CaptainDuke commented on a change in pull request #8479:
URL: https://github.com/apache/tvm/pull/8479#discussion_r680378831
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File path: python/tvm/topi/cuda/scatter.py
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@@ -787,44 +791,94 @@ def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
for i in data_ptr.shape:
fused_shape *= i
- # For now we avoid parallizing over dimensions indexed by `indices` as
- # there may be repeated indices and hadling parallel accumulation can
- # be hard. So we parallelize over X_M .. X_{N-1} instead. This will
- # work well when these dimensions are large enough to saturate memory
- # bandwidth, but performance will be bad when these dimensions are
- # small.
- bx = te.thread_axis("blockIdx.x")
- tx = te.thread_axis("threadIdx.x")
max_threads =
int(tvm.target.Target.current(allow_none=False).max_num_threads)
tdim = min(max_threads, fused_updates_dimension)
- ib.scope_attr(tx, "thread_extent", tdim)
- bdim = ceil_div(fused_updates_dimension, tdim)
- ib.scope_attr(bx, "thread_extent", bdim)
-
- # Copy data into the output. This loop writes to the same portions of
- # memory as the following loop, so we do not need a memory sync.
- with ib.for_range(0, ceil_div(fused_shape, fused_updates_dimension),
name="i") as i:
- index = i * fused_updates_dimension + bx * tdim + tx
- with ib.if_scope(bx * tdim + tx < fused_updates_dimension):
+
+ with ib.new_scope():
+ bdim = ceil_div(fused_shape, tdim)
+ bx = te.thread_axis("blockIdx.x")
+ tx = te.thread_axis("threadIdx.x")
+ ib.scope_attr(bx, "thread_extent", bdim)
+ ib.scope_attr(tx, "thread_extent", tdim)
+
+ index = bx * tdim + tx
+ with ib.if_scope(index < fused_shape):
out[index] = data[index]
- with ib.for_range(0, fused_indices_dimension) as i:
- j = bx * tdim + tx
- with ib.if_scope(j < fused_updates_dimension):
- offset = fused_updates_dimension
- index = j # This is x_M, .. x_{N-1} part of the index into
out.
- # Build up the indices[0, y_0, .. y_{K-1}], .. indices[M-1,
y_0, .. y_{K-1}] part
- # of the index into out.
- for l in reversed(range(indices_ptr.shape[0].value)):
- # indices[i * l * fused_indices_dimension] = indices[l,
y_0, ... y_{k-1}]
- index += offset * indices[i + l * fused_indices_dimension]
- offset *= data_ptr.shape[l]
- if mode == "update":
- out[index] = updates[i * fused_updates_dimension + j]
- elif mode == "add":
- out[index] += updates[i * fused_updates_dimension + j]
- else:
- raise NotImplementedError("scatter_nd mode not in [update,
add]:", mode)
+ # For better performance, we introduce blockIdx.y to implement
for-loops
+ # within one thread.
+ # The code is parallel over the scattered indices, so we use atomic_add
+ # to guarantee correctness when mode=="add"
+
+ # For now, atomic is not supported by target "vulkan", "metal", or
"cuda" with "int64"
+ # So we fallback to normal algorithm, using "+=" rather than atomic_add
+
+ # TODO (CaptainDuke):
+ # Since multiple threads compete for the same write index, which leads
to
+ # non-determinstic output for update mode. We could add a new attribute
+ # "allow_non_deterministic" to scatter_nd op, which is False by
default.
+ # And change ONNX frontend to emit scatter_op with
allow_non_deterministic = True,
Review comment:
Done
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