File size: 7,172 Bytes
a402b9b | 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 | import unittest
import torch
from torch.nn.functional import scaled_dot_product_attention
from sglang.test.test_utils import CustomTestCase
torch.manual_seed(1234)
class TestExtendAttention(CustomTestCase):
def _run_sdpa_forward_extend(
self,
query: torch.Tensor,
output: torch.Tensor,
k_cache: torch.Tensor,
v_cache: torch.Tensor,
req_to_token: torch.Tensor,
req_pool_indices: torch.Tensor,
seq_lens: torch.Tensor,
extend_prefix_lens: torch.Tensor,
extend_seq_lens: torch.Tensor,
scaling=None,
enable_gqa=False,
causal=False,
):
assert seq_lens.shape[0] == extend_prefix_lens.shape[0]
assert seq_lens.shape[0] == extend_seq_lens.shape[0]
# [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
query = query.movedim(0, query.dim() - 2)
start_q, start_kv = 0, 0
for seq_idx in range(seq_lens.shape[0]):
extend_seq_len_q = extend_seq_lens[seq_idx]
prefill_seq_len_q = extend_prefix_lens[seq_idx]
seq_len_kv = seq_lens[seq_idx]
end_q = start_q + extend_seq_len_q
end_kv = start_kv + seq_len_kv
per_req_query = query[:, start_q:end_q, :]
per_req_query_redudant = torch.empty(
(per_req_query.shape[0], seq_len_kv, per_req_query.shape[2]),
dtype=per_req_query.dtype,
device=per_req_query.device,
)
per_req_query_redudant[:, prefill_seq_len_q:, :] = per_req_query
# get key and value from cache. per_req_tokens contains the kv cache
# index for each token in the sequence.
req_pool_idx = req_pool_indices[seq_idx]
per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]
per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
per_req_out_redudant = (
scaled_dot_product_attention(
per_req_query_redudant.unsqueeze(0),
per_req_key.unsqueeze(0),
per_req_value.unsqueeze(0),
enable_gqa=enable_gqa,
scale=scaling,
is_causal=causal,
)
.squeeze(0)
.movedim(query.dim() - 2, 0)
)
output[start_q:end_q, :, :] = per_req_out_redudant[prefill_seq_len_q:, :, :]
start_q, start_kv = end_q, end_kv
return output
def _test_extend_attention_once(self, B, N_CTX, H_Q, H_KV, D, DV, mla=False):
dtype = torch.bfloat16
b_seq_len_prefix = torch.randint(1, N_CTX // 2, (B,), dtype=torch.int32)
if mla:
b_seq_len_prefix.zero_()
b_seq_len_extend = torch.randint(1, N_CTX // 2, (B,), dtype=torch.int32)
b_seq_len = b_seq_len_prefix + b_seq_len_extend
max_len_in_batch = torch.max(b_seq_len, 0)[0].item()
b_req_idx = torch.arange(B, dtype=torch.int32)
req_to_tokens = torch.empty((B, max_len_in_batch), dtype=torch.int32)
b_start_loc = torch.zeros((B,), dtype=torch.int32)
b_start_loc[1:] = torch.cumsum(b_seq_len[:-1], 0)
b_start_loc_extend = torch.zeros((B,), dtype=torch.int32)
b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
for i in range(B):
req_to_tokens[i, : b_seq_len[i]] = torch.arange(
b_start_loc[i], b_start_loc[i] + b_seq_len[i]
)
total_token_num = torch.sum(b_seq_len).item()
extend_token_num = torch.sum(b_seq_len_extend).item()
H_BUF = 1 if mla else H_KV
k_buffer = torch.randn((total_token_num, H_BUF, D), dtype=dtype)
v_buffer = torch.randn((total_token_num, H_BUF, DV), dtype=dtype)
k_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype)
v_extend = torch.empty((extend_token_num, H_KV, DV), dtype=dtype)
q_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype)
for i in range(B):
extend_start_in_buffer = b_start_loc[i] + b_seq_len_prefix[i]
extend_end_in_buffer = b_start_loc[i] + b_seq_len[i]
extend_start = b_start_loc_extend[i]
extend_end = b_start_loc_extend[i] + b_seq_len_extend[i]
k_extend[extend_start:extend_end] = k_buffer[
extend_start_in_buffer:extend_end_in_buffer
]
v_extend[extend_start:extend_end] = v_buffer[
extend_start_in_buffer:extend_end_in_buffer
]
q_extend[extend_start:extend_end] = torch.randn(
(b_seq_len_extend[i], H_Q, D), dtype=dtype
)
# q_extend, k_extend, v_extend, k_buffer and v_buffer supports non-contiguous tensors
q_extend = q_extend.transpose(0, 1).contiguous().transpose(0, 1)
k_extend = k_extend.transpose(0, 1).contiguous().transpose(0, 1)
v_extend = v_extend.transpose(0, 1).contiguous().transpose(0, 1)
k_buffer = k_buffer.transpose(0, 1).contiguous().transpose(0, 1)
v_buffer = v_buffer.transpose(0, 1).contiguous().transpose(0, 1)
b_seq_len_extend = b_seq_len - b_seq_len_prefix
b_start_loc_extend = torch.zeros_like(b_seq_len)
b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
max_len_extend = torch.max(b_seq_len_extend, 0)[0].item()
sm_scale = 1.0 / (D**0.5)
logit_cap = 0.0
# handle index type
b_req_idx = b_req_idx.to(torch.int64)
b_seq_len = b_seq_len.to(torch.int64)
enable_gqa = H_Q != H_KV
o_ref = torch.empty((extend_token_num, H_Q, DV), dtype=dtype)
self._run_sdpa_forward_extend(
q_extend,
o_ref,
k_buffer,
v_buffer,
req_to_tokens,
b_req_idx,
b_seq_len,
b_seq_len_prefix,
b_seq_len_extend,
scaling=sm_scale,
enable_gqa=enable_gqa,
causal=True,
)
o_extend = torch.empty((extend_token_num, H_Q, DV), dtype=dtype)
torch.ops.sgl_kernel.extend_attention_cpu(
q_extend,
k_extend,
v_extend,
o_extend,
k_buffer,
v_buffer,
req_to_tokens,
b_req_idx,
b_seq_len,
b_seq_len_extend,
b_start_loc_extend,
max_len_extend,
sm_scale,
logit_cap,
)
torch.testing.assert_close(o_ref, o_extend, atol=1e-2, rtol=1e-2)
def test_extend_attention(self):
for is_mla in [True, False]:
self._test_extend_attention_once(1, 123, 1, 1, 128, 96, is_mla)
self._test_extend_attention_once(1, 123, 16, 1, 128, 96, is_mla)
self._test_extend_attention_once(4, 1230, 16, 4, 128, 96, is_mla)
self._test_extend_attention_once(1, 9000, 16, 1, 32, 32, is_mla)
if __name__ == "__main__":
unittest.main()
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