[MLU]add elementwise op (#43491)

paddle/fluid/operators/elementwise/elementwise_min_op_mlu.cc

+/* Copyright (c) 2022 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/elementwise/elementwise_mlu.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class ElementwiseMinMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    MLUBinaryOp<MINIMUM, T>(ctx);
+  }
+};
+
+template <typename T>
+class ElementwiseMinGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
+
+    const auto& x_dims = x->dims();
+    const auto& y_dims = y->dims();
+    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
+                     : axis);
+    int max_dim = std::max(x_dims.size(), y_dims.size());
+    std::vector<int> x_dims_array(max_dim);
+    std::vector<int> y_dims_array(max_dim);
+    std::vector<int> out_dims_array(max_dim);
+    GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
+                           y_dims_array.data(), out_dims_array.data(), max_dim,
+                           axis);
+
+    // mask = LessEqual(x, y)
+    Tensor mask(x->dtype());
+    mask.Resize(phi::make_ddim(out_dims_array));
+    mask.mutable_data<T>(ctx.GetPlace());
+
+    cnnlDataType_t data_type = ToCnnlDataType<T>();
+    MLUCnnlTensorDesc x_desc(max_dim, x_dims_array.data(), data_type);
+    MLUCnnlTensorDesc y_desc(max_dim, y_dims_array.data(), data_type);
+    MLUCnnlTensorDesc mask_desc(max_dim, out_dims_array.data(), data_type);
+    MLUCnnl::Logic(ctx, CNNL_LOGIC_OP_LE, x_desc.get(), GetBasePtr(x),
+                   y_desc.get(), GetBasePtr(y), mask_desc.get(),
+                   GetBasePtr(&mask));
+
+    // dx = Mul(dz, mask)
+    Tensor dx_temp(x->dtype());
+    dx_temp.Resize(dout->dims());
+    dx_temp.mutable_data<T>(ctx.GetPlace());
+    MLUCnnlTensorDesc dout_desc(*dout);
+    MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, data_type,
+                                    CNNL_NOT_PROPAGATE_NAN);
+    MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(), GetBasePtr(dout),
+                      dout_desc.get(), GetBasePtr(&mask), dout_desc.get(),
+                      GetBasePtr(&dx_temp), data_type);
+
+    // dy = Sub(dz, dx)
+    Tensor dy_temp(y->dtype());
+    dy_temp.Resize(dout->dims());
+    dy_temp.mutable_data<T>(ctx.GetPlace());
+    MLUCnnlOpTensorDesc sub_op_desc(CNNL_OP_TENSOR_SUB, data_type,
+                                    CNNL_NOT_PROPAGATE_NAN);
+    MLUCnnl::OpTensor(ctx, sub_op_desc.get(), dout_desc.get(), GetBasePtr(dout),
+                      dout_desc.get(), GetBasePtr(&dx_temp), dout_desc.get(),
+                      GetBasePtr(&dy_temp), data_type);
+
+    if (dx) {
+      if (dx->dims() != dout->dims()) {
+        dx->mutable_data<T>(ctx.GetPlace());
+        std::vector<int> reduce_axes;
+        GetReduceAxes(axis, dx_temp.dims(), dx->dims(), &reduce_axes);
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, data_type, CNNL_NOT_PROPAGATE_NAN,
+            CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dx_desc(*dx);
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(&dx_temp), 0,
+                        nullptr, nullptr, dx_desc.get(), GetBasePtr(dx));
+      } else {
+        dx->ShareDataWith(dx_temp);
+      }
+    }
+
+    if (dy) {
+      if (dy->dims() != dout->dims()) {
+        dy->mutable_data<T>(ctx.GetPlace());
+        std::vector<int> reduce_axes;
+        GetReduceAxes(axis, dy_temp.dims(), dy->dims(), &reduce_axes);
+        MLUCnnlReduceDesc reduction_desc(
+            reduce_axes, CNNL_REDUCE_ADD, data_type, CNNL_NOT_PROPAGATE_NAN,
+            CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
+        MLUCnnlTensorDesc dy_desc(*dy);
+        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
+                        nullptr, dout_desc.get(), GetBasePtr(&dy_temp), 0,
+                        nullptr, nullptr, dy_desc.get(), GetBasePtr(dy));
+      } else {
+        dy->ShareDataWith(dy_temp);
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(elementwise_min, ops::ElementwiseMinMLUKernel<int>,
+                       ops::ElementwiseMinMLUKernel<float>,
+                       ops::ElementwiseMinMLUKernel<plat::float16>);
+
+REGISTER_OP_MLU_KERNEL(elementwise_min_grad,
+                       ops::ElementwiseMinGradMLUKernel<int>,
+                       ops::ElementwiseMinGradMLUKernel<float>,
+                       ops::ElementwiseMinGradMLUKernel<plat::float16>);

paddle/fluid/operators/elementwise/elementwise_mlu.h

@@ -109,6 +109,7 @@ enum BINARY_FUNCTOR {
   DIV,
   DIVNONAN,
   MAXIMUM,
+  MINIMUM,
 };
 
 template <BINARY_FUNCTOR func>
@@ -137,6 +138,15 @@ inline void MLUBinary<MAXIMUM>(
   MLUCnnl::Maximum(ctx, x_desc, x, y_desc, y, out_desc, out);
 }
 
+template <>
+inline void MLUBinary<MINIMUM>(
+    const framework::ExecutionContext& ctx, cnnlComputationPreference_t prefer,
+    const cnnlTensorDescriptor_t in1_desc, const void* in1,
+    const cnnlTensorDescriptor_t in2_desc, const void* in2,
+    const cnnlTensorDescriptor_t out_desc, void* out) {
+  MLUCnnl::Minimum(ctx, in1_desc, in1, in2_desc, in2, out_desc, out);
+}
+
 template <BINARY_FUNCTOR Functor, typename T>
 void MLUBinaryOp(const framework::ExecutionContext& ctx) {
   auto* x = ctx.Input<Tensor>("X");

paddle/fluid/platform/device/mlu/device_context.cc

@@ -40,6 +40,7 @@ MLUDeviceContext::MLUDeviceContext(MLUPlace place) : place_(place) {
   compute_capability_ = GetMLUComputeCapability(place_.device);
   driver_version_ = GetMLUDriverVersion(place_.device);
   runtime_version_ = GetMLURuntimeVersion(place_.device);
+  cnnl_version_ = GetMLUCnnlVersion(place_.device);
 
   LOG_FIRST_N(WARNING, 1) << "Please NOTE: device: " << place_.device
                           << ", MLU Compute Capability: "
@@ -50,7 +51,10 @@ MLUDeviceContext::MLUDeviceContext(MLUPlace place) : place_(place) {
                           << driver_version_ % 100 << ", Runtime API Version: "
                           << runtime_version_ / 10000 << "."
                           << (runtime_version_ / 100) % 100 << "."
-                          << runtime_version_ % 100;
+                          << runtime_version_ % 100
+                          << ", Cnnl API Version: " << cnnl_version_ / 10000
+                          << "." << (cnnl_version_ / 100) % 100 << "."
+                          << cnnl_version_ % 100;
 
   default_ctx_.reset(new MLUContext(place_));
 }

paddle/fluid/platform/device/mlu/device_context.h

@@ -134,6 +134,7 @@ class MLUDeviceContext : public DeviceContext {
   int compute_capability_;
   int driver_version_;
   int runtime_version_;
+  int cnnl_version_;
   MLUPlace place_;
   std::shared_ptr<MLUContext> default_ctx_;
 

paddle/fluid/platform/device/mlu/mlu_info.cc

@@ -116,6 +116,13 @@ int GetMLURuntimeVersion(int id) {
   return x * 10000 + y * 100 + z;
 }
 
+int GetMLUCnnlVersion(int id) {
+  CheckDeviceId(id);
+  int x, y, z;
+  cnnlGetLibVersion(&x, &y, &z);
+  return x * 10000 + y * 100 + z;
+}
+
 int GetMLUCurrentDeviceId() {
   int device_id;
   PADDLE_ENFORCE_MLU_SUCCESS(cnrtGetDevice(&device_id));

paddle/fluid/platform/device/mlu/mlu_info.h

@@ -46,6 +46,9 @@ int GetMLUDriverVersion(int id);
 //! Get the runtime version of the ith MLU.
 int GetMLURuntimeVersion(int id);
 
+//! Get the cnnl version of the ith MLU.
+int GetMLUCnnlVersion(int id);
+
 //! Get the total number of MLU devices in system.
 int GetMLUDeviceCount();
 

python/paddle/fluid/tests/unittests/mlu/test_elementwise_min_op_mlu.py

+#  Copyright (c) 2022 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, skip_check_grad_ci
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import paddle.fluid.core as core
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestElementwiseMinOp(OpTest):
+
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "elementwise_min"
+        self.init_dtype()
+        self.init_input_output()
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.outputs = {'Out': self.out}
+        self.attrs = {'axis': self.axis}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_input_output(self):
+        # If x and y have the same value, the min() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.sgn = np.random.choice([-1, 1], [13, 17]).astype(self.dtype)
+        self.y = self.x + self.sgn * np.random.uniform(0.1, 1, [13, 17]).astype(
+            self.dtype)
+        self.out = np.minimum(self.x, self.y)
+        self.axis = -1
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad_normal(self):
+        if self.dtype == np.float16:
+            self.check_grad_with_place(self.place, ['X', 'Y'],
+                                       'Out',
+                                       max_relative_error=0.5)
+        else:
+            self.check_grad_with_place(
+                self.place,
+                ['X', 'Y'],
+                'Out',
+            )
+
+    def test_check_grad_ingore_x(self):
+        if self.dtype == np.float16:
+            self.check_grad_with_place(self.place, ['Y'],
+                                       'Out',
+                                       no_grad_set=set("X"),
+                                       max_relative_error=0.9)
+        else:
+            self.check_grad_with_place(
+                self.place,
+                ['Y'],
+                'Out',
+                no_grad_set=set("X"),
+            )
+
+    def test_check_grad_ingore_y(self):
+        if self.dtype == np.float16:
+            self.check_grad_with_place(self.place, ['X'],
+                                       'Out',
+                                       no_grad_set=set("Y"),
+                                       max_relative_error=0.1)
+        else:
+            self.check_grad_with_place(
+                self.place,
+                ['X'],
+                'Out',
+                no_grad_set=set("Y"),
+            )
+
+
+class TestElementwiseMinOpFp16(TestElementwiseMinOp):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestElementwiseMinOp_Vector(TestElementwiseMinOp):
+
+    def init_input_output(self):
+        self.x = np.random.uniform(1, 2, (100, )).astype(self.dtype)
+        self.sgn = np.random.choice([-1, 1], (100, )).astype(self.dtype)
+        self.y = self.x + self.sgn * np.random.uniform(0.1, 1, (100, )).astype(
+            self.dtype)
+        self.out = np.minimum(self.x, self.y)
+        self.axis = -1
+
+
+class TestElementwiseMinOpFp16_Vector(TestElementwiseMinOp_Vector):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestElementwiseMinOp_scalar(TestElementwiseMinOp):
+
+    def init_input_output(self):
+        self.x = np.random.random_integers(-5, 5, [10, 3, 4]).astype(self.dtype)
+        self.y = np.array([0.5]).astype(self.dtype)
+        self.out = np.minimum(self.x, self.y)
+        self.axis = -1
+
+
+@skip_check_grad_ci(
+    reason="[skip shape check] Use y_shape(1) to test broadcast.")
+class TestElementwiseMinOpFp16_scalar(TestElementwiseMinOp_scalar):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestElementwiseMinOp_broadcast(TestElementwiseMinOp):
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0.5, 1, (2, 3, 100)).astype(self.dtype)
+        self.sgn = np.random.choice([-1, 1], (100, )).astype(self.dtype)
+        self.y = self.x[0, 0, :] + self.sgn * \
+            np.random.uniform(1, 2, (100, )).astype(self.dtype)
+        self.out = np.minimum(self.x, self.y.reshape(1, 1, 100))
+        self.axis = -1
+
+
+class TestElementwiseMinOpFp16_broadcast(TestElementwiseMinOp_broadcast):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestElementwiseMinOpNet(unittest.TestCase):
+
+    def _test(self, run_mlu=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')
+
+            c = paddle.minimum(a, b)
+
+            fc_1 = fluid.layers.fc(input=c, 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)
+            sgd = fluid.optimizer.SGD(learning_rate=0.01)
+            sgd.minimize(loss)
+
+        if run_mlu:
+            place = paddle.device.MLUPlace(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_mlu(self):
+        cpu_pred, cpu_loss = self._test(False)
+        mlu_pred, mlu_loss = self._test(True)
+
+        self.assertTrue(np.allclose(mlu_pred, cpu_pred))
+        self.assertTrue(np.allclose(mlu_loss, cpu_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()