Mousius commented on code in PR #12006:
URL: https://github.com/apache/tvm/pull/12006#discussion_r917864293
##########
tests/python/contrib/test_cmsisnn/test_conv2d.py:
##########
@@ -540,6 +547,135 @@ def test_depthwise_int8(
)
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+@pytest.mark.parametrize("strides, dilation", [((1, 1), (1, 1))])
+@pytest.mark.parametrize("relu_type", ["RELU", "NONE"])
+@pytest.mark.parametrize("depth_multiplier", [1, 3])
+@pytest.mark.parametrize(
+ "input_zero_point, input_scale, kernel_scale",
+ [
+ (
+ 10,
+ 0.0128,
+ [0.11, 0.22],
+ ),
+ (
+ -64,
+ 1,
+ [1, 0.0256, 1.37],
+ ),
+ ],
+)
+def test_relay_conv2d_cmsisnn_depthwise_int8(
+ padding,
+ strides,
+ dilation,
+ relu_type,
+ input_zero_point,
+ input_scale,
+ kernel_scale,
+ depth_multiplier,
+):
+ """Tests QNN Depthwise int8 op via CMSIS-NN"""
+ interface_api = "c"
+ use_unpacked_api = True
+ test_runner = AOT_USMP_CORSTONE300_RUNNER
+
+ dtype = "int8"
+ in_min, in_max = get_range_for_dtype_str(dtype)
+
+ ifm_shape = (1, 24, 24, 1)
+ groups = ifm_shape[3]
+ weight_format = "HWIO"
+ (kernel_h, kernel_w) = (3, 3)
+ kernel_shape = (kernel_h, kernel_w, ifm_shape[3], depth_multiplier)
+ out_channels = ifm_shape[3] * depth_multiplier
+ enable_bias = True
+ ks_len = len(kernel_scale)
+ kernel_zero_point = 0
+ kernel_scale = [kernel_scale[i % ks_len] for i in range(out_channels)]
+
+ output_scale, output_zero_point = get_conv2d_qnn_params(
+ kernel_shape,
+ input_scale,
+ input_zero_point,
+ kernel_scale,
+ kernel_zero_point,
+ dtype,
+ dtype,
+ dtype,
+ True,
+ )
+
+ 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)
+
+ # generate reference 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)
+
+ # validate presence of depthwise convolution
+ compiled_models = compile_models(
+ AOTTestModel(
+ module=cmsisnn_mod,
+ inputs=inputs,
+ outputs=output_list,
+ params=params,
+ output_tolerance=1,
+ ),
+ interface_api,
+ use_unpacked_api,
+ pass_config=test_runner.pass_config,
+ )
+
+ cmsisnn_tir_mod = None
+ for target, mod in
compiled_models[0].executor_factory.lowered_ir_mods.items():
+ if target.kind.name == "cmsis-nn":
+ cmsisnn_tir_mod = mod
+
+ cmsisnn_func = cmsisnn_tir_mod["tvmgen_default_cmsis_nn_main_0"]
+ call_extern = None
+ if isinstance(cmsisnn_func.body, tvm.tir.stmt.Evaluate):
+ call_extern = cmsisnn_func.body.value
+ else:
+ call_extern = cmsisnn_func.body.body.value
Review Comment:
Isn't this stable between test runs?
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