diff --git a/paddle/fluid/operators/rmsprop_op.h b/paddle/fluid/operators/rmsprop_op.h index 406730407d4bf9e39857f8ea617e6044c47d3ffa..797cd45fdcdbd5c3567d1676f37e148304ee6e2d 100644 --- a/paddle/fluid/operators/rmsprop_op.h +++ b/paddle/fluid/operators/rmsprop_op.h @@ -131,21 +131,21 @@ template class RmspropOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { - using Tensor = framework::LoDTensor; + using LoDTensor = framework::LoDTensor; auto *grad_var = ctx.InputVar("Grad"); - auto *param_out = ctx.Output("ParamOut"); - auto *moment_out = ctx.Output("MomentOut"); - auto *mean_square_out = ctx.Output("MeanSquareOut"); + auto *param_out = ctx.Output("ParamOut"); + auto *moment_out = ctx.Output("MomentOut"); + auto *mean_square_out = ctx.Output("MeanSquareOut"); auto epsilon = static_cast(ctx.Attr("epsilon")); auto rho = static_cast(ctx.Attr("decay")); auto momentum = static_cast(ctx.Attr("momentum")); bool centered = ctx.Attr("centered"); - auto &p_tensor = *ctx.Input("Param"); - auto &ms_tensor = *ctx.Input("MeanSquare"); - auto &lr_tensor = *ctx.Input("LearningRate"); - auto &mom_tensor = *ctx.Input("Moment"); + auto &p_tensor = *ctx.Input("Param"); + auto &ms_tensor = *ctx.Input("MeanSquare"); + auto &lr_tensor = *ctx.Input("LearningRate"); + auto &mom_tensor = *ctx.Input("Moment"); PADDLE_ENFORCE_EQ(&p_tensor, param_out, "Param and ParamOut must be the same Tensor"); @@ -157,8 +157,8 @@ class RmspropOpKernel : public framework::OpKernel { auto &dev_ctx = ctx.template device_context(); size_t limit = static_cast(ms_tensor.numel()); - if (grad_var->IsType()) { - auto &grad_tensor = grad_var->Get(); + if (grad_var->IsType()) { + auto &grad_tensor = grad_var->Get(); if (std::is_same::value) { auto &place = @@ -176,9 +176,9 @@ class RmspropOpKernel : public framework::OpKernel { ms_out.device(place) = rho * ms + (1 - rho) * g * g; if (centered) { - auto &mg_tensor = *ctx.Input("MeanGrad"); + auto &mg_tensor = *ctx.Input("MeanGrad"); auto mg = EigenVector::Flatten(mg_tensor); - auto *mean_grad_out = ctx.Output("MeanGradOut"); + auto *mean_grad_out = ctx.Output("MeanGradOut"); PADDLE_ENFORCE(&mg_tensor, mean_grad_out, "MeanGrad and MeanGradOut must be the same Tensor"); auto mg_out = EigenVector::Flatten(*mean_grad_out); @@ -196,8 +196,8 @@ class RmspropOpKernel : public framework::OpKernel { DenseRmspropGradFunctor grad_func(grad_tensor.data()); platform::ForRange for_range(dev_ctx, limit); if (centered) { - auto &mg_tensor = *ctx.Input("MeanGrad"); - auto *mean_grad_out = ctx.Output("MeanGradOut"); + auto &mg_tensor = *ctx.Input("MeanGrad"); + auto *mean_grad_out = ctx.Output("MeanGradOut"); PADDLE_ENFORCE(&mg_tensor, mean_grad_out, "MeanGrad and MeanGradOut must be the same Tensor"); for_range(CenteredRmspropFunctor>( @@ -241,8 +241,8 @@ class RmspropOpKernel : public framework::OpKernel { row_numel, row_count); if (centered) { - auto &mg_tensor = *ctx.Input("MeanGrad"); - auto *mean_grad_out = ctx.Output("MeanGradOut"); + auto &mg_tensor = *ctx.Input("MeanGrad"); + auto *mean_grad_out = ctx.Output("MeanGradOut"); PADDLE_ENFORCE(&mg_tensor, mean_grad_out, "MeanGrad and MeanGradOut must be the same Tensor"); for_range(CenteredRmspropFunctor>( diff --git a/python/paddle/fluid/tests/unittests/test_rmsprop_op.py b/python/paddle/fluid/tests/unittests/test_rmsprop_op.py index 70848e4e2239e2be160bb0c1a28a5aecd01a87dc..335d595f3d655f1a2f089e9490022fab76858cb5 100644 --- a/python/paddle/fluid/tests/unittests/test_rmsprop_op.py +++ b/python/paddle/fluid/tests/unittests/test_rmsprop_op.py @@ -19,29 +19,72 @@ import unittest import numpy as np import paddle.fluid.core as core from paddle.fluid.op import Operator +import paddle.fluid as fluid + + +def create_selected_rows_and_tensor(scope, place, height, row_num, + embedding_size): + sr = scope.var("@selected_rows@").get_selected_rows() + tensor = scope.var("grad").get_tensor() + + rows = np.random.random_integers( + low=0, high=height - 1, size=[row_num, ]).astype('int64') + sr_val = np.random.random(size=[row_num, embedding_size]).astype('float32') + + sr.set_height(height) + sr.set_rows(rows) + sr.get_tensor().set(sr_val, place) + + tensor_val = np.zeros(shape=[height, embedding_size], dtype='float32') + for i in range(row_num): + row = rows[i] + tensor_val[row, :] = tensor_val[row, :] + sr_val[i, :] + + tensor.set(tensor_val, place) + return tensor_val, sr_val class TestBase(unittest.TestCase): - def setup(self, centered, epsilon=1e-6): + def setup(self, + place, + is_sparse, + centered, + size, + row_num=None, + epsilon=1e-6): np.random.seed(5) # fix seed + self.scope = fluid.global_scope() + self.place = place + self.param_name = "param" - self.param = np.random.random((123, 321)).astype("float32") + self.param = np.random.random(size).astype("float32") self.mean_square_name = "mean_square" - self.mean_square = np.random.random((123, 321)).astype("float32") + self.mean_square = np.random.uniform( + low=1, high=2, size=size).astype("float32") self.mean_grad_name = "mean_grad" - self.mean_grad = np.random.random((123, 321)).astype("float32") + self.mean_grad = np.random.random(size).astype("float32") self.lr_name = "lr" self.learning_rate = np.array([0.01]).astype("float32") self.grad_name = "grad" - self.grad = np.random.random((123, 321)).astype("float32") + + self.is_sparse = is_sparse + if self.is_sparse: + self.grad_sr_name = "@selected_rows@" + self.grad, self.grad_sr = create_selected_rows_and_tensor( + self.scope, place, size[0], row_num, size[1]) + else: + self.grad = np.random.random(size).astype("float32") + grad_tensor = self.scope.var(self.grad_name).get_tensor() + grad_tensor.set(self.grad, place) self.moment_name = "moment" - self.moment = np.zeros((123, 321)).astype("float32") + self.moment = np.random.uniform( + low=0, high=1, size=size).astype("float32") self.epsilon = epsilon self.decay = 0.9 @@ -61,118 +104,119 @@ class TestBase(unittest.TestCase): self.param_out = self.param - self.moment_out - def check(self, - actual_t, - expect_t, - place, - out_name, - atol=1e-5, - equal_nan=False): - self.assertTrue( - np.allclose( - actual_t, expect_t, atol=atol, equal_nan=equal_nan), - "Output (" + out_name + ") has diff at " + str(place) + "\nExpect " - + str(expect_t) + "\n" + "But Got" + str(actual_t)) - - -class TestRmspropOp(TestBase): - def check_with_place(self, place, centered, epsilon): - self.setup(centered, epsilon) - scope = core.Scope() - # create and initialize Param Variable - param = scope.var(self.param_name).get_tensor() - param.set(self.param, place) + self.param_tensor = self.scope.var(self.param_name).get_tensor() + self.param_tensor.set(self.param, place) - mean_square = scope.var(self.mean_square_name).get_tensor() - mean_square.set(self.mean_square, place) + self.mean_square_tensor = self.scope.var( + self.mean_square_name).get_tensor() + self.mean_square_tensor.set(self.mean_square, place) - lr = scope.var(self.lr_name).get_tensor() + lr = self.scope.var(self.lr_name).get_tensor() lr.set(self.learning_rate, place) - grad = scope.var(self.grad_name).get_tensor() - grad.set(self.grad, place) + self.moment_tensor = self.scope.var(self.moment_name).get_tensor() + self.moment_tensor.set(self.moment, place) - moment = scope.var(self.moment_name).get_tensor() - moment.set(self.moment, place) + if self.centered: + self.mean_grad_tensor = self.scope.var( + self.mean_grad_name).get_tensor() + self.mean_grad_tensor.set(self.mean_grad, place) - # create and run sgd operator + def check(self, actual_t, expect_t, place, out_name, atol=1e-5): + self.assertTrue( + np.allclose( + actual_t, expect_t, atol=atol), + "Output (" + out_name + ") has diff at " + str(place) + "\nExpect " + + str(expect_t) + "\n" + "But Got" + str(actual_t)) - if self.centered: - mean_grad = scope.var(self.mean_grad_name).get_tensor() - mean_grad.set(self.mean_grad, place) - - rmsprop_op = Operator( - "rmsprop", - Param=self.param_name, - Grad=self.grad_name, - MeanSquare=self.mean_square_name, - MeanGrad=self.mean_grad_name, - Moment=self.moment_name, - LearningRate=self.lr_name, - ParamOut=self.param_name, - MeanSquareOut=self.mean_square_name, - MomentOut=self.moment_name, - MeanGradOut=self.mean_grad_name, - epsilon=self.epsilon, - decay=self.decay, - momentum=self.momentum, - centered=True) - else: - rmsprop_op = Operator( - "rmsprop", - Param=self.param_name, - Grad=self.grad_name, - MeanSquare=self.mean_square_name, - Moment=self.moment_name, - LearningRate=self.lr_name, - ParamOut=self.param_name, - MeanSquareOut=self.mean_square_name, - MomentOut=self.moment_name, - epsilon=self.epsilon, - decay=self.decay, - momentum=self.momentum, - centered=False) - - rmsprop_op.run(scope, place) - - atol = 1e-5 - equal_nan = False + +class TestRmspropOp(TestBase): + def check_with_place(self, + place, + is_sparse, + centered, + size, + row_num=None, + epsilon=1e-6): + self.setup(place, is_sparse, centered, size, row_num, epsilon) + self.run_and_check() + + def run_and_check(self): + grad_name = self.grad_sr_name if self.is_sparse else self.grad_name + + kwargs = { + 'Param': self.param_name, + 'Grad': grad_name, + 'MeanSquare': self.mean_square_name, + 'Moment': self.moment_name, + 'LearningRate': self.lr_name, + 'ParamOut': self.param_name, + 'MeanSquareOut': self.mean_square_name, + 'MomentOut': self.moment_name, + 'epsilon': self.epsilon, + 'decay': self.decay, + 'momentum': self.momentum, + 'centered': self.centered + } if self.centered: - atol = 1e-3 - equal_nan = True + kwargs['MeanGrad'] = self.mean_grad_name + kwargs['MeanGradOut'] = self.mean_grad_name + + rmsprop_op = Operator('rmsprop', **kwargs) + atol = 1e-6 + + rmsprop_op.run(self.scope, self.place) self.check( - np.array(mean_square), self.ms_out, place, self.mean_square_name) + np.array(self.mean_square_tensor), self.ms_out, self.place, + self.mean_square_name) self.check( - np.array(moment), + np.array(self.moment_tensor), self.moment_out, - place, + self.place, self.moment_name, - atol=atol, - equal_nan=equal_nan) + atol=atol) self.check( - np.array(param), + np.array(self.param_tensor), self.param_out, - place, + self.place, self.param_name, - atol=atol, - equal_nan=equal_nan) + atol=atol) if self.centered: self.check( - np.array(mean_grad), self.mg_out, place, self.mean_grad_name) + np.array(self.mean_grad_tensor), self.mg_out, self.place, + self.mean_grad_name) def test_rmsprop(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) + + size = (128, 320) for place in places: - self.check_with_place(place, False, 1e-6) - self.check_with_place(place, False, 1e-10) - self.check_with_place(place, True, 1e-6) - self.check_with_place(place, True, 1e-10) + for centered in [False, True]: + with fluid.scope_guard(core.Scope()): + self.check_with_place( + place, is_sparse=False, centered=centered, size=size) + + with fluid.scope_guard(core.Scope()): + self.check_with_place( + place, + is_sparse=True, + centered=centered, + row_num=512, + size=size) + + with fluid.scope_guard(core.Scope()): + self.check_with_place( + place, + is_sparse=True, + centered=centered, + row_num=60, + size=size) if __name__ == "__main__":