device))
assert torch.allclose(result[1], torch.tensor([[2.0], [2.0], [2.0]], device=device))
# And current_length reflects valid frames so far.
cl = buffer.current_length
assert torch.equal(cl.cpu(), torch.tensor([1, 1]))
def test_current_length_counts_and_clamps(device):
"""current_length counts per-batch valid frames and clamps to max_len."""
buffer = CircularBuffer(max_len=4, batch_size=3, device=device)
# Two appends -> length 2 everywhere.
for _ in range(2):
buffer.append(torch.arange(3, dtype=torch.float32, device=device).unsqueeze(-1))
assert torch.equal(buffer.current_length.cpu(), torch.tensor([2, 2, 2]))
# Reset middle row -> it becomes 0.
buffer.reset(batch_ids=[1])
assert torch.equal(buffer.current_length.cpu(), torch.tensor([2, 0, 2]))
# One more append -> rows [0,2] become 3; row 1 becomes 1.
buffer.append(torch.arange(3, dtype=torch.float32, device=device).unsqueeze(-1))
assert torch.equal(buffer.current_length.cpu(), torch.tensor([3, 1, 3]))
# Fill beyond capacity -> clamp to max_len.
for _ in range(5):
buffer.append(torch.arange(3, dtype=torch.float32, device=device).unsqueeze(-1))
assert torch.equal(buffer.current_length.cpu(), torch.tensor([4, 4, 4]))
def test_reset_all_none_path(device):
"""reset(None) zeros the entire buffer and counters without indexing."""
buffer = CircularBuffer(max_len=3, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0], [2.0]], device=device))
buffer.append(torch.tensor([[3.0], [4.0]], device=device))
buffer.reset() # None -> reset all.
# Counters are zero.
assert torch.equal(buffer.current_length.cpu(), torch.tensor([0, 0]))
# Buffer zeros (safe to read even after reset because storage exists).
result = buffer.buffer
assert torch.count_nonzero(result) == 0
def test_getitem_lifo_and_clamp(device):
"""__getitem__ returns lagged frames per-batch (LIFO), clamping when needed."""
buffer = CircularBuffer(max_len=3, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0], [10.0]], device=device)) # t0
buffer.append(torch.tensor([[2.0], [20.0]], device=device)) # t1
buffer.append(torch.tensor([[3.0], [30.0]], device=device)) # t2
# Lag 0 for batch 0 (-> 3), lag 2 for batch 1 (-> oldest 10).
out = buffer[torch.tensor([0, 2], device=device)]
assert torch.allclose(out, torch.tensor([[3.0], [10.0]], device=device))
# Clamp: huge lag for batch 0 -> oldest (1), lag 1 for batch 1 -> 20.
out = buffer[torch.tensor([99, 1], device=device)]
assert torch.allclose(out, torch.tensor([[1.0], [20.0]], device=device))
def test_backfill_after_per_batch_reset(device):
"""After resetting a row, the next append back-fills its entire history for that row."""
buffer = CircularBuffer(max_len=3, batch_size=2, device=device)
buffer.append(torch.tensor([[1.0], [10.0]], device=device)) # t0
buffer.append(torch.tensor([[2.0], [20.0]], device=device)) # t1
# Reset only batch row 1; row 0 remains with 2 valid frames.
buffer.reset(batch_ids=[1])
assert torch.equal(buffer.current_length.cpu(), torch.tensor([2, 0]))
# Next append: row 0 gets new value; row 1 is "first push" -> back-filled.
buffer.append(torch.tensor([[3.0], [99.0]], device=device)) # t2
hist = buffer.buffer # shape (2, 3, 1)
# Row 0 keeps real chronology [1, 2, 3].
assert torch.allclose(
hist[0].squeeze(-1), torch.tensor([1.0, 2.0, 3.0], device=device)
)
# Row 1 is back-filled to all 99s.
assert torch.allclose(
hist[1].squeeze(-1), torch.tensor([99.0, 99.0, 99.0], device=device)
)
def test_errors_and_types(device):
"""Error paths: wrong batch, pre-append access, and bad key size."""
buffer = CircularBuffer(max_len=2, batch_size=2, device=device)
# Wrong batch size on append.
with pytest.raises(ValueError):
buffer.append(torch.tensor([[1.0]], device=device)) # batch_size=1 wrong
# buffer property before first append.
with pytest.raises(RuntimeError):
_ = CircularBuffer(max_len=1, batch_size=1, device=device).buffer
# __getitem__ before any valid pushes.
with pytest.raises(RuntimeError):
_ = buffer[torch.tensor([0, 0], device=device)]
# Now append once so storage exists and counters > 0.
buffer.append(torch.tensor([[1.0], [2.0]], device=device))
# __getitem__ with wrong key length.
with pytest.raises(ValueError):
_ = buffer[torch.tensor([0], device=device)]
def test_dtype_and_device_preserved(device):
"""Buffer preserves dtype and device."""
buffer = CircularBuffer(max_len=2, batch_size=2, device=device)
x = torch.tensor([[1.0], [2.0]], dtype=torch.float32, device=device)
buffer.append(x)
assert buffer.buffer.dtype == torch.float32
assert buffer.buffer.device.type == torch.device(device).type
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