[CMSIS-NN] Separated symmetric and asymmetric padding tests for Conv2D (

#9963)

tests/python/contrib/test_cmsisnn/test_conv2d.py

@@ -124,22 +124,118 @@ def make_model(
 
 
 @tvm.testing.requires_cmsisnn
-@pytest.mark.parametrize("ifm_shape", [(1, 25, 25, 12), (1, 64, 100, 4)])
-@pytest.mark.parametrize("kernel_size", [(5, 5)])
 @pytest.mark.parametrize("padding", ["SAME", "VALID"])
-@pytest.mark.parametrize("strides, dilation", [((2, 2), (1, 1))])
 @pytest.mark.parametrize("relu_type", ["RELU"])
 @pytest.mark.parametrize("enable_bias", [True, False])
 @pytest.mark.parametrize(
     "input_zero_point, input_scale, kernel_scale, out_channels",
     [(10, 0.0128, [0.11, 0.22], 2), (-64, 1, [1, 0.0256, 1.37], 3)],
 )
-def test_conv2d_int8(
-    ifm_shape,
-    kernel_size,
+def test_conv2d_symmetric_padding_int8(
+    padding,
+    enable_bias,
+    relu_type,
+    input_zero_point,
+    input_scale,
+    kernel_scale,
+    out_channels,
+):
+    interface_api = "c"
+    use_unpacked_api = True
+    test_runner = AOT_CORSTONE300_RUNNER
+
+    ifm_shape = (1, 64, 100, 4)
+    kernel_size = (3, 3)
+    strides = (1, 1)
+    dilation = (1, 1)
+    dtype = "int8"
+    groups = 1
+    weight_format = "HWIO"
+    kernel_h = kernel_size[0]
+    kernel_w = kernel_size[1]
+    kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
+    kernel_zero_point = 0
+    in_min, in_max = get_range_for_dtype_str(dtype)
+
+    output_scale, output_zero_point = get_conv2d_qnn_params(
+        kernel_shape,
+        input_scale,
+        input_zero_point,
+        kernel_scale,
+        kernel_zero_point,
+        dtype,
+        dtype,
+        dtype,
+    )
+
+    model, params = make_model(
+        ifm_shape,
+        kernel_shape,
+        input_zero_point,
+        input_scale,
+        kernel_zero_point,
+        kernel_scale,
+        output_zero_point,
+        output_scale,
+        padding,
+        strides,
+        dilation,
+        groups,
+        dtype,
+        dtype,
+        out_channels,
+        weight_format,
+        enable_bias,
+        relu_type,
+    )
+    orig_mod = make_module(model)
+    cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
+
+    # validate pattern matching
+    attrs = [
+        cmsisnn_mod[var.name_hint].attrs
+        for var in cmsisnn_mod.get_global_vars()
+        if cmsisnn_mod[var.name_hint].attrs
+    ]
+    assert any(attrs), "At least one function with external attributes was expected."
+
+    compilers = [
+        key == "Compiler" and value == "cmsis-nn" for attr in attrs for key, value in attr.items()
+    ]
+    assert any(compilers), "Module does not contain function for cmsis-nn target."
+
+    assert count_num_calls(orig_mod) == count_num_calls(
+        cmsisnn_mod
+    ), "Number of calls changed during partitioning"
+
+    # validate the output
+    rng = np.random.default_rng(12345)
+    inputs = {"input": rng.integers(in_min, high=in_max, size=ifm_shape, dtype=dtype)}
+    output_list = generate_ref_data(orig_mod["main"], inputs, params)
+    compile_and_run(
+        AOTTestModel(
+            module=cmsisnn_mod,
+            inputs=inputs,
+            outputs=output_list,
+            params=params,
+            output_tolerance=1,
+        ),
+        test_runner,
+        interface_api,
+        use_unpacked_api,
+    )
+
+
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+@pytest.mark.parametrize("relu_type", ["RELU", "NONE"])
+@pytest.mark.parametrize("enable_bias", [True, False])
+@pytest.mark.parametrize(
+    "input_zero_point, input_scale, kernel_scale, out_channels",
+    [(10, 0.0128, [0.11, 0.22], 2), (-64, 1, [1, 0.0256, 1.37], 3)],
+)
+def test_conv2d_asymmetric_padding_int8(
     padding,
-    strides,
-    dilation,
     enable_bias,
     relu_type,
     input_zero_point,
@@ -151,6 +247,10 @@ def test_conv2d_int8(
     use_unpacked_api = True
     test_runner = AOT_CORSTONE300_RUNNER
 
+    ifm_shape = (1, 25, 25, 12)
+    kernel_size = (5, 5)
+    strides = (2, 2)
+    dilation = (1, 1)
     dtype = "int8"
     groups = 1
     weight_format = "HWIO"