arina-grovety commented on code in PR #14765:
URL: https://github.com/apache/tvm/pull/14765#discussion_r1196374217
##########
tests/python/contrib/test_ethosu/test_legalize.py:
##########
@@ -462,6 +463,118 @@ def verify(ext_func):
verify(mod["tvmgen_default_ethos_u_main_0"])
+def test_tflite_conv2d_with_separate_channel_padding_legalize():
+ dtype = "int8"
+ ifm_shape = (1, 55, 34, 3)
+ kernel_shape = (3, 2)
+ strides = (1, 1)
+ dilation = (2, 1)
+ padding_ch = (1, 1)
+
+ class ArePadOnGraph(ExprVisitor):
+ """
+ Visits the Graph recursively and checks if it contains 'nn.pad' op
+ """
+
+ def __init__(self):
+ ExprVisitor.__init__(self)
+ self.on_graph = False
+
+ def visit_call(self, call):
+ if isinstance(call.op, tvm.ir.Op):
+ if str(call.op.name) == "nn.pad":
+ self.on_graph = True
+
+ return super().visit_call(call)
+
+ def are_pad_on_graph(self, subgraph) -> bool:
+ """
+ This function recursively visits the graph and checks if 'nn.pad'
op is ongraph
+ """
+ self.visit(subgraph)
+ return self.on_graph
+
+ def create_tflite_graph_single():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ tf_strides = [1, strides[0], strides[1], 1]
+ op = tf.pad(
+ x,
+ [[0, 0], [0, 0], [0, 0], [padding_ch[0], padding_ch[1]]],
+ "CONSTANT",
+ )
+ # HWIO
+ weight_shape = [
+ kernel_shape[0],
+ kernel_shape[1],
+ ifm_shape[3] + padding_ch[0] + padding_ch[1],
+ 3,
+ ]
+ weight = tf.constant(np.random.uniform(size=weight_shape),
dtype=tf.float32)
+ return tf.nn.conv2d(
+ op,
+ weight,
+ strides=tf_strides,
+ padding="VALID",
+ dilations=dilation,
+ )
+
+ 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):
+
+ assert ArePadOnGraph().are_pad_on_graph(ext_func.body) == True
+
+ conv2d_pattern_table = [
+ (
+ ethosu.ChannelPadParams.composite_name,
+ ethosu.pad_pattern(),
+ lambda pat: ethosu.ChannelPadParams(pat).is_valid(),
+ ),
+ (
+ ethosu.QnnConv2DParams.composite_name,
+ ethosu.qnn_conv2d_pattern(),
+ lambda pat: ethosu.QnnConv2DParams(pat).is_valid(),
+ ),
+ ]
+
+ tflite_graph = create_tflite_graph_single()
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+ mod, conv_params = relay.frontend.from_tflite(
+ tflite_model,
+ shape_dict={"input": ifm_shape},
+ dtype_dict={"input": dtype},
+ )
+
+ mod["main"] = bind_params_by_name(mod["main"], conv_params)
+ mod = partition_ethosu_by_table(mod, conv2d_pattern_table)
+
+ mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
+ legalize.Conv2DRewriter(), mod["tvmgen_default_ethos_u_main_0"]
+ )
+
+ verify(mod["tvmgen_default_ethos_u_main_0"])
Review Comment:
Hi @ekalda, thank you for the review!
Yes, it is. Here we check that the pad by channel does not merge with
conv2d, as it happens with the spatial pad.
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