mbrookhart commented on a change in pull request #6839: URL: https://github.com/apache/tvm/pull/6839#discussion_r539700585
########## File path: python/tvm/topi/cuda/nms.py ########## @@ -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 don't see the sort? It's faster, but it's not correct, that PR also disabled the relevant tests I need to ensure ONNX works properly. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org