[Numpy] Numpy compatible slicing

src/operator/tensor/matrix_op-inl.h

@@ -685,13 +685,13 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
     << "Static array size=" << ndim
     << " is not equal to data shape ndim=" << dshape.ndim();
 
-  if (param_step.ndim() != 0) {
+  if (param_step.ndim() > 0) {
     CHECK_EQ(param_step.ndim(), param_begin.ndim())
       << "step and begin must have the same length";
   }
 
   for (int i = 0; i < param_begin.ndim(); ++i) {
-    index_t s = param_step.ndim() != 0U && param_step[i].has_value() ? param_step[i].value() : 1;
+    index_t s = param_step.ndim() > 0 && param_step[i].has_value() ? param_step[i].value() : 1;
     CHECK_NE(s, 0) << "slice op step[" << i << "] cannot be 0";
 
     index_t b = 0, e = 0;
@@ -700,58 +700,54 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
       b = param_begin[i].has_value() ? param_begin[i].value() : (s < 0 ? len - 1 : 0);
       e = param_end[i].has_value() ? param_end[i].value() : (s < 0 ? -1 : len);
 
-      // checking upper and lower bounds for begin
       if (b < 0) {
         b += len;
-        CHECK_GE(b, 0) << "slicing with begin[" << i << "]=" << b - len
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
       }
-      CHECK_LT(b, len) << "slicing with begin[" << i << "]=" << b
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
-
-      // checking upper and lower bounds for end
       if (e < 0 && param_end[i].has_value()) {
-        if (!(s < 0 && e == -1)) {
-          // Keep end=-1 as one-beyond-limits index for negative stride
-          e += len;
-        }
-        CHECK_GE(e, 0) << "slicing with end[" << i << "]=" << e - len
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
+        e += len;
       }
-      CHECK_LE(e, len) << "slicing with end[" << i << "]=" << e
-                       << " exceeds limit of input dimension[" << i << "]=" << len;
 
-      // checking begin==end case which is not supported
-      CHECK_NE(b, e) << "slicing with begin[" << i << "]=end[" << i << "]="
-                     << e << " results in an empty tensor and is not supported";
+      // move the begin and end to correct position for calculating dim size
+      b = (b < 0 && s > 0) ? 0 : b;
+      b = (b > len - 1 && s < 0) ? len - 1 : b;
+      // if the start value lead to empty tensor under step s, use -1 for indication
+      b = (b < 0 || b > len - 1) ? -1 : b;
+      e = e > -1 ? e : -1;
+      e = e > len ? len : e;
+    } else if (len == 0) {
+      b = 0;
+      e = 0;
     }
 
     (*begin)[i] = b;
     (*end)[i] = e;
     (*step)[i] = s;
   }
 
-  for (index_t i = param_begin.ndim(); i < dshape.ndim(); ++i) {
+  for (int i = param_begin.ndim(); i < dshape.ndim(); ++i) {
     (*begin)[i] = 0;
     (*end)[i] = dshape[i];
     (*step)[i] = 1;
   }
 }
 
-inline void SetSliceOpOutputDimSize(const index_t i, const int b,
+inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
+                                    const index_t i, const int b,
                                     const int e, const int s,
                                     mxnet::TShape* oshape) {
-  if (e != b) {
+  if (!mxnet::dim_size_is_known(dshape, i)) {
+    (*oshape)[i] = -1;
+    return;
+  }
+  if (e != b && b >= 0) {
     if (s > 0) {
-      CHECK_LT(b, e) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
-                     << e << ", and step[" << i << "]=" << s << " is invalid";
-      (*oshape)[i] = (e - b - 1) / s + 1;
+      (*oshape)[i] = e > b ? (e - b - 1) / s + 1 : 0;
     } else {
-      CHECK_LT(e, b) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
-                     << e << ", and step[" << i << "]=" << s << " is invalid";
-      (*oshape)[i] = (b - e - 1) / (-s) + 1;
+      (*oshape)[i] = e < b ? (b - e - 1) / (-s) + 1 : 0;
     }
-  }  // else leave oshape[i] as 0 for partial infer
+  } else {
+      (*oshape)[i] = 0;
+  }
 }
 
 inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
@@ -761,6 +757,7 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1U);
   const mxnet::TShape& dshape = (*in_attrs)[0];
   if (!mxnet::ndim_is_known(dshape)) return false;
+  CHECK_GT(dshape.ndim(), 0) << "slice only works for ndim > 0";
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   mxnet::TShape oshape = dshape;
 
@@ -769,12 +766,12 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &oshape);
+      SetSliceOpOutputDimSize(dshape, i, b, e, s, &oshape);
     }
   })
 
   SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
-  return shape_is_known(oshape);
+  return shape_is_known(dshape) && shape_is_known(oshape);
 }
 
 template<int ndim, int req, typename xpu>
@@ -852,6 +849,7 @@ void SliceOpForward(const nnvm::NodeAttrs& attrs,
   Stream<xpu>* s = ctx.get_stream<xpu>();
   const TBlob& data = inputs[0];
   const TBlob& out = outputs[0];
+  if (out.Size() == 0) return;
   const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
   MXNET_NDIM_SWITCH(data.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
@@ -951,6 +949,7 @@ void SliceOpBackward(const nnvm::NodeAttrs& attrs,
   } else if (req[0] == kWriteInplace) {
     LOG(FATAL) << "_slice_backward does not support kWriteInplace";
   }
+  if (ograd.Size() == 0) return;
   MXNET_NDIM_SWITCH(ograd.ndim(), ndim, {
     common::StaticArray<index_t, ndim> begin, end, step;
     GetIndexRange(igrad.shape_, param.begin, param.end, param.step, &begin, &end, &step);
@@ -982,7 +981,7 @@ inline bool SliceAssignOpShape(const nnvm::NodeAttrs& attrs,
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+      SetSliceOpOutputDimSize(dshape, i, b, e, s, &vshape);
     }
   })
   SHAPE_ASSIGN_CHECK(*in_attrs, 1, vshape);
@@ -1121,7 +1120,7 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
     GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
     for (index_t i = 0; i < param.begin.ndim(); ++i) {
       const int b = begin[i], e = end[i], s = step[i];
-      SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+      SetSliceOpOutputDimSize(data.shape_, i, b, e, s, &vshape);
     }
     MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
       mxnet_op::Kernel<slice_assign_scalar<ndim>, xpu>::Launch(s, vshape.FlatTo2D()[0],

src/operator/tensor/matrix_op.cc

@@ -506,6 +506,7 @@ Example::
                                                             [5.,  7.],
                                                             [1.,  3.]]
 )code" ADD_FILELINE)
+.add_alias("_npx_slice")
 .set_attr_parser(ParamParser<SliceParam>)
 .set_attr<mxnet::FInferShape>("FInferShape", SliceOpShape)
 .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)

tests/python/unittest/test_numpy_op.py

@@ -92,6 +92,63 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_npx_slice():
+    class TestSlice(HybridBlock):
+        def __init__(self, begin, end, step):
+            super(TestSlice, self).__init__()
+            self._begin = begin
+            self._end = end
+            self._step = step
+
+        def hybrid_forward(self, F, a):
+            return F.npx.slice(a, begin=self._begin, end=self._end, step=self._step)
+
+    shape = (8, 16, 9, 9)
+    np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+    configs = [
+        ([], [], None),
+        ([], [], []),
+        ([1], [4], None),
+        ([1], [10], [3]),
+        ([10], [0], [-2]),
+        ([None], [None], [None]),
+        ([None], [None], [-1]),
+        ([10], [None], [-1]),
+        ([1, 0, 3], [-2, 10, -4], [None, 2, 3]),
+        ([-2, -3, -5, -6], [1, 3, 4, 5], None),
+        ([-2, -3, -5, -6], [1, 3, 4, 5], [-1, -2, -3, -4]),
+        ([2, -3, -5, -6], [2, 3, 4, 5], None),
+        ([2, -3, -5, 5], [3, 3, 4, 5], None),
+    ]
+
+    for hybridize in [True, False]:
+        for config in configs:
+            start, end, step = config[0], config[1], config[2]
+            test_slice = TestSlice(begin=start, end=end, step=step)
+            if hybridize:
+                test_slice.hybridize()
+
+            a = np.array(np_array, dtype=np_array.dtype)
+            a.attach_grad()
+            basic_index = tuple([
+                slice(start[i], end[i], step[i]) if step is not None else slice(start[i], end[i])
+                for i in range(len(start))
+            ])
+            expected_ret = np_array[basic_index]
+            with mx.autograd.record():
+                y = test_slice(a)
+
+            assert same(y.asnumpy(), expected_ret)
+
+            # test backward
+            mx.autograd.backward(y)
+            expected_grad = _np.zeros(shape)
+            expected_grad[basic_index] = 1
+            assert same(a.grad.asnumpy(), expected_grad)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

tests/python/unittest/test_operator.py

@@ -7519,15 +7519,6 @@ def test_slice_forward_backward(a, index):
     for index in index_list:
         test_slice_forward_backward(arr, index)
 
-    def test_begin_equals_end(shape, begin, end, step):
-        in_arr = mx.nd.arange(np.prod(shape)).reshape(shape=shape)
-        out_arr = mx.nd.slice(in_arr, begin=begin, end=end, step=step)
-
-    assertRaises(MXNetError, test_begin_equals_end, (4,), (2,), (2,), (1,))
-    assertRaises(MXNetError, test_begin_equals_end, (1, 5), (None, 3), (None, 3), (-1, 1))
-    assertRaises(MXNetError, test_begin_equals_end, (3, 4, 5), (1, 3, 1), (3, 3, 1), (1, -3, 2))
-    assertRaises(MXNetError, test_begin_equals_end, (2, 4), (None, 2), (None, 2), (1, -1))
-
     # check numeric gradient
     in_data = np.arange(36).reshape(2, 2, 3, 3)
     data = mx.sym.Variable('data')