diff --git a/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py b/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py index 9c1271c1cd7baba67397999193086e6df52183bc..924143049efc1315f4f2dc27a7e648ecc0b4e253 100644 --- a/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py +++ b/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py @@ -167,7 +167,13 @@ class NameVisitor(gast.NodeVisitor): # var_a = func2(x) # - if isinstance(var_name_to_ctxs[name][0], gast.Load): + is_created = False + for ctx in var_name_to_ctxs[name]: + if isinstance(ctx, gast.Store): + is_created = True + + if isinstance(var_name_to_ctxs[name][0], + gast.Load) and is_created: loop_var_names.add(name) create_var_names.add(name) diff --git a/python/paddle/fluid/dygraph/dygraph_to_static/utils.py b/python/paddle/fluid/dygraph/dygraph_to_static/utils.py index 6e44a26e050f34b50f5e08cc4bcd18813a9938d9..2fac616673ddf3d11c538b16ef78121822d4df38 100644 --- a/python/paddle/fluid/dygraph/dygraph_to_static/utils.py +++ b/python/paddle/fluid/dygraph/dygraph_to_static/utils.py @@ -883,6 +883,8 @@ class ForNodeVisitor(object): self.node.iter.func, gast.Attribute) and self.node.iter.func.attr == 'numpy': return True + elif isinstance(self.node.iter, gast.Subscript): + return True else: return False diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_for_enumerate.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_for_enumerate.py index a74c56fc31766ccf39cbcbee6b1138573fe9de6a..18995238a3c0589328bcc838f1cb7a04acbf61d7 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_for_enumerate.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_for_enumerate.py @@ -159,6 +159,7 @@ def for_enumerate_var_numpy_with_start_continue(x_array): def for_iter_var(x_array): z = fluid.layers.fill_constant([1], 'int32', 0) x_array = fluid.dygraph.to_variable(x_array) + for x in x_array: z = z + x return z @@ -221,6 +222,17 @@ def for_enumerate_var_with_nested_range(x_array): return x +# 16. for iter var[idx] +@paddle.jit.to_static +def for_iter_var_idx(x_array): + z = fluid.layers.fill_constant([1], 'int32', 0) + x_array = fluid.dygraph.to_variable(x_array) + + for x in x_array[0:]: + z = z + x + return z + + class TestTransformBase(unittest.TestCase): def setUp(self): self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( @@ -343,6 +355,11 @@ class TestForIterVar(TestForIterVarNumpy): self.dygraph_func = for_iter_var +class TestForIterVarIdx(TestForIterVarNumpy): + def set_test_func(self): + self.dygraph_func = for_iter_var_idx + + class TestForEnumerateVar(TestForIterVarNumpy): def set_test_func(self): self.dygraph_func = for_enumerate_var diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py index b6aa73d37639b89d0d374e34725c526f119b1064..bc235ca8606499aa02743f4b627d478f007f4ed8 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py @@ -86,11 +86,15 @@ def for_loop_dyfunc(max_len): def for_loop_dyfunc2(max_len): # Test case: a variable is used and created in loop, but used before created + x = fluid.layers.fill_constant(shape=[1, 2], dtype="int32", value=1) + for i in range(max_len): if i > 1: s = a a = 1 - ret = fluid.layers.fill_constant(shape=[1], dtype="int32", value=s) + q, _ = x.shape # test var x.shape only used but not created in loop + + ret = fluid.layers.fill_constant(shape=[1], dtype="int32", value=s + q) return ret diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py index cd075d44117ca936db264e1e207285105c55d406..14fa75e458f8d45f573c78ec28c21d327234c2b8 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py @@ -16,63 +16,63 @@ from __future__ import print_function import unittest import numpy as np -import paddle.fluid as fluid -from paddle.fluid.dygraph.jit import dygraph_to_static_func + +import paddle SEED = 2020 np.random.seed(SEED) +prog_trans = paddle.jit.ProgramTranslator() +@paddle.jit.to_static def test_slice_without_control_flow(x): # Python slice will not be transformed. - x = fluid.dygraph.to_variable(x) + x = paddle.to_tensor(x) a = [x] - a[0] = fluid.layers.fill_constant(shape=[2], value=2, dtype="float32") - return a + a[0] = paddle.full(shape=[2], fill_value=2, dtype="float32") + return a[0] +@paddle.jit.to_static def test_slice_in_if(x): - x = fluid.dygraph.to_variable(x) + x = paddle.to_tensor(x) a = [] if x.numpy()[0] > 0: a.append(x) else: - a.append( - fluid.layers.fill_constant( - shape=[1, 2], value=9, dtype="int64")) + a.append(paddle.full(shape=[1, 2], fill_value=9, dtype="int64")) if x.numpy()[0] > 0: a[0] = x - out = a[0:] + out = a[0] return out -def test_slice_in_while_loop(x, iter_num): - x = fluid.dygraph.to_variable(x) - iter_num_var = fluid.layers.fill_constant( - shape=[1], value=iter_num, dtype="int32") +@paddle.jit.to_static +def test_slice_in_while_loop(x, iter_num=3): + x = paddle.to_tensor(x) + iter_num_var = paddle.full(shape=[1], fill_value=iter_num, dtype="int32") a = [] i = 0 - # Note: `i < iter_num` can't be supported in dygraph mode now, - # but PR22892 is fixing it https://github.com/PaddlePaddle/Paddle/pull/22892. - # If PR22892 merged, change `i < iter_num.numpy()[0]` to `i < iter_num`. - while i < iter_num_var.numpy()[0]: + + while i < iter_num_var: a.append(x) i += 1 i = 0 while i < iter_num_var.numpy()[0]: - a[i] = fluid.layers.fill_constant(shape=[2], value=2, dtype="float32") + a[i] = paddle.full(shape=[2], fill_value=2, dtype="float32") i += 1 out = a[0:iter_num] - return out + return out[0] -def test_slice_in_for_loop(x, iter_num): - x = fluid.dygraph.to_variable(x) +@paddle.jit.to_static +def test_slice_in_for_loop(x, iter_num=3): + x = paddle.to_tensor(x) a = [] - # Use `fill_constant` so that static analysis can analyze the type of iter_num is Tensor - iter_num = fluid.layers.fill_constant( - shape=[1], value=iter_num, dtype="int32" + # Use `paddle.full` so that static analysis can analyze the type of iter_num is Tensor + iter_num = paddle.full( + shape=[1], fill_value=iter_num, dtype="int32" ) # TODO(liym27): Delete it if the type of parameter iter_num can be resolved for i in range(iter_num): @@ -87,35 +87,31 @@ def test_slice_in_for_loop(x, iter_num): class TestSliceWithoutControlFlow(unittest.TestCase): def setUp(self): self.input = np.random.random((3)).astype('int32') - self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( - ) else fluid.CPUPlace() + self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda( + ) else paddle.CPUPlace() self.init_dygraph_func() + paddle.disable_static() def init_dygraph_func(self): self.dygraph_func = test_slice_without_control_flow def run_dygraph_mode(self): - with fluid.dygraph.guard(): - res = self.dygraph_func(self.input) - if isinstance(res, (list, tuple)): - res = res[0] - return res.numpy() + return self._run(to_static=False) - def run_static_mode(self): - main_program = fluid.Program() - with fluid.program_guard(main_program): - tensor_list = dygraph_to_static_func(self.dygraph_func)(self.input) - exe = fluid.Executor(self.place) - static_res = exe.run(main_program, fetch_list=tensor_list[0]) + def _run(self, to_static): + prog_trans.enable(to_static) + res = self.dygraph_func(self.input) + return res.numpy() - return static_res[0] + def run_static_mode(self): + return self._run(to_static=True) def test_transformed_static_result(self): static_res = self.run_static_mode() dygraph_res = self.run_dygraph_mode() self.assertTrue( np.allclose(dygraph_res, static_res), - msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res, + msg='dygraph_res is {}\nstatic_res is {}'.format(dygraph_res, static_res)) @@ -123,69 +119,16 @@ class TestSliceInIf(TestSliceWithoutControlFlow): def init_dygraph_func(self): self.dygraph_func = test_slice_in_if - def run_static_mode(self): - main_program = fluid.Program() - with fluid.program_guard(main_program): - tensor_array = dygraph_to_static_func(self.dygraph_func)(self.input) - static_out = fluid.layers.array_read( - tensor_array, - i=fluid.layers.fill_constant( - shape=[1], value=0, dtype='int64')) - exe = fluid.Executor(self.place) - numpy_res = exe.run(main_program, fetch_list=static_out) - return numpy_res[0] - class TestSliceInWhileLoop(TestSliceWithoutControlFlow): - def setUp(self): - self.iter_num = 3 - self.input = np.random.random((3)).astype('int32') - self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( - ) else fluid.CPUPlace() - self.init_dygraph_func() - def init_dygraph_func(self): self.dygraph_func = test_slice_in_while_loop - def run_dygraph_mode(self): - with fluid.dygraph.guard(): - var_res = self.dygraph_func(self.input, self.iter_num) - if not isinstance(var_res, list): - var_res = [var_res] - numpy_res = [ele.numpy() for ele in var_res] - return numpy_res - - def run_static_mode(self): - main_program = fluid.Program() - with fluid.program_guard(main_program): - tensor_array = dygraph_to_static_func(self.dygraph_func)( - self.input, self.iter_num) - static_outs = [] - for i in range(self.iter_num): - static_outs.append( - fluid.layers.array_read( - tensor_array, - i=fluid.layers.fill_constant( - shape=[1], value=i, dtype='int64'))) - - exe = fluid.Executor(self.place) - numpy_res = exe.run(main_program, fetch_list=static_outs) - return numpy_res - class TestSliceInForLoop(TestSliceInWhileLoop): def init_dygraph_func(self): self.dygraph_func = test_slice_in_for_loop - def run_static_mode(self): - main_program = fluid.Program() - with fluid.program_guard(main_program): - static_out = dygraph_to_static_func(self.dygraph_func)( - self.input, self.iter_num) - exe = fluid.Executor(self.place) - numpy_res = exe.run(main_program, fetch_list=static_out) - return numpy_res - if __name__ == '__main__': unittest.main()