File size: 4,063 Bytes
d02d576 | 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 | import pytest
import torch
import torch.nn.functional as F
from sgl_kernel import tree_speculative_sampling_target_only
test_cases = [
(
1,
1,
[3, -1, -1, 4, 5, 18, 11, -1, -1, -1, 12, 18],
[[0, 3, 4, 5], [6, 10, 11, -1]],
[3, 2],
),
(
0, # threshold_single
0, # threshold_acc
[1, 2, 18, -1, -1, -1, 11, -1, -1, -1, 12, 18],
[[0, 1, 2, -1], [6, 10, 11, -1]],
[2, 2],
),
]
@pytest.mark.parametrize(
"threshold_single, threshold_acc, expected_predicts, expected_accept_index, expected_accept_token_num",
test_cases,
)
def test_tree_speculative_sampling_target_only(
threshold_single,
threshold_acc,
expected_predicts,
expected_accept_index,
expected_accept_token_num,
):
"""
Tests the tree_speculative_sampling_target_only function using Pytest parameterization.
"""
device = "cuda"
candidates = torch.tensor(
[
[0, 1, 2, 3, 4, 5],
[7, 8, 9, 10, 11, 12],
],
dtype=torch.int64,
device=device,
)
retrive_index = torch.tensor(
[
[0, 1, 2, 3, 4, 5],
[6, 7, 8, 9, 10, 11],
],
dtype=torch.int64,
device=device,
)
retrive_next_token = torch.tensor(
[
[1, 2, -1, 4, 5, -1],
[4, 2, 3, -1, 5, -1],
],
dtype=torch.int64,
device=device,
)
retrive_next_sibling = torch.tensor(
[
[-1, 3, -1, -1, -1, -1],
[-1, -1, -1, -1, 1, -1],
],
dtype=torch.int64,
device=device,
)
target_logits = torch.full((2, 6, 20), 1, dtype=torch.float32, device=device)
target_logits[0, 0, 3] = 10
target_logits[0, 3, 4] = 10
target_logits[0, 4, 5] = 10
target_logits[1, 0, 11] = 10
target_logits[1, 4, 12] = 10
for i in range(target_logits.shape[0]):
for j in range(target_logits.shape[1]):
if torch.max(target_logits[i, j]) < 10:
target_logits[i, j, 18] = 10
temperatures = torch.tensor([0.01, 0.01], dtype=torch.float32, device=device)
bs, num_draft_tokens = candidates.shape
num_spec_step = len(expected_accept_index[0])
predict_shape = (len(expected_predicts),)
predicts = torch.full(predict_shape, -1, dtype=torch.int32, device=device)
accept_index = torch.full((bs, num_spec_step), -1, dtype=torch.int32, device=device)
accept_token_num = torch.full((bs,), 0, dtype=torch.int32, device=device)
expanded_temperature = temperatures.unsqueeze(1).unsqueeze(1)
target_probs = F.softmax(target_logits / expanded_temperature, dim=-1)
draft_probs = torch.full_like(target_probs, 0, dtype=torch.float32, device=device)
coins = torch.rand(bs, num_draft_tokens, device=device, dtype=torch.float32)
coins_for_final_sampling = torch.rand(bs, device=device).to(torch.float32)
tree_speculative_sampling_target_only(
predicts=predicts,
accept_index=accept_index,
accept_token_num=accept_token_num,
candidates=candidates,
retrive_index=retrive_index,
retrive_next_token=retrive_next_token,
retrive_next_sibling=retrive_next_sibling,
uniform_samples=coins,
uniform_samples_for_final_sampling=coins_for_final_sampling,
target_probs=target_probs,
draft_probs=draft_probs,
threshold_single=threshold_single,
threshold_acc=threshold_acc,
deterministic=True,
)
assert (
predicts.tolist() == expected_predicts
), f"Predicts mismatch for thresholds ({threshold_single}, {threshold_acc})"
assert (
accept_index.tolist() == expected_accept_index
), f"Accept index mismatch for thresholds ({threshold_single}, {threshold_acc})"
assert (
accept_token_num.tolist() == expected_accept_token_num
), f"Accept token num mismatch for thresholds ({threshold_single}, {threshold_acc})"
if __name__ == "__main__":
pytest.main([__file__])
|