trevor-m commented on a change in pull request #6108:
URL: https://github.com/apache/incubator-tvm/pull/6108#discussion_r459138745
##########
File path: tests/python/relay/test_op_level5.py
##########
@@ -270,9 +270,9 @@ def verify_get_valid_counts(dshape, score_threshold,
id_index, score_index):
intrp = relay.create_executor("debug", ctx=ctx, target=target)
out = intrp.evaluate(func)(np_data)
tvm.testing.assert_allclose(out[0].asnumpy(), np_out1, rtol=1e-3,
atol=1e-04)
- # get_valid_count for cuda, opencl doesn't do data rearrangement
- if target in ['cuda', 'opencl']:
- return
+ # get_valid_count for opencl doesn't do data rearrangement
+ if target in ['opencl']:
Review comment:
OpenCL shares the cuda implementation, so you can enable this test too.
##########
File path: topi/python/topi/cuda/nms.py
##########
@@ -93,44 +93,41 @@ def get_valid_counts_ir(data, valid_count, out, out_indices,
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)
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
+ nthread_tx = batch_size
+ nthread_bx = 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)
+ tid = tx
+
+ # each thread process one batch
+ valid_count[tid] = 0
+ data_base_ind = tid * num_anchors * elem_length
+ ind_base_ind = tid * num_anchors
+ with ib.for_range(0, num_anchors) as anchor_ind:
+ with ib.for_range(0, elem_length) as k:
+ out[data_base_ind + anchor_ind * elem_length + k] = -one
+ out_indices[ind_base_ind + anchor_ind] = -one_count
+
+ with ib.for_range(0, num_anchors) as anchor_ind:
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",
Review comment:
Since we are no longer using atomic_add, should we remove those
intrinsic definitions?
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