Laurawly commented on a change in pull request #6839: URL: https://github.com/apache/tvm/pull/6839#discussion_r539704122
########## 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: Actually, I mentioned in PR #5339 that the model is `ssd_resnet50_v1` with (1, 3, 512, 512) input shape and with Thrust turned on in build. And you can use TVM profiling tool to get the timing for each op. ---------------------------------------------------------------- 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