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Refactor sdpa_with_kv_cache tests #4064

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edea73e
Refactor sdpa_with_kv_cache tests
kimishpatel Jun 25, 2024
be02bb6
Update on "Refactor sdpa_with_kv_cache tests"
kimishpatel Jun 26, 2024
76ac850
Update on "Refactor sdpa_with_kv_cache tests"
kimishpatel Jun 26, 2024
75ddbbf
Update on "Refactor sdpa_with_kv_cache tests"
kimishpatel Jun 27, 2024
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15 changes: 14 additions & 1 deletion examples/models/llama2/custom_ops/op_sdpa.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -400,7 +400,20 @@ void cpu_flash_attention(
static_cast<accum_t>(0),
qk_data,
kvBlockSize);
// Apply causal mask, fill unused with -inf
// Apply causal mask, fill unused, i.e. future values, with -inf
// Say you have q @ k.T size = [16, 32]
// With qblock size = 4, say you are processing
// q seq len dim = 8:11.
// Say kvSplitSize = 4
// Then for causal mask, the entries that needs to be
// ignored are
// [8, 9:31], [9, 10:31], [10, 10:31], [11, 11:31]
// Following condition says that num_keys = 8 + 4 =12
// (num_keys - n) <= kvSplitSize
// num_keys <= n + kvSplitSize
// If n + kvSplitSize is larger than 12, then some
// entries need masked out. In our example n = 4
// will qualify for that
if (is_causal && num_keys - n <= kvSplitSize) {
for (int32_t row = 0; row < qBlockSize; ++row) {
int64_t last_col = m + row - n;
Expand Down
121 changes: 39 additions & 82 deletions examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,54 +12,52 @@
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]
q = q.transpose(1, 2)
k_cache[:, start_pos : start_pos + seq_len, :, :] = k
v_cache[:, start_pos : start_pos + seq_len, :, :] = v
sliced_k_cache = k_cache[:, : start_pos + seq_len, :, :]
sliced_v_cache = v_cache[:, : start_pos + seq_len, :, :]
sliced_k_cache = sliced_k_cache.transpose(1, 2)
sliced_v_cache = sliced_v_cache.transpose(1, 2)

num_heads_q = q.size(1)
num_heads_kv = sliced_k_cache.size(1)
if num_heads_q != num_heads_kv:
assert (
num_heads_q % num_heads_kv == 0
), f"{num_heads_q} not divisible by {num_heads_kv}"
n_reps = num_heads_q // num_heads_kv
if n_reps > 1:
sliced_k_cache = sliced_k_cache.repeat_interleave(n_reps, dim=1)
sliced_v_cache = sliced_v_cache.repeat_interleave(n_reps, dim=1)
out = F.scaled_dot_product_attention(
q, sliced_k_cache, sliced_v_cache, attn_mask=attn_mask
)
out = out.transpose(1, 2)
return out


class SDPATest(unittest.TestCase):

def setUp(self):
torch.manual_seed(42)
self.k_cache = torch.zeros((1, 5, 8, 4))
self.v_cache = torch.zeros((1, 5, 8, 4))
self.k_cache = torch.zeros((1, 10, 8, 4))
self.v_cache = torch.zeros((1, 10, 8, 4))
self.mask = torch.full(
(5, 5),
(10, 10),
float("-inf"),
)
self.mask = torch.triu(self.mask, diagonal=1)

def _sdpa_with_kv_cache_ref(self, q, k, v, k_cache, v_cache, mask, start_pos):
print(f"at start_pos:{start_pos}")
print(q)
print(k)
print(v)
attn_mask = mask[start_pos].view((1, -1))
attn_mask = attn_mask[:, : start_pos + 1]
q = q.transpose(1, 2)
k_cache[:, start_pos] = k
v_cache[:, start_pos] = v
sliced_k_cache = k_cache[:, : start_pos + 1, :, :]
sliced_v_cache = v_cache[:, : start_pos + 1, :, :]
sliced_k_cache = sliced_k_cache.transpose(1, 2)
sliced_v_cache = sliced_v_cache.transpose(1, 2)
# print(sliced_k_cache.size())
# print(torch.matmul(q, sliced_k_cache.transpose(2, 3)))
# print("q @ k")
# qk = torch.matmul(q, sliced_k_cache.transpose(2, 3))
# qk_softmax = torch.softmax(qk, dim=-1)
# qkv = torch.matmul(qk_softmax, sliced_v_cache)
# print(qk)
# print(qk_softmax)
# print(qkv)
out = F.scaled_dot_product_attention(
q, sliced_k_cache, sliced_v_cache, attn_mask=attn_mask
)
out = out.transpose(1, 2)
print(out)
print(f"-------- start pos {start_pos} done -----")
return out

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))
ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -72,7 +70,7 @@ def test_sdpa_with_cache_no_mqa_2(self):
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))

ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -85,7 +83,7 @@ def test_sdpa_with_cache_no_mqa_3(self):
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))

ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -98,7 +96,7 @@ def test_sdpa_with_cache_no_mqa_4(self):
k = torch.rand((1, 1, 8, 4))
v = torch.rand((1, 1, 8, 4))

ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -124,52 +122,11 @@ def setUp(self):
)
self.mask = torch.triu(self.mask, diagonal=1)

def _sdpa_with_kv_cache_ref(self, q, k, v, k_cache, v_cache, mask, start_pos):
print(f"at start_pos:{start_pos}")
print(q)
print(k)
print(v)
attn_mask = mask[start_pos].view((1, -1))
attn_mask = attn_mask[:, : start_pos + 1]
q = q.transpose(1, 2)
k_cache[:, start_pos] = k
v_cache[:, start_pos] = v
sliced_k_cache = k_cache[:, : start_pos + 1, :, :]
sliced_v_cache = v_cache[:, : start_pos + 1, :, :]
sliced_k_cache = sliced_k_cache.transpose(1, 2)
sliced_v_cache = sliced_v_cache.transpose(1, 2)
# print(sliced_k_cache.size())
# print(torch.matmul(q, sliced_k_cache.transpose(2, 3)))
# print("q @ k")
# qk = torch.matmul(q, sliced_k_cache.transpose(2, 3))
# qk_softmax = torch.softmax(qk, dim=-1)
# qkv = torch.matmul(qk_softmax, sliced_v_cache)
# print(qk)
# print(qk_softmax)
# print(qkv)
num_heads_q = q.size(1)
num_heads_kv = sliced_k_cache.size(1)
if num_heads_q != num_heads_kv:
assert (
num_heads_q % num_heads_kv == 0
), f"{num_heads_q} not divisible by {num_heads_kv}"
n_reps = num_heads_q // num_heads_kv
if n_reps > 1:
sliced_k_cache = sliced_k_cache.repeat_interleave(n_reps, dim=1)
sliced_v_cache = sliced_v_cache.repeat_interleave(n_reps, dim=1)
out = F.scaled_dot_product_attention(
q, sliced_k_cache, sliced_v_cache, attn_mask=attn_mask
)
out = out.transpose(1, 2)
print(out)
print(f"-------- start pos {start_pos} done -----")
return out

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))
ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -181,7 +138,7 @@ 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))
ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand All @@ -196,7 +153,7 @@ 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))
ref_output = self._sdpa_with_kv_cache_ref(
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(
Expand Down