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, deps = 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)
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)
node = name_scopes.transform(node, ctx)
return node, deps
def test_basic_for(self):
def test_fn(l):
s = 0
for e in l:
s += e
return s
node = self.parse_and_analyze(test_fn, {})
node = for_loops.transform(node, self.ctx)
with self.compiled(node) as result:
l = [1, 2, 3]
self.assertEqual(test_fn(l), result.test_fn(l))
l = []
self.assertEqual(test_fn(l), result.test_fn(l))
def test_basic_for(self):
def test_fn(l):
s = 0
for e in l:
s += e
return s
node = self.parse_and_analyze(test_fn, {})
node = for_loops.transform(node, self.ctx)
with self.compiled(node) as result:
l = [1, 2, 3]
self.assertEqual(test_fn(l), result.test_fn(l))
l = []
self.assertEqual(test_fn(l), result.test_fn(l))
def test_for_with_iterated_expression(self):
eval_count = [0]
def count_evals(x):
eval_count[0] += 1
return x
def test_fn(n):
s = 0
for e in count_evals(range(n)):
s += e
return s
node = self.parse_and_analyze(test_fn, {'count_evals': count_evals})
node = for_loops.transform(node, self.ctx)
with self.compiled(node) as result:
result.count_evals = count_evals
self.assertEqual(test_fn(5), result.test_fn(5))
# count_evals ran twice, once for test_fn and another for result.test_fn
self.assertEqual(eval_count[0], 2)
def test_for_with_iterated_expression(self):
eval_count = [0]
def count_evals(x):
eval_count[0] += 1
return x
def test_fn(n):
s = 0
for e in count_evals(range(n)):
s += e
return s
node = self.parse_and_analyze(test_fn, {'count_evals': count_evals})
node = for_loops.transform(node, self.ctx)
with self.compiled(node) as result:
result.count_evals = count_evals
self.assertEqual(test_fn(5), result.test_fn(5))
# count_evals ran twice, once for test_fn and another for result.test_fn
self.assertEqual(eval_count[0], 2)
