Skip to content

More test refactor to enable attnetion mask #4065

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
kimishpatel wants to merge 4 commits into from
Closed

More test refactor to enable attnetion mask #4065

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
1cca1d6
More test refactor to enable attnetion mask
kimishpatel Jun 25, 2024
fceeba3
Update on "More test refactor to enable attnetion mask"
kimishpatel Jun 26, 2024
1c1307c
Update on "More test refactor to enable attnetion mask"
kimishpatel Jun 26, 2024
7da38bc
Update on "More test refactor to enable attnetion mask"
kimishpatel Jun 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion examples/models/llama2/custom_ops/op_sdpa_with_kv_cache_test.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,6 @@ attn_mask=attn_mask)

/*
Missing tests:
1. Add back tests with attention masks
2. Add different seq lengths on Q
3. Test for different batch sizes
4. Apply causal attention
Expand Down
146 changes: 123 additions & 23 deletions examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,7 @@
from .sdpa_with_kv_cache import custom_ops_lib # noqa


def _sdpa_with_kv_cache_ref(q, k, v, k_cache, v_cache, mask, start_pos):
seq_len = q.size(1)
attn_mask = mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]
def _sdpa_with_kv_cache_ref(q, k, v, k_cache, v_cache, attn_mask, start_pos, seq_len):
q = q.transpose(1, 2)
k_cache[:, start_pos : start_pos + seq_len, :, :] = k
v_cache[:, start_pos : start_pos + seq_len, :, :] = v
Expand Down Expand Up @@ -52,59 +49,150 @@ def setUp(self):
float("-inf"),
)
self.mask = torch.triu(self.mask, diagonal=1)
self.use_mask_with_custom_op = False

def test_sdpa_with_cache_no_mqa_1(self):
q = torch.rand((1, 1, 8, 4))
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))
start_pos = 0
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]
ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 0
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 0, 1, None, 0, False
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
start_pos,
seq_len,
attn_mask,
0,
False,
)
else:
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, False
)
self.assertTrue(torch.allclose(ref_output, op_output))

def test_sdpa_with_cache_no_mqa_2(self):
q = torch.rand((1, 1, 8, 4))
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))
start_pos = 1
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]

ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 1
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 1, 1, None, 0, False
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
start_pos,
seq_len,
attn_mask,
0,
False,
)
else:
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, False
)

self.assertTrue(torch.allclose(ref_output, op_output))

def test_sdpa_with_cache_no_mqa_3(self):
q = torch.rand((1, 1, 8, 4))
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))
start_pos = 2
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]

ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 2
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 2, 1, None, 0, False
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
start_pos,
seq_len,
attn_mask,
0,
False,
)
else:
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, False
)
self.assertTrue(torch.allclose(ref_output, op_output))

def test_sdpa_with_cache_no_mqa_4(self):
q = torch.rand((1, 1, 8, 4))
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))
start_pos = 3
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]

ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 3
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 3, 1, None, 0, False
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
if self.use_mask_with_custom_op:
attn_mask = attn_mask.contiguous()
op_output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.k_cache,
self.v_cache,
start_pos,
seq_len,
attn_mask,
0,
False,
)
else:
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, False
)
self.assertTrue(torch.allclose(ref_output, op_output))


class SDPAWithAttentionMaskTest(SDPATest):

def setUp(self):
torch.manual_seed(42)
self.k_cache = torch.zeros((1, 10, 8, 4))
self.v_cache = torch.zeros((1, 10, 8, 4))
self.mask = torch.full(
(10, 10),
100.642,
)
self.use_mask_with_custom_op = True


class SDPATestWithMQA(unittest.TestCase):

def setup_caches(self):
Expand All @@ -126,8 +214,12 @@ def test_sdpa_with_cache_mqa_1(self):
q = torch.rand((1, 1, self.n_heads_q, 4))
k = torch.rand((1, 1, self.n_heads_kv, 4))
v = torch.rand((1, 1, self.n_heads_kv, 4))
start_pos = 0
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]
ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 0
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 0, 1, None, 0, False
Expand All @@ -138,8 +230,12 @@ def test_sdpa_with_cache_mqa_2(self):
q = torch.rand((1, 1, self.n_heads_q, 4))
k = torch.rand((1, 1, self.n_heads_kv, 4))
v = torch.rand((1, 1, self.n_heads_kv, 4))
start_pos = 1
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]
ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 1
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 1, 1, None, 0, False
Expand All @@ -153,8 +249,12 @@ def test_sdpa_with_cache_mqa_3(self):
q = torch.rand((1, 1, self.n_heads_q, 4))
k = torch.rand((1, 1, self.n_heads_kv, 4))
v = torch.rand((1, 1, self.n_heads_kv, 4))
start_pos = 1
seq_len = q.size(1)
attn_mask = self.mask[start_pos : start_pos + seq_len, :]
attn_mask = attn_mask[:, : start_pos + seq_len]
ref_output = _sdpa_with_kv_cache_ref(
q, k, v, self.k_cache, self.v_cache, self.mask, 1
q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
)
op_output = torch.ops.llama.sdpa_with_kv_cache(
q, k, v, self.k_cache, self.v_cache, 1, 1, None, 0, False
Expand Down