Parallelize cumsum in get_valid_counts (apache#7123)

* Parallelize cumsum in get_valid_counts * make the scan loop exclusive * switch to directly using exclusive scan * perform inner loop of final writes on anchor threads * fix flaky test fix lint * remove final cuda kernel Co-authored-by: masa <[email protected]>
TusharKanekiDey · Jan 20, 2021 · db2c7fa · db2c7fa
1 parent 3c5a91c
commit db2c7fa
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Showing 4 changed files with 109 additions and 52 deletions.
diff --git a/python/tvm/topi/cuda/nms.py b/python/tvm/topi/cuda/nms.py
@@ -95,7 +95,7 @@ def get_valid_boxes_ir(data, valid_boxes, score_threshold, id_index, score_index
     max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
     with ib.new_scope():
         nthread_tx = max_threads
-        nthread_bx = num_anchors // max_threads + 1
+        nthread_bx = ceil_div(num_anchors, max_threads)
         nthread_by = batch_size
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
@@ -151,31 +151,103 @@ def get_valid_indices_ir(valid_boxes, valid_count, valid_indices):
     valid_indices = ib.buffer_ptr(valid_indices)
 
     max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+
+    # Copy boxes to valid_indices
     with ib.new_scope():
         nthread_tx = max_threads
-        nthread_bx = batch_size // max_threads + 1
+        nthread_bx = ceil_div(num_anchors, max_threads)
+        nthread_by = batch_size
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
+        by = te.thread_axis("blockIdx.y")
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
-        tid = bx * max_threads + tx
-        # TODO(mbrookhart): Parallelize the sum and cumsum here
-        current_index = ib.allocate("int32", (1,), name="current_index", scope="local")
-        with ib.if_scope(tid < batch_size):
-            current_index[0] = 0
-            valid_count[tid] = 0
-            with ib.for_range(0, num_anchors) as j:
-                idx = tid * num_anchors + j
-                valid_count[tid] = valid_count[tid] + valid_boxes[idx]
-                with ib.if_scope(valid_boxes[idx] == 1):
-                    valid_indices[idx] = current_index[0]
-                    current_index[0] = current_index[0] + 1
-                with ib.else_scope():
-                    valid_indices[idx] = -1
+        ib.scope_attr(by, "thread_extent", nthread_by)
+        tid = bx * nthread_tx + tx
+        with ib.if_scope(tid < num_anchors):
+            valid_indices[by, tid] = valid_boxes[by, tid]
+
+    nthread_tx = max_threads
+    nthread_bx = ceil_div(num_anchors, max_threads)
+    nthread_by = batch_size
+
+    ## The following algorithm performs parallel exclusive scan to get
+    ## a tensor that can later be used to select valid indices
+    # Up Sweep of exclusive scan
+    lim = tvm.tir.generic.cast(
+        tvm.tir.ceil(tvm.tir.log2(tvm.tir.generic.cast(num_anchors, "float64"))), "int64"
+    )
+    with ib.for_range(0, lim, dtype="int64") as l2_width:
+        width = 2 << l2_width
+
+        with ib.new_scope():
+            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",
+                tvm.tir.generic.cast(ceil_div(num_anchors, max_threads * width), "int32"),
+            )
+            tid = bx * nthread_tx + tx
+
+            by = te.thread_axis("blockIdx.y")
+            ib.scope_attr(by, "thread_extent", nthread_by)
+            start = ib.allocate("int64", (1,), name="start", scope="local")
+            middle = ib.allocate("int64", (1,), name="middle", scope="local")
+            end = ib.allocate("int64", (1,), name="end", scope="local")
+            start[0] = width * tid
+            with ib.if_scope(start[0] < num_anchors):
+                middle[0] = start[0] + tvm.tir.indexdiv(width, 2)
+                end[0] = tvm.te.min(start[0] + width, num_anchors)
+                with ib.if_scope(middle[0] < num_anchors):
+                    valid_indices[by * num_anchors + end[0] - 1] += valid_indices[
+                        by * num_anchors + middle[0] - 1
+                    ]
+
+    # Down Sweep of exclusive scan
+    with ib.new_scope():
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(bx, "thread_extent", batch_size)
+        with ib.if_scope(bx < batch_size):
+            valid_count[bx] = valid_indices[(bx + 1) * num_anchors - 1]
+            valid_indices[(bx + 1) * num_anchors - 1] = 0
+
+    with ib.for_range(0, lim, dtype="int64") as l2_width:
+        width = 2 << (lim - l2_width - 1)
+
+        with ib.new_scope():
+            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",
+                tvm.tir.generic.cast(ceil_div(num_anchors, max_threads * width), "int32"),
+            )
+            tid = bx * nthread_tx + tx
+
+            by = te.thread_axis("blockIdx.y")
+            ib.scope_attr(by, "thread_extent", nthread_by)
+            start = ib.allocate("int64", (1,), name="start", scope="local")
+            middle = ib.allocate("int64", (1,), name="middle", scope="local")
+            end = ib.allocate("int64", (1,), name="end", scope="local")
+            tmp = ib.allocate("int32", (1,), name="end", scope="local")
+            start[0] = width * tid
+            with ib.if_scope(tvm.tir.all(start[0] < num_anchors)):
+                middle[0] = start[0] + tvm.tir.indexdiv(width, 2)
+                end[0] = tvm.tir.min(start[0] + width, num_anchors)
+                with ib.if_scope(middle[0] < num_anchors):
+                    tmp[0] = valid_indices[by * num_anchors + middle[0] - 1]
+                    valid_indices[by * num_anchors + middle[0] - 1] = valid_indices[
+                        by * num_anchors + end[0] - 1
+                    ]
+                    valid_indices[by * num_anchors + end[0] - 1] += tmp[0]
+
     return ib.get()
 
 
-def get_valid_counts_ir(data, valid_indices, out, out_indices):
+def get_valid_counts_ir(data, valid_indices, valid_boxes, out, out_indices):
     """Low level IR to get valid count of bounding boxes
     given a score threshold. Also prepares to move valid boxes to the
     top of input data.
@@ -203,8 +275,9 @@ def get_valid_counts_ir(data, valid_indices, out, out_indices):
     ib = tvm.tir.ir_builder.create()
 
     data = ib.buffer_ptr(data)
-
     valid_indices = ib.buffer_ptr(valid_indices)
+    valid_boxes = ib.buffer_ptr(valid_boxes)
+
     out = ib.buffer_ptr(out)
     out_indices = ib.buffer_ptr(out_indices)
     one = tvm.tir.const(1, dtype=out.dtype)
@@ -213,41 +286,36 @@ def get_valid_counts_ir(data, valid_indices, out, out_indices):
     nthread_tx = max_threads
     nthread_bx = num_anchors // max_threads + 1
     nthread_by = batch_size
-    nthread_bz = elem_length
     with ib.new_scope():
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
         by = te.thread_axis("blockIdx.y")
-        bz = te.thread_axis("blockIdx.z")
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
         ib.scope_attr(by, "thread_extent", nthread_by)
-        ib.scope_attr(bz, "thread_extent", nthread_bz)
         tid = bx * max_threads + tx
         with ib.if_scope(tid < num_anchors):
             i = by
             j = tid
-            k = bz
-            out[(i * num_anchors + j) * elem_length + k] = -one
+            with ib.for_range(0, elem_length) as k:
+                out[(i * num_anchors + j) * elem_length + k] = -one
             out_indices[i * num_anchors + j] = -1
     with ib.new_scope():
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
         by = te.thread_axis("blockIdx.y")
-        bz = te.thread_axis("blockIdx.z")
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
         ib.scope_attr(by, "thread_extent", nthread_by)
-        ib.scope_attr(bz, "thread_extent", nthread_bz)
         tid = bx * max_threads + tx
         with ib.if_scope(tid < num_anchors):
             i = by
             j = tid
-            k = bz
-            with ib.if_scope(valid_indices[i, tid] >= 0):
-                out[(i * num_anchors + valid_indices[i, tid]) * elem_length + k] = data[
-                    (i * num_anchors + j) * elem_length + k
-                ]
+            with ib.if_scope(valid_boxes[i, tid] > 0):
+                with ib.for_range(0, elem_length) as k:
+                    out[(i * num_anchors + valid_indices[i, tid]) * elem_length + k] = data[
+                        (i * num_anchors + j) * elem_length + k
+                    ]
                 out_indices[i * num_anchors + valid_indices[i, tid]] = j
     return ib.get()
 
@@ -321,10 +389,10 @@ def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
 
     out, out_indices = te.extern(
         [data.shape, (batch_size, num_anchors)],
-        [data, valid_indices],
-        lambda ins, outs: get_valid_counts_ir(ins[0], ins[1], outs[0], outs[1]),
+        [data, valid_indices, valid_boxes],
+        lambda ins, outs: get_valid_counts_ir(ins[0], ins[1], ins[2], outs[0], outs[1]),
         dtype=["int32", data.dtype],
-        in_buffers=[data_buf, valid_indices_buf],
+        in_buffers=[data_buf, valid_indices_buf, valid_boxes_buf],
         out_buffers=[out_buf, out_indices_buf],
         name="get_valid_counts",
         tag="get_valid_counts_gpu",

diff --git a/python/tvm/topi/vision/nms.py b/python/tvm/topi/vision/nms.py
@@ -213,7 +213,7 @@ def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
     out_indices: tvm.te.Tensor or numpy NDArray
         Related index in input data.
     """
-    if isinstance(score_threshold, float):
+    if isinstance(score_threshold, (float, int)):
         score_threshold = tvm.tir.const(score_threshold, dtype=data.dtype)
     id_index_const = tvm.tir.const(id_index, "int32")
     score_index_const = tvm.tir.const(score_index, "int32")

diff --git a/tests/python/relay/test_op_level5.py b/tests/python/relay/test_op_level5.py
@@ -313,10 +313,8 @@ def verify_get_valid_counts(dshape, score_threshold, id_index, score_index):
         for target, ctx in tvm.testing.enabled_targets():
             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 opencl doesn't do data rearrangement
-            if target in ["opencl"]:
-                return
             tvm.testing.assert_allclose(out[1].asnumpy(), np_out2, rtol=1e-3, atol=1e-04)
             tvm.testing.assert_allclose(out[2].asnumpy(), np_out3, rtol=1e-3, atol=1e-04)
 

diff --git a/tests/python/topi/python/test_topi_vision.py b/tests/python/topi/python/test_topi_vision.py
@@ -105,27 +105,18 @@ def check_device(device):
         tvm_out1 = tvm.nd.array(np.zeros(np_out1.shape, dtype="int32"), ctx)
         tvm_out2 = tvm.nd.array(np.zeros(np_out2.shape, dtype=dtype), ctx)
         tvm_out3 = tvm.nd.array(np.zeros(np_out3.shape, dtype="int32"), ctx)
-        if device == "llvm":
-            f = tvm.build(s, [data, outs[0], outs[1], outs[2]], device)
-            f(tvm_input_data, tvm_out1, tvm_out2, tvm_out3)
-            tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out3.asnumpy(), np_out3, rtol=1e-3)
-        else:
-            f = tvm.build(s, [data, outs[0], outs[1]], device)
-            f(tvm_input_data, tvm_out1, tvm_out2)
-            tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
+
+        f = tvm.build(s, [data, outs[0], outs[1], outs[2]], device)
+        f(tvm_input_data, tvm_out1, tvm_out2, tvm_out3)
+        tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
+        tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
+        tvm.testing.assert_allclose(tvm_out3.asnumpy(), np_out3, rtol=1e-3)
 
     for device in ["llvm", "cuda", "opencl"]:
         check_device(device)
 
 
 @tvm.testing.uses_gpu
-@pytest.mark.skip(
-    "Skip this test as it is intermittent."
-    "See https://github.com/apache/tvm/pull/4901#issuecomment-595040094"
-)
 def test_get_valid_counts():
     verify_get_valid_counts((1, 1000, 5), 0.5, -1, 0)
     verify_get_valid_counts((1, 2500, 6), 0, 0, 1)