Generalized reshape_like operator (apache#11928)

* first commit

* fix documentation

* changed static_cast<bool>(end) to end.has_value()
fixed documentation issues

* change begin from int to optional

* test None as lhs

src/operator/tensor/elemwise_unary_op.h

@@ -476,6 +476,34 @@ void HardSigmoidBackward(const nnvm::NodeAttrs& attrs,
   });
 }
 
+struct ReshapeLikeParam : public dmlc::Parameter<ReshapeLikeParam> {
+  dmlc::optional<int> lhs_begin, rhs_begin, lhs_end, rhs_end;
+  DMLC_DECLARE_PARAMETER(ReshapeLikeParam) {
+    DMLC_DECLARE_FIELD(lhs_begin)
+        .set_default(dmlc::optional<int>())
+        .describe(
+            "Defaults to 0. "
+            "The beginning index along which the lhs dimensions are to be "
+            "reshaped. Supports negative indices.");
+    DMLC_DECLARE_FIELD(lhs_end)
+        .set_default(dmlc::optional<int>())
+        .describe("Defaults to None. "
+                  "The ending index along which the lhs dimensions are to be "
+                  "used for reshaping. Supports negative indices.");
+    DMLC_DECLARE_FIELD(rhs_begin)
+        .set_default(dmlc::optional<int>())
+        .describe("Defaults to 0. "
+                  "The beginning index along which the rhs dimensions are to "
+                  "be used for "
+                  "reshaping. Supports negative indices.");
+    DMLC_DECLARE_FIELD(rhs_end)
+        .set_default(dmlc::optional<int>())
+        .describe("Defaults to None. "
+                  "The ending index along which the rhs dimensions are to be "
+                  "used for reshaping. Supports negative indices.");
+  }
+};
+
 /*! \brief Unary compute */
 #define MXNET_OPERATOR_REGISTER_UNARY(__name$)                      \
   NNVM_REGISTER_OP(__name$)                                         \

src/operator/tensor/elemwise_unary_op_basic.cc

@@ -350,10 +350,109 @@ NNVM_REGISTER_OP(_identity_with_attr_like_rhs)
 .add_argument("lhs", "NDArray-or-Symbol", "First input.")
 .add_argument("rhs", "NDArray-or-Symbol", "Second input.");
 
+void ReshapeLikeRangeCanonicalize(int ndims, const char *side,
+                                  const dmlc::optional<int> &begin,
+                                  const dmlc::optional<int> &end, int *cbegin,
+                                  int *cend) {
+  *cbegin = begin.has_value() ? begin.value() : 0;
+  if (*cbegin < 0)
+    *cbegin += ndims;
+
+  if (!end.has_value()) {
+    *cend = ndims;
+  } else {
+    *cend = end.value();
+    if (*cend < 0) {
+      *cend += ndims;
+    }
+  }
+  CHECK(*cend <= ndims) << "Invalid end for " << side << "_end=" << end
+                        << " as dimension number is " << ndims;
+  CHECK((*cbegin < *cend)) << "Invalid begin, end, get " << side
+                           << "_begin=" << begin << ", " << side
+                           << "_end=" << end;
+
+  CHECK(*cend >= 0) << "Invalid end for " << side << "_end=" << end;
+  CHECK(*cbegin >= 0) << "Invalid begin for " << side << "_begin=" << begin;
+}
+
+void GetReshapeLikeParams(const ReshapeLikeParam &param, const TShape &lshape,
+                          const TShape &rshape, int *lhs_begin, int *lhs_end,
+                          int *rhs_begin, int *rhs_end) {
+  // LHS params
+  ReshapeLikeRangeCanonicalize(lshape.ndim(), "lhs", param.lhs_begin,
+                               param.lhs_end, lhs_begin, lhs_end);
+  // RHS params
+  ReshapeLikeRangeCanonicalize(rshape.ndim(), "rhs", param.rhs_begin,
+                               param.rhs_end, rhs_begin, rhs_end);
+}
 
+bool ReshapeLikeShapeCompute(const nnvm::NodeAttrs &attrs,
+                             std::vector<TShape> *in_attrs,
+                             std::vector<TShape> *out_attrs) {
+  const ReshapeLikeParam &param = nnvm::get<ReshapeLikeParam>(attrs.parsed);
+  const TShape &lshape = (*in_attrs)[0];
+  const TShape &rshape = (*in_attrs)[1];
+  int lhs_begin, lhs_end, rhs_begin, rhs_end;
+  GetReshapeLikeParams(param, lshape, rshape, &lhs_begin, &lhs_end, &rhs_begin,
+                       &rhs_end);
+
+  int lhsrank = static_cast<int>(lshape.ndim());
+  int orank = lhsrank + (rhs_end - rhs_begin) - (lhs_end - lhs_begin);
+  TShape oshape(orank);
+
+  for (int i = 0; i < lhs_begin; ++i)
+    oshape[i] = lshape[i];
+
+  int opos = lhs_begin;
+  for (int i = rhs_begin; i < rhs_end; ++i) {
+    oshape[opos] = rshape[i];
+    opos += 1;
+  }
+
+  for (int i = lhs_end; i < lhsrank; ++i) {
+    oshape[opos] = lshape[i];
+    opos += 1;
+  }
+
+  CHECK_EQ((*in_attrs)[0].Size(), oshape.Size())
+      << "Cannot reshape lhs with shape " << (*in_attrs)[0] << "to new "
+      << "shape " << oshape << " because they have different "
+      << "size.";
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
+  return true;
+}
+
+DMLC_REGISTER_PARAMETER(ReshapeLikeParam);
 NNVM_REGISTER_OP(reshape_like)
-.describe("Reshape lhs to have the same shape as rhs.")
+.describe(R"code(Reshape some or all dimensions of `lhs` to have the same shape as some or all dimensions of `rhs`.
+
+Returns a **view** of the `lhs` array with a new shape without altering any data.
+
+Example::
+
+  x = [1, 2, 3, 4, 5, 6]
+  y = [[0, -4], [3, 2], [2, 2]]
+  reshape_like(x, y) = [[1, 2], [3, 4], [5, 6]]
+
+More precise control over how dimensions are inherited is achieved by specifying \
+slices over the `lhs` and `rhs` array dimensions. Only the sliced `lhs` dimensions \
+are reshaped to the `rhs` sliced dimensions, with the non-sliced `lhs` dimensions staying the same. 
+
+  Examples::
+
+  - lhs shape = (30,7), rhs shape = (15,2,4), lhs_begin=0, lhs_end=1, rhs_begin=0, rhs_end=2, output shape = (15,2,7)
+  - lhs shape = (3, 5), rhs shape = (1,15,4), lhs_begin=0, lhs_end=2, rhs_begin=1, rhs_end=2, output shape = (15)
+
+Negative indices are supported, and `None` can be used for either `lhs_end` or `rhs_end` to indicate the end of the range.
+
+  Example::
+
+  - lhs shape = (30, 12), rhs shape = (4, 2, 2, 3), lhs_begin=-1, lhs_end=None, rhs_begin=1, rhs_end=None, output shape = (30, 2, 2, 3)
+
+)code" ADD_FILELINE)
 .set_num_inputs(2)
+.set_attr_parser(ParamParser<ReshapeLikeParam>)
 .set_attr<nnvm::FListInputNames>("FListInputNames",
   [](const NodeAttrs& attrs) { return std::vector<std::string>{"lhs", "rhs"}; })
 .set_attr<nnvm::FInplaceOption>(
@@ -365,19 +464,7 @@ NNVM_REGISTER_OP(reshape_like)
 .set_attr<nnvm::FIgnoreInputs>("FIgnoreInputs",
     [](const NodeAttrs& attrs) { return std::vector<uint32_t>(1, 1); })
 .set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
-.set_attr<nnvm::FInferShape>("FInferShape",
-    [](const nnvm::NodeAttrs& attrs,
-       std::vector<TShape> *in_attrs,
-       std::vector<TShape> *out_attrs) {
-      if ((*in_attrs)[0].ndim()) {
-        CHECK_EQ((*in_attrs)[0].Size(), (*in_attrs)[1].Size())
-            << "Cannot reshape lhs with shape " << (*in_attrs)[0] << "to rhs "
-            << "with shape " << (*in_attrs)[1] << " because they have different "
-            << "size.";
-      }
-      SHAPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[1]);
-      return true;
-    })
+.set_attr<nnvm::FInferShape>("FInferShape", ReshapeLikeShapeCompute)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
 .set_attr<nnvm::FGradient>(
     "FGradient",  [](const nnvm::NodePtr& n,
@@ -438,7 +525,8 @@ Example::
     TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
     return out_attrs->at(0) != -1;
   })
-.add_argument("data", "NDArray-or-Symbol", "Input Array.");
+.add_argument("data", "NDArray-or-Symbol", "Input Array.")
+.add_arguments(ReshapeLikeParam::__FIELDS__());
 
 void SizeComputeCPU(const nnvm::NodeAttrs& attrs,
                     const OpContext& ctx,

tests/python/unittest/test_operator.py

@@ -2131,6 +2131,59 @@ def test_reshape_new(src_shape, shape_args, reverse, dst_shape):
     assert_allclose(exe.grad_arrays[0].asnumpy(), out_grad_npy.reshape((5, 4, 3, 7)))
 
 
+@with_seed()
+def test_reshape_like():
+    def test_reshape_like_new(lhs_shape, rhs_shape, lbeg, lend, rbeg, rend, dst_shape):
+        lhs = mx.sym.Variable("lhs")
+        rhs = mx.sym.Variable("rhs")
+        net = mx.sym.reshape_like(lhs, rhs, lhs_begin=lbeg, lhs_end=lend, rhs_begin=rbeg, rhs_end=rend)
+        js = net.tojson()
+        net = mx.sym.load_json(js)
+        _, output_shape, __ = net.infer_shape(lhs=lhs_shape, rhs=rhs_shape)
+
+        assert output_shape[0] == dst_shape, \
+            'LHS Shape = %s, RHS Shape = %s, lhs_begin = %s, lhs_end = %s, rhs_begin= %s, rhs_end= %s'\
+            %(str(lhs_shape), str(rhs_shape), str(lbeg), str(lend), str(rbeg), str(rend))
+
+        lhs_npy = np.random.rand(*lhs_shape)
+        rhs_npy = np.random.rand(*rhs_shape)
+        grad_npy = np.random.rand(*dst_shape)
+
+        exe = net.simple_bind(default_context(), lhs=lhs_shape, rhs=rhs_shape)
+        exe.arg_dict['lhs'][:] = lhs_npy
+        exe.arg_dict['rhs'][:] = rhs_npy
+        exe.forward(is_train=True)
+        assert np.square(exe.outputs[0].asnumpy() - lhs_npy.reshape(dst_shape)).mean() < 1E-7, \
+            'LHS Shape = %s, RHS Shape = %s, lhs_begin = %s, lhs_end = %s, rhs_begin= %s, rhs_end= %s'\
+            %(str(lhs_shape), str(rhs_shape), str(lbeg), str(lend), str(rbeg), str(rend))
+        exe.backward(out_grads=mx.nd.array(grad_npy))
+        assert np.square(exe.grad_dict['lhs'].asnumpy() - grad_npy.reshape(lhs_shape)).mean() < 1E-7, \
+            'LHS Shape = %s, RHS Shape = %s, lhs_begin = %s, lhs_end = %s, rhs_begin= %s, rhs_end= %s'\
+            %(str(lhs_shape), str(rhs_shape), str(lbeg), str(lend), str(rbeg), str(rend))
+    # Test new api (Using shape)
+    test_cases = [
+        [(30,), (15,2,4), 0, None, 0, 2, (15,2)],
+        [(30,), (15,2,4), None, 1, None, 2, (15,2)],
+        [(30,7), (15,2,4), 0, 1, 0, 2, (15,2,7)],
+        [(3,5), (1,15,4), 0, 2, 1, 2, (15,)],
+        [(3,5), (1,15,4), 0, None, 1, -1, (15,)],
+        [(30,12), (4,2,2,3), -1, None, 1, None, (30,2,2,3)],
+        [(1,1,7,3,1,1), (81,1,1,21), 1, -1, 1, None, (1,1,1,21,1)]
+    ]
+    # for test_case in test_cases:
+    for test_case in test_cases:
+        test_reshape_like_new(*test_case)
+
+    # Test old api
+    lhs = mx.sym.Variable("lhs")
+    rhs = mx.sym.Variable("rhs")
+    net = mx.sym.reshape_like(lhs, rhs)
+    js = net.tojson()
+    net = mx.sym.load_json(js)
+    _, output_shape, __ = net.infer_shape(lhs=(40, 30), rhs=(30,20,2))
+    assert(output_shape[0] == (30,20,2))
+
+
 @with_seed()
 def test_reduce():
     sample_num = 500