dchauhan-arm commented on a change in pull request #10345:
URL: https://github.com/apache/tvm/pull/10345#discussion_r822613407
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File path: tests/python/contrib/test_ethosu/test_legalize.py
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@@ -2421,5 +2421,108 @@ def verify(ext_func):
verify(mod["tvmgen_default_ethos_u_main_0"])
[email protected]("ifm_shape", [(1, 14), (1, 151)])
[email protected]("ofm_channels", [32, 64])
[email protected]("use_bias", [True, False])
[email protected]("activation_function", ["RELU", "NONE"])
+def test_tflite_fully_connected(
+ ifm_shape,
+ ofm_channels,
+ use_bias,
+ activation_function,
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def fully_connected(self, x):
+ bias_shape = ofm_channels
+ bias = tf.constant(np.random.uniform(size=bias_shape),
dtype=tf.float32)
+ w = tf.constant(
+ np.random.uniform(size=[ifm_shape[1], ofm_channels]),
+ dtype=tf.float32,
+ )
+ x = tf.matmul(x, w)
+ if use_bias:
+ x = tf.nn.bias_add(x, bias)
+ if activation_function:
+ x = tf.nn.relu(x)
+ return x
+
+ model = Model()
+ concrete_func = model.fully_connected.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.args[0]
+ ofm_channels = op.attrs.ofm_channels
+
+ # check IFM
+ ifm = op.args[0].checked_type
+ assert [ifm.shape[2], ifm.shape[3]] == list(ifm_shape)
+ assert str(ifm.dtype) == dtype
+
+ # check OFM
+ ofm = op.checked_type
+ assert [ofm.shape[2], ofm.shape[3]] == [1, ofm_channels]
+ assert str(ofm.dtype) == dtype
+
+ # check weights
+ weights_ohwi = op.args[1].data.asnumpy()
+ assert str(weights_ohwi.dtype) == dtype
+ assert list(weights_ohwi) == [ofm_channels, 1, 1, ifm_shape[1]]
Review comment:
ack! good spot on the list(ifm.shape)
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