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_print(self): def test_fn(a): print(a) node = self.parse_and_analyze(test_fn, {'print': print}) node = builtin_functions.transform(node, self.ctx) result = compiler.ast_to_object(node) result.test_fn('a') self.assertTrue(isinstance(node.body[0].body[0].value, gast.Call))
def test_len(self): def test_fn(a): return len(a) node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node, self.ctx) with self.compiled(node, array_ops.shape) as result: with self.test_session() as sess: self.assertEqual( 3, sess.run(result.test_fn(constant_op.constant([0, 0, 0]))))
def test_len(self): def test_fn(a): return len(a) node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node) result = compiler.ast_to_object(node) setattr(result, 'tf', array_ops) with self.test_session() as sess: self.assertEqual( 3, sess.run(result.test_fn(constant_op.constant([0, 0, 0]))))
def test_len(self): def test_fn(a): return len(a) node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node, self.ctx) with self.compiled(node, array_ops.shape) as result: with self.test_session() as sess: self.assertEqual(3, sess.run( result.test_fn(constant_op.constant([0, 0, 0]))))
def test_len(self): def test_fn(a): return len(a) node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node, self.ctx) result = compiler.ast_to_object(node) setattr(result, 'tf', array_ops) with self.test_session() as sess: self.assertEqual(3, sess.run( result.test_fn(constant_op.constant([0, 0, 0]))))
def test_print(self): def test_fn(a): print(a) node = self.parse_and_analyze(test_fn, {'print': print}) node = builtin_functions.transform(node, self.ctx) with self.compiled(node) as result: try: out_capturer = six.StringIO() sys.stdout = out_capturer result.test_fn('a') self.assertEqual(out_capturer.getvalue(), 'a\n') finally: sys.stdout = sys.__stdout__
def test_print_with_py_func(self): def test_fn(a, b, c): print(a, b, c) node = self.parse_and_analyze(test_fn, {'print': print}) node = builtin_functions.transform(node, self.ctx) # Note: it's relevant not to include logging_ops.Print here, to verify # that py_func is used. with self.compiled(node, script_ops.py_func) as result: with self.test_session() as sess: try: out_capturer = six.StringIO() sys.stdout = out_capturer result.test_fn('a', 1, [2, 3]) sess.run(sess.graph.get_operations()) self.assertEqual(out_capturer.getvalue(), 'a 1 [2, 3]\n') finally: sys.stdout = sys.__stdout__
def test_print_tuple(self): def test_fn(a, b, c): print(a, b, c) node = self.parse_and_analyze(test_fn, {'print': print}) node = builtin_functions.transform(node, self.ctx) with self.compiled(node) as result: try: out_capturer = six.StringIO() sys.stdout = out_capturer result.test_fn('a', 1, [2, 3]) # It appears that the print output looks odd only under Python 2. if six.PY2: self.assertEqual(out_capturer.getvalue(), "('a', 1, [2, 3])\n") else: self.assertEqual(out_capturer.getvalue(), 'a 1 [2, 3]\n') finally: sys.stdout = sys.__stdout__