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new e2e1696c74 [Relay] Handle pad value coming from Tensor instead of
scalar (#14735)
e2e1696c74 is described below
commit e2e1696c742957d2ac3b508b438a82c85e141794
Author: Krzysztof Parzyszek <[email protected]>
AuthorDate: Fri Apr 28 16:57:18 2023 -0500
[Relay] Handle pad value coming from Tensor instead of scalar (#14735)
* [Relay] Handle pad value coming from Tensor instead of scalar
The PadCompute function would pass empty index to obtain the pad value.
This caused a crash when the pad value was given in a tensor with the
following message:
Check failed: shape.size() == indices.size() (1 vs. 0)
: Tensor dimension mismatch in read ndim = 1, indices.size=0
* Move test to tests/python/relay/test_op_level2.py
---
src/relay/op/nn/pad.cc | 2 +-
tests/python/relay/test_op_level2.py | 19 +++++++++++++++++++
2 files changed, 20 insertions(+), 1 deletion(-)
diff --git a/src/relay/op/nn/pad.cc b/src/relay/op/nn/pad.cc
index 365873d2fd..8cfb369901 100644
--- a/src/relay/op/nn/pad.cc
+++ b/src/relay/op/nn/pad.cc
@@ -177,7 +177,7 @@ Array<te::Tensor> PadCompute(const Attrs& attrs, const
Array<te::Tensor>& inputs
pad_after.push_back(pad_width[i][1]);
}
te::Tensor cast_pad_value = topi::cast(inputs[1], inputs[0]->dtype);
- const PrimExpr& pad_value = cast_pad_value(Array<PrimExpr>());
+ const PrimExpr& pad_value =
cast_pad_value(Array<PrimExpr>(inputs[1]->shape.size(), 0));
return Array<te::Tensor>{topi::pad(inputs[0], pad_before, pad_after,
pad_value, "T_pad",
topi::kElementWise, param->pad_mode)};
}
diff --git a/tests/python/relay/test_op_level2.py
b/tests/python/relay/test_op_level2.py
index 434b4fa0a0..0a0ae561ab 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -1444,6 +1444,25 @@ def test_pad_run_dynamic_pad_value():
_test_run("int32")
+def test_pad_value_in_array():
+ A = relay.var("A", shape=(32, 32), dtype="int8")
+
+ # Extract pad value from an array
+ p0 = relay.Constant(tvm.nd.array(np.array([2], dtype="int8")))
+ p1 = relay.nn.pad(A, pad_value=p0, pad_width=((1, 1), (1, 1)))
+
+ func = relay.Function(relay.analysis.free_vars(p1), p1)
+ mod = tvm.IRModule.from_expr(func)
+
+ target = "llvm"
+ lib = relay.build(
+ mod,
+ tvm.target.Target(target, host=target),
+ runtime=relay.backend.Runtime("cpp"),
+ executor=relay.backend.Executor("aot", {"unpacked-api": False,
"interface-api": "packed"}),
+ )
+
+
@tvm.testing.uses_gpu
@pytest.mark.parametrize("dtype", ["float32", "float16"])
def test_lrn(executor_kind, dtype):
