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 test_uncompiled_modules(self):
def test_fn(a):
a = math_ops.multiply(a, constant_op.constant(2))
a = math_ops.add(a, constant_op.constant(1))
return a
node = self.parse_and_analyze(
test_fn, {
'math_ops': math_ops,
'constant_op': constant_op
},
namer=TestNamer())
node = call_trees.transform(node, self.ctx,
set(((math_ops.__name__,),
(constant_op.__name__,))), ())
result = compiler.ast_to_object(node)
setattr(result, 'math_ops', math_ops)
setattr(result, 'constant_op', constant_op)
with self.test_session() as sess:
# Not renamed, because the converter doesn't rename the definition itself.
# (the caller is responsible for that).
result_tensor = result.test_fn(constant_op.constant(1))
result_val = sess.run(result_tensor)
self.assertEquals(3, result_val)
def test_uncompiled_modules(self):
def test_fn(a):
a = math_ops.multiply(a, constant_op.constant(2))
a = math_ops.add(a, constant_op.constant(1))
return a
node = self.parse_and_analyze(test_fn, {
'math_ops': math_ops,
'constant_op': constant_op
},
namer=TestNamer())
node = call_trees.transform(
node, self.ctx,
set(((math_ops.__name__, ), (constant_op.__name__, ))), ())
result = compiler.ast_to_object(node)
setattr(result, 'math_ops', math_ops)
setattr(result, 'constant_op', constant_op)
with self.test_session() as sess:
# Not renamed, because the converter doesn't rename the definition itself.
# (the caller is responsible for that).
result_tensor = result.test_fn(constant_op.constant(1))
result_val = sess.run(result_tensor)
self.assertEquals(3, result_val)
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_py_func_wrap_known_function(self):
def test_fn():
return np.random.binomial(2, 0.5)
node = self.parse_and_analyze(test_fn, {'np': np})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node, dtypes.int64) as result:
result.np = np
with self.test_session() as sess:
self.assertTrue(isinstance(result.test_fn(), ops.Tensor))
self.assertIn(sess.run(result.test_fn()), (0, 1, 2))
def test_dynamic_function(self):
def test_fn_1():
raise ValueError('This should be masked by the mock.')
def test_fn_2(f):
return f() + 3
node = self.parse_and_analyze(test_fn_2, {})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
# 10 = 7 (from the mock) + 3 (from test_fn_2)
self.assertEquals(10, result.test_fn_2(test_fn_1))
def test_py_func_wrap_known_function(self):
def test_fn():
return np.random.binomial(2, 0.5)
node = self.parse_and_analyze(test_fn, {'np': np})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node, dtypes.int64) as result:
result.np = np
with self.test_session() as sess:
self.assertTrue(isinstance(result.test_fn(), ops.Tensor))
self.assertIn(sess.run(result.test_fn()), (0, 1, 2))
def test_dynamic_function(self):
def test_fn_1():
raise ValueError('This should be masked by the mock.')
def test_fn_2(f):
return f() + 3
node = self.parse_and_analyze(test_fn_2, {})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
# 10 = 7 (from the mock) + 3 (from test_fn_2)
self.assertEquals(10, result.test_fn_2(test_fn_1))
def test_simple_methods(self):
class TestClass(object):
def test_fn_1(self, a):
return a + 1
def test_fn_2(self, a):
return self.test_fn_1(a) + 1
node = self.parse_and_analyze(
TestClass.test_fn_2, {'TestClass': TestClass},
arg_types={'self': (TestClass.__name__, TestClass)})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
tc = TestClass()
self.assertEquals(3, result.test_fn_2(tc, 1))
def test_simple_methods(self):
class TestClass(object):
def test_fn_1(self, a):
return a + 1
def test_fn_2(self, a):
return self.test_fn_1(a) + 1
node = self.parse_and_analyze(
TestClass.test_fn_2, {'TestClass': TestClass},
arg_types={'self': (TestClass.__name__, TestClass)})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
tc = TestClass()
self.assertEquals(3, result.test_fn_2(tc, 1))
def test_basic(self):
def test_fn_1(_):
raise ValueError(
'This should not be called in the compiled verison.')
def renamed_test_fn_1(a):
return a + 1
def test_fn_2(a):
return test_fn_1(a) + 1
node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1})
node = call_trees.transform(node, TestNamer(), {}, (), ())
result = compiler.ast_to_object(node)
# Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 manually.
setattr(result, 'renamed_test_fn_1', renamed_test_fn_1)
self.assertEquals(3, result.test_fn_2(1))
def test_basic(self):
def test_fn_1(_):
raise ValueError('This should not be called in the compiled verison.')
def renamed_test_fn_1(a):
return a + 1
def test_fn_2(a):
return test_fn_1(a) + 1
node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
# Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1
# manually.
result.renamed_test_fn_1 = renamed_test_fn_1
self.assertEquals(3, result.test_fn_2(1))
def test_basic(self):
def test_fn_1(_):
raise ValueError(
'This should not be called in the compiled verison.')
def renamed_test_fn_1(a):
return a + 1
def test_fn_2(a):
return test_fn_1(a) + 1
node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
# Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1
# manually.
result.renamed_test_fn_1 = renamed_test_fn_1
self.assertEquals(3, result.test_fn_2(1))
def test_basic(self):
def test_fn_1(_):
raise ValueError('This should not be called in the compiled verison.')
def renamed_test_fn_1(a):
return a + 1
def test_fn_2(a):
return test_fn_1(a) + 1
node = self.parse_and_analyze(
test_fn_2, {'test_fn_1': test_fn_1}, namer=TestNamer())
node = call_trees.transform(node, self.ctx, (), ())
result = compiler.ast_to_object(node)
# Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 manually.
setattr(result, 'renamed_test_fn_1', renamed_test_fn_1)
self.assertEquals(3, result.test_fn_2(1))
def test_py_func_wrap_no_retval(self):
def test_fn(a):
setattr(a, 'foo', 'bar')
node = self.parse_and_analyze(test_fn, {'setattr': setattr})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
with self.test_session() as sess:
# The function has no return value, so we do some tricks to grab the
# generated py_func node and ensure its effect only happens at graph
# execution.
class Dummy(object):
pass
a = Dummy()
result.test_fn(a)
self.assertFalse(hasattr(a, 'foo'))
sess.run(sess.graph.get_operations()[0])
self.assertEquals('bar', a.foo)
def test_uncompiled_modules(self):
def test_fn(a):
a = math_ops.multiply(a, constant_op.constant(2))
a = math_ops.add(a, constant_op.constant(1))
return a
node = self.parse_and_analyze(test_fn, {
'math_ops': math_ops,
'constant_op': constant_op
})
node = call_trees.transform(
node, self.ctx,
set(((math_ops.__name__, ), (constant_op.__name__, ))), ())
with self.compiled(node) as result:
result.math_ops = math_ops
result.constant_op = constant_op
with self.test_session() as sess:
# Not renamed, because the converter doesn't rename the definition
# itself (the caller is responsible for that).
result_tensor = result.test_fn(constant_op.constant(1))
self.assertEquals(3, sess.run(result_tensor))
def test_py_func_wrap_no_retval(self):
def test_fn(a):
setattr(a, 'foo', 'bar')
node = self.parse_and_analyze(test_fn, {'setattr': setattr})
node = call_trees.transform(node, self.ctx, (), ())
with self.compiled(node) as result:
with self.test_session() as sess:
# The function has no return value, so we do some tricks to grab the
# generated py_func node and ensure its effect only happens at graph
# execution.
class Dummy(object):
pass
a = Dummy()
result.test_fn(a)
self.assertFalse(hasattr(a, 'foo'))
sess.run(sess.graph.get_operations()[0])
self.assertEquals('bar', a.foo)
def test_uncompiled_modules(self):
def test_fn(a):
a = math_ops.multiply(a, constant_op.constant(2))
a = math_ops.add(a, constant_op.constant(1))
return a
node = self.parse_and_analyze(test_fn, {
'math_ops': math_ops,
'constant_op': constant_op
})
node = call_trees.transform(node, self.ctx,
set(((math_ops.__name__,),
(constant_op.__name__,))), ())
with self.compiled(node) as result:
result.math_ops = math_ops
result.constant_op = constant_op
with self.test_session() as sess:
# Not renamed, because the converter doesn't rename the definition
# itself (the caller is responsible for that).
result_tensor = result.test_fn(constant_op.constant(1))
self.assertEquals(3, sess.run(result_tensor))
