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Parallelize cumsum in get_valid_counts #7123

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Dec 31, 2020
Merged

Parallelize cumsum in get_valid_counts #7123

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944ee3c
Parallelize cumsum in get_valid_counts
Dec 17, 2020
45ba3cb
make the scan loop exclusive
Dec 18, 2020
9b5b1e1
switch to directly using exclusive scan
Dec 18, 2020
67db9cd
perform inner loop of final writes on anchor threads
Dec 18, 2020
86988e8
fix flaky test
Dec 18, 2020
eafdf9d
remove final cuda kernel
Dec 21, 2020
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134 changes: 101 additions & 33 deletions python/tvm/topi/cuda/nms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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")
Expand Down Expand Up @@ -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
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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.
Expand Down Expand Up @@ -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)
Expand All @@ -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()

Expand Down Expand Up @@ -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",
Expand Down
2 changes: 1 addition & 1 deletion python/tvm/topi/vision/nms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)):
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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")
Expand Down
4 changes: 1 addition & 3 deletions tests/python/relay/test_op_level5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand Down
21 changes: 6 additions & 15 deletions tests/python/topi/python/test_topi_vision.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down