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diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc
new file mode 100644
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+++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc
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+/* Copyright 2017 The TensorFlow 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.
+==============================================================================*/
+
+// XLA implementations of Random ops
+// TODO(misard,phawkins): handle random number generator seeds/states correctly.
+// TODO(misard,phawkins): add tests.
+
+#include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/xla_compilation_device.h"
+#include "tensorflow/compiler/tf2xla/xla_helpers.h"
+#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+
+namespace tensorflow {
+namespace {
+
+class RandomUniformOp : public XlaOpKernel {
+ public:
+  explicit RandomUniformOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    TensorShape shape;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &shape));
+
+    const DataType dtype = output_type(0);
+    xla::Shape xla_shape;
+    OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(dtype, shape, &xla_shape));
+
+    xla::ComputationBuilder* b = ctx->builder();
+    xla::ComputationDataHandle result = b->RngUniform(
+        XlaHelpers::Zero(b, dtype), XlaHelpers::One(b, dtype), xla_shape);
+
+    ctx->SetOutput(0, result);
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(RandomUniformOp);
+};
+
+REGISTER_XLA_OP("RandomUniform", RandomUniformOp);
+
+class RandomUniformIntOp : public XlaOpKernel {
+ public:
+  explicit RandomUniformIntOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    TensorShape shape;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &shape));
+    xla::Shape xla_shape;
+    OP_REQUIRES_OK(ctx,
+                   TensorShapeToXLAShape(input_type(1), shape, &xla_shape));
+
+    const TensorShape minval_shape = ctx->InputShape(1);
+    const TensorShape maxval_shape = ctx->InputShape(2);
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(minval_shape),
+                errors::InvalidArgument("minval must be 0-D, got shape ",
+                                        minval_shape.DebugString()));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(maxval_shape),
+                errors::InvalidArgument("maxval must be 0-D, got shape ",
+                                        maxval_shape.DebugString()));
+
+    auto minval = ctx->Input(1);
+    auto maxval = ctx->Input(2);
+    ctx->SetOutput(0, ctx->builder()->RngUniform(minval, maxval, xla_shape));
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(RandomUniformIntOp);
+};
+
+REGISTER_XLA_OP("RandomUniformInt", RandomUniformIntOp);
+
+class RandomStandardNormalOp : public XlaOpKernel {
+ public:
+  explicit RandomStandardNormalOp(OpKernelConstruction* ctx)
+      : XlaOpKernel(ctx) {}
+
+  void Compile(XlaOpKernelContext* ctx) override {
+    const DataType dtype = output_type(0);
+
+    TensorShape shape;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &shape));
+    xla::Shape xla_shape;
+    OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(dtype, shape, &xla_shape));
+
+    xla::ComputationBuilder* b = ctx->builder();
+
+    // Normal distribution with a mean of 0 and a standard deviation of 1:
+    xla::ComputationDataHandle result = b->RngNormal(
+        XlaHelpers::Zero(b, dtype), XlaHelpers::One(b, dtype), xla_shape);
+
+    ctx->SetOutput(0, result);
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(RandomStandardNormalOp);
+};
+
+REGISTER_XLA_OP("RandomStandardNormal", RandomStandardNormalOp);
+
+}  // anonymous namespace
+}  // namespace tensorflow