tkonolige commented on a change in pull request #8030:
URL: https://github.com/apache/tvm/pull/8030#discussion_r633766017
##########
File path: python/tvm/topi/cuda/sparse.py
##########
@@ -105,13 +105,22 @@ def _callback(op):
return s
-def schedule_cuda_transpose(s, out):
+def schedule_transpose(outs):
Review comment:
moved to transform.py
##########
File path: python/tvm/relay/op/strategy/cuda.py
##########
@@ -1068,3 +1070,23 @@ def unique_strategy_cuda(attrs, inputs, out_type,
target):
name="unique.cuda",
)
return strategy
+
+
+@schedule_transpose.register(["cuda", "gpu", "rocm"])
+def schedule_transpose_cuda(attrs, outs, target):
+ """
+ Transpose cuda strategy
+ Dispatches to and optimized schedule if the transpose is standalone (not
fused).
+ """
+ warp_size = int(Target.current(allow_none=False).thread_warp_size)
+ if (
Review comment:
As far as I can tell, there is not a better way to do this. There is a
way to add implementations based on input sizes, but these are not on a
per-target basis. If you know a better way, let me know.
##########
File path: tests/python/topi/python/test_topi_transform.py
##########
@@ -870,6 +871,30 @@ def test_transpose():
verify_transpose((3, 10), None)
[email protected]_targets
+def test_transpose_schedule(target, dev):
+ shape = (100, 34)
+ x = relay.var("x", relay.TensorType(shape, "float32"))
+ f = relay.transpose(x)
+ ex = relay.create_executor(
+ kind="graph", mod=tvm.IRModule.from_expr(relay.Function([x], f)),
device=dev, target=target
+ )
+ r = np.random.rand(*shape)
+ tvm.testing.assert_allclose(ex.evaluate()(r).asnumpy(), np.transpose(r))
+
+ # make sure schedule does not fire here
Review comment:
It is more like a wish. Ideally we would be able to know which schedules
were used, but there is to way to introspect on what was used. I've updated the
comment to reflect this.
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