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[Runtime][Builtin] Using float32 accumulation in attention kernel #16667

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merged 1 commit into from
Mar 4, 2024
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[Runtime][Builtin] Using float32 accumulation in attention kernel #16667

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12 changes: 6 additions & 6 deletions tests/python/relax/test_runtime_builtin_paged_attention_kv_cache_tir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -902,7 +902,7 @@ def batch_prefill_paged_kv(
i, j, k = T.axis.remap("SSR", [li, lj, lk])
with T.init():
S_local[i, j] = 0.0
S_local[i, j] += Q_smem[i, k] * K_smem[j, k] * attn_score_scaling_factor * sm_scale
S_local[i, j] += T.cast(Q_smem[i, k], "float32") * T.cast(K_smem[j, k], "float32") * attn_score_scaling_factor * sm_scale
T.tvm_storage_sync("shared")
for li, lj in T.grid(tile_x, tile_z):
with T.block("S_store"):
Expand Down Expand Up @@ -960,7 +960,7 @@ def batch_prefill_paged_kv(
i, j, k = T.axis.remap("SSR", [li, lj, lk])
with T.init():
O_local[i, j] *= T.exp2(m_prev_smem[i] - m_smem[i])
O_local[i, j] += S_smem[i, k] * V_smem[k, j]
O_local[i, j] += S_smem[i, k] * T.cast(V_smem[k, j], "float32")

# Store O from smem to gmem
for li, lj in T.grid(tile_x, tile_y):
Expand Down Expand Up @@ -1196,7 +1196,7 @@ def batch_decode_paged_kv(
# compute S = Q * K * sm_scale
S_reduce_local[0] = 0
for vec in T.serial(VEC_SIZE):
S_reduce_local[0] += Q_local[vec] * K_local[vec] * attn_score_scaling_factor * sm_scale
S_reduce_local[0] += T.cast(Q_local[vec], "float32") * T.cast(K_local[vec], "float32") * attn_score_scaling_factor * sm_scale

with T.block("block_cross_thread"):
T.reads(S_reduce_local[0])
Expand Down Expand Up @@ -1230,7 +1230,7 @@ def batch_decode_paged_kv(
for vec in T.vectorized(VEC_SIZE):
V_local[vec] = V_smem[tz * bdy * tile_size_per_bdx + j, tx * VEC_SIZE + vec]
for vec in T.vectorized(VEC_SIZE):
O_local[vec] += V_local[vec] * S_local[j]
O_local[vec] += T.cast(V_local[vec], "float32") * S_local[j]

if bdz > 1:
# allreduce over bdz
Expand Down Expand Up @@ -1445,7 +1445,7 @@ def batch_prefill_ragged_kv(
i, j, k = T.axis.remap("SSR", [li, lj, lk])
with T.init():
S_local[i, j] = 0.0
S_local[i, j] += Q_smem[i, k] * K_smem[j, k] * attn_score_scaling_factor * sm_scale
S_local[i, j] += T.cast(Q_smem[i, k], "float32") * T.cast(K_smem[j, k], "float32") * attn_score_scaling_factor * sm_scale
T.tvm_storage_sync("shared")
for li, lj in T.grid(tile_x, tile_z):
with T.block("S_store"):
Expand Down Expand Up @@ -1503,7 +1503,7 @@ def batch_prefill_ragged_kv(
i, j, k = T.axis.remap("SSR", [li, lj, lk])
with T.init():
O_local[i, j] *= T.exp2(m_prev_smem[i] - m_smem[i])
O_local[i, j] += S_smem[i, k] * V_smem[k, j]
O_local[i, j] += S_smem[i, k] * T.cast(V_smem[k, j], "float32")

# Store O from smem to gmem
for li, lj in T.grid(tile_x, tile_y):
Expand Down