【NPU】Support npu kernel for matmul op

paddle/fluid/operators/matmul_v2_op_npu.cc

@@ -0,0 +1,151 @@
+/* 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/matmul_v2_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class MatMulV2NPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    bool transpose_x = ctx.Attr<bool>("trans_x");
+    bool transpose_y = ctx.Attr<bool>("trans_y");
+
+    if (x->dims().size() == 2) {
+      out->mutable_data<T>(ctx.GetPlace());
+
+      auto runner = NpuOpRunner(
+          "MatMul", {*x, *y}, {*out},
+          {{"transpose_x1", transpose_x}, {"transpose_x2", transpose_y}});
+
+      auto stream =
+          ctx.template device_context<paddle::platform::NPUDeviceContext>()
+              .stream();
+      runner.Run(stream);
+
+    } else if (x->dims().size() > 2) {
+      out->mutable_data<T>(ctx.GetPlace());
+
+      auto runner =
+          NpuOpRunner("BatchMatMul", {*x, *y}, {*out},
+                      {{"adj_x1", transpose_x}, {"adj_x2", transpose_y}});
+
+      auto stream =
+          ctx.template device_context<paddle::platform::NPUDeviceContext>()
+              .stream();
+      runner.Run(stream);
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class MatMulV2GradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    auto* dout = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+    bool transpose_y = ctx.Attr<bool>("trans_y");
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    if (x->dims().size() == 2) {
+      if (transpose_y) {
+        dx->mutable_data<T>(ctx.GetPlace());
+        auto runner_dx =
+            NpuOpRunner("MatMul", {*dout, *y}, {*dx},
+                        {{"transpose_x1", false}, {"transpose_x2", false}});
+
+        runner_dx.Run(stream);
+
+        dy->mutable_data<T>(ctx.GetPlace());
+        auto runner_dy =
+            NpuOpRunner("MatMul", {*dout, *x}, {*dy},
+                        {{"transpose_x1", true}, {"transpose_x2", false}});
+
+        runner_dy.Run(stream);
+
+      } else {
+        dx->mutable_data<T>(ctx.GetPlace());
+        auto runner_dx =
+            NpuOpRunner("MatMul", {*dout, *y}, {*dx},
+                        {{"transpose_x1", false}, {"transpose_x2", true}});
+
+        runner_dx.Run(stream);
+
+        dy->mutable_data<T>(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) {
+        dx->mutable_data<T>(ctx.GetPlace());
+        auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx},
+                                     {{"adj_x1", false}, {"adj_x2", false}});
+
+        runner_dx.Run(stream);
+
+        dy->mutable_data<T>(ctx.GetPlace());
+        auto runner_dy = NpuOpRunner("BatchMatMul", {*dout, *x}, {*dy},
+                                     {{"adj_x1", true}, {"adj_x2", false}});
+
+        runner_dy.Run(stream);
+
+      } else {
+        dx->mutable_data<T>(ctx.GetPlace());
+        auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx},
+                                     {{"adj_x1", false}, {"adj_x2", true}});
+
+        runner_dx.Run(stream);
+
+        dy->mutable_data<T>(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<paddle::platform::NPUDeviceContext, float>,
+    ops::MatMulV2NPUKernel<paddle::platform::NPUDeviceContext,
+                           paddle::platform::float16>);
+REGISTER_OP_NPU_KERNEL(
+    matmul_v2_grad,
+    ops::MatMulV2GradNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::MatMulV2GradNPUKernel<paddle::platform::NPUDeviceContext,
+                               paddle::platform::float16>);

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()