biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Core][Model runner refactoring 1/N] Refactor attn metadata term (#4518)

Browse Source
This commit is contained in:
SangBin Cho
2024-05-04 02:20:12 +09:00
committed by GitHub
parent 2d7bce9cd5
commit 3521ba4f25
27 changed files with 554 additions and 525 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
tests/kernels/test_prefix_prefill.py
View File

@@ -51,12 +51,12 @@ def test_contexted_kv_attention(
cache_size = 640
block_size = 32
max_block_per_request = 64
subquery_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
query_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
ctx_lens = [random.randint(16, MAX_CTX_LEN) for _ in range(BS)]
seq_lens = [a + b for a, b in zip(subquery_lens, ctx_lens)]
seq_lens = [a + b for a, b in zip(query_lens, ctx_lens)]
num_kv_heads = num_heads // num_queries_per_kv
num_tokens = sum(subquery_lens)
num_tokens = sum(query_lens)
query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
query.uniform_(-1e-3, 1e-3)
output = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
@@ -75,15 +75,15 @@ def test_contexted_kv_attention(
num_kv_heads,
head_size,
dtype=dtype)
k = torch.zeros(sum(subquery_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(subquery_lens), num_kv_heads, head_size, dtype=dtype)
k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
values = torch.arange(0, cache_size, dtype=torch.long)
values = values[torch.randperm(cache_size)]
block_table = values[:BS * max_block_per_request].view(
BS, max_block_per_request)
b_seq_len = torch.tensor(seq_lens, dtype=torch.long)
b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long)
b_start_loc = torch.cumsum(torch.tensor([0] + subquery_lens[:-1],
b_start_loc = torch.cumsum(torch.tensor([0] + query_lens[:-1],
dtype=torch.long),
dim=0)
max_input_len = MAX_SEQ_LEN
@@ -92,7 +92,7 @@ def test_contexted_kv_attention(
dtype=torch.long),
dim=0)
for i in range(BS):
for j in range(subquery_lens[i]):
for j in range(query_lens[i]):
k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
j])
v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
@@ -178,7 +178,7 @@ def test_contexted_kv_attention(
value = value.unsqueeze(0)
attn_bias = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
subquery_lens, seq_lens)
query_lens, seq_lens)
if sliding_window > 0:
attn_bias = attn_bias.make_local_attention_from_bottomright(
sliding_window)