diff --git a/paddle/fluid/operators/matmul_v2_op_npu.cc b/paddle/fluid/operators/matmul_v2_op_npu.cc new file mode 100644 index 0000000000000000000000000000000000000000..d3022056a47ded99e63aa05c1aca8e9b31ccc3fe --- /dev/null +++ b/paddle/fluid/operators/matmul_v2_op_npu.cc @@ -0,0 +1,160 @@ +/* Copyright (c) 2021 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. */ + +#include +#include + +#include "paddle/fluid/operators/matmul_v2_op.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace operators { + +template +class MatMulV2NPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* out = ctx.Output("Out"); + bool transpose_x = ctx.Attr("trans_x"); + bool transpose_y = ctx.Attr("trans_y"); + + if (x->dims().size() == 2) { + out->mutable_data(ctx.GetPlace()); + + auto runner = NpuOpRunner( + "MatMul", {*x, *y}, {*out}, + {{"transpose_x1", transpose_x}, {"transpose_x2", transpose_y}}); + + auto stream = + ctx.template device_context() + .stream(); + runner.Run(stream); + + } else if (x->dims().size() > 2) { + out->mutable_data(ctx.GetPlace()); + + auto runner = + NpuOpRunner("BatchMatMul", {*x, *y}, {*out}, + {{"adj_x1", transpose_x}, {"adj_x2", transpose_y}}); + + auto stream = + ctx.template device_context() + .stream(); + runner.Run(stream); + } + } +}; + +template +class MatMulV2GradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* dout = ctx.Input(framework::GradVarName("Out")); + auto* dx = ctx.Output(framework::GradVarName("X")); + auto* dy = ctx.Output(framework::GradVarName("Y")); + bool transpose_y = ctx.Attr("trans_y"); + auto stream = + ctx.template device_context() + .stream(); + + if (x->dims().size() == 2) { + if (transpose_y) { + if (dx) { + dx->mutable_data(ctx.GetPlace()); + auto runner_dx = + NpuOpRunner("MatMul", {*dout, *y}, {*dx}, + {{"transpose_x1", false}, {"transpose_x2", false}}); + + runner_dx.Run(stream); + } + if (dy) { + dy->mutable_data(ctx.GetPlace()); + auto runner_dy = + NpuOpRunner("MatMul", {*dout, *x}, {*dy}, + {{"transpose_x1", true}, {"transpose_x2", false}}); + + runner_dy.Run(stream); + } + + } else { + if (dx) { + dx->mutable_data(ctx.GetPlace()); + auto runner_dx = + NpuOpRunner("MatMul", {*dout, *y}, {*dx}, + {{"transpose_x1", false}, {"transpose_x2", true}}); + + runner_dx.Run(stream); + } + if (dy) { + dy->mutable_data(ctx.GetPlace()); + auto runner_dy = + NpuOpRunner("MatMul", {*x, *dout}, {*dy}, + {{"transpose_x1", true}, {"transpose_x2", false}}); + + runner_dy.Run(stream); + } + } + } else if (x->dims().size() > 2) { + if (transpose_y) { + if (dx) { + dx->mutable_data(ctx.GetPlace()); + auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx}, + {{"adj_x1", false}, {"adj_x2", false}}); + + runner_dx.Run(stream); + } + if (dy) { + dy->mutable_data(ctx.GetPlace()); + auto runner_dy = NpuOpRunner("BatchMatMul", {*dout, *x}, {*dy}, + {{"adj_x1", true}, {"adj_x2", false}}); + + runner_dy.Run(stream); + } + } else { + if (dx) { + dx->mutable_data(ctx.GetPlace()); + auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx}, + {{"adj_x1", false}, {"adj_x2", true}}); + + runner_dx.Run(stream); + } + if (dy) { + dy->mutable_data(ctx.GetPlace()); + auto runner_dy = NpuOpRunner("BatchMatMul", {*x, *dout}, {*dy}, + {{"adj_x1", true}, {"adj_x2", false}}); + runner_dy.Run(stream); + } + } + } + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + matmul_v2, + ops::MatMulV2NPUKernel, + ops::MatMulV2NPUKernel); +REGISTER_OP_NPU_KERNEL( + matmul_v2_grad, + ops::MatMulV2GradNPUKernel, + ops::MatMulV2GradNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..b27b9c0b9756072c42fa7269f73821c18a7cc37e --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py @@ -0,0 +1,210 @@ +# Copyright (c) 2021 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 numpy as np +import unittest +import sys +sys.path.append("..") +from op_test import OpTest +import paddle +import paddle.fluid as fluid + +paddle.enable_static() +SEED = 2021 + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +def reference_matmul(X, Y, transpose_X=False, transpose_Y=False): + """Reference forward implementation using np.matmul.""" + # np.matmul does not support the transpose flags, so we manually + # transpose X and Y appropriately. + if transpose_X: + if X.ndim == 1: + X = X.reshape((X.size, )) + elif X.ndim == 2: + X = X.T + else: + dim = [i for i in range(len(X.shape))] + dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1] + X = np.transpose(X, tuple(dim)) + if transpose_Y: + if Y.ndim == 1: + Y = Y.reshape((Y.size, )) + else: + dim = [i for i in range(len(Y.shape))] + dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1] + Y = np.transpose(Y, tuple(dim)) + + Out = np.matmul(X, Y) + if not Out.shape: + # We do not support 0-dimensional Tensors (scalars). So where + # np.matmul outputs a scalar, we must convert to a Tensor of + # shape (1, ) instead. + # Everywhere else, we are compatible with np.matmul. + Out = np.array([Out], dtype="float64") + return Out + + +class TestMatMul(OpTest): + def config(self): + self.x_shape = (100, 24) + self.y_shape = (24, 100) + self.trans_x = False + self.trans_y = False + + def setUp(self): + self.set_npu() + self.op_type = "matmul_v2" + self.place = paddle.NPUPlace(0) + self.init_dtype() + self.config() + np.random.seed(SEED) + x = np.random.random(self.x_shape).astype(self.dtype) + y = np.random.random(self.y_shape).astype(self.dtype) + # -0.1 ~ 0.1 + x = -0.1 + 0.2 * x + y = -0.1 + 0.2 * y + result = reference_matmul(x, y, self.trans_x, self.trans_y) + result = result.astype(self.dtype) + self.inputs = { + 'X': x, + 'Y': y, + } + self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y} + self.outputs = {'Out': result} + + def set_npu(self): + self.__class__.use_npu = True + self.__class__.no_need_check_grad = True + + def init_dtype(self): + self.dtype = np.float32 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) + + + # TODO(ascendrc): Add grad test + # def test_check_grad(self): + # if self.dtype == np.float16: + # return + # self.check_grad(['X'], 'Out') + # +class TestMatMul2(TestMatMul): + """ + case 2 + """ + + def config(self): + self.x_shape = (32, 24) + self.y_shape = (32, 24) + self.trans_x = False + self.trans_y = True + + +class TestMatMul3(TestMatMul): + """ + case 3 + """ + + def init_dtype(self): + self.dtype = np.float16 + + +class TestMatMul4(TestMatMul): + """ + case 4 dim=3 + """ + + def config(self): + self.x_shape = (2, 3, 4) + self.y_shape = (2, 4, 3) + self.trans_x = False + self.trans_y = False + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestMatMulNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(2, 3)).astype('float32') + b_np = np.random.random(size=(2, 3)).astype('float32') + c_np = np.random.random(size=(3, 2)).astype('float32') + d_np = np.random.random(size=(3, 2)).astype('float32') + label_np = np.random.randint(2, size=(2, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[2, 3], dtype='float32') + b = paddle.static.data(name="b", shape=[2, 3], dtype='float32') + c = paddle.static.data(name="c", shape=[3, 2], dtype='float32') + d = paddle.static.data(name="d", shape=[3, 2], dtype='float32') + label = paddle.static.data( + name="label", shape=[2, 1], dtype='int64') + + sum_1 = paddle.add(a, b) + sum_2 = paddle.add(c, d) + result = paddle.matmul(sum_1, sum_2) + + fc_1 = fluid.layers.fc(input=result, size=8) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run(main_prog, + feed={ + "a": a_np, + "b": b_np, + "c": c_np, + "d": d_np, + "label": label_np + }, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred)) + self.assertTrue(np.allclose(npu_loss, cpu_loss)) + + +if __name__ == '__main__': + unittest.main()