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[Executorch][llama] bug fix for custom sdpa for attention bias #10284

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merged 3 commits into from
Apr 21, 2025
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[Executorch][llama] bug fix for custom sdpa for attention bias #10284

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e6b5f52
[Executorch][llama] bug fix for custom sdpa for attention bias
kimishpatel Apr 17, 2025
1194cc1
Update on "[Executorch][llama] bug fix for custom sdpa for attention …
kimishpatel Apr 18, 2025
6af8457
Update on "[Executorch][llama] bug fix for custom sdpa for attention …
kimishpatel Apr 21, 2025
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3 changes: 2 additions & 1 deletion extension/llm/custom_ops/op_sdpa.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -400,7 +400,8 @@ Tensor& custom_sdpa_out_impl(

ET_CHECK_MSG(q.dim() == 4, "query must be a 4D tensor");

const int64_t num_keys_for_causal_attention = start_pos + seq_len;
const int64_t num_keys_for_causal_attention =
attn_mask.has_value() ? -1 : start_pos + seq_len;

ET_KERNEL_CHECK(
ctx,
Expand Down
283 changes: 0 additions & 283 deletions extension/llm/custom_ops/op_sdpa_with_kv_cache_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -524,289 +524,6 @@ TEST(OpScaledDotProductAttentionTest, LargerTest) {
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_5, 1e-4, 1e-4);
}

TEST(OpScaledDotProductAttentionTest, BasicTestWithAttnMask) {
TensorFactory<executorch::aten::ScalarType::Float> tfFloat;

executorch::aten::Tensor query = tfFloat.make(
{1, 1, 4, 4},
{0.8823,
0.9150,
0.3829,
0.9593,
0.3904,
0.6009,
0.2566,
0.7936,
0.9408,
0.1332,
0.9346,
0.5936,
0.8694,
0.5677,
0.7411,
0.4294});
executorch::aten::Tensor key = tfFloat.make(
{1, 1, 4, 4},
{0.8854,
0.5739,
0.2666,
0.6274,
0.2696,
0.4414,
0.2969,
0.8317,
0.1053,
0.2695,
0.3588,
0.1994,
0.5472,
0.0062,
0.9516,
0.0753});
executorch::aten::Tensor value = tfFloat.make(
{1, 1, 4, 4},
{0.8860,
0.5832,
0.3376,
0.8090,
0.5779,
0.9040,
0.5547,
0.3423,
0.6343,
0.3644,
0.7104,
0.9464,
0.7890,
0.2814,
0.7886,
0.5895});
executorch::aten::Tensor attn_mask = tfFloat.make({1, 1}, {0});
executorch::aten::Tensor key_cache_0 = tfFloat.zeros({1, 5, 4, 4});
executorch::aten::Tensor value_cache_0 = tfFloat.zeros({1, 5, 4, 4});
executorch::aten::Tensor key_cache_1 = tfFloat.zeros({1, 5, 4, 4});
executorch::aten::Tensor value_cache_1 = tfFloat.zeros({1, 5, 4, 4});
executorch::aten::Tensor key_cache_2 = tfFloat.zeros({1, 5, 4, 4});
executorch::aten::Tensor value_cache_2 = tfFloat.zeros({1, 5, 4, 4});
double dropout_p = 0;
bool is_causal = false;
executorch::aten::optional<double> scale;

// start pos: 0 layer id 0
executorch::aten::Tensor ret_expected_0 = tfFloat.make(
{1, 1, 4, 4},
{0.8860,
0.5832,
0.3376,
0.8090,
0.5779,
0.9040,
0.5547,
0.3423,
0.6343,
0.3644,
0.7104,
0.9464,
0.7890,
0.2814,
0.7886,
0.5895});

std::vector<int32_t> out_size = {1, 1, 4, 4};
executorch::aten::Tensor out = tfFloat.zeros(out_size);
executorch::aten::Tensor ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_0,
value_cache_0,
0,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_0, 1e-4, 1e-4);

// start pos: 0 layer id 2
executorch::aten::Tensor ret_expected_1 = tfFloat.make(
{1, 1, 4, 4},
{0.8860,
0.5832,
0.3376,
0.8090,
0.5779,
0.9040,
0.5547,
0.3423,
0.6343,
0.3644,
0.7104,
0.9464,
0.7890,
0.2814,
0.7886,
0.5895});
out = tfFloat.zeros(out_size);
ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_2,
value_cache_2,
0,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_1, 1e-4, 1e-4);

attn_mask = tfFloat.make({1, 2}, {0, 0});
// start pos: 1 layer id 0
executorch::aten::Tensor ret_expected_2 = tfFloat.make(
{1, 1, 4, 4},
{0.8860,
0.5832,
0.3376,
0.8090,
0.5779,
0.9040,
0.5547,
0.3423,
0.6343,
0.3644,
0.7104,
0.9464,
0.7890,
0.2814,
0.7886,
0.5895});
out = tfFloat.zeros(out_size);
ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_0,
value_cache_0,
1,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_2, 1e-4, 1e-4);

// start pos: 1 layer id 1
executorch::aten::Tensor ret_expected_3 = tfFloat.make(
{1, 1, 4, 4},
{0.6486,
0.4270,
0.2472,
0.5922,
0.3669,
0.5740,
0.3522,
0.2173,
0.3635,
0.2088,
0.4071,
0.5423,
0.5110,
0.1822,
0.5107,
0.3817});
out = tfFloat.zeros(out_size);
ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_1,
value_cache_1,
1,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_3, 1e-4, 1e-4);

attn_mask = tfFloat.make({1, 3}, {0, 0, 0});
// start pos: 2 layer id 1
executorch::aten::Tensor ret_expected_4 = tfFloat.make(
{1, 1, 4, 4},
{0.7490,
0.4930,
0.2854,
0.6838,
0.4489,
0.7021,
0.4308,
0.2659,
0.4622,
0.2655,
0.5176,
0.6895,
0.6202,
0.2212,
0.6199,
0.4634});
out = tfFloat.zeros(out_size);
ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_1,
value_cache_1,
2,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_4, 1e-4, 1e-4);

// start pos: 2 layer id 2
executorch::aten::Tensor ret_expected_5 = tfFloat.make(
{1, 1, 4, 4},
{0.7490,
0.4930,
0.2854,
0.6838,
0.4489,
0.7021,
0.4308,
0.2659,
0.4622,
0.2655,
0.5176,
0.6895,
0.6202,
0.2212,
0.6199,
0.4634});
out = tfFloat.zeros(out_size);
ret = op_sdpa_with_kv_cache(
query,
key,
value,
key_cache_2,
value_cache_2,
2,
1,
attn_mask,
dropout_p,
is_causal,
scale,
out);
EXPECT_TENSOR_CLOSE_WITH_TOL(ret, ret_expected_5, 1e-4, 1e-4);
}

TEST(OpScaledDotProductAttentionTest, SequenceTest) {
TensorFactory<executorch::aten::ScalarType::Float> tfFloat;

Expand Down
24 changes: 16 additions & 8 deletions extension/llm/custom_ops/test_sdpa_with_kv_cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,12 +67,14 @@ def test_sdpa_with_cache_no_mqa_1(self):
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
sliced_k_cache = self.k_cache[:, : start_pos + seq_len, :, :]
sliced_v_cache = self.v_cache[:, : start_pos + seq_len, :, :]
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
sliced_k_cache,
sliced_v_cache,
start_pos,
seq_len,
attn_mask,
Expand Down Expand Up @@ -108,12 +110,14 @@ def test_sdpa_with_cache_no_mqa_2(self):
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
sliced_k_cache = self.k_cache[:, : start_pos + seq_len, :, :]
sliced_v_cache = self.v_cache[:, : start_pos + seq_len, :, :]
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
sliced_k_cache,
sliced_v_cache,
start_pos,
seq_len,
attn_mask,
Expand Down Expand Up @@ -150,12 +154,14 @@ def test_sdpa_with_cache_no_mqa_3(self):
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
sliced_k_cache = self.k_cache[:, : start_pos + seq_len, :, :]
sliced_v_cache = self.v_cache[:, : start_pos + seq_len, :, :]
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
sliced_k_cache,
sliced_v_cache,
start_pos,
seq_len,
attn_mask,
Expand Down Expand Up @@ -191,12 +197,14 @@ def test_sdpa_with_cache_no_mqa_4(self):
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
sliced_k_cache = self.k_cache[:, : start_pos + seq_len, :, :]
sliced_v_cache = self.v_cache[:, : start_pos + seq_len, :, :]
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
sliced_k_cache,
sliced_v_cache,
start_pos,
seq_len,
attn_mask,
Expand Down
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