ashutosh-arm commented on code in PR #11732:
URL: https://github.com/apache/tvm/pull/11732#discussion_r899109224
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tests/python/contrib/test_cmsisnn/test_scalar_to_tensor_constant.py:
##########
@@ -256,6 +256,47 @@ def test_all_primary_operands_tensor_constants():
assert tvm.ir.structural_equal(mod[global_var].body,
new_mod[global_var].body)
[email protected]_cmsisnn
+def test_duplicate_constant_arguments():
+ """Tests the pass when repeating operands are arguments to the binary op"""
+ dtype = "int8"
+ shape = (1, 3, 3, 32)
+ operand0 = generate_variable("operand0", shape, dtype)
+ operand1 = generate_variable("operand0", shape, dtype)
+ binary_op = make_binary_op(
+ relay.qnn.op.add,
+ operand0,
+ operand0,
+ input_0_scale=0.0128,
+ input_0_zero_point=32,
+ input_1_scale=0.256,
+ input_1_zero_point=-64,
+ )
+
+ local_func = relay.Function([operand0, operand1], binary_op,
relay.TensorType(shape, dtype))
+ local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_add")
+
+ rng = np.random.default_rng(12345)
+ arg0 = relay.const(rng.integers(-128, high=127, size=shape, dtype=dtype))
+ call_local_func = relay.Call(local_func, [arg0, arg0])
+ extern_func = relay.Function([], call_local_func, relay.TensorType(shape,
dtype))
+
+ global_var = relay.GlobalVar("external_function")
+ extern_func = set_external_func_attr(extern_func, "cmsis-nn",
global_var.name_hint)
+ call_extern_func = relay.Call(global_var, [])
+ main_func = relay.Function([], call_extern_func, relay.TensorType(shape,
dtype))
+ main_var = relay.GlobalVar("main")
+
+ mod = tvm.IRModule()
+ mod[global_var] = extern_func
+ mod[main_var] = main_func
+
+ mod = relay.transform.InferType()(mod)
+ mod = ScalarToTensorConstants()(mod)
+ new_mod = relay.transform.InferType()(mod)
+ assert tvm.ir.structural_equal(mod[global_var].body,
new_mod[global_var].body)
Review Comment:
Before this commit, it was producing a different body in the end due to an
error in the code. Since the relay model in this test does not contain any
scalar constants, expectation is that the pass should not affect the graph.
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