def node_to_graph(node, ctx, nocompile_decorators):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: A Python AST node representing the code to convert.
ctx: An EntityContext object.
nocompile_decorators: A tuple containing decorators to be stripped from
functions during conversion.
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Verify arguments for correctness.
# TODO(mdan): Factor out common elements.
# These include:
# * code move between blocks
# * visiting blocks in transformers
# Certain steps, especially canonicalization, insert new symbols into the
# tree, which must be accounted. Although less efficient, it is most robust
# to re-run the analysis.
node = _static_analysis_pass(node, ctx)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
ctx.source_code = None
node = decorators.transform(node, nocompile_decorators)
node = break_statements.transform(node, ctx)
node = asserts.transform(node, ctx)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = continue_statements.transform(node, ctx)
ctx.namespace['len'] = len
node = _static_analysis_pass(node, ctx)
node = for_loops.transform(node, ctx)
# for_loops may insert new global references.
node = builtin_functions.transform(node, ctx)
# TODO(mdan): Kept for CL consistency. Remove.
# builtin_functions may insert new global references.
ctx.namespace['print'] = print
node = _static_analysis_pass(node, ctx)
node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES,
nocompile_decorators)
node = control_flow.transform(node, ctx)
# control_flow may create new symbols and change scopes.
node = _static_analysis_pass(node, ctx)
node = logical_expressions.transform(node)
node = side_effect_guards.transform(node, ctx)
return node
def node_to_graph(node, ctx, nocompile_decorators):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: A Python AST node representing the code to convert.
ctx: An EntityContext object.
nocompile_decorators: A tuple containing decorators to be stripped from
functions during conversion.
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Verify arguments for correctness.
# TODO(mdan): Factor out common elements.
# These include:
# * code move between blocks
# * visiting blocks in transformers
# Certain steps, especially canonicalization, insert new symbols into the
# tree, which must be accounted. Although less efficient, it is most robust
# to re-run the analysis.
node = _static_analysis_pass(node, ctx)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
ctx.source_code = None
node = decorators.transform(node, nocompile_decorators)
node = break_canonicalization.transform(node, ctx)
node = asserts.transform(node, ctx)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = continue_canonicalization.transform(node, ctx)
ctx.namespace['len'] = len
node = _static_analysis_pass(node, ctx)
node = for_canonicalization.transform(node, ctx)
# for_canonicalization may insert new global references.
node = builtin_functions.transform(node, ctx)
# builtin_functions may insert new global references.
ctx.namespace['print'] = print
node = _static_analysis_pass(node, ctx)
node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES,
nocompile_decorators)
node = control_flow.transform(node, ctx)
# control_flow may create new symbols and change scopes.
node = _static_analysis_pass(node, ctx)
node = logical_expressions.transform(node)
node = side_effect_guards.transform(node, ctx)
return node
def node_to_graph(node, ctx, nocompile_decorators):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: A Python AST node representing the code to convert.
ctx: An EntityContext object.
nocompile_decorators: A tuple containing decorators to be stripped from
functions during conversion.
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Verify arguments for correctness.
# TODO(mdan): Factor out common elements.
# These include:
# * keeping track of symbols that have been created
# * marking nodes (e.g. py_func wrappers) to suppress further processing
# * code move between blocks
# * insertion of new global references
# * visiting blocks in transformers
# Certain steps, especially canonicalization, insert new symbols into the
# tree, which must be accounted. Although less efficient, it is most robust
# to re-run the analysis.
node = _static_analysis_pass(node, ctx)
node = decorators.transform(node, nocompile_decorators)
node = break_canonicalization.transform(node, ctx.namer)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = continue_canonicalization.transform(node, ctx.namer)
ctx.namespace['len'] = len
node = _static_analysis_pass(node, ctx)
node = for_canonicalization.transform(node, ctx.namer)
# for_canonicalization may insert new global references.
node = builtin_functions.transform(node)
# builtin_functions may insert new global references.
ctx.namespace['print'] = print
node = _static_analysis_pass(node, ctx)
node = print_functions.transform(node)
node = call_trees.transform(node, ctx.namer, ctx.namespace,
config.DEFAULT_UNCOMPILED_MODULES,
nocompile_decorators)
node = control_flow.transform(node, ctx.namer)
node = logical_expressions.transform(node)
node = side_effect_guards.transform(node, ctx.namer)
return node
def test_transform(self):
def test_fn(a):
state_ops.assign(a, a + 1)
return a
node = self.parse_and_analyze(test_fn, {'state_ops': state_ops})
node = side_effect_guards.transform(node, TestNamer())
result = compiler.ast_to_object(node)
setattr(result, 'state_ops', state_ops)
# TODO(mdan): Configure the namespaces instead of doing these hacks.
ops.identity = array_ops.identity
setattr(result, 'tf', ops)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
self.assertEqual(3, sess.run(result.test_fn(v)))
def _transform_and_compile(self, test_fn):
ns = {
'control_flow_ops': control_flow_ops,
'constant_op': constant_op,
'gen_math_ops': gen_math_ops,
'ops': ops,
'state_ops': state_ops,
}
node = self.parse_and_analyze(
test_fn, ns,
namer=TestNamer())
node = side_effect_guards.transform(node, self.ctx)
result = compiler.ast_to_object(node)
self.attach_namespace(result, **ns)
result.tf = self.make_fake_tf(array_ops.identity, control_flow_ops.Assert,
gen_math_ops.greater,
ops.control_dependencies, ops.Tensor)
result.py2tf_utils = utils
return result.test_fn, node
def test_side_effect_on_tensor(self):
tf = None
def test_fn(a):
tf.Assert(a > 0, ['expected in throw'])
return a
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, control_flow_ops.Assert) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
# NOTE: In this case we can also capture the side effect because the
# argument is a tensor ans we can wrap it inside an identity.
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
'expected in throw'):
sess.run(result.test_fn(constant_op.constant(-1)))
def test_side_effect_on_return_only_variable(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
return a
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: We don't expect the assignment to execute in this case, because
# variables cannot be reliably guarded.
self.assertEqual(2, sess.run(result.test_fn(v)))
def test_transform(self):
def test_fn(a):
state_ops.assign(a, a + 1)
return a
node = self.parse_and_analyze(test_fn, {'state_ops': state_ops})
node = side_effect_guards.transform(node, TestNamer())
result = compiler.ast_to_object(node)
setattr(result, 'state_ops', state_ops)
# TODO(mdan): Configure the namespaces instead of doing these hacks.
ops.identity = array_ops.identity
setattr(result, 'tf', ops)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
self.assertEqual(3, sess.run(result.test_fn(v)))
def test_side_effect_on_tensor(self):
tf = None
def test_fn(a):
tf.Assert(a > 0, ['expected in throw'])
return a
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, control_flow_ops.Assert) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
# NOTE: In this case we can also capture the side effect because the
# argument is a tensor ans we can wrap it inside an identity.
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
'expected in throw'):
sess.run(result.test_fn(constant_op.constant(-1)))
def test_side_effect_on_return_only_variable(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
return a
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: We don't expect the assignment to execute in this case, because
# variables cannot be reliably guarded.
self.assertEqual(2, sess.run(result.test_fn(v)))
def test_side_effect_on_used_variable(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
return a + 1
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: Unlike test_side_effect_on_return_only_variable, the variable
# was used in the local scope and so we could catch the assign's side
# effect.
self.assertEqual(4, sess.run(result.test_fn(v)))
def test_side_effect_on_used_variable(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
return a + 1
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: Unlike test_side_effect_on_return_only_variable, the variable
# was used in the local scope and so we could catch the assign's side
# effect.
self.assertEqual(4, sess.run(result.test_fn(v)))
def test_multiline_block(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
b = a + 1
tf.assign(a, b + 1)
c = b + 1
d = c + 1
return d
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
self.assertEqual(6, sess.run(result.test_fn(v)))
def test_multiline_block(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
b = a + 1
tf.assign(a, b + 1)
c = b + 1
d = c + 1
return d
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
self.assertEqual(6, sess.run(result.test_fn(v)))
def test_multiline_block_unsafe(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
b = a + 1
tf.assign(a, a + 1)
c = b + 1
d = c + 1
return d
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: This intentionally highlights the flakiness. The test should be
# tightened down once that is solved.
self.assertTrue(sess.run(result.test_fn(v)) in (6, 7))
def test_multiline_block_unsafe(self):
tf = None
def test_fn(a):
tf.assign(a, a + 1)
b = a + 1
tf.assign(a, a + 1)
c = b + 1
d = c + 1
return d
node = self.parse_and_analyze(test_fn, {})
node = side_effect_guards.transform(node, self.ctx)
with self.compiled(node, state_ops.assign) as result:
self.assertEqual(len(node.body[0].body), 1)
with self.test_session() as sess:
v = variables.Variable(2)
sess.run(v.initializer)
# NOTE: This intentionally highlights the flakiness. The test should be
# tightened down once that is solved.
self.assertTrue(sess.run(result.test_fn(v)) in (6, 7))
