[NPU] add npu kernel for softmax_with_cross_entropy

paddle/fluid/operators/npu_op_runner.h

@@ -82,6 +82,8 @@ class NpuOpRunner {
   aclopAttr *attr_{nullptr};
 };
 
+aclDataType ConvertToNpuDtype(framework::proto::VarType::Type dtype);
+
 }  // namespace operators
 }  // namespace paddle
 #endif

paddle/fluid/operators/softmax_with_cross_entropy_op_npu.cc

@@ -0,0 +1,203 @@
+/* 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 "paddle/fluid/operators/math/softmax.h"
+#include <memory>
+#include <string>
+#include "paddle/fluid/operators/math/cross_entropy.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+#include "paddle/fluid/operators/softmax_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class SoftmaxWithCrossEntropyNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* logits = ctx.Input<Tensor>("Logits");
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* softmax = ctx.Output<Tensor>("Softmax");
+    auto* loss = ctx.Output<Tensor>("Loss");
+
+    int cls_num = logits->dims()[1];
+    const int rank = logits->dims().size();
+    const int axis = CanonicalAxis(ctx.Attr<int>("axis"), rank);
+    std::vector<int> axes;
+    for (auto i = axis; i < logits->dims().size(); ++i) {
+      axes.push_back(i);
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    // softmax
+    softmax->mutable_data<T>(ctx.GetPlace());
+    auto runner_softmax =
+        NpuOpRunner("SoftmaxV2", {*logits}, {*softmax}, {{"axes", axes}});
+    runner_softmax.Run(stream);
+
+    // cast label from int64/int32 to int32
+    Tensor tmp_labels(framework::proto::VarType::INT32);
+    if (labels->type() != framework::proto::VarType::INT32) {
+      tmp_labels.Resize(labels->dims());
+      tmp_labels.mutable_data(ctx.GetPlace(), framework::proto::VarType::INT32);
+      auto dst_dtype = ConvertToNpuDtype(framework::proto::VarType::INT32);
+      auto runner_cast_label =
+          NpuOpRunner("Cast", {*labels}, {tmp_labels},
+                      {{"dst_type", static_cast<int>(dst_dtype)}});
+      runner_cast_label.Run(stream);
+      labels = &tmp_labels;
+    }
+
+    // on and off
+    Tensor on_tensor(framework::proto::VarType::INT32);
+    on_tensor.mutable_data<int>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<int>{static_cast<int>(1)},
+                     ctx.device_context(), &on_tensor);
+    Tensor off_tensor(framework::proto::VarType::INT32);
+    off_tensor.mutable_data<int>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<int>{static_cast<int>(0)},
+                     ctx.device_context(), &off_tensor);
+
+    // one_hot
+    Tensor tmp_onehot(on_tensor.type());
+    tmp_onehot.Resize(logits->dims());
+    tmp_onehot.mutable_data<int>(ctx.GetPlace());
+
+    auto runner_onehot =
+        NpuOpRunner("OneHotD", {*labels, on_tensor, off_tensor}, {tmp_onehot},
+                    {{"axis", -1}, {"depth", cls_num}});
+    runner_onehot.Run(stream);
+
+    // cast one_hot from int32 to T
+    Tensor cast_onehot(logits->type());
+    cast_onehot.Resize(tmp_onehot.dims());
+    cast_onehot.mutable_data<T>(ctx.GetPlace());
+    auto dst_dtype = ConvertToNpuDtype(logits->type());
+    auto runner_cast_onehot =
+        NpuOpRunner("Cast", {tmp_onehot}, {cast_onehot},
+                    {{"dst_type", static_cast<int>(dst_dtype)}});
+    runner_cast_onehot.Run(stream);
+
+    // SoftmaxCrossEntropyWithLogits
+    Tensor backprop(logits->type());
+    backprop.Resize(logits->dims());
+    backprop.mutable_data<T>(ctx.GetPlace());
+
+    loss->mutable_data<T>(ctx.GetPlace());
+
+    // SoftmaxCrossEntropyWithLogits requires loss to be of shape [batch_size]
+    auto loss_dims = loss->dims();
+    loss->Resize({loss_dims[0]});
+    auto runner_s = NpuOpRunner("SoftmaxCrossEntropyWithLogits",
+                                {*logits, cast_onehot}, {*loss, backprop}, {});
+    runner_s.Run(stream);
+    loss->Resize(loss_dims);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class SoftmaxWithCrossEntropyGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* labels = ctx.Input<Tensor>("Label");
+    auto* softmax = ctx.Input<Tensor>("Softmax");
+    auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
+    auto* logits_grad = ctx.Output<Tensor>(framework::GradVarName("Logits"));
+
+    int cls_num = softmax->dims()[1];
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    // cast label from int64/int32 to int32
+    Tensor tmp_labels(framework::proto::VarType::INT32);
+    if (labels->type() != framework::proto::VarType::INT32) {
+      tmp_labels.Resize(labels->dims());
+      tmp_labels.mutable_data(ctx.GetPlace(), framework::proto::VarType::INT32);
+      auto dst_dtype = ConvertToNpuDtype(framework::proto::VarType::INT32);
+      auto runner_cast_label =
+          NpuOpRunner("Cast", {*labels}, {tmp_labels},
+                      {{"dst_type", static_cast<int>(dst_dtype)}});
+      runner_cast_label.Run(stream);
+      labels = &tmp_labels;
+    }
+
+    // on and off
+    Tensor on_tensor(framework::proto::VarType::INT32);
+    on_tensor.mutable_data<int>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<int>{static_cast<int>(1)},
+                     ctx.device_context(), &on_tensor);
+    Tensor off_tensor(framework::proto::VarType::INT32);
+    off_tensor.mutable_data<int>({1}, ctx.GetPlace());
+    TensorFromVector(std::vector<int>{static_cast<int>(0)},
+                     ctx.device_context(), &off_tensor);
+
+    // one_hot
+    Tensor tmp_onehot(on_tensor.type());
+    tmp_onehot.Resize(softmax->dims());
+    tmp_onehot.mutable_data<int>(ctx.GetPlace());
+
+    auto runner_onehot =
+        NpuOpRunner("OneHotD", {*labels, on_tensor, off_tensor}, {tmp_onehot},
+                    {{"axis", -1}, {"depth", cls_num}});
+    runner_onehot.Run(stream);
+
+    // cast one_hot from int32 to T
+    Tensor cast_onehot(softmax->type());
+    cast_onehot.Resize(tmp_onehot.dims());
+    cast_onehot.mutable_data<T>(ctx.GetPlace());
+    auto dst_dtype = ConvertToNpuDtype(softmax->type());
+    auto runner_cast_onehot =
+        NpuOpRunner("Cast", {tmp_onehot}, {cast_onehot},
+                    {{"dst_type", static_cast<int>(dst_dtype)}});
+    runner_cast_onehot.Run(stream);
+
+    // sub
+    Tensor tmp_sub(softmax->type());
+    tmp_sub.Resize(softmax->dims());
+    tmp_sub.mutable_data<T>(ctx.GetPlace());
+    auto runner_sub =
+        NpuOpRunner("Sub", {*softmax, cast_onehot}, {tmp_sub}, {});
+
+    runner_sub.Run(stream);
+    // mul
+    logits_grad->mutable_data<T>(ctx.GetPlace());
+    auto runner_mul =
+        NpuOpRunner("Mul", {*loss_grad, tmp_sub}, {*logits_grad}, {});
+    runner_mul.Run(stream);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    softmax_with_cross_entropy,
+    ops::SoftmaxWithCrossEntropyNPUKernel<paddle::platform::NPUDeviceContext,
+                                          float>,
+    ops::SoftmaxWithCrossEntropyNPUKernel<paddle::platform::NPUDeviceContext,
+                                          paddle::platform::float16>);
+REGISTER_OP_NPU_KERNEL(
+    softmax_with_cross_entropy_grad,
+    ops::SoftmaxWithCrossEntropyGradNPUKernel<
+        paddle::platform::NPUDeviceContext, float>,
+    ops::SoftmaxWithCrossEntropyGradNPUKernel<
+        paddle::platform::NPUDeviceContext, paddle::platform::float16>);

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

@@ -0,0 +1,159 @@
+#  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
+from test_softmax_op import stable_softmax
+from test_softmax_with_cross_entropy_op import cross_entropy
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestSoftmaxWithCrossEntropyOp(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def initParams(self):
+        self.set_npu()
+        self.op_type = "softmax_with_cross_entropy"
+        self.numeric_stable_mode = False
+        self.place = paddle.NPUPlace(0)
+        self.soft_label = False
+        self.init_dtype()
+        self.axis = -1
+        self.ignore_index = -1
+        self.shape = [41, 37]
+        np.random.seed(SEED)
+
+    def setUp(self):
+        self.initParams()
+
+        logits = getattr(
+            self, "logits",
+            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
+        softmax = np.apply_along_axis(stable_softmax, self.axis, logits)
+
+        if self.soft_label:
+            labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
+            labels /= np.sum(labels, axis=self.axis, keepdims=True)
+        else:
+            axis_dim = self.shape[self.axis]
+            self.shape[self.axis] = 1
+            labels = np.random.randint(0, axis_dim, self.shape, dtype="int64")
+
+        loss = cross_entropy(softmax, labels, self.soft_label, self.axis,
+                             self.ignore_index)
+
+        self.inputs = {"Logits": logits, "Label": labels}
+        self.outputs = {
+            "Softmax": softmax.astype(self.dtype),
+            "Loss": loss.astype(self.dtype)
+        }
+        self.attrs = {
+            "numeric_stable_mode": self.numeric_stable_mode,
+            "soft_label": self.soft_label,
+            "ignore_index": self.ignore_index,
+        }
+
+        if self.axis != -1:
+            self.attrs['axis'] = self.axis
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+    # TODO(ascendrc): Add grad test
+    # def test_check_grad(self):
+    #     if self.dtype == np.float16:
+    #         return
+    #     self.check_grad(['X'], 'Out')
+    #
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestPowNet(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)
+
+            cost = fluid.layers.softmax_with_cross_entropy(prediction, 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,
+                      "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()