return AttrCvt(op_name="copy", ignores=['message'])(inputs, attr)
return _impl
+def _assert():
+ # ToDo: In general people want asserts to be gone from TensorFlow graphs
+ # when they are optimizing them, so converting it to a no-op is
+ # reasonable. However, it would be nice to have the option to keep them
+ # once Relay gets a Halt or Assert op.
+ return _no_op()
+
+def _no_op():
+ def _impl(inputs, attr, params):
+ # ToDo: This should really be an op that returns nothing, which could
+ # be represented as an empty tuple. It turns out that TVM
+ # infrastructure doesn't like running functions that return None and
+ # also don't like running functions that return an empty tuple. So it
+ # doesn't work, but it should be made to work and then this could be
+ # improved. In the mean time, it is hard to imagine a case where it
+ # matters in any real way that a no-op is converted to a constant 0.
+ return tvm.relay.const(0)
+ return _impl
def _matmul():
def _impl(inputs, attr, params):
'All' : _reduce('all'),
'ArgMax' : _argx(_op.argmax, 'argmax'),
'ArgMin' : _argx(_op.argmin, 'argmin'),
+ 'Assert' : _assert(),
'AvgPool' : _pooling('avg_pool'),
'BatchMatMul' : _batch_matmul(),
'BatchMatMulV2' : _batch_matmul(),
'Mod' : _elemwise('mod'),
'Mul' : _elemwise('multiply'),
'Neg' : AttrCvt('negative'),
+ 'NoOp' : _no_op(),
'NotEqual' : _broadcast('not_equal'),
'OneHot' : _one_hot(),
'Pack' : _pack(),
if np_array.dtype == np.dtype(object):
# Object types are generally tensorflow DT_STRING (DecodeJpeg op).
# Just leave it as placeholder.
- self._nodes[name] = [_expr.var(name, shape=shape[name], dtype='uint8')]
-
+ if shape:
+ var_shape = shape[name]
+ else:
+ var_shape = tensor_util.TensorShapeProtoToList(value.tensor.tensor_shape)
+ self._nodes[name] = [_expr.var(name, shape=var_shape, dtype='uint8')]
return
array_ndim = len(np_array.shape)
--- /dev/null
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Unit tests for converting TensorFlow debugging ops to Relay."""
+import tensorflow as tf
+import numpy as np
+from tvm import relay
+from tvm.relay.frontend.tensorflow import from_tensorflow
+
+def run_relay(graph, *vars):
+ mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
+ ex = relay.create_executor('debug', mod=mod)
+ return ex.evaluate()(*vars)
+
+def test_assert_true():
+ g = tf.Graph()
+ with g.as_default():
+ x = tf.placeholder(tf.float32, shape=())
+ assert_op = tf.Assert(tf.less_equal(x, x), ["it failed"])
+
+ with tf.Session() as sess:
+ x_value = np.random.rand()
+ assert sess.run(assert_op, feed_dict={x: x_value}) is None
+
+ # In TVM, tf.assert is converted to a no-op which is actually a 0,
+ # though it should probably be none or an empty tuple.
+ #
+ # ToDo: It appears that the frontend converter gets confused here and
+ # entirely eliminates all operands from main(). Likely because x <= x
+ # is always true, so the placeholder can be eliminated. But TF doesn't
+ # do that, it's happening in Relay, and that optimization shouldn't
+ # affect the arity of the main function. We should have to pass in
+ # x_value here.
+ np.testing.assert_allclose(0, run_relay(g).asnumpy())
+
+def test_assert_true_var_capture():
+ g = tf.Graph()
+ with g.as_default():
+ x = tf.placeholder(tf.float32, shape=())
+
+ # It turns out that tf.assert() creates a large and complex subgraph if
+ # you capture a variable as part of the error message. So we need to
+ # test that, too.
+ assert_op = tf.Assert(tf.less_equal(x, x), ["it failed", x])
+
+ with tf.Session() as sess:
+ x_value = np.random.rand()
+ assert sess.run(assert_op, feed_dict={x: x_value}) is None
+
+ # ToDo: The frontend converter gets confused here as well, thinking
+ # that it needs to be told what x is twice. It also notes the output of
+ # the graph as a boolean, which is not correct - as you can see above,
+ # TF believes that the value of this graph is None. In addition, the
+ # arity of the translated function should be 1, not 2.
+ np.testing.assert_allclose(True, run_relay(g, x_value, x_value).asnumpy())
+
+def test_assert_false():
+ g = tf.Graph()
+ with g.as_default():
+ assert_op = tf.Assert(tf.constant(False), ["it failed"])
+
+ with tf.Session() as sess:
+ try:
+ print(sess.run(assert_op))
+ assert False # TF should have thrown an exception
+ except tf.errors.InvalidArgumentError as e:
+ assert "it failed" in e.message
+
+ # In TVM, tf.assert is converted to a no-op which is actually a 0,
+ # though it should probably be none or an empty tuple. For the same
+ # reason, there should not be an error here, even though the assertion
+ # argument is false.
+ np.testing.assert_allclose(0, run_relay(g).asnumpy())
+
+
+if __name__ == "__main__":
+ test_assert_true()
+ test_assert_true_var_capture()
+ test_assert_false()
+
--- /dev/null
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Unit tests for converting TensorFlow debugging ops to Relay."""
+import tensorflow as tf
+import numpy as np
+from tvm import relay
+from tvm.relay.frontend.tensorflow import from_tensorflow
+
+def run_relay(graph):
+ mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
+ ex = relay.create_executor('debug', mod=mod)
+ return ex.evaluate()(**params)
+
+def test_no_op():
+ g = tf.Graph()
+ with g.as_default():
+ no_op = tf.no_op()
+ with tf.Session() as sess:
+ # In TF, the type of a no-op is None.
+ assert sess.run(no_op) is None
+
+ # In TVM, no-op is currently translated to 0, though it should
+ # probably be none or an empty tuple.
+ np.testing.assert_allclose(0, run_relay(g).asnumpy())
+
+
+if __name__ == "__main__":
+ test_no_op()
+
projects / platform / upstream / tvm.git / commitdiff