[GitHub] [tvm] altanh commented on a change in pull request #8030: [TOPI] Custom schedule for standalone transpose in cuda

[GitBox <gi...@apache.org>]

altanh commented on a change in pull request #8030:
URL: https://github.com/apache/tvm/pull/8030#discussion_r632736914



##########
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:
       feels a bit weird to have this in `sparse.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:
       is there a more principled way to do this? like maybe with an OpStrategy or something

##########
File path: vta/tutorials/autotvm/tune_relay_vta.py
##########
@@ -357,7 +357,7 @@ def tune_and_evaluate(tuning_opt):
     )
 
     # filter out non-packed conv2d task
-    tasks = list(filter(lambda t: len(t.args[0][1]) > 4, tasks))
+    tasks = list(filter(lambda t: len(t.args[0][1]) > 4 and "conv" in t.name, tasks))

Review comment:
       what happened here, did this transpose change introduce a new task or something?

##########
File path: tests/python/topi/python/test_topi_transform.py
##########
@@ -870,6 +871,30 @@ def test_transpose():
     verify_transpose((3, 10), None)
 
 
+@tvm.testing.parametrize_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:
       is this a TODO? Also I wonder if it would be good to parametrize the test shape by warp size (rather than hard coding) for future proofing




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