[GitHub] [tvm] Mousius commented on a diff in pull request #11732: [CMSIS-NN] Fixed the case with repeating operands in the QNN binary ops

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

Mousius commented on code in PR #11732:
URL: https://github.com/apache/tvm/pull/11732#discussion_r899042991


##########
tests/python/contrib/test_cmsisnn/test_binary_ops.py:
##########
@@ -145,6 +145,58 @@ def test_op_int8(
     )
 
 
+@skip_if_no_reference_system
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("op", [relay.qnn.op.mul, relay.qnn.op.add])
+@pytest.mark.parametrize("relu_type", ["RELU", "NONE"])
+def test_same_input_to_binary_op(op, relu_type):
+    """Tests QNN binary operator for CMSIS-NN where both inputs are the same"""
+    interface_api = "c"
+    use_unpacked_api = True
+    test_runner = AOT_USMP_CORSTONE300_RUNNER
+
+    dtype = "int8"
+    shape = [1, 16, 16, 3]
+    input_ = generate_variable("input")
+    input_scale = 0.256
+    input_zero_point = 33
+
+    model = make_model(
+        op,
+        input_,
+        input_,
+        input_scale,
+        input_zero_point,
+        input_scale,
+        input_zero_point,
+        relu_type,
+    )
+    orig_mod = make_module(model)
+
+    cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
+
+    # validate pattern matching
+    assert_partitioned_function(orig_mod, cmsisnn_mod)
+
+    # validate the output
+    in_min, in_max = get_range_for_dtype_str(dtype)
+    inputs = {
+        "input": np.random.randint(in_min, high=in_max, size=shape, dtype=dtype),
+    }
+    output_list = generate_ref_data(orig_mod["main"], inputs)
+    compile_and_run(

Review Comment:
   Should we not test that the internal functions only have 1 parameter each? 



##########
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)
 
 
+@tvm.testing.requires_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)

Review Comment:
   ```suggestion
       operand0 = generate_variable("operand0", shape, dtype)
       operand1 = generate_variable("operand1", shape, dtype)
   ```



##########
python/tvm/relay/op/contrib/cmsisnn.py:
##########
@@ -229,17 +229,18 @@ def qnn_max_pool2d_pattern():
     def check_qnn_max_pool2d(pattern):
         """Check if max pool2d is supported by CMSIS-NN."""
         output = pattern
-        input_var = _find_last(pattern)
+        input_op = None

Review Comment:
   We can remove this as it's always set later.



##########
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)
 
 
+@tvm.testing.requires_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:
   What is this checking? It appears to just check the body hasn't changed after `InferType`?



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