Laurawly commented on a change in pull request #6839:
URL: https://github.com/apache/tvm/pull/6839#discussion_r537977579
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File path: python/tvm/topi/cuda/nms.py
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@@ -97,47 +97,44 @@ def get_valid_counts_ir(
valid_count = ib.buffer_ptr(valid_count)
out = ib.buffer_ptr(out)
out_indices = ib.buffer_ptr(out_indices)
- atomic_add_return = ib.allocate(
- valid_count.dtype, (1,), name="atomic_add_return", scope="local"
- )
- one_count = tvm.tir.const(1, dtype=valid_count.dtype)
one = tvm.tir.const(1, dtype=out.dtype)
- score_threshold = tvm.ir.make_node("FloatImm", dtype="float32",
value=score_threshold)
+ if isinstance(score_threshold, float):
+ score_threshold = tvm.ir.make_node("FloatImm", dtype="float32",
value=score_threshold)
id_index = tvm.ir.make_node("IntImm", dtype="int32", value=id_index)
score_index = tvm.ir.make_node("IntImm", dtype="int32", value=score_index)
max_threads =
int(tvm.target.Target.current(allow_none=False).max_num_threads)
- nthread_tx = max_threads
- nthread_bx = batch_size * num_anchors // max_threads + 1
- tx = te.thread_axis("threadIdx.x")
- bx = te.thread_axis("blockIdx.x")
- ib.scope_attr(tx, "thread_extent", nthread_tx)
- ib.scope_attr(bx, "thread_extent", nthread_bx)
- tid = bx * max_threads + tx
- idxd = tvm.tir.indexdiv
-
- # initialize valid_count
- with ib.if_scope(tid < batch_size):
- valid_count[tid] = 0
- with ib.if_scope(tid < batch_size * num_anchors):
- i = idxd(tid, num_anchors)
- with ib.if_scope(
- tvm.tir.all(
- data[tid * elem_length + score_index] > score_threshold,
- tvm.tir.any(id_index < 0, data[tid * elem_length + id_index]
>= 0),
- )
- ):
- atomic_add_return[0] = atomic_add(
- tvm.tir.call_intrin("handle", "tir.address_of",
valid_count[i]), one_count
- )
- with ib.for_range(0, elem_length) as k:
- out[tid * elem_length + k] = data[tid * elem_length + k]
- out_indices[tid + k] = tid + k
- with ib.else_scope():
- with ib.for_range(0, elem_length) as k:
- out[tid * elem_length + k] = -one
- out_indices[tid + k] = -one_count
-
+ with ib.new_scope():
+ nthread_tx = max_threads
+ nthread_bx = batch_size // max_threads + 1
+ tx = te.thread_axis("threadIdx.x")
+ bx = te.thread_axis("blockIdx.x")
+ ib.scope_attr(tx, "thread_extent", nthread_tx)
+ ib.scope_attr(bx, "thread_extent", nthread_bx)
+ tid = bx * max_threads + tx
+ with ib.if_scope(tid < batch_size):
+ valid_count[tid] = 0
+ i = tid
+ with ib.for_range(0, num_anchors) as j:
+ score = data[(i * num_anchors + j) * elem_length + score_index]
+ with ib.if_scope(
+ tvm.tir.all(
+ score > score_threshold,
+ tvm.tir.any(
+ id_index < 0, data[(i * num_anchors + j) *
elem_length + id_index] >= 0
+ ),
+ )
+ ):
+ with ib.for_range(0, elem_length) as k:
+ out[(i * num_anchors + valid_count[i]) * elem_length +
k] = data[
+ (i * num_anchors + j) * elem_length + k
+ ]
+ out_indices[i * num_anchors + valid_count[i]] = j
+ valid_count[i] += 1
Review comment:
I see. So we lose parallelism in `num_anchors` (could be quite large)
compared with the original implementation. Are we able to keep the level of
parallelism while getting the correct output indices?
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