diff --git a/paddle/fluid/eager/auto_code_generator/eager_generator.cc b/paddle/fluid/eager/auto_code_generator/eager_generator.cc index 4018c0c0918de..db1dbaa2aa7e1 100644 --- a/paddle/fluid/eager/auto_code_generator/eager_generator.cc +++ b/paddle/fluid/eager/auto_code_generator/eager_generator.cc @@ -2108,6 +2108,10 @@ static std::string GenerateSingleOpBase( GRAD_OUTS_CONTENT_TEMPLATE, grad_output_name, grads_position); } else { + if (dispensable_input_name_set.count(fwd_name) && + grad_ins_fwd_slotname_map.count(fwd_name)) { + continue; + } size_t fwd_input_position = fwd_inputs_name_pos_map.at(fwd_name); if (duplicable_input_name_set.count(fwd_name) && !is_op_base_per_duplicable_input) { @@ -2144,6 +2148,42 @@ static std::string GenerateSingleOpBase( BWD_OUTS_MAP_TEMPLATE, outs_name, outs_contents_str); generated_grad_function_body += outs_map_str; generated_grad_function_body += "\n"; + for (auto iter : grad_outs) { + const std::string& grad_output_name = iter.first; + + if (grad_outs_slotname_map.count(grad_output_name)) { + // Fwd Tensor + const std::string& fwd_name = grad_outs_slotname_map.at(grad_output_name); + if (fwd_inputs_name_pos_map.count(fwd_name)) { + if (dispensable_input_name_set.count(fwd_name) && + grad_ins_fwd_slotname_map.count(fwd_name)) { + if (duplicable_input_name_set.count(fwd_name) && + !is_op_base_per_duplicable_input) { + size_t fwd_input_position = fwd_inputs_name_pos_map.at(fwd_name); + const char* DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE = + " if(%s.size() > 0) %s[\"%s\"] = egr::EagerUtils::CreateVars( " + "this->OutputMeta()[%d].size() );\n"; + generated_grad_function_body += paddle::string::Sprintf( + DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE, fwd_name, outs_name, + grad_output_name, fwd_input_position); + } else { + const char* DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE = + " if(%s.initialized()) %s[\"%s\"] = " + "{std::make_shared(egr::Controller::" + "Instance().GenerateUniqueName())};\n"; + generated_grad_function_body += paddle::string::Sprintf( + DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE, fwd_name, outs_name, + grad_output_name); + } + } + } + } else { + PADDLE_THROW(platform::errors::Fatal( + "Detected mismatched slot names." + "Unable to find forward slot name that matches %s", + grad_output_name)); + } + } VLOG(6) << "Generated Outs Map"; diff --git a/paddle/fluid/pybind/eager_method.cc b/paddle/fluid/pybind/eager_method.cc index 198e042e2c67b..c1e1bfc88c41c 100644 --- a/paddle/fluid/pybind/eager_method.cc +++ b/paddle/fluid/pybind/eager_method.cc @@ -703,8 +703,6 @@ static PyObject* tensor_method__setitem_eager_tensor(TensorObject* self, } }); - // TODO(pangyoki) add inplace(BumpInplaceVersion) if need - // 1. Check argumnets bool parse_index = true; @@ -753,12 +751,6 @@ static PyObject* tensor_method__setitem_eager_tensor(TensorObject* self, if (PyCheckTensor(value_obj)) { value_tensor = reinterpret_cast(value_obj)->tensor; - - // pass the stop_gradient from value to tensor - if (!egr::EagerUtils::autograd_meta(&value_tensor)->StopGradient() && - egr::EagerUtils::autograd_meta(&self->tensor)->StopGradient()) { - egr::EagerUtils::autograd_meta(&self->tensor)->SetStopGradient(false); - } } else if (py::isinstance(value_obj)) { paddle::experimental::Tensor value_tensor_tmp( std::make_shared(), @@ -858,8 +850,18 @@ static PyObject* tensor_method__setitem_eager_tensor(TensorObject* self, { // Release gil and do tracing py::gil_scoped_release release; - self->tensor = set_value_dygraph_function(self->tensor, value_tensor, {}, - {}, {}, attrs); + // use inplace set_value_ operator + self->tensor = set_value__dygraph_function(self->tensor, value_tensor, {}, + {}, {}, attrs); + } + if (PyCheckTensor(value_obj)) { + // pass the stop_gradient from value to tensor. + // pass stop gradient should be done after CheckInplace in + // set_value__dygraph_function. + if (!egr::EagerUtils::autograd_meta(&value_tensor)->StopGradient() && + egr::EagerUtils::autograd_meta(&self->tensor)->StopGradient()) { + egr::EagerUtils::autograd_meta(&self->tensor)->SetStopGradient(false); + } } } else { auto self_numpy = TensorToPyArray(*self_tensor); @@ -1179,6 +1181,15 @@ static PyObject* tensor__inplace_version(TensorObject* self, PyObject* args, EAGER_CATCH_AND_THROW_RETURN_NULL } +static PyObject* tensor__bump_inplace_version(TensorObject* self, + PyObject* args, + PyObject* kwargs) { + EAGER_TRY + self->tensor.bump_inplace_version(); + return Py_None; + EAGER_CATCH_AND_THROW_RETURN_NULL +} + static PyObject* tensor_method_is_selected_rows(TensorObject* self, PyObject* args, PyObject* kwargs) { @@ -1287,6 +1298,9 @@ PyMethodDef variable_methods[] = { /***the method of sparse tensor****/ {"_inplace_version", (PyCFunction)(void (*)(void))tensor__inplace_version, METH_VARARGS | METH_KEYWORDS, NULL}, + {"_bump_inplace_version", + (PyCFunction)(void (*)(void))tensor__bump_inplace_version, + METH_VARARGS | METH_KEYWORDS, NULL}, {"is_selected_rows", (PyCFunction)(void (*)(void))tensor_method_is_selected_rows, METH_VARARGS | METH_KEYWORDS, NULL}, diff --git a/paddle/phi/api/lib/tensor.cc b/paddle/phi/api/lib/tensor.cc index b9b6ca36f673b..cfa8e80e84541 100644 --- a/paddle/phi/api/lib/tensor.cc +++ b/paddle/phi/api/lib/tensor.cc @@ -364,11 +364,7 @@ void Tensor::bump_inplace_version() { auto &inplace_version_counter = std::dynamic_pointer_cast(impl_) ->InplaceVersionCounter(); - VLOG(3) << "yoki: before bump inplace version: " - << inplace_version_counter.CurrentVersion(); inplace_version_counter.Bump(); - VLOG(3) << "yoki: after bump inplace version: " - << inplace_version_counter.CurrentVersion(); } else { PADDLE_THROW(phi::errors::Unimplemented( "bump_inplace_version is only supported on DenseTensor now.")); @@ -380,8 +376,6 @@ uint32_t Tensor::current_inplace_version() { auto &inplace_version_counter = std::dynamic_pointer_cast(impl_) ->InplaceVersionCounter(); - VLOG(3) << "yoki: print version: " - << inplace_version_counter.CurrentVersion(); return inplace_version_counter.CurrentVersion(); } else { PADDLE_THROW(phi::errors::Unimplemented( diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index 26a1076a64cf7..1372f894cdf0f 100755 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -961,7 +961,6 @@ set_tests_properties(test_bicubic_interp_op PROPERTIES TIMEOUT 120) set_tests_properties(test_deformable_conv_op PROPERTIES TIMEOUT 120) set_tests_properties(test_nearest_interp_op PROPERTIES TIMEOUT 120) set_tests_properties(test_profiler PROPERTIES TIMEOUT 120) -set_tests_properties(test_inplace_eager_fluid PROPERTIES TIMEOUT 120) set_tests_properties(test_inplace_softmax_with_cross_entropy PROPERTIES TIMEOUT 120) set_tests_properties(test_cross_entropy2_op PROPERTIES TIMEOUT 120) set_tests_properties(test_fetch_unmerged PROPERTIES TIMEOUT 120) diff --git a/python/paddle/fluid/tests/unittests/test_inplace.py b/python/paddle/fluid/tests/unittests/test_inplace.py index 316db18753511..617e9811d630f 100644 --- a/python/paddle/fluid/tests/unittests/test_inplace.py +++ b/python/paddle/fluid/tests/unittests/test_inplace.py @@ -19,10 +19,11 @@ import paddle import paddle.fluid.core as core +from paddle.fluid.framework import _test_eager_guard, in_dygraph_mode class TestInplace(unittest.TestCase): - def test_forward_version(self): + def func_test_forward_version(self): with paddle.fluid.dygraph.guard(): var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32)) self.assertEqual(var.inplace_version, 0) @@ -30,7 +31,11 @@ def test_forward_version(self): var[0] = 1.1 self.assertEqual(var.inplace_version, 1) - paddle.assign(paddle.ones(shape=[3]), var) + # TODO1: assign don't support inplace in temporary + if in_dygraph_mode(): + var[0] = 2 + else: + paddle.assign(paddle.ones(shape=[3]), var) # NOTE(liym27): assign(input, output) is an inplace operation for output. # There is inplace-related processing for api assign, var.inplace_version should be 2 not 1. @@ -39,7 +44,12 @@ def test_forward_version(self): var[2] = 3 self.assertEqual(var.inplace_version, 3) - def test_backward_error(self): + def test_forward_version(self): + with _test_eager_guard(): + self.func_test_forward_version() + self.func_test_forward_version() + + def func_test_backward_error(self): # It raises an error because the inplace operator will result # in incorrect gradient computation. with paddle.fluid.dygraph.guard(): @@ -55,13 +65,25 @@ def test_backward_error(self): var_d = var_b**2 loss = paddle.nn.functional.relu(var_c + var_d) - with self.assertRaisesRegexp( - RuntimeError, - "received tensor_version:{} != wrapper_version_snapshot:{}". - format(1, 0)): - loss.backward() + if in_dygraph_mode(): + with self.assertRaisesRegexp( + RuntimeError, + "received current_inplace_version:{} != inplace_version_snapshot_:{}". + format(1, 0)): + loss.backward() + else: + with self.assertRaisesRegexp( + RuntimeError, + "received tensor_version:{} != wrapper_version_snapshot:{}". + format(1, 0)): + loss.backward() - def test_backward_success_1(self): + def test_backward_error(self): + with _test_eager_guard(): + self.func_test_backward_error() + self.func_test_backward_error() + + def func_test_backward_success_1(self): # var_b is modified inplace before using it, the inplace operator doesn't result # in incorrect gradient computation. with paddle.fluid.dygraph.guard(): @@ -76,7 +98,12 @@ def test_backward_success_1(self): loss = var_c.sum() loss.backward() - def test_backward_success_2(self): + def test_backward_success_1(self): + with _test_eager_guard(): + self.func_test_backward_success_1() + self.func_test_backward_success_1() + + def func_test_backward_success_2(self): # Although var_b is modified inplace after using it, it does not used in gradient computation. # The inplace operator doesn't result in incorrect gradient computation. with paddle.fluid.dygraph.guard(): @@ -94,6 +121,12 @@ def test_backward_success_2(self): loss.backward() + def test_backward_success_2(self): + # TODO2: need to process no_need_buffer in eager mode + # with _test_eager_guard(): + # self.func_test_backward_success_2() + self.func_test_backward_success_2() + class TestDygraphInplace(unittest.TestCase): def setUp(self): @@ -113,7 +146,7 @@ def non_inplace_api_processing(self, var): def inplace_api_processing(self, var): return paddle.squeeze_(var) - def test_inplace_api(self): + def func_test_inplace_api(self): var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) inplace_var = self.inplace_api_processing(var) self.assertTrue(id(var) == id(inplace_var)) @@ -121,7 +154,12 @@ def test_inplace_api(self): inplace_var[0] = 2. self.assertTrue(np.array_equal(var.numpy(), inplace_var.numpy())) - def test_forward_version(self): + def test_inplace_api(self): + with _test_eager_guard(): + self.func_test_inplace_api() + self.func_test_inplace_api() + + def func_test_forward_version(self): with paddle.fluid.dygraph.guard(): var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) self.assertEqual(var.inplace_version, 0) @@ -135,7 +173,12 @@ def test_forward_version(self): inplace_var = self.inplace_api_processing(inplace_var) self.assertEqual(var.inplace_version, 3) - def test_leaf_inplace_var_error(self): + def test_forward_version(self): + with _test_eager_guard(): + self.func_test_forward_version() + self.func_test_forward_version() + + def func_test_leaf_inplace_var_error(self): with paddle.fluid.dygraph.guard(): var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) var.stop_gradient = False @@ -145,7 +188,12 @@ def leaf_inplace_error(): self.assertRaises(ValueError, leaf_inplace_error) - def test_backward_error(self): + def test_leaf_inplace_var_error(self): + with _test_eager_guard(): + self.func_test_leaf_inplace_var_error() + self.func_test_leaf_inplace_var_error() + + def func_test_backward_error(self): # It raises an error because the inplace operator will result # in incorrect gradient computation. with paddle.fluid.dygraph.guard(): @@ -159,13 +207,25 @@ def test_backward_error(self): self.inplace_api_processing(var_b) loss = paddle.nn.functional.relu(var_c) - with self.assertRaisesRegexp( - RuntimeError, - "received tensor_version:{} != wrapper_version_snapshot:{}". - format(1, 0)): - loss.backward() + if in_dygraph_mode(): + with self.assertRaisesRegexp( + RuntimeError, + "received current_inplace_version:{} != inplace_version_snapshot_:{}". + format(1, 0)): + loss.backward() + else: + with self.assertRaisesRegexp( + RuntimeError, + "received tensor_version:{} != wrapper_version_snapshot:{}". + format(1, 0)): + loss.backward() - def test_backward_success_1(self): + def test_backward_error(self): + with _test_eager_guard(): + self.func_test_backward_error() + self.func_test_backward_error() + + def func_test_backward_success_1(self): # var_b is modified inplace before using it, the inplace operator doesn't result # in incorrect gradient computation. grad_var_a, grad_var_a_inplace = 0, 1 @@ -196,7 +256,12 @@ def test_backward_success_1(self): self.assertTrue(self.np_compare(grad_var_a_inplace, grad_var_a)) - def test_backward_success_2(self): + def test_backward_success_1(self): + with _test_eager_guard(): + self.func_test_backward_success_1() + self.func_test_backward_success_1() + + def func_test_backward_success_2(self): # Although var_b is modified inplace after using it, it does not used in gradient computation. # The inplace operator doesn't result in incorrect gradient computation. grad_var_a, grad_var_a_inplace = 0, 1 @@ -221,8 +286,7 @@ def test_backward_success_2(self): var_b = var_a**2 - var_c = self.non_inplace_api_processing( - var_b) # var_b is modified inplace before using it + var_c = self.non_inplace_api_processing(var_b) var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b loss = var_d.sum() @@ -231,6 +295,11 @@ def test_backward_success_2(self): grad_var_a = var_a.grad.numpy() self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a)) + def test_backward_success_2(self): + with _test_eager_guard(): + self.func_test_backward_success_2() + self.func_test_backward_success_2() + class TestDygraphInplaceUnsqueeze(TestDygraphInplace): def non_inplace_api_processing(self, var): @@ -391,26 +460,29 @@ class TestDygraphInplaceAdd(TestDygraphInplace): def init_data(self): self.input_var_numpy = np.random.rand(2, 3, 4) self.dtype = "float32" - input_var_numpy_2 = np.random.rand(2, 3, 4).astype(self.dtype) - self.input_var_2 = paddle.to_tensor(input_var_numpy_2) + self.input_var_numpy_2 = np.random.rand(2, 3, 4).astype(self.dtype) def non_inplace_api_processing(self, var): - return var.add(self.input_var_2) + input_var_2 = paddle.to_tensor(self.input_var_numpy_2) + return var.add(input_var_2) def inplace_api_processing(self, var): - return var.add_(self.input_var_2) + input_var_2 = paddle.to_tensor(self.input_var_numpy_2) + return var.add_(input_var_2) class TestDygraphInplaceSubtract(TestDygraphInplaceAdd): def non_inplace_api_processing(self, var): - return var.subtract(self.input_var_2) + input_var_2 = paddle.to_tensor(self.input_var_numpy_2) + return var.subtract(input_var_2) def inplace_api_processing(self, var): - return var.subtract_(self.input_var_2) + input_var_2 = paddle.to_tensor(self.input_var_numpy_2) + return var.subtract_(input_var_2) class TestLossIsInplaceVar(unittest.TestCase): - def test_loss_is_inplace_var(self): + def func_test_loss_is_inplace_var(self): with paddle.fluid.dygraph.guard(): var_a = paddle.ones((2, 2)) var_a.stop_gradient = False @@ -433,9 +505,14 @@ def test_loss_is_inplace_var(self): self.assertTrue(np.array_equal(inplace_grad_var_a, grad_var_a)) + def test_loss_is_inplace_var(self): + with _test_eager_guard(): + self.func_test_loss_is_inplace_var() + self.func_test_loss_is_inplace_var() + class TestContinuouslyInplace(unittest.TestCase): - def test_continuously_inplace(self): + def func_test_continuously_inplace(self): a = paddle.rand([2, 3]) a.stop_gradient = False b = a * 2 @@ -446,6 +523,11 @@ def test_continuously_inplace(self): b.backward() + def test_continuously_inplace(self): + with _test_eager_guard(): + self.func_test_continuously_inplace() + self.func_test_continuously_inplace() + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_inplace_eager_fluid.py b/python/paddle/fluid/tests/unittests/test_inplace_eager_fluid.py deleted file mode 100644 index 45232ae4e4600..0000000000000 --- a/python/paddle/fluid/tests/unittests/test_inplace_eager_fluid.py +++ /dev/null @@ -1,574 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import print_function - -import unittest -import numpy as np - -import paddle -import paddle.fluid.core as core -from paddle.fluid.framework import _test_eager_guard - - -class TestDygraphInplace(unittest.TestCase): - def setUp(self): - self.init_data() - self.set_np_compare_func() - - def init_data(self): - self.input_var_numpy = np.random.uniform(-5, 5, [10, 20, 1]) - self.dtype = "float32" - - def set_np_compare_func(self): - self.np_compare = np.array_equal - - def non_inplace_api_processing(self, var): - return paddle.squeeze(var) - - def inplace_api_processing(self, var): - return paddle.squeeze_(var) - - def test_inplace_api(self): - with _test_eager_guard(): - var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) - inplace_var = self.inplace_api_processing(var) - self.assertTrue(id(var) == id(inplace_var)) - - inplace_var.exp_() - self.assertTrue(np.array_equal(var.numpy(), inplace_var.numpy())) - - def test_forward_version(self): - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) - self.assertEqual(var.inplace_version, 0) - - inplace_var = self.inplace_api_processing(var) - self.assertEqual(var.inplace_version, 1) - - inplace_var.exp_() - self.assertEqual(var.inplace_version, 2) - - inplace_var = self.inplace_api_processing(inplace_var) - self.assertEqual(var.inplace_version, 3) - - def test_leaf_inplace_var_error(self): - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype) - var.stop_gradient = False - - def leaf_inplace_error(): - self.inplace_api_processing(var) - - self.assertRaises(ValueError, leaf_inplace_error) - - def test_backward_error(self): - # It raises an error because the inplace operator will result - # in incorrect gradient computation. - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - - # Here, the gradient computation will use the value of var_b - var_c = var_b**2 - self.inplace_api_processing(var_b) - - loss = paddle.nn.functional.relu(var_c) - with self.assertRaisesRegexp( - RuntimeError, - "received current_inplace_version:{} != inplace_version_snapshot_:{}". - format(1, 0)): - loss.backward() - - def test_backward_success_1(self): - # var_b is modified inplace before using it, the inplace operator doesn't result - # in incorrect gradient computation. - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - # Here, the gradient computation will use the value of var_b - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_c = self.non_inplace_api_processing(var_b) - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a = var_a.grad.numpy() - - self.assertTrue(self.np_compare(grad_var_a_inplace, grad_var_a)) - - def test_backward_success_2(self): - # Although var_b is modified inplace after using it, it does not used in gradient computation. - # The inplace operator doesn't result in incorrect gradient computation. - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - - var_c = self.non_inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a = var_a.grad.numpy() - self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a)) - - # inplace + hook - def test_backward_success_3(self): - # var_b is modified inplace before using it, the inplace operator doesn't result - # in incorrect gradient computation. - def double_hook(grad): - grad = grad * 2 - return grad - - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - helper = var_a.register_hook(double_hook) - - var_b = var_a**2 - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - # Here, the gradient computation will use the value of var_b - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - helper = var_a.register_hook(double_hook) - - var_b = var_a**2 - var_c = self.non_inplace_api_processing(var_b) - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a = var_a.grad.numpy() - - self.assertTrue(self.np_compare(grad_var_a_inplace, grad_var_a)) - - # inplace + hook - def test_backward_success_4(self): - # Although var_b is modified inplace after using it, it does not used in gradient computation. - # The inplace operator doesn't result in incorrect gradient computation. - def double_hook(grad): - grad = grad * 2 - return grad - - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - var_a.register_hook(double_hook) - - var_b = var_a**2 - - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - var_a.register_hook(double_hook) - - var_b = var_a**2 - - var_c = self.non_inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a = var_a.grad.numpy() - self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a)) - - # inplace + hook - def test_backward_success_5(self): - # var_b is modified inplace before using it, the inplace operator doesn't result - # in incorrect gradient computation. - def double_hook(grad): - grad = grad * 2 - return grad - - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_b.register_hook(double_hook) - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - # Here, the gradient computation will use the value of var_b - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_b.register_hook(double_hook) - var_c = self.non_inplace_api_processing(var_b) - var_d = var_c**2 - loss = var_d.sum() - loss.backward() - grad_var_a = var_a.grad.numpy() - - self.assertTrue(self.np_compare(grad_var_a_inplace, grad_var_a)) - - # inplace + hook - def test_backward_success_6(self): - # Although var_b is modified inplace before using it, it does not used in gradient computation. - # The inplace operator doesn't result in incorrect gradient computation. - def double_hook(grad): - grad = grad * 2 - return grad - - grad_var_a, grad_var_a_inplace = 0, 1 - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_b.register_hook(double_hook) - var_c = self.inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a_inplace = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.to_tensor(self.input_var_numpy).astype( - self.dtype) - var_a.stop_gradient = False - - var_b = var_a**2 - var_b.register_hook(double_hook) - var_c = self.non_inplace_api_processing( - var_b) # var_b is modified inplace before using it - - var_d = var_c + var_c # Here, the grad op of sum doesn't use the value of var_b - loss = var_d.sum() - - loss.backward() - grad_var_a = var_a.grad.numpy() - self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a)) - - -class TestDygraphInplaceUnsqueeze(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.unsqueeze(var, -1) - - def inplace_api_processing(self, var): - return paddle.unsqueeze_(var, -1) - - -class TestDygraphInplaceReshape(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.reshape(var, [-1]) - - def inplace_api_processing(self, var): - return paddle.reshape_(var, [-1]) - - -class TestDygraphInplaceFlatten(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.flatten() - - def inplace_api_processing(self, var): - return var.flatten_() - - -""" -# This case will fail while using `_C_ops.final_state_scatter`. -class TestDygraphInplaceScatter(TestDygraphInplace): - def init_data(self): - self.input_var_numpy = np.array([[1, 1], [2, 2], [3, 3]]) - self.dtype = "float32" - - def non_inplace_api_processing(self, var): - index = paddle.to_tensor([2, 1, 0, 1], dtype='int64') - updates = paddle.to_tensor( - [[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32') - - return paddle.scatter(var, index, updates, overwrite=False) - - def inplace_api_processing(self, var): - index = paddle.to_tensor([2, 1, 0, 1], dtype='int64') - updates = paddle.to_tensor( - [[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32') - - return paddle.scatter_(var, index, updates, overwrite=False) -""" - - -class TestDygraphInplaceElu(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.nn.functional.elu(var) - - def inplace_api_processing(self, var): - return paddle.nn.functional.elu_(var) - - -class TestDygraphInplaceRelu(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.nn.functional.relu(var) - - def inplace_api_processing(self, var): - return paddle.nn.functional.relu_(var) - - -class TestDygraphInplaceSoftmax(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.nn.functional.softmax(var) - - def inplace_api_processing(self, var): - return paddle.nn.functional.softmax_(var) - - -class TestDygraphInplaceTanh(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return paddle.tanh(var) - - def inplace_api_processing(self, var): - return paddle.tanh_(var) - - -class TestDygraphInplaceCeil(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.ceil() - - def inplace_api_processing(self, var): - return var.ceil_() - - -class TestDygraphInplaceFloor(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.floor() - - def inplace_api_processing(self, var): - return var.floor_() - - -class TestDygraphInplaceExp(TestDygraphInplace): - def set_np_compare_func(self): - self.np_compare = np.allclose - - def non_inplace_api_processing(self, var): - return var.exp() - - def inplace_api_processing(self, var): - return var.exp_() - - -class TestDygraphInplaceReciprocal(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.reciprocal() - - def inplace_api_processing(self, var): - return var.reciprocal_() - - -class TestDygraphInplaceRound(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.round() - - def inplace_api_processing(self, var): - return var.round_() - - -class TestDygraphInplaceSqrt(TestDygraphInplace): - def init_data(self): - self.input_var_numpy = np.random.uniform(0, 5, [10, 20, 1]) - self.dtype = "float32" - - def non_inplace_api_processing(self, var): - return var.sqrt() - - def inplace_api_processing(self, var): - return var.sqrt_() - - -class TestDygraphInplaceRsqrt(TestDygraphInplaceSqrt): - def non_inplace_api_processing(self, var): - return var.rsqrt() - - def inplace_api_processing(self, var): - return var.rsqrt_() - - -class TestDygraphInplaceClip(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.clip(0.6, 1.5) - - def inplace_api_processing(self, var): - return var.clip_(0.6, 1.5) - - -class TestDygraphInplaceScale(TestDygraphInplace): - def non_inplace_api_processing(self, var): - return var.scale(scale=2.0, bias=3.0) - - def inplace_api_processing(self, var): - return var.scale_(scale=2.0, bias=3.0) - - -class TestDygraphInplaceAdd(TestDygraphInplace): - def init_data(self): - self.input_var_numpy = np.random.rand(2, 3, 4) - self.dtype = "float32" - self.input_var_numpy_2 = np.random.rand(2, 3, 4).astype(self.dtype) - - def non_inplace_api_processing(self, var): - input_var_2 = paddle.to_tensor(self.input_var_numpy_2) - return var.add(input_var_2) - - def inplace_api_processing(self, var): - input_var_2 = paddle.to_tensor(self.input_var_numpy_2) - return var.add_(input_var_2) - - -class TestDygraphInplaceSubtract(TestDygraphInplaceAdd): - def non_inplace_api_processing(self, var): - input_var_2 = paddle.to_tensor(self.input_var_numpy_2) - return var.subtract(input_var_2) - - def inplace_api_processing(self, var): - input_var_2 = paddle.to_tensor(self.input_var_numpy_2) - return var.subtract_(input_var_2) - - -class TestLossIsInplaceVar(unittest.TestCase): - def test_loss_is_inplace_var(self): - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.ones((2, 2)) - var_a.stop_gradient = False - - var_b = var_a * 2 - loss = var_b.tanh_() - - loss.backward() - inplace_grad_var_a = var_a.grad.numpy() - - with paddle.fluid.dygraph.guard(): - with _test_eager_guard(): - var_a = paddle.ones((2, 2)) - var_a.stop_gradient = False - - var_b = var_a * 2 - loss = var_b.tanh() - - loss.backward() - grad_var_a = var_a.grad.numpy() - - self.assertTrue(np.array_equal(inplace_grad_var_a, grad_var_a)) - - -class TestContinuouslyInplace(unittest.TestCase): - def test_continuously_inplace(self): - with _test_eager_guard(): - a = paddle.rand([2, 3]) - a.stop_gradient = False - b = a * 2 - - b.reshape_([-1]) - b.reshape_([2, 3]) - b.reshape_([-1]) - - b.backward() - - -if __name__ == '__main__': - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_set_value_op.py b/python/paddle/fluid/tests/unittests/test_set_value_op.py index 782ac3835facc..8f9801780cd9d 100644 --- a/python/paddle/fluid/tests/unittests/test_set_value_op.py +++ b/python/paddle/fluid/tests/unittests/test_set_value_op.py @@ -1011,10 +1011,7 @@ def func_test_dynamic(self): loss.backward() self.assertTrue(var.grad.shape == x.grad[0, :, 0, 0].shape) - # - # TODO(pangyoki) add inplace and delete if - if _in_legacy_dygraph(): - self.assertTrue((0 == x.grad[0, :, 0, 0]).all()) + self.assertTrue((0 == x.grad[0, :, 0, 0]).all()) def test_dynamic(self): with _test_eager_guard(): @@ -1192,8 +1189,8 @@ def set_value5(t, value): x[0, :] = value - self.assertTrue(~x.stop_gradient) - self.assertTrue(~x.is_leaf) + self.assertTrue(not x.stop_gradient) + self.assertTrue(not x.is_leaf) def test_consistent_with_competitor(self): with _test_eager_guard(): diff --git a/python/paddle/fluid/variable_index.py b/python/paddle/fluid/variable_index.py index 80c18e6aade93..e6990e25a08af 100644 --- a/python/paddle/fluid/variable_index.py +++ b/python/paddle/fluid/variable_index.py @@ -674,8 +674,7 @@ def _setitem_impl_(var, item, value): "paddle.Tensor to a paddle.Tensor, but received {}".format( type(value))) - if paddle.fluid.framework._in_legacy_dygraph(): - # TODO(pangyoki) add inplace(BumpInplaceVersion) if need + if paddle.fluid.framework._non_static_mode(): var._bump_inplace_version() cur_block = default_main_program().current_block()