Mousius commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r740352529
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File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -458,7 +458,102 @@ def verify(ext_func):
mod = partition_ethosu_by_table(mod, depthwise_pattern_table)
mod["tvmgen_default_ethosu_main_0"] = dataflow_pattern.rewrite(
- legalize.EthosuDepthwiseConv2DRewriter(),
mod["tvmgen_default_ethosu_main_0"]
+ legalize.DepthwiseConv2DRewriter(), mod["tvmgen_default_ethosu_main_0"]
+ )
+ verify(mod["tvmgen_default_ethosu_main_0"])
+
+
[email protected]("pooling_type", ["MAX", "AVG"])
[email protected]("ifm_shape", [[1, 9, 12, 3], [1, 10, 20, 2]])
[email protected]("strides", [[1, 2], [2, 3]])
[email protected]("pool_shape", [[1, 2], [2, 3]])
[email protected]("activation_function", ["NONE", "RELU"])
[email protected]("padding", ["SAME", "VALID"])
+def test_tflite_pool2d_legalize(
+ ifm_shape, pooling_type, strides, pool_shape, activation_function, padding
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ if pooling_type == "MAX":
+ op = tf.nn.max_pool(x, pool_shape, strides, padding)
+ elif pooling_type == "AVG":
+ op = tf.nn.avg_pool(x, pool_shape, strides, padding)
+ if activation_function == "RELU":
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.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):
+ ofm_shape = infra.compute_ofm_shape(ifm_shape, padding, pool_shape,
strides)
+ op = ext_func.body
+ assert list(op.args[0].checked_type.shape) == ifm_shape
+ assert op.args[0].checked_type.dtype == dtype
+ assert list(op.checked_type.shape) == ofm_shape
+ assert op.checked_type.dtype == dtype
+ assert op.attrs.pooling_type == pooling_type
+ assert list(op.attrs.strides) == strides
+ assert list(op.attrs.padding) == infra.compute_padding_shape(
+ ifm_shape, ofm_shape, padding, pool_shape, strides
+ )
+ assert list(op.attrs.pool_shape) == pool_shape
+ assert op.attrs.ofm_channels == ifm_shape[3]
+ if activation_function == "RELU":
+ assert str(op.attrs.activation) == "CLIP"
+
+ if pooling_type == "MAX":
+ rewriter = legalize.MaxPoolingRewriter()
+ pattern_table = [
+ (
+ ethosu.MaxPool2DParams.composite_name,
+ ethosu.qnn_maxpool2d_pattern(),
+ lambda pat: ethosu.MaxPool2DParams(pat).is_valid(),
+ ),
+ ]
+ elif pooling_type == "AVG":
+ rewriter = legalize.AvgPoolingRewriter()
+ pattern_table = [
+ (
+ ethosu.AvgPool2DParams.composite_name,
+ ethosu.qnn_avgpool2d_pattern(),
+ lambda pat: ethosu.AvgPool2DParams(pat).is_valid(),
+ ),
+ ]
Review comment:
Test cases aren't necessarily limited to being per operator, to get good
coverage I usually find you need a lot of smaller tests rather than one large
one - branches are then an indicator where you might need two small tests for
the operator rather than one large one as the branch itself may need testing.
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