lamb_op_xpu;test=kunlun (#31012)

* lamb_op_xpu;test=kunlun

* modify lamb_op_xpu.cc;test=kunlun

* delete atol lamb_op_xpu; test=kunlun

* update xpu.cmake;test=kunlun

* test_error 1e-5,lamb_op_xpu;test=kunlun

* error1e-5,lamb_op_xpu,test=kunlun

* delete atol lamb_xpu;test=kunlun

* modify atol,lamb_op_xpy;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu, XPUOptest;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu,modify xpu_cmake; test=kunlun

* lamb_op_xpu;test=kunlun

* lamb_op_xpu,modify xpucmake;test=kunlun

cmake/external/xpu.cmake

@@ -13,7 +13,7 @@ if(NOT XPU_SDK_ROOT)
   elseif(WITH_SUNWAY)
       SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/sunway/xpu_2021_01_13.tar.gz" CACHE STRING "" FORCE)
   else()
-      SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_02_19.tar.gz" CACHE STRING "" FORCE)
+      SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_02_27.tar.gz" CACHE STRING "" FORCE)
   endif()
 
   SET(XPU_SOURCE_DIR              "${THIRD_PARTY_PATH}/xpu")

paddle/fluid/operators/optimizers/lamb_op_xpu.cc

+/* Copyright (c) 2016 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 "paddle/fluid/operators/optimizers/lamb_op.h"
+#include "gflags/gflags.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+#ifdef PADDLE_WITH_XPU
+template <typename DeviceContext, typename T>
+class LambOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using paddle::framework::LoDTensor;
+    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())));
+
+    using paddle::framework::LoDTensor;
+
+    // inputs
+    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
+    T weight_decay = static_cast<T>(ctx.Attr<float>("weight_decay"));
+    T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
+    T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
+    auto& param = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Param"), "Input",
+                                  "Param", "Lamb");
+    auto* grad_var = ctx.InputVar("Grad");
+    auto& mom1 = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Moment1"), "Input",
+                                 "Moment1", "Lamb");
+    auto& mom2 = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Moment2"), "Input",
+                                 "Moment2", "Lamb");
+    auto& lr = GET_DATA_SAFELY(ctx.Input<LoDTensor>("LearningRate"), "Input",
+                               "LearningRate", "Lamb");
+
+    auto& beta1_pow = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Beta1Pow"), "Input",
+                                      "Beta1Pow", "Lamb");
+    auto& beta2_pow = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Beta2Pow"), "Input",
+                                      "Beta2Pow", "Lamb");
+
+    auto& param_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("ParamOut"),
+                                      "Output", "ParamOut", "Lamb");
+    auto& mom1_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Moment1Out"),
+                                     "Output", "Moment1Out", "Lamb");
+    auto& mom2_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Moment2Out"),
+                                     "Output", "Moment2Out", "Lamb");
+    auto& beta1_pow_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Beta1PowOut"),
+                                          "Output", "Beta1PowOut", "Lamb");
+    auto& beta2_pow_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("Beta2PowOut"),
+                                          "Output", "Beta2PowOut", "Lamb");
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+
+    if (grad_var->IsType<framework::LoDTensor>()) {
+      auto& grad = *ctx.Input<LoDTensor>("Grad");
+      int r = xpu::lamb(dev_ctx.x_context(), grad.template data<T>(),
+                        mom1.template data<T>(), mom2.template data<T>(),
+                        param.template data<T>(), beta1_pow.template data<T>(),
+                        beta2_pow.template data<T>(), beta1, beta2, epsilon,
+                        weight_decay, lr.template data<T>(),
+                        mom1_out.template mutable_data<T>(ctx.GetPlace()),
+                        mom2_out.template mutable_data<T>(ctx.GetPlace()),
+                        param_out.template mutable_data<T>(ctx.GetPlace()),
+                        beta1_pow_out.template mutable_data<T>(ctx.GetPlace()),
+                        beta2_pow_out.template mutable_data<T>(ctx.GetPlace()),
+                        param.numel());
+
+      if (r == xpu::Error_t::INVALID_PARAM) {
+        PADDLE_ENFORCE_EQ(
+            r, xpu::Error_t::SUCCESS,
+            platform::errors::InvalidArgument(
+                "XPU kernel error of LambOp, error message: INVALID_PARAM, "
+                "please check your input & output."));
+      } else if (r == xpu::Error_t::RUNTIME_ERROR) {
+        PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                          platform::errors::Unavailable(
+                              "XPU kernel error of LambOp, error message: "
+                              "RUNTIME_ERROR, please check whether Baidu "
+                              "Kunlun Card is properly installed."));
+      } else if (r == xpu::Error_t::NO_ENOUGH_WORKSPACE) {
+        PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                          platform::errors::ResourceExhausted(
+                              "XPU kernel error of LambOp, error "
+                              "message: NO_ENOUGH_WORKSPACE, XPU "
+                              "has no enough memory."));
+      } else {
+        PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
+                          platform::errors::ResourceExhausted(
+                              "XPU kernel error of LambOp, error "
+                              "message: OTHER "
+                              "XPU API returns error code: %d.",
+                              r));
+      }
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Variable type not supported by lamb_op. Expect LoDTensor, "
+          "but got %s",
+          framework::ToTypeName(param_var->Type())));
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    lamb, ops::LambOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
+#endif

python/paddle/fluid/tests/unittests/xpu/test_lamb_op_xpu.py

+#   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 sys
+sys.path.append("..")
+import unittest
+import numpy as np
+from op_test_xpu import XPUOpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+import paddle
+
+
+class TestLambOp1(XPUOpTest):
+    def set_attrs(self):
+        self.attrs = {
+            'epsilon': 1e-6,
+            'beta1': 0.9,
+            'beta2': 0.999,
+            'weight_decay': 0.01
+        }
+
+    def setUp(self):
+        '''Test Lamb Op with supplied attributes
+        '''
+        self.op_type = "lamb"
+        param = np.random.uniform(-1, 1, 5000).astype("float32")
+        grad = np.random.uniform(-1, 1, 5000).astype("float32")
+        moment1 = np.random.uniform(-1, 1, 5000).astype("float32")
+        moment2 = np.random.random(5000).astype("float32")
+
+        self.set_attrs()
+        learning_rate = 0.001
+        beta1_pow = self.attrs['beta1']
+        beta2_pow = self.attrs['beta2']
+
+        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")
+        }
+
+        param_out, moment1_out, moment2_out, \
+            beta1_pow_out, beta2_pow_out = lamb_step(self.inputs, self.attrs)
+
+        self.outputs = {
+            'Moment1Out': moment1_out,
+            'Moment2Out': moment2_out,
+            'ParamOut': param_out,
+            'Beta1PowOut': beta1_pow_out,
+            'Beta2PowOut': beta2_pow_out
+        }
+
+    def test_check_output(self):
+        self.check_output_with_place(paddle.XPUPlace(0))
+
+
+def lamb_step(inputs, attributes):
+    '''
+    Simulate one step of the lamb optimizer
+    :param inputs: dict of inputs
+    :param attributes: dict of attributes
+    :return tuple: tuple of output param, moment1, moment2,
+    beta1 power accumulator and beta2 power accumulator
+    '''
+    param = inputs['Param']
+    grad = inputs['Grad']
+    moment1 = inputs['Moment1']
+    moment2 = inputs['Moment2']
+    lr = inputs['LearningRate']
+    beta1_pow = inputs['Beta1Pow']
+    beta2_pow = inputs['Beta2Pow']
+
+    beta1 = attributes['beta1']
+    beta2 = attributes['beta2']
+    epsilon = attributes['epsilon']
+    weight_decay = attributes['weight_decay']
+
+    moment1_out = beta1 * moment1 + (1 - beta1) * grad
+    moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
+
+    moment1_unbiased = moment1_out / (1 - beta1_pow)
+    moment2_unbiased = moment2_out / (1 - beta2_pow)
+
+    r_1 = np.linalg.norm(param)
+    r_2 = np.linalg.norm(moment1_unbiased / (np.sqrt(moment2_unbiased) + epsilon
+                                             ) + weight_decay * param)
+    if r_1 > 0.0 and r_2 > 0.0:
+        lr_t = lr * r_1 / r_2
+    else:
+        lr_t = 1.0
+
+    param_out = param - lr_t * (moment1_unbiased / (
+        np.sqrt(moment2_unbiased) + epsilon) + weight_decay * param)
+
+    beta1_pow_out = beta1_pow * beta1
+    beta2_pow_out = beta2_pow * beta2
+
+    return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()

tools/static_mode_white_list.py

@@ -695,4 +695,5 @@ STATIC_MODE_TESTING_LIST = [
     'test_shape_op_xpu',
     'test_slice_op_xpu',
     'test_generate_proposals_v2_op',
+    'test_lamb_op_xpu',
 ]