def test_index_access_multiple_definitions(self):
def test_fn(l):
if l:
l = []
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
def_, = anno.getanno(node.body[0].body[0].targets[0],
anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.float32')
}
with self.assertRaises(transformer.AutographParseError):
slices.transform(node, ctx)
def test_index_access_multiple_definitions(self):
def test_fn(l):
if l:
l = []
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
def_, = anno.getanno(node.body[0].body[0].body[0].targets[0],
anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.float32')
}
with self.assertRaises(transformer.AutographParseError):
slices.transform(node, ctx)
def test_index_access(self):
def test_fn(l):
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
node = slices.transform(node, ctx)
with self.compiled(node, {}, dtypes.int32) as result:
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def test_index_access(self):
def test_fn(l):
return l[1]
node, ctx = self.prepare(test_fn, {})
def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS)
def_.directives[directives.set_element_type] = {
'dtype': parser.parse_expression('tf.int32')
}
node = slices.transform(node, ctx)
with self.compiled(node, {}, dtypes.int32) as result:
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2],
element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def test_index_access(self):
def test_fn(l):
utils.set_element_type(l, dtypes.int32)
return l[1]
node = self.parse_and_analyze(
test_fn,
{
'utils': utils,
'dtypes': dtypes
},
include_type_analysis=True,
)
node = slices.transform(node, self.ctx)
with self.compiled(node, dtypes.int32) as result:
result.utils = utils
result.dtypes = dtypes
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
def test_index_access(self):
def test_fn(l):
utils.set_element_type(l, dtypes.int32)
return l[1]
node = self.parse_and_analyze(
test_fn,
{
'utils': utils,
'dtypes': dtypes
},
include_type_analysis=True,
)
node = slices.transform(node, self.ctx)
with self.compiled(node, dtypes.int32) as result:
result.utils = utils
result.dtypes = dtypes
with self.test_session() as sess:
tl = list_ops.tensor_list_from_tensor(
[1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32))
y = result.test_fn(tl)
self.assertEqual(2, sess.run(y))
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)
# TODO(mdan): Clean this up.
# Some intermediate analyses are not required, and some comments got orphaned.
# TODO(mdan): We may assume all converters require analysis to be re-done.
# 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 = ifexp.transform(node, ctx)
node, deps = decorators.transform(node, nocompile_decorators)
node = break_statements.transform(node, ctx)
node = _static_analysis_pass(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 = single_return.transform(node, ctx)
node = _static_analysis_pass(node, ctx)
node = lists.transform(node, ctx)
node = _static_analysis_pass(node, ctx)
node = slices.transform(node, ctx)
node = builtin_functions.transform(node, ctx)
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, ctx)
node = side_effect_guards.transform(node, ctx)
node = name_scopes.transform(node, ctx)
return node, deps
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)
# TODO(mdan): Clean this up.
# Some intermediate analyses are not required, and some comments got orphaned.
# TODO(mdan): We may assume all converters require analysis to be re-done.
# 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 = ifexp.transform(node, ctx)
node, deps = decorators.transform(node, nocompile_decorators)
node = break_statements.transform(node, ctx)
node = _static_analysis_pass(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 = single_return.transform(node, ctx)
node = _static_analysis_pass(node, ctx)
node = lists.transform(node, ctx)
node = _static_analysis_pass(node, ctx)
node = slices.transform(node, ctx)
node = builtin_functions.transform(node, ctx)
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, ctx)
node = side_effect_guards.transform(node, ctx)
node = name_scopes.transform(node, ctx)
return node, deps
