Mark Shields
c8a6089073
* This continues the work outlined in the RFC https://discuss.tvm.apache.org/t/rfc-relay-tecompiler-rewrite-existing-compile-engine-to-match-updated-compiler-flow/9233 This gets about halfway there for the Interpreter: * Remove direct access to TECompiler from interpreter, and instead call tec::LowerTEExpr when 'preparing' a module and expression for evaluation. * Make clear there's no phase distinction between create_interpreter and evaluate on the Python side -- both must be prepared together as a single IRModule. * But in return make sure the result of evaluate on the Python side is a packed func ready to directly apply 'simple' arguments to an already interpreted closure. * The interpreter builds and caches primitive TIR functions (and their corresponding dynamic shape functions) as packed funcs as they are encountered. * Cleanup uses of interpreter for constant folding on the C++ side. Future work: * Fold LoweredModule into IRModule so tec::LowerTEExpr is just another pass. * Get rid of the implicit caching of lowered functions in TECompiler. * Make calling convention from Relay to TIR explicit, and remove all the function attribute hackery currently needed so the interpreter can correctly invoke lowered functions as it encounters them. * Make TECompiler private. Though could do this now it will make migrating the VM and AOT uses of CompilerEngine harder. Force a gc between sphinx-gallery items to reclaim GPU memory. (#8722) GPU memory is only released once the PackedFunc for evaling the model is gced by Python. In CI we're noticing intermittent 'CUDA: Out of memory' failures while processing the tutorials, and tracing showed there was no gc happening between items. Not confident this will solve the problem but worth a try. * Get rid of logs spam.
100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Unit tests for converting TensorFlow debugging ops to Relay."""
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try:
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import tensorflow.compat.v1 as tf
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tf.disable_v2_behavior()
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except ImportError:
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import tensorflow as tf
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import numpy as np
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from tvm import relay
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from tvm.relay.frontend.tensorflow import from_tensorflow
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def run_relay(graph, shape_dict=None, *vars):
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mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True), shape=shape_dict)
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return relay.create_executor("debug", mod=mod).evaluate()(*vars)
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def test_assert_true():
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g = tf.Graph()
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shape = (1, 2)
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with g.as_default():
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x = tf.placeholder(tf.float32, shape=shape, name="input")
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assert_op = tf.Assert(tf.reduce_all(tf.less_equal(x, x)), ["it failed"])
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with tf.Session() as sess:
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x_value = np.random.rand(*shape)
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assert sess.run(assert_op, feed_dict={x: x_value}) is None
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# In TVM, tf.assert is converted to a no-op which is actually a 0,
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# though it should probably be none or an empty tuple.
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#
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# ToDo: It appears that the frontend converter gets confused here and
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# entirely eliminates all operands from main(). Likely because x <= x
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# is always true, so the placeholder can be eliminated. But TF doesn't
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# do that, it's happening in Relay, and that optimization shouldn't
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# affect the arity of the main function. We should have to pass in
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# x_value here.
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np.testing.assert_allclose(0, run_relay(g, {"input": shape}).numpy())
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def test_assert_true_var_capture():
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g = tf.Graph()
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with g.as_default():
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x = tf.placeholder(tf.float32, shape=())
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# It turns out that tf.assert() creates a large and complex subgraph if
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# you capture a variable as part of the error message. So we need to
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# test that, too.
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assert_op = tf.Assert(tf.less_equal(x, x), ["it failed", x])
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with tf.Session() as sess:
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x_value = np.random.rand()
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assert sess.run(assert_op, feed_dict={x: x_value}) is None
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# TODO: The frontend converter notes the output of
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# the graph as a boolean, which is not correct - as you can see above,
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# TF believes that the value of this graph is None.
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np.testing.assert_allclose(True, run_relay(g, None, x_value).numpy())
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def test_assert_false():
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g = tf.Graph()
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with g.as_default():
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assert_op = tf.Assert(tf.constant(False), ["it failed"])
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with tf.Session() as sess:
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try:
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print(sess.run(assert_op))
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assert False # TF should have thrown an exception
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except tf.errors.InvalidArgumentError as e:
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assert "it failed" in e.message
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# In TVM, tf.assert is converted to a no-op which is actually a 0,
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# though it should probably be none or an empty tuple. For the same
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# reason, there should not be an error here, even though the assertion
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# argument is false.
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np.testing.assert_allclose(0, run_relay(g).numpy())
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if __name__ == "__main__":
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test_assert_true()
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test_assert_true_var_capture()
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test_assert_false()
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