No Op Optimizer (#12390)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12390

Introduce a no op optimizer for when we don't want updates to happen, but don't want to affect downstream processes.

Reviewed By: mlappelbaum

Differential Revision: D10209812

fbshipit-source-id: 2af4ebc0fb42e78ea851c3a9f4860f3d224037b6

caffe2/operators/data_couple.cc

@@ -0,0 +1,17 @@
+#include "caffe2/operators/data_couple.h"
+
+namespace caffe2 {
+REGISTER_CPU_OPERATOR(DataCouple, DataCoupleOp<CPUContext>);
+
+OPERATOR_SCHEMA(DataCouple)
+    .EnforceOneToOneInplace()
+    .SetDoc(R"DOC(
+
+A one to one operator that takes an arbitrary number of input and output blobs
+such that each input blob is inplace with it's matching output blob. It then proceedes
+to do nothing with each of these operators. This serves two purposes. It can make it
+appear as if a blob has been written to, as well as can tie together different blobs
+in a data dependency
+
+)DOC");
+} // namespace caffe2

caffe2/operators/data_couple.h

@@ -0,0 +1,22 @@
+#ifndef CAFFE2_OPERATORS_NO_OP_OPTIMIZER_OP_H_
+#define CAFFE2_OPERATORS_NO_OP_OPTIMIZER_OP_H_
+
+#include "caffe2/core/context.h"
+#include "caffe2/core/operator.h"
+
+namespace caffe2 {
+
+template <class Context>
+class DataCoupleOp : public Operator<Context> {
+ public:
+  USE_SIMPLE_CTOR_DTOR(DataCoupleOp)
+
+  bool RunOnDevice() override {
+    // Actually does nothing...
+    return true;
+  }
+};
+
+} // namespace caffe2
+
+#endif // CAFFE2_OPERATORS_NO_OP_OPTIMIZER_OP_H_

caffe2/operators/data_couple_gpu.cu

@@ -0,0 +1,6 @@
+#include "caffe2/core/context_gpu.h"
+#include "caffe2/operators/data_couple.h"
+
+namespace caffe2 {
+REGISTER_CUDA_OPERATOR(DataCouple, DataCoupleOp<CUDAContext>);
+}

caffe2/python/operator_test/data_couple_op_test.py

@@ -0,0 +1,30 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from caffe2.python import core, workspace
+from caffe2.python.test_util import TestCase
+import numpy as np
+
+
+class TestDataCoupleOp(TestCase):
+
+    def test_data_couple_op(self):
+        param_array = np.random.rand(10, 10)
+        gradient_array = np.random.rand(10, 10)
+        extra_array = np.random.rand(10, 10)
+        workspace.FeedBlob("param", param_array)
+        workspace.FeedBlob("gradient", gradient_array)
+        workspace.FeedBlob("extraBlob", extra_array)
+
+        workspace.RunOperatorOnce(core.CreateOperator(
+            "DataCouple",
+            ["param", "gradient", "extraBlob"],
+            ["param", "gradient"]))
+
+        result1 = workspace.FetchBlob('param')
+        result2 = workspace.FetchBlob('gradient')
+
+        self.assertFalse((result1 - param_array).any())
+        self.assertFalse((result2 - gradient_array).any())