[NPU] add npu kernel for adam

paddle/fluid/operators/optimizers/adam_op_npu.cc

@@ -0,0 +1,161 @@
+/* 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 <memory>
+#include <string>
+
+#include "paddle/fluid/operators/npu_op_runner.h"
+#include "paddle/fluid/operators/optimizers/adam_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename DeviceContext, typename T>
+class AdamNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
+                      platform::errors::InvalidArgument(
+                          "The Var(%s)'s type should be LoDTensor, "
+                          "but the received is %s",
+                          ctx.InputNames("Param").front(),
+                          framework::ToTypeName(param_var->Type())));
+    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
+    auto* param = ctx.Input<LoDTensor>("Param");
+    auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
+                      platform::errors::InvalidArgument(
+                          "The Grad(%s)'s type should be LoDTensor, "
+                          "but the received is %s",
+                          ctx.InputNames("Grad").front(),
+                          framework::ToTypeName(param_var->Type())));
+    auto* grad = ctx.Input<LoDTensor>("Grad");
+    auto* mom1 = ctx.Input<LoDTensor>("Moment1");
+    auto* mom2 = ctx.Input<LoDTensor>("Moment2");
+    auto* lr = ctx.Input<LoDTensor>("LearningRate");
+
+    auto* beta1_pow = ctx.Input<LoDTensor>("Beta1Pow");
+    auto* beta2_pow = ctx.Input<LoDTensor>("Beta2Pow");
+
+    auto* param_out = ctx.Output<LoDTensor>("ParamOut");
+    auto* mom1_out = ctx.Output<LoDTensor>("Moment1Out");
+    auto* mom2_out = ctx.Output<LoDTensor>("Moment2Out");
+    auto* beta1_pow_out = ctx.Output<LoDTensor>("Beta1PowOut");
+    auto* beta2_pow_out = ctx.Output<LoDTensor>("Beta2PowOut");
+
+    param_out->mutable_data<T>(ctx.GetPlace());
+    mom1_out->mutable_data<T>(ctx.GetPlace());
+    mom2_out->mutable_data<T>(ctx.GetPlace());
+    beta1_pow_out->mutable_data<T>(ctx.GetPlace());
+    beta2_pow_out->mutable_data<T>(ctx.GetPlace());
+
+    T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
+    if (ctx.HasInput("Beta1Tensor")) {
+      auto* beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
+      PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(Beta1Tensor) size must be 1, but get %d",
+                            beta1_tensor->numel()));
+      beta1 = static_cast<T>(GetAttrFromTensor(beta1_tensor));
+    }
+    T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
+    if (ctx.HasInput("Beta2Tensor")) {
+      auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
+      PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1,
+                        platform::errors::InvalidArgument(
+                            "Input(Beta2Tensor) size must be 1, but get %d",
+                            beta2_tensor->numel()));
+      beta2 = static_cast<T>(GetAttrFromTensor(beta2_tensor));
+    }
+    VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel()
+            << "beta2_pow.numel() : " << beta2_pow->numel();
+    VLOG(3) << "param.numel(): " << param->numel();
+
+    PADDLE_ENFORCE_EQ(beta1_pow_out->numel(), 1,
+                      platform::errors::InvalidArgument(
+                          "beta1 pow output size should be 1, but received "
+                          "value is:%d.",
+                          beta1_pow_out->numel()));
+
+    PADDLE_ENFORCE_EQ(beta2_pow_out->numel(), 1,
+                      platform::errors::InvalidArgument(
+                          "beta2 pow output size should be 1, but received "
+                          "value is:%d.",
+                          beta2_pow_out->numel()));
+
+    // reshape
+    Tensor beta1_tensor(framework::proto::VarType::FP32);
+    beta1_tensor.mutable_data<float>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<T>{beta1}, ctx.device_context(),
+                     &beta1_tensor);
+    Tensor beta2_tensor(framework::proto::VarType::FP32);
+    beta2_tensor.mutable_data<float>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<T>{beta2}, ctx.device_context(),
+                     &beta2_tensor);
+
+    Tensor epsilon_tensor(framework::proto::VarType::FP32);
+    epsilon_tensor.mutable_data<T>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<T>{epsilon}, ctx.device_context(),
+                     &epsilon_tensor);
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    auto runner =
+        NpuOpRunner("ApplyAdamD",
+                    {
+                        *param, *mom1, *mom2, *beta1_pow, *beta2_pow, *lr,
+                        beta1_tensor, beta2_tensor, epsilon_tensor, *grad,
+                    },
+                    {
+                        *param_out, *mom1_out, *mom2_out,
+                    },
+                    {});
+    runner.Run(stream);
+
+    // NOTE(zhiqiu): ApplyAdamD updates params inplace, so
+    // if param and param_out is not same, we need to do copy.
+    if (param_out->data<T>() != param->data<T>()) {
+      ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+      framework::TensorCopySync(*param, ctx.GetPlace(), param_out);
+    }
+    if (mom1_out->data<T>() != mom1->data<T>()) {
+      ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+      framework::TensorCopySync(*mom1, ctx.GetPlace(), mom1_out);
+    }
+    if (mom2_out->data<T>() != mom2->data<T>()) {
+      ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+      framework::TensorCopySync(*mom2, ctx.GetPlace(), mom2_out);
+    }
+    auto runner_m1 =
+        NpuOpRunner("Mul", {*beta1_pow, beta1_tensor}, {*beta1_pow_out}, {});
+    runner_m1.Run(stream);
+    auto runner_m2 =
+        NpuOpRunner("Mul", {*beta2_pow, beta2_tensor}, {*beta2_pow_out}, {});
+    runner_m2.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    adam, ops::AdamNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::AdamNPUKernel<paddle::platform::NPUDeviceContext,
+                       paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_adam_op_npu.py

@@ -0,0 +1,148 @@
+#  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.
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from test_adam_op import adam_step
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestSGD(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.place = paddle.NPUPlace(0)
+        self.op_type = "adam"
+        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
+        # The second moment is positive
+        moment2 = np.random.random((102, 105)).astype("float32")
+
+        learning_rate = 0.004
+        beta1 = 0.78
+        beta2 = 0.836
+        epsilon = 1e-4
+        beta1_pow = beta1**10
+        beta2_pow = beta2**10
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Moment1': moment1,
+            'Moment2': moment2,
+            'LearningRate': np.array([learning_rate]).astype("float32"),
+            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+        }
+
+        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+
+        param_out, moment1_out, \
+            moment2_out = adam_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
+        }
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False)
+
+
+'''
+# TODO(zhiqiu): The following test may let 0-3 card down.
+# we need to analyze it and open it.
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestNet(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=(32, 32)).astype('float32')
+        b_np = np.random.random(size=(32, 32)).astype('float32')
+        label_np = np.random.randint(2, size=(32, 1)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
+            b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            sum = paddle.add(a, b)
+            z = paddle.pow(sum, 2.0)
+
+            fc_1 = fluid.layers.fc(input=z, size=128)
+            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)
+            adam = fluid.optimizer.Adam(learning_rate=0.01)
+            adam.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,
+                      "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()