[CMSIS-NN] Aligned scale computation with TFLM to fix numerical mismatch

src/relay/backend/contrib/cmsisnn/generate_constants.cc

@@ -123,8 +123,8 @@ class GenerateConstantsMutator : public MixedModeMutator {
     // Obtain input and output scales from Relay's Requantization
     int64_t out_channels = conv2d_attrs->channels.as<IntImmNode>()->value;
     float output_scale = GetScalarFromConstant<float>(requantize_call->args[3]);
-    auto input_scales = tvm::relay::qnn::GetFloatVectorFromConstant(requantize_call->args[1]);
-    ICHECK(input_scales.size() == static_cast<size_t>(out_channels));
+    auto input_scale = GetScalarFromConstant<float>(conv2d_call->args[4]);
+    auto filter_scales = tvm::relay::qnn::GetFloatVectorFromConstant(conv2d_call->args[5]);
 
     // Calculate requantization multiplier and shift
     Device dev{DLDeviceType::kDLCPU, 0};
@@ -134,10 +134,10 @@ class GenerateConstantsMutator : public MixedModeMutator {
     int32_t* multiplier = static_cast<int32_t*>(multiplier_nda->data);
     int32_t* shift = static_cast<int32_t*>(shift_nda->data);
     for (int i = 0; i < out_channels; ++i) {
-      double quantized_multiplier =
-          static_cast<double>(input_scales[i]) / static_cast<double>(output_scale);
+      double effective_output_scale =
+          static_cast<double>(input_scale) * filter_scales[i] / static_cast<double>(output_scale);
       std::tie(*(multiplier + i), *(shift + i)) =
-          tvm::relay::qnn::GetFixedPointMultiplierShift(quantized_multiplier);
+          tvm::relay::qnn::GetFixedPointMultiplierShift(effective_output_scale);
     }
 
     // Create constants from requantization multiplier and shift

tests/python/contrib/test_cmsisnn/test_conv2d.py

@@ -304,6 +304,94 @@ def test_conv2d_asymmetric_padding_int8(
     )
 
 
+# This test expects assertion as the output should mismatch between TVM and CMSIS-NN
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+def test_conv2d_numerical_mismatch_int8(padding):
+    interface_api = "c"
+    use_unpacked_api = True
+    test_runner = AOT_USMP_CORSTONE300_RUNNER
+
+    out_channels = 3
+    ifm_shape = (1, 64, 100, 4)
+    input_zero_point = -64
+    input_scale = 1
+    strides = (1, 1)
+    dilation = (1, 1)
+    dtype = "int8"
+    groups = 1
+    relu_type = "RELU"
+    weight_format = "HWIO"
+    kernel_size = (3, 3)
+    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
+    kernel_scale = [1, 0.0256, 1.37]
+    enable_bias = False
+    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
+    assert_partitioned_function(orig_mod, cmsisnn_mod)
+
+    # DO_NOT_RANDOMIZE: validate the output with fixed input
+    seed = 0
+    random_state = np.random.RandomState(seed)
+    input_data = random_state.randint(in_min, in_max, ifm_shape, dtype)
+    inputs = {"input": input_data}
+    output_list = generate_ref_data(orig_mod["main"], inputs, params)
+    try:
+        compile_and_run(
+            AOTTestModel(
+                module=cmsisnn_mod,
+                inputs=inputs,
+                outputs=output_list,
+                params=params,
+                output_tolerance=0,
+            ),
+            test_runner,
+            interface_api,
+            use_unpacked_api,
+        )
+        exit(-1)
+    except AssertionError:
+        return
+
+
 @pytest.mark.skip(reason="See https://github.com/apache/tvm/issues/10314")
 @tvm.testing.requires_cmsisnn
 @pytest.mark.parametrize("ifm_shape", [(1, 28, 28, 12), (1, 64, 100, 4)])