uniform_random op for mlu (#39450)

paddle/fluid/operators/mlu/mlu_baseop.cc

@@ -923,11 +923,12 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
 
 /* static */ void MLUCnnl::RandomUniform(
     const ExecutionContext& ctx, const int num, const cnnlDataType_t data_type,
-    const cnnlRandGenerator_t mlu_generator, void* output) {
+    const cnnlRandGenerator_t mlu_generator, const float min, const float max,
+    void* output) {
   cnnlHandle_t handle = GetHandleFromCTX(ctx);
 
   PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandGenerateUniform(
-      handle, mlu_generator, data_type, nullptr, num, 0, 1, output));
+      handle, mlu_generator, data_type, nullptr, num, min, max, output));
 }
 
 /* static */ void MLUCnnl::TopK(

paddle/fluid/operators/mlu/mlu_baseop.h

@@ -513,7 +513,7 @@ class MLUCnnl {
   static void RandomUniform(const ExecutionContext& ctx, const int num,
                             const cnnlDataType_t data_type,
                             const cnnlRandGenerator_t mlu_generator,
-                            void* output);
+                            const float min, const float max, void* output);
 
   static void Cumsum(const ExecutionContext& ctx, const int axis,
                      const bool exclusive, const bool reverse,

paddle/fluid/operators/uniform_random_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 "paddle/fluid/operators/uniform_random_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class MLUUniformRandomKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    framework::Tensor *tensor = nullptr;
+    auto out_var = ctx.OutputVar("Out");
+
+    std::vector<int64_t> new_shape;
+    auto list_new_shape_tensor =
+        ctx.MultiInput<framework::Tensor>("ShapeTensorList");
+    if (list_new_shape_tensor.size() > 0 || ctx.HasInput("ShapeTensor")) {
+      if (ctx.HasInput("ShapeTensor")) {
+        auto *shape_tensor = ctx.Input<framework::Tensor>("ShapeTensor");
+        new_shape = GetNewDataFromShapeTensor(shape_tensor);
+      } else if (list_new_shape_tensor.size() > 0) {
+        new_shape = GetNewDataFromShapeTensorList(list_new_shape_tensor);
+      }
+    }
+
+    if (out_var->IsType<pten::SelectedRows>()) {
+      auto *selected_rows = out_var->GetMutable<pten::SelectedRows>();
+      tensor = selected_rows->mutable_value();
+      auto shape = ctx.Attr<std::vector<int64_t>>("shape");
+      if (!new_shape.empty()) shape = new_shape;
+      tensor->Resize(framework::make_ddim(shape));
+      selected_rows->mutable_rows()->reserve(shape[0]);
+    } else if (out_var->IsType<framework::LoDTensor>()) {
+      tensor = out_var->GetMutable<framework::LoDTensor>();
+      if (!new_shape.empty()) tensor->Resize(framework::make_ddim(new_shape));
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Expected type of Output(out) in uniform_random_op must be Tensor, "
+          "SelectedRows. But got "
+          "unsupport type: %s.",
+          framework::ToTypeName(out_var->Type())));
+    }
+
+    tensor->mutable_data<T>(ctx.GetPlace());
+    int64_t size = tensor->numel();
+    const float min = static_cast<T>(ctx.Attr<float>("min"));
+    const float max = static_cast<T>(ctx.Attr<float>("max"));
+    unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
+    // make mlu seed
+    MLUCnnlRandomGeneratorDesc random_desc(/*is_mlu200=*/false, seed);
+    cnnlDataType_t data_type = ToCnnlDataType(tensor->type());
+    MLUCnnl::RandomUniform(ctx, size, /*data type=*/data_type,
+                           random_desc.get(), min, max, GetBasePtr(tensor));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_MLU_KERNEL(uniform_random,
+                       paddle::operators::MLUUniformRandomKernel<float>);

python/paddle/fluid/tests/unittests/mlu/test_uniform_random_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 sys
+import subprocess
+import unittest
+import numpy as np
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid.core as core
+import paddle
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+from test_uniform_random_op import TestUniformRandomOp, TestUniformRandomOpSelectedRows
+
+paddle.enable_static()
+
+
+def output_hist(out):
+    hist, _ = np.histogram(out, range=(-5, 10))
+    hist = hist.astype("float32")
+    hist /= float(out.size)
+    prob = 0.1 * np.ones((10))
+    return hist, prob
+
+
+class TestMLUUniformRandomOp(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "uniform_random"
+        self.init_dtype()
+        self.inputs = {}
+        self.init_attrs()
+        self.outputs = {"Out": np.zeros((1000, 784)).astype(self.dtype)}
+
+    def init_attrs(self):
+        self.attrs = {
+            "shape": [1000, 784],
+            "min": -5.0,
+            "max": 10.0,
+            "seed": 10
+        }
+        self.output_hist = output_hist
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_customized(self.verify_output, self.place)
+
+    def verify_output(self, outs):
+        hist, prob = self.output_hist(np.array(outs[0]))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
+
+
+class TestMLUUniformRandomOpSelectedRows(unittest.TestCase):
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_mlu():
+            places.append(core.MLUPlace(0))
+        return places
+
+    def test_check_output(self):
+        for place in self.get_places():
+            self.check_with_place(place)
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+        out = scope.var("X").get_selected_rows()
+        paddle.seed(10)
+        op = Operator(
+            "uniform_random",
+            Out="X",
+            shape=[1000, 784],
+            min=-5.0,
+            max=10.0,
+            seed=10)
+        op.run(scope, place)
+        self.assertEqual(out.get_tensor().shape(), [1000, 784])
+        hist, prob = output_hist(np.array(out.get_tensor()))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
+
+
+if __name__ == "__main__":
+    unittest.main()