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Update: migrate scope3 sort/gather to tensor-level API and reduce MAX_SEQ #140

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Merged
zhangqi-chen merged 1 commit into from
Apr 21, 2026
+34 −41
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Update: migrate scope3 sort/gather to tensor-level API and reduce MAX_SEQ #140

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75 changes: 34 additions & 41 deletions examples/models/deepseek_v3_2/deepseek_v3_2_decode_front_scope3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@


BATCH = 16
MAX_SEQ = 8192
MAX_SEQ = 4096
INDEX_HEAD_DIM = 128
INDEX_TOPK = 2048
CACHE_ROWS_IDX = BATCH * MAX_SEQ
Expand All @@ -45,7 +45,10 @@
Q_VALID = 1
Q_PAD = 16

# sort32 + 4 mrgsort iterations (block_len 64,256,1024,4096) sort MAX_SEQ=8192.
# sort32 + 4 mrgsort iterations (block_len 64,256,1024,4096) sort SORT_LEN=8192.
# SORT_LEN > MAX_SEQ so the full sort buffer is pre-filled with -inf (Stage 0)
# and only [0, ctx_len) contains real scores; tail stays -inf.
SORT_LEN = 8192
MRGSORT_ITERS = 4

# -inf sentinel for score tail. FP32 lowest, since ptoas rejects literal -inf.
Expand All @@ -61,7 +64,7 @@ def deepseek_v3_2_decode_front_scope3(
q_idx: pl.Tensor[[BATCH, INDEX_HEAD_DIM], pl.BF16],
k_cache_idx: pl.Tensor[[CACHE_ROWS_IDX, INDEX_HEAD_DIM], pl.BF16],
seq_lens: pl.Tensor[[BATCH], pl.INT32],
idx_init: pl.Tensor[[1, MAX_SEQ], pl.UINT32],
idx_init: pl.Tensor[[1, SORT_LEN], pl.UINT32],
topk_vals_out: pl.Tensor[[BATCH, INDEX_TOPK], pl.FP32],
topk_idx_out: pl.Tensor[[BATCH, INDEX_TOPK], pl.INT32],
) -> tuple[
Expand Down Expand Up @@ -89,19 +92,20 @@ def deepseek_v3_2_decode_front_scope3(
)

# Transient GM buffers.
scores = pl.create_tensor([BATCH, MAX_SEQ], dtype=pl.FP32)
sorted_gm = pl.create_tensor([BATCH, 2 * MAX_SEQ], dtype=pl.FP32)
# scores is [BATCH, SORT_LEN]: Stage 0 fills the full row with -inf
# so the [MAX_SEQ, SORT_LEN) tail is always -inf for the sort.
scores = pl.create_tensor([BATCH, SORT_LEN], dtype=pl.FP32)
sorted_gm = pl.create_tensor([BATCH, 2 * SORT_LEN], dtype=pl.FP32)
raw_idx_gm = pl.create_tensor([BATCH, INDEX_TOPK], dtype=pl.INT32)

for b in pl.range(0, BATCH, 1):
ctx_len = pl.tensor.read(seq_lens, [b])
ctx_blocks = (ctx_len + SEQ_TILE - 1) // SEQ_TILE

# Stage 0: pre-fill scores[b] with -inf. Stage 2's parallel
# loop only covers [0, ctx_blocks), so untouched tail slots
# keep the create_tensor default of 0.0 without this.
# Stage 0: pre-fill scores[b, 0:SORT_LEN] with -inf so both the
# untouched ctx tail and the [MAX_SEQ, SORT_LEN) pad are -inf.
with pl.at(level=pl.Level.CORE_GROUP):
neg_inf_row = pl.full([1, MAX_SEQ], dtype=pl.FP32, value=FP32_NEG_INF)
neg_inf_row = pl.full([1, SORT_LEN], dtype=pl.FP32, value=FP32_NEG_INF)
scores = pl.assemble(scores, neg_inf_row, [b, 0])

# Stage 1: tiled QK matmul into all_scores[sb*Q_PAD, 0] (row 0 valid).
Expand Down Expand Up @@ -133,43 +137,32 @@ def deepseek_v3_2_decode_front_scope3(
tile_padded = pl.fillpad(tile_valid, pad_value=pl.PadValue.min)
scores = pl.assemble(scores, tile_padded, [b, s0])

# Stage 3: sort32 + 4 mrgsort iterations. Result is [1, 2*MAX_SEQ]
# interleaved (val, idx). Stored to GM because vec→vec tile slicing
# is not supported on a2a3, so downstream gather re-loads from GM.
# Distinct SSA names for each mrgsort so the final tile is the one
# that gets stored.
# Stage 3: sort32 + 4 mrgsort iterations (tensor-level). Operates
# directly on GM slices; result is [1, 2*SORT_LEN] interleaved
# (val, idx). Stored to sorted_gm for gather in Stage 4.
with pl.at(level=pl.Level.CORE_GROUP):
score_row = pl.load(scores, offsets=[b, 0], shapes=[1, MAX_SEQ])
idx_row = pl.load(idx_init, offsets=[0, 0], shapes=[1, MAX_SEQ])
sorted0 = pl.tile.sort32(score_row, idx_row)
sorted1 = pl.tile.mrgsort(sorted0, block_len=64)
sorted2 = pl.tile.mrgsort(sorted1, block_len=256)
sorted3 = pl.tile.mrgsort(sorted2, block_len=1024)
sorted4 = pl.tile.mrgsort(sorted3, block_len=4096)
sorted_gm = pl.store(
sorted4, offsets=[b, 0], output_tensor=sorted_gm
)

# Stage 4: GM-load the first INDEX_TOPK pairs (2*INDEX_TOPK cols),
# gather P0101/P1010 to split vals / idx bits, store to outputs.
score_row = pl.slice(scores, [1, SORT_LEN], [b, 0])
sorted_t = pl.tensor.sort32(score_row, idx_init)
sorted_t = pl.tensor.mrgsort(sorted_t, block_len=64)
sorted_t = pl.tensor.mrgsort(sorted_t, block_len=256)
sorted_t = pl.tensor.mrgsort(sorted_t, block_len=1024)
sorted_t = pl.tensor.mrgsort(sorted_t, block_len=4096)
sorted_gm = pl.assemble(sorted_gm, sorted_t, [b, 0])

# Stage 4: gather P0101/P1010 to split vals / idx bits from the
# first INDEX_TOPK pairs (2*INDEX_TOPK cols) in sorted_gm.
with pl.at(level=pl.Level.CORE_GROUP):
topk_pairs = pl.load(
sorted_gm, offsets=[b, 0], shapes=[1, 2 * INDEX_TOPK]
)
topk_v = pl.tile.gather(
topk_pairs = pl.slice(sorted_gm, [1, 2 * INDEX_TOPK], [b, 0])
topk_v = pl.tensor.gather(
topk_pairs, mask_pattern=pl.tile.MaskPattern.P0101
)
topk_i_raw = pl.tile.gather(
topk_i_raw = pl.tensor.gather(
topk_pairs,
mask_pattern=pl.tile.MaskPattern.P1010,
output_dtype=pl.INT32,
)
topk_vals_out = pl.store(
topk_v, offsets=[b, 0], output_tensor=topk_vals_out
)
raw_idx_gm = pl.store(
topk_i_raw, offsets=[b, 0], output_tensor=raw_idx_gm
)
topk_vals_out = pl.assemble(topk_vals_out, topk_v, [b, 0])
raw_idx_gm = pl.assemble(raw_idx_gm, topk_i_raw, [b, 0])

# Stage 5: GM reload + valid_shape fillpad to mark idx slots past
# ctx_len with PadValue.min (= INT32_MIN < 0).
Expand Down Expand Up @@ -198,7 +191,7 @@ def golden_decode_front_scope3(tensors):
topk_vals_out = tensors["topk_vals_out"]
topk_idx_out = tensors["topk_idx_out"]

scores = torch.full((BATCH, MAX_SEQ), FP32_NEG_INF, dtype=torch.float32)
scores = torch.full((BATCH, SORT_LEN), FP32_NEG_INF, dtype=torch.float32)
for b in range(BATCH):
ctx_len = int(seq_lens[b].item())
q_b = q_idx[b : b + 1]
Expand Down Expand Up @@ -233,7 +226,7 @@ def init_k_cache_idx():
return torch.rand(CACHE_ROWS_IDX, INDEX_HEAD_DIM) - 0.5

def init_idx_init():
return torch.arange(MAX_SEQ, dtype=torch.int32).unsqueeze(0)
return torch.arange(SORT_LEN, dtype=torch.int32).unsqueeze(0)

def init_topk_vals_out():
return torch.zeros((BATCH, INDEX_TOPK), dtype=torch.float32)
Expand All @@ -250,7 +243,7 @@ def init_topk_idx_out():
init_value=init_k_cache_idx,
),
TensorSpec("seq_lens", [BATCH], torch.int32, init_value=seq_lens_data),
TensorSpec("idx_init", [1, MAX_SEQ], torch.int32, init_value=init_idx_init),
TensorSpec("idx_init", [1, SORT_LEN], torch.int32, init_value=init_idx_init),
TensorSpec(
"topk_vals_out",
[BATCH, INDEX_TOPK],
Expand Down
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