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Optimize Qwen3 decode scope2 SV path #114

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Optimize Qwen3 decode scope2 SV path #114

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42 changes: 15 additions & 27 deletions examples/models/qwen3/qwen3_32b_decode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -252,10 +252,9 @@ def qwen3_decode(

# Stage 2: QK matmul for all active sb blocks.
all_raw_scores = pl.create_tensor([max_ctx_blocks * Q_HEAD_PAD, SEQ_TILE], dtype=pl.FP32)
all_exp_padded = pl.create_tensor([max_ctx_blocks * Q_HEAD_PAD, SEQ_TILE], dtype=pl.BF16)
all_oi_tmp = pl.create_tensor([max_ctx_blocks * Q_HEAD_PAD, head_dim], dtype=pl.FP32)
all_cur_mi = pl.create_tensor([max_ctx_blocks * Q_HEAD_BATCH, 1], dtype=pl.FP32)
all_cur_li = pl.create_tensor([max_ctx_blocks * Q_HEAD_BATCH, 1], dtype=pl.FP32)
all_cur_mi = pl.create_tensor([max_ctx_blocks * Q_HEAD_PAD, 1], dtype=pl.FP32)
all_cur_li = pl.create_tensor([max_ctx_blocks * Q_HEAD_PAD, 1], dtype=pl.FP32)
with pl.at(level=pl.Level.CORE_GROUP, optimization=pl.chunked_loop_optimizer):
for sb in pl.parallel(ctx_blocks, chunk=SB_BATCH):
s0 = sb * SEQ_TILE
Expand All @@ -268,16 +267,17 @@ def qwen3_decode(
raw_scores = pl.matmul(q_padded, k_tile, b_trans=True, out_dtype=pl.FP32)
all_raw_scores = pl.assemble(all_raw_scores, raw_scores, [sb * Q_HEAD_PAD, 0])

# Stage 3: softmax for all active sb blocks.
# Stage 3: softmax + SV matmul for all active sb blocks.
with pl.at(level=pl.Level.CORE_GROUP, optimization=pl.chunked_loop_optimizer):
for sb in pl.parallel(ctx_blocks, chunk=SB_BATCH):
s0 = sb * SEQ_TILE
cache_row0 = b * num_kv_heads * max_seq + kvh * max_seq + s0
valid_len = pl.min(SEQ_TILE, ctx_len - s0)
scores_valid = pl.slice(
all_raw_scores,
[Q_HEAD_BATCH, SEQ_TILE],
[Q_HEAD_PAD, SEQ_TILE],
[sb * Q_HEAD_PAD, 0],
valid_shape=[Q_HEAD_BATCH, valid_len],
valid_shape=[Q_HEAD_PAD, valid_len],
)
scores_padded = pl.fillpad(scores_valid, pad_value=pl.PadValue.min)
scores = pl.mul(scores_padded, attn_scale)
Expand All @@ -286,41 +286,29 @@ def qwen3_decode(
exp_scores_bf16 = pl.cast(exp_scores, target_type=pl.BF16)
exp_scores_fp32 = pl.cast(exp_scores_bf16, target_type=pl.FP32)
cur_li = pl.row_sum(exp_scores_fp32)
all_exp_padded = pl.assemble(all_exp_padded, exp_scores_bf16, [sb * Q_HEAD_PAD, 0])
all_cur_mi = pl.assemble(all_cur_mi, cur_mi, [sb * Q_HEAD_BATCH, 0])
all_cur_li = pl.assemble(all_cur_li, cur_li, [sb * Q_HEAD_BATCH, 0])

# Stage 4: SV matmul for all active sb blocks.
with pl.at(level=pl.Level.CORE_GROUP, optimization=pl.chunked_loop_optimizer):
for sb in pl.parallel(ctx_blocks, chunk=SB_BATCH):
s0 = sb * SEQ_TILE
cache_row0 = b * num_kv_heads * max_seq + kvh * max_seq + s0
exp_tile = pl.slice(
all_exp_padded,
[Q_HEAD_PAD, SEQ_TILE],
[sb * Q_HEAD_PAD, 0],
)
v_tile = pl.slice(
v_cache,
[SEQ_TILE, head_dim],
[cache_row0, 0],
)
oi_tmp = pl.matmul(exp_tile, v_tile, out_dtype=pl.FP32)
oi_tmp = pl.matmul(exp_scores_bf16, v_tile, out_dtype=pl.FP32)
all_oi_tmp = pl.assemble(all_oi_tmp, oi_tmp, [sb * Q_HEAD_PAD, 0])
all_cur_mi = pl.assemble(all_cur_mi, cur_mi, [sb * Q_HEAD_PAD, 0])
all_cur_li = pl.assemble(all_cur_li, cur_li, [sb * Q_HEAD_PAD, 0])

# Stage 5: online softmax accumulation and normalisation.
# Stage 4: online softmax accumulation and normalisation.
with pl.at(level=pl.Level.CORE_GROUP):
oi = pl.slice(all_oi_tmp, [Q_HEAD_BATCH, head_dim], [0, 0])
mi = pl.slice(all_cur_mi, [Q_HEAD_BATCH, 1], [0, 0])
li = pl.slice(all_cur_li, [Q_HEAD_BATCH, 1], [0, 0])
for sb in pl.range(1, ctx_blocks):
oi_tmp_valid = pl.slice(all_oi_tmp, [Q_HEAD_BATCH, head_dim], [sb * Q_HEAD_PAD, 0])
cur_mi = pl.slice(all_cur_mi, [Q_HEAD_BATCH, 1], [sb * Q_HEAD_BATCH, 0])
cur_li = pl.slice(all_cur_li, [Q_HEAD_BATCH, 1], [sb * Q_HEAD_BATCH, 0])
mi_new = pl.maximum(mi, cur_mi)
cur_mi_valid = pl.slice(all_cur_mi, [Q_HEAD_BATCH, 1], [sb * Q_HEAD_PAD, 0])
cur_li_valid = pl.slice(all_cur_li, [Q_HEAD_BATCH, 1], [sb * Q_HEAD_PAD, 0])
mi_new = pl.maximum(mi, cur_mi_valid)
alpha = pl.exp(pl.sub(mi, mi_new))
beta = pl.exp(pl.sub(cur_mi, mi_new))
li = pl.add(pl.mul(alpha, li), pl.mul(beta, cur_li))
beta = pl.exp(pl.sub(cur_mi_valid, mi_new))
li = pl.add(pl.mul(alpha, li), pl.mul(beta, cur_li_valid))
oi = pl.add(pl.row_expand_mul(oi, alpha),
pl.row_expand_mul(oi_tmp_valid, beta))
mi = mi_new
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