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Support FlashAttention3 page_size > 1 and topk > 1 case with paged attn and spec decode (#7725)

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Yubo Wang
2025-11-25 19:44:41 -08:00
committed by GitHub
parent ca5c8b16f6
commit 18fb51583f
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python/sglang/test/speculative/test_spec_utils.py Normal file
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import unittest
import numpy as np
import torch
from sglang.srt.mem_cache.memory_pool import copy_all_layer_kv_cache_tiled
from sglang.srt.speculative.spec_utils import assign_draft_cache_locs
from sglang.srt.utils import next_power_of_2
BYTES_PER_TILE = 128
class TestSpecUtils(unittest.TestCase):
def setUp(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.data_ptrs = torch.zeros(2, 1, dtype=torch.uint64, device=self.device)
self.k_cache = [
torch.zeros((100, 1, 1), dtype=torch.float32, device=self.device)
]
self.v_cache = [
torch.zeros((100, 1, 1), dtype=torch.float32, device=self.device)
]
self.k_cache[0][:11, 0, 0] = torch.tensor(
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
dtype=torch.float32,
device=self.device,
)
self.v_cache[0][:11, 0, 0] = torch.tensor(
[-0.0, -0.1, -0.2, -0.3, -0.4, -0.5, -0.6, -0.7, -0.8, -0.9, -1.0],
dtype=torch.float32,
device=self.device,
)
self.data_ptrs[0, 0] = self.k_cache[0].data_ptr()
self.data_ptrs[1, 0] = self.v_cache[0].data_ptr()
self.data_strides = torch.tensor(
[
np.prod(x.shape[1:]) * x.dtype.itemsize
for x in self.k_cache + self.v_cache
],
device=self.device,
dtype=torch.int64,
)
def test_assign_draft_cache_locs_single_seq(self):
# Testing Setup: req_to_token starting from 4
# 4,5,6,7,{8,9,10}, 8,9,10 is the last partial page, 3 tokens < page_size=4
# next kv cache will be stored starting 11,12,13...
device = self.device
num_seqs = 1
page_size = 4
speculative_num_steps = 5
topk = 8
seq_lens_num = 7
extend_lens_num = 61 # includes the duplicated last page
req_pool_indices = torch.arange(num_seqs, dtype=torch.int32, device=device)
req_to_token = torch.zeros((num_seqs, 100), dtype=torch.int32, device=device)
req_to_token[0, :seq_lens_num] = torch.tensor(
[4, 5, 6, 7, 8, 9, 10], device=device
)
seq_lens = torch.tensor([seq_lens_num], dtype=torch.int32, device=device)
extend_lens = torch.tensor([extend_lens_num], dtype=torch.int32, device=device)
num_new_pages_per_topk = torch.tensor([2], dtype=torch.int32, device=device)
out_cache_loc = torch.arange(11, 11 + extend_lens_num, device=device)
last_page_lens = torch.tensor([3], dtype=torch.int32, device=device)
last_page_lens_cumsum = torch.cumsum(last_page_lens, dim=0)
duplicate_cache_len = last_page_lens.sum().item() * (topk - 1)
target_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
source_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
assign_draft_cache_locs[(num_seqs,)](
req_pool_indices,
req_to_token,
seq_lens,
extend_lens,
num_new_pages_per_topk,
out_cache_loc,
source_cache_loc,
target_cache_loc,
last_page_lens_cumsum,
duplicate_cache_len,
req_to_token.shape[1],
topk,
speculative_num_steps,
page_size,
next_power_of_2(num_seqs),
next_power_of_2(speculative_num_steps + page_size),
)
out_cache_loc = out_cache_loc[: num_seqs * topk * speculative_num_steps]
expected_source_cache_loc = torch.tensor(
[8, 9, 10] * (topk - 1), device=device, dtype=torch.int32
)
assert torch.allclose(source_cache_loc, expected_source_cache_loc)
copy_all_layer_kv_cache_tiled[(len(self.data_ptrs),)](
self.data_ptrs,
self.data_strides,
target_cache_loc,
source_cache_loc,
len(target_cache_loc),
next_power_of_2(len(target_cache_loc)),
BYTES_PER_TILE,
)
assert torch.allclose(
self.k_cache[0][16:19, 0, 0],
torch.tensor(
[0.8, 0.9, 1.0],
dtype=torch.float32,
device=device,
),
)
assert torch.allclose(
self.v_cache[0][16:19, 0, 0],
torch.tensor(
[-0.8, -0.9, -1.0],
dtype=torch.float32,
device=device,
),
)
def test_assign_draft_cache_locs_multi_seq(self):
device = self.device
num_seqs = 3
page_size = 4
speculative_num_steps = 5
topk = 8
req_pool_indices = torch.arange(num_seqs, dtype=torch.int32, device=device)
req_to_token = torch.zeros((num_seqs, 100), dtype=torch.int32, device=device)
seq_lens = torch.tensor([8, 7, 5], dtype=torch.int32, device=device)
extend_lens = torch.tensor([64, 64, 64], dtype=torch.int32, device=device)
num_new_pages_per_topk = torch.tensor(
[2, 2, 2], dtype=torch.int32, device=device
)
req_to_token = torch.zeros((num_seqs, 100), dtype=torch.int32, device=device)
req_to_token[0, :8] = torch.tensor([4, 5, 6, 7, 8, 9, 10, 11], device=device)
req_to_token[1, :7] = torch.tensor([4, 5, 6, 7, 8, 9, 10], device=device)
req_to_token[2, :5] = torch.tensor([4, 5, 6, 7, 8], device=device)
last_page_lens = torch.tensor([0, 3, 1], dtype=torch.int32, device=device)
last_page_lens_cumsum = torch.cumsum(last_page_lens, dim=0)
duplicate_cache_len = last_page_lens.sum().item() * (topk - 1)
out_cache_loc = torch.arange(
12, 12 + torch.sum(extend_lens), dtype=torch.int32, device=device
)
target_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
source_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
assign_draft_cache_locs[(num_seqs,)](
req_pool_indices,
req_to_token,
seq_lens,
extend_lens,
num_new_pages_per_topk,
out_cache_loc,
source_cache_loc,
target_cache_loc,
last_page_lens_cumsum,
duplicate_cache_len,
req_to_token.shape[1],
topk,
speculative_num_steps,
page_size,
next_power_of_2(num_seqs),
next_power_of_2(speculative_num_steps + page_size),
)
out_cache_loc = out_cache_loc[: num_seqs * topk * speculative_num_steps]
# fmt: off
expected_out_cache_loc = torch.tensor([
12, 13, 14, 15, 16,
20, 21, 22, 23, 24,
28, 29, 30, 31, 32,
36, 37, 38, 39, 40,
44, 45, 46, 47, 48,
52, 53, 54, 55, 56,
60, 61, 62, 63, 64,
68, 69, 70, 71, 72,
76, 77, 78, 79, 80,
84, 85, 86, 87, 88,
92, 93, 94, 95, 96,
100, 101, 102, 103, 104,
108, 109, 110, 111, 112,
116, 117, 118, 119, 120,
124, 125, 126, 127, 128,
132, 133, 134, 135, 136,
140, 141, 142, 143, 144,
148, 149, 150, 151, 152,
156, 157, 158, 159, 160,
164, 165, 166, 167, 168,
172, 173, 174, 175, 176,
180, 181, 182, 183, 184,
188, 189, 190, 191, 192,
196, 197, 198, 199, 200
], device=device, dtype=torch.int32)
expected_source_cache_loc = torch.tensor([8, 9, 10] * 7 + [8] * 7, device=device, dtype=torch.int32)
expected_target_cache_loc = torch.tensor([
81, 82, 83, 89, 90, 91, 97, 98, 99, 105, 106, 107, 113, 114,
115, 121, 122, 123, 129, 130, 131, 147, 155, 163, 171, 179, 187, 195
], device=device, dtype=torch.int32)
# fmt: on
assert torch.allclose(out_cache_loc, expected_out_cache_loc)
assert torch.allclose(source_cache_loc, expected_source_cache_loc)
assert torch.allclose(target_cache_loc, expected_target_cache_loc)
copy_all_layer_kv_cache_tiled[(len(self.data_ptrs),)](
self.data_ptrs,
self.data_strides,
target_cache_loc,
source_cache_loc,
len(target_cache_loc),
next_power_of_2(len(target_cache_loc)),
BYTES_PER_TILE,
)
assert torch.allclose(
self.k_cache[0][81:84, 0, 0],
torch.tensor(
[0.8, 0.9, 1.0],
dtype=torch.float32,
device=device,
),
)
assert torch.allclose(
self.v_cache[0][81:84, 0, 0],
torch.tensor(
[-0.8, -0.9, -1.0],
dtype=torch.float32,
device=device,
),
)
def test_assign_draft_cache_locs_page_size_1(self):
# Test to make sure page_size=1 not affected
device = self.device
num_seqs = 1
page_size = 1
speculative_num_steps = 5
topk = 8
seq_lens_num = 7
extend_lens_num = topk * speculative_num_steps
req_pool_indices = torch.arange(num_seqs, dtype=torch.int32, device=device)
req_to_token = torch.zeros((num_seqs, 100), dtype=torch.int32, device=device)
req_to_token[0, :seq_lens_num] = torch.tensor(
[4, 5, 6, 7, 8, 9, 10], device=device
)
seq_lens = torch.tensor([seq_lens_num], dtype=torch.int32, device=device)
extend_lens = torch.tensor([extend_lens_num], dtype=torch.int32, device=device)
num_new_pages_per_topk = torch.tensor([2], dtype=torch.int32, device=device)
out_cache_loc = torch.arange(11, 11 + extend_lens_num, device=device)
last_page_lens = torch.tensor([3], dtype=torch.int32, device=device)
duplicate_cache_len = 0
target_cache_loc = None
source_cache_loc = None
last_page_lens_cumsum = None
assign_draft_cache_locs[(num_seqs,)](
req_pool_indices,
req_to_token,
seq_lens,
extend_lens,
num_new_pages_per_topk,
out_cache_loc,
source_cache_loc,
target_cache_loc,
last_page_lens_cumsum,
duplicate_cache_len,
req_to_token.shape[1],
topk,
speculative_num_steps,
page_size,
next_power_of_2(num_seqs),
next_power_of_2(speculative_num_steps + page_size),
)
out_cache_loc = out_cache_loc[: num_seqs * topk * speculative_num_steps]
expected_out_cache_loc = torch.arange(11, 11 + extend_lens_num, device=device)
assert torch.allclose(out_cache_loc, expected_out_cache_loc)
def test_assign_draft_cache_locs_page_size_gt_spec_steps(self):
device = self.device
num_seqs = 1
page_size = 16
speculative_num_steps = 4
topk = 3
seq_lens_num = 12
pool_len = 256
req_pool_indices = torch.arange(num_seqs, dtype=torch.int32, device=device)
req_to_token = torch.zeros(
(num_seqs, pool_len), dtype=torch.int32, device=device
)
req_to_token[0, :seq_lens_num] = torch.arange(
seq_lens_num, dtype=torch.int32, device=device
)
seq_lens = torch.tensor([seq_lens_num], dtype=torch.int32, device=device)
last_page_len = seq_lens_num % page_size
last_page_lens = torch.tensor([last_page_len], dtype=torch.int32, device=device)
last_page_lens_cumsum = torch.cumsum(last_page_lens, dim=0)
num_new_pages_per_topk_val = (
last_page_len + speculative_num_steps + page_size - 1
) // page_size
num_new_pages_per_topk = torch.tensor(
[num_new_pages_per_topk_val], dtype=torch.int32, device=device
)
extend_lens_num = num_new_pages_per_topk_val * page_size * topk
extend_lens = torch.tensor([extend_lens_num], dtype=torch.int32, device=device)
out_cache_loc = torch.arange(
2000, 2000 + extend_lens_num, dtype=torch.int32, device=device
)
duplicate_cache_len = last_page_lens.sum().item() * (topk - 1)
target_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
source_cache_loc = torch.zeros(
duplicate_cache_len, dtype=torch.int32, device=device
)
assign_draft_cache_locs[(num_seqs,)](
req_pool_indices,
req_to_token,
seq_lens,
extend_lens,
num_new_pages_per_topk,
out_cache_loc,
source_cache_loc,
target_cache_loc,
last_page_lens_cumsum,
duplicate_cache_len,
req_to_token.shape[1],
topk,
speculative_num_steps,
page_size,
next_power_of_2(num_seqs),
next_power_of_2(speculative_num_steps + page_size),
)
trimmed = out_cache_loc[: num_seqs * topk * speculative_num_steps]
expected = []
for topk_id in range(topk):
start = seq_lens_num + topk_id * num_new_pages_per_topk_val * page_size
expected.append(
req_to_token[0, start : start + speculative_num_steps].clone()
)
expected_out_cache_loc = torch.cat(expected)
assert torch.allclose(trimmed, expected_out_cache_loc)
if __name__ == "__main__":
unittest.main()