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Update extend/decode attention kernel for CPU in sgl-kernel and add UTs (#6405)

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Co-authored-by: mingfeima <mingfei.ma@intel.com>
This commit is contained in:
YanbingJiang
2025-05-20 12:23:17 +08:00
committed by GitHub
parent 83f2d9d4ed
commit 32cc66efa5
4 changed files with 464 additions and 19 deletions
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122
sgl-kernel/csrc/cpu/decode.cpp
View File

@@ -34,6 +34,19 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ acc,
}
}
template <typename scalar_t>
inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ src, int64_t size) {
using bVec = at::vec::Vectorized<scalar_t>;
int64_t d = 0;
for (; d <= size - bVec::size(); d += bVec::size()) {
bVec out_bvec = bVec::loadu(src + d);
out_bvec.store(out + d);
}
for (; d < size; ++d) {
out[d] = src[d];
}
}
// GEMM handles query @ key (indexed) x scale
// A : [M, K]
// B : [N, K] indexed
@@ -611,8 +624,11 @@ void decode_attention_kernel_impl(
scalar_t* __restrict__ output,
float* __restrict__ attn_logits,
const scalar_t* __restrict__ query,
const scalar_t* __restrict__ k_buffer,
const scalar_t* __restrict__ v_buffer,
scalar_t* __restrict__ k_buffer,
scalar_t* __restrict__ v_buffer,
const scalar_t* __restrict__ key,
const scalar_t* __restrict__ value,
const int64_t* __restrict__ loc,
const index_t* __restrict__ req_to_token,
const int64_t* __restrict__ req_pool_indices,
const int64_t* __restrict__ seq_lens,
@@ -625,11 +641,33 @@ void decode_attention_kernel_impl(
int64_t k_strideH,
int64_t v_strideN,
int64_t v_strideH,
int64_t nk_strideN,
int64_t nk_strideH,
int64_t nv_strideN,
int64_t nv_strideH,
float scaling,
float logit_cap,
int64_t max_num_reqs,
int64_t max_context_len,
int64_t max_total_num_tokens) {
at::parallel_for(0, batches * num_heads, 0, [&](int64_t begin, int64_t end) {
int64_t bs{0}, head_id{0};
data_index_init(begin, bs, batches, head_id, num_heads);
for (int64_t i = begin; i < end; i++) {
int64_t loc_val = loc[bs];
scalar_t* k_buffer_ptr = k_buffer + loc_val * k_strideN + head_id * k_strideH;
scalar_t* v_buffer_ptr = v_buffer + loc_val * v_strideN + head_id * v_strideH;
const scalar_t* new_key_ptr = key + bs * nk_strideN + head_id * nk_strideH;
const scalar_t* new_value_ptr = value + bs * nv_strideN + head_id * nv_strideH;
copy_stub<scalar_t>(k_buffer_ptr, new_key_ptr, head_size);
copy_stub<scalar_t>(v_buffer_ptr, new_value_ptr, head_size_v);
// move to the next index
data_index_step(bs, batches, head_id, num_heads);
}
});
using Vec = at::vec::Vectorized<float>;
// block length for k_buffer and v_buffer
@@ -791,8 +829,11 @@ void decode_attention_grouped_kernel_impl(
scalar_t* __restrict__ output,
float* __restrict__ attn_logits,
const scalar_t* __restrict__ query,
const scalar_t* __restrict__ k_buffer,
const scalar_t* __restrict__ v_buffer,
scalar_t* __restrict__ k_buffer,
scalar_t* __restrict__ v_buffer,
const scalar_t* __restrict__ key,
const scalar_t* __restrict__ value,
const int64_t* __restrict__ loc,
const index_t* __restrict__ req_to_token,
const int64_t* __restrict__ req_pool_indices,
const int64_t* __restrict__ seq_lens,
@@ -806,11 +847,33 @@ void decode_attention_grouped_kernel_impl(
int64_t k_strideH,
int64_t v_strideN,
int64_t v_strideH,
int64_t nk_strideN,
int64_t nk_strideH,
int64_t nv_strideN,
int64_t nv_strideH,
float scaling,
float logit_cap,
int64_t max_num_reqs,
int64_t max_context_len,
int64_t max_total_num_tokens) {
at::parallel_for(0, batches * num_heads_kv, 0, [&](int64_t begin, int64_t end) {
int64_t bs{0}, head_kv_id{0};
data_index_init(begin, bs, batches, head_kv_id, num_heads_kv);
for (int64_t i = begin; i < end; i++) {
int64_t loc_val = loc[bs];
scalar_t* k_buffer_ptr = k_buffer + loc_val * k_strideN + head_kv_id * k_strideH;
scalar_t* v_buffer_ptr = v_buffer + loc_val * v_strideN + head_kv_id * v_strideH;
const scalar_t* new_key_ptr = key + bs * nk_strideN + head_kv_id * nk_strideH;
const scalar_t* new_value_ptr = value + bs * nv_strideN + head_kv_id * nv_strideH;
copy_stub<scalar_t>(k_buffer_ptr, new_key_ptr, head_size);
copy_stub<scalar_t>(v_buffer_ptr, new_value_ptr, head_size_v);
// move to the next index
data_index_step(bs, batches, head_kv_id, num_heads_kv);
}
});
using Vec = at::vec::Vectorized<float>;
// block length for k_buffer and v_buffer
@@ -833,14 +896,12 @@ void decode_attention_grouped_kernel_impl(
// partition the heads into blocks for parallel
const int64_t num_groups = num_heads / num_heads_kv;
const int64_t num_blocks = div_up(num_heads, std::min(BLOCK_H, num_groups));
const int64_t num_groups_per_block = div_up(num_groups, BLOCK_H);
const int64_t num_heads_per_block = std::min(num_groups, BLOCK_H);
const int64_t num_blocks = div_up(num_groups, BLOCK_H);
// parallel on [batches, num_blocks, num_kv_splits]
at::parallel_for(0, batches * num_blocks * num_kv_splits, 0, [&](int64_t begin, int64_t end) {
int64_t bs{0}, head_id{0}, kv_id{0};
data_index_init(begin, bs, batches, head_id, num_blocks, kv_id, num_kv_splits);
// parallel on [batches, num_heads_kv, num_blocks, num_kv_splits]
at::parallel_for(0, batches * num_heads_kv * num_blocks * num_kv_splits, 0, [&](int64_t begin, int64_t end) {
int64_t bs{0}, head_kv_id{0}, block_id{0}, kv_id{0};
data_index_init(begin, bs, batches, head_kv_id, num_heads_kv, block_id, num_blocks, kv_id, num_kv_splits);
alignas(64) float s_i[BLOCK_H * BLOCK_N];
float* __restrict__ s_delta = s_i;
@@ -850,15 +911,13 @@ void decode_attention_grouped_kernel_impl(
alignas(64) float m_delta[BLOCK_H];
for (int64_t i = begin; i < end; ++i) {
const int64_t h_start = head_id * num_heads_per_block;
const int64_t h_end = std::min(h_start + num_heads_per_block, num_heads);
const int64_t h_start = head_kv_id * num_groups + block_id * BLOCK_H;
const int64_t h_end = head_kv_id * num_groups + std::min(block_id * BLOCK_H + BLOCK_H, num_groups);
const int64_t h_size = h_end - h_start;
// get query
const scalar_t* __restrict__ q_ptr = query + bs * q_strideM + h_start * q_strideH;
// kv head id and valid block head size
int64_t head_kv_id = head_id / num_groups_per_block;
int64_t seq_len_kv = seq_lens[bs];
int64_t req_pool_id = req_pool_indices[bs];
TORCH_CHECK(seq_len_kv <= max_context_len, "seq_len_kv out of scope!");
@@ -952,7 +1011,7 @@ void decode_attention_grouped_kernel_impl(
}
// move to the next index
data_index_step(bs, batches, head_id, num_blocks, kv_id, num_kv_splits);
data_index_step(bs, batches, head_kv_id, num_heads_kv, block_id, num_blocks, kv_id, num_kv_splits);
}
});
@@ -1004,9 +1063,12 @@ void decode_attention_grouped_kernel_impl(
//
void decode_attention_cpu(
at::Tensor& query,
at::Tensor& output,
at::Tensor& k_buffer,
at::Tensor& v_buffer,
at::Tensor& output,
at::Tensor& key,
at::Tensor& value,
at::Tensor& loc,
at::Tensor& attn_logits,
at::Tensor& req_to_token,
at::Tensor& req_pool_indices,
@@ -1021,9 +1083,15 @@ void decode_attention_cpu(
CHECK_INPUT(query);
CHECK_LAST_DIM_CONTIGUOUS_INPUT(k_buffer);
CHECK_LAST_DIM_CONTIGUOUS_INPUT(v_buffer);
// for MLA, key and value shares the same storage and value could be non-contiguous
CHECK_LAST_DIM_CONTIGUOUS_INPUT(key);
CHECK_LAST_DIM_CONTIGUOUS_INPUT(value);
CHECK_DIM(3, query);
CHECK_DIM(3, k_buffer);
CHECK_DIM(3, v_buffer);
CHECK_DIM(3, key);
CHECK_DIM(3, value);
CHECK_DIM(1, loc);
int64_t num_seqs = seq_lens.size(0);
int64_t max_num_reqs = req_to_token.size(0);
@@ -1037,6 +1105,7 @@ void decode_attention_cpu(
int64_t num_kv_splits = attn_logits.size(2);
CHECK_EQ(loc.numel(), num_seqs);
CHECK_EQ(attn_logits.size(0), num_seqs);
CHECK_EQ(attn_logits.size(1), num_heads);
CHECK_EQ(attn_logits.size(3), head_size_v + 1);
@@ -1047,6 +1116,11 @@ void decode_attention_cpu(
int64_t k_strideH = k_buffer.stride(1);
int64_t v_strideN = v_buffer.stride(0);
int64_t v_strideH = v_buffer.stride(1);
// strides for new key and value
int64_t nk_strideN = key.stride(0);
int64_t nk_strideH = key.stride(1);
int64_t nv_strideN = value.stride(0);
int64_t nv_strideH = value.stride(1);
// check index data types
const auto index_dtype = req_to_token.scalar_type();
@@ -1070,6 +1144,9 @@ void decode_attention_cpu(
query.data_ptr<scalar_t>(),
k_buffer.data_ptr<scalar_t>(),
v_buffer.data_ptr<scalar_t>(),
key.data_ptr<scalar_t>(),
value.data_ptr<scalar_t>(),
loc.data_ptr<int64_t>(),
req_to_token.data_ptr<index_t>(),
req_pool_indices.data_ptr<int64_t>(),
seq_lens.data_ptr<int64_t>(),
@@ -1082,6 +1159,10 @@ void decode_attention_cpu(
k_strideH,
v_strideN,
v_strideH,
nk_strideN,
nv_strideH,
nv_strideN,
nv_strideH,
sm_scale,
logit_cap,
max_num_reqs,
@@ -1095,6 +1176,9 @@ void decode_attention_cpu(
query.data_ptr<scalar_t>(),
k_buffer.data_ptr<scalar_t>(),
v_buffer.data_ptr<scalar_t>(),
key.data_ptr<scalar_t>(),
value.data_ptr<scalar_t>(),
loc.data_ptr<int64_t>(),
req_to_token.data_ptr<index_t>(),
req_pool_indices.data_ptr<int64_t>(),
seq_lens.data_ptr<int64_t>(),
@@ -1108,6 +1192,10 @@ void decode_attention_cpu(
k_strideH,
v_strideN,
v_strideH,
nk_strideN,
nk_strideH,
nv_strideN,
nv_strideH,
sm_scale,
logit_cap,
max_num_reqs,

7
sgl-kernel/csrc/cpu/torch_extension_cpu.cpp
View File

@@ -49,9 +49,12 @@ std::tuple<at::Tensor, at::Tensor> biased_grouped_topk_cpu(
// attention
void decode_attention_cpu(
at::Tensor& query,
at::Tensor& output,
at::Tensor& k_cache,
at::Tensor& v_cahce,
at::Tensor& v_cache,
at::Tensor& output,
at::Tensor& key,
at::Tensor& value,
at::Tensor& loc,
at::Tensor& attn_logits,
at::Tensor& req_to_token,
at::Tensor& req_pool_indices,

167
test/srt/cpu/test_decode.py Normal file
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@@ -0,0 +1,167 @@
import unittest
import torch
from sgl_kernel.common_ops import decode_attention_cpu as decode_attention
from torch.nn.functional import scaled_dot_product_attention
from sglang.test.test_utils import CustomTestCase
class TestDecodeAttention(CustomTestCase):
def _run_sdpa_forward_decode(
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,
scaling=None,
enable_gqa=False,
causal=False,
):
# [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]):
seq_len_q = 1
seq_len_kv = seq_lens[seq_idx]
end_q = start_q + seq_len_q
end_kv = start_kv + seq_len_kv
per_req_query = query[:, start_q:end_q, :]
# 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 = (
scaled_dot_product_attention(
per_req_query.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
start_q, start_kv = end_q, end_kv
return output
def _test_grouped_decode_attention_once(self, B, H_Q, H_KV, D, D_V, device):
dtype = torch.bfloat16
# This represents the number of tokens already in the sequence
seq_len = 1024
total_tokens = B * seq_len
sm_scale = 1.0 / (D**0.5)
logit_cap = 0.0
num_kv_splits = 8
enable_gqa = H_Q != H_KV
# q represents the new token being generated, one per batch
q = torch.randn(B, H_Q, D, dtype=dtype, device=device)
# k_buffer and v_buffer represent all previous tokens
k_buffer = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=device)
v_buffer = torch.randn(total_tokens, H_KV, D_V, dtype=dtype, device=device)
key = torch.randn(B, H_KV, D, dtype=dtype)
value = torch.randn(B, H_KV, D_V, dtype=dtype)
loc = torch.randint(0, 10, (B,)).to(torch.int64)
# set kv cache
k_buffer[loc] = key
v_buffer[loc] = value
# o will have the same shape as q
o = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)
o_grouped = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)
req_to_token = (
torch.arange(total_tokens, device=device)
.reshape(B, seq_len)
.to(torch.int32)
)
b_req_idx = torch.arange(B, device=device).to(torch.int64)
b_seq_len = torch.full((B,), seq_len, device=device).to(torch.int64)
attn_logits = torch.empty(
(B, H_Q, num_kv_splits, D_V + 1),
dtype=torch.float32,
device=device,
)
# k_buffer, v_buffer, key and value supports non-contiguous tensors
k_buffer = k_buffer.transpose(0, 1).contiguous().transpose(0, 1)
v_buffer = v_buffer.transpose(0, 1).contiguous().transpose(0, 1)
key = key.transpose(0, 1).contiguous().transpose(0, 1)
value = value.transpose(0, 1).contiguous().transpose(0, 1)
decode_attention(
q,
k_buffer,
v_buffer,
o,
key,
value,
loc,
attn_logits,
req_to_token,
b_req_idx,
b_seq_len,
sm_scale,
logit_cap,
)
self._run_sdpa_forward_decode(
q,
o_grouped,
k_buffer,
v_buffer,
req_to_token,
b_req_idx,
b_seq_len,
scaling=sm_scale,
enable_gqa=enable_gqa,
)
cos_sim = torch.nn.functional.cosine_similarity(
o.flatten(), o_grouped.flatten(), dim=0
)
self.assertGreater(cos_sim.item(), 0.99)
torch.testing.assert_close(o, o_grouped, atol=3e-2, rtol=1e-6)
def _test_grouped_decode_attention(self, device="cuda"):
configs = [
(2, 16, 16, 64, 64),
(2, 16, 1, 16, 16),
(2, 32, 8, 33, 55),
(2, 16, 1, 64, 64),
(2, 64, 1, 13, 13),
(2, 128, 1, 80, 80),
(2, 128, 2, 512, 512),
(1, 16, 1, 576, 512),
(1, 16, 16, 576, 512),
(1, 22, 1, 576, 512),
(1, 40, 8, 128, 128),
]
for B, H_Q, H_KV, D, D_V in configs:
self._test_grouped_decode_attention_once(
B, H_Q, H_KV, D, D_V, device=device
)
def test_grouped_decode_attention(self):
self._test_grouped_decode_attention("cpu")
if __name__ == "__main__":
unittest.main()

187
test/srt/cpu/test_extend.py Normal file
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@@ -0,0 +1,187 @@
import unittest
import torch
from sgl_kernel.common_ops import extend_attention_cpu as extend_attention
from torch.nn.functional import scaled_dot_product_attention
from sglang.test.test_utils import CustomTestCase
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
)
# k_extend, v_extend, k_buffer and v_buffer supports non-contiguous tensors
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)
extend_attention(
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)
if __name__ == "__main__":
unittest.main()