NicolaLancellotti commented on a change in pull request #9209:
URL: https://github.com/apache/tvm/pull/9209#discussion_r723468737
##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -327,12 +341,118 @@ def create_graph_single_unsupported_ifm_layout(
for test_case in test_cases:
mod, conv_params = test_case[0](*test_case[1])
- mod = partition_for_ethosu(mod)
+ mod = ethosu.partition_for_ethosu(mod)
with pytest.raises(
tvm._ffi.base.TVMError, match="EthosUCodegenError: Unsupported
Layout NCHW"
):
mod = legalize.LegalizeEthosUConv2D()(mod)
[email protected]("ifm_shape", [(1, 299, 299, 3), (1, 123, 17, 7)])
[email protected]("kernel_shape", [(7, 3), (22, 5)])
[email protected]("padding", ["SAME", "VALID"])
[email protected]("strides, dilation", [((1, 1), (2, 1)), ((3, 2), (1,
1))])
[email protected]("activation", ["RELU", None])
+def test_tflite_depthwise2d_legalize(
+ ifm_shape, kernel_shape, padding, strides, dilation, activation
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def depthwise2d(self, x):
+ weight_shape = [kernel_shape[0], kernel_shape[1],
ifm_shape[3], 1]
+ weight = tf.constant(np.random.uniform(size=weight_shape),
dtype=tf.float32)
+ # The input strides to the TensorFlow API needs to be of shape
1x4
+ tf_strides = [1, strides[0], strides[1], 1]
+ op = tf.nn.depthwise_conv2d(
+ x, weight, strides=tf_strides, padding=padding,
dilations=dilation
+ )
+ if activation:
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.depthwise2d.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32)
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(ifm_shape))
+ yield [data.astype(np.float32)]
+
+ converter =
tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops =
[tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ def verify(ext_func):
+ op = ext_func.body
+ ofm_channels = op.attrs.ofm_channels
+
+ # check IFM
+ ifm = op.args[0].checked_type
+ assert list(ifm.shape) == list(ifm_shape)
+ assert str(ifm.dtype) == dtype
+ assert ifm.shape[3] == ofm_channels
+
+ # check OFM
+ ofm = op.checked_type
+ expected_ofm_shape = infra.compute_ofm_shape(
+ ifm_shape, padding, kernel_shape, strides, dilation
+ )
+ assert list(ofm.shape) == list(expected_ofm_shape)
+ assert str(ofm.dtype) == dtype
+ assert ofm.shape[3] == ofm_channels
+
+ # check weights
+ weights_ohwi = op.args[1].data.asnumpy()
+ assert str(weights_ohwi.dtype) == dtype
+ assert weights_ohwi.shape[0] == ofm_channels
+ assert weights_ohwi.shape[1] == kernel_shape[0]
+ assert weights_ohwi.shape[2] == kernel_shape[1]
+ assert weights_ohwi.shape[3] == 1 # only depth multiplier 1 is
supported
+
+ # Check that scale_bias matches weight tensor
+ assert list(op.args[2].checked_type.shape)[0] == ofm_channels
+
+ expected_padding = infra.compute_padding_shape(
+ ifm_shape, expected_ofm_shape, padding, kernel_shape, strides,
dilation
+ )
+ assert list(op.attrs.padding) == list(expected_padding)
+ assert op.attrs.ofm_channels == ofm_channels
+ assert list(op.attrs.strides) == list(strides)
+ assert list(op.attrs.dilation) == list(dilation)
+ if activation == "RELU":
+ assert str(op.attrs.activation) == "CLIP"
+
+ depthwise_pattern_table = [
+ (
+ "ethosu.depthwise2d",
Review comment:
```suggestion
"ethosu.QnnDepthwise2DParams.composite_name",
```
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