Dynamic Strided Slice

python/tvm/relay/frontend/keras.py

@@ -617,8 +617,8 @@ def _convert_cropping(inexpr, keras_layer, _):
         raise tvm.error.OpNotImplemented(
             'Operator {} is not supported for frontend Keras.'.format(crop_type))
     int32_max = np.iinfo(np.int32).max
-    return _op.strided_slice(inexpr, begin=_expr.const([0, 0, crop_t, crop_l]), \
-        end=_expr.const([int32_max, int32_max, in_h-crop_b, in_w-crop_r]))
+    return _op.strided_slice(inexpr, begin=[0, 0, crop_t, crop_l], \
+        end=[int32_max, int32_max, in_h-crop_b, in_w-crop_r])
 
 
 def _convert_batchnorm(inexpr, keras_layer, etab):

python/tvm/relay/frontend/mxnet.py

@@ -500,11 +500,11 @@ def _mx_slice(inputs, attrs):
     for i, ed in enumerate(end):
         if ed is None:
             end[i] = input_shape[i]
-    new_attrs = {'begin': _expr.const(list(begin), dtype="int32"),
-                 'end': _expr.const(list(end), dtype="int32")}
+    new_attrs = {'begin': list(begin),
+                 'end': list(end)}
     if stride is not None:
         stride = (x if x is not None else 1 for x in stride)
-        new_attrs['strides'] = _expr.const(list(stride), dtype="int32")
+        new_attrs['strides'] = list(stride)
     return _op.strided_slice(inputs[0], **new_attrs)
 
 
@@ -545,8 +545,8 @@ def _mx_slice_axis(inputs, attrs):
             begin.append(ax_beg)
             end.append(ax_end)
     return _op.strided_slice(inputs[0],
-                             _expr.const(begin, dtype="int32"),
-                             _expr.const(end, dtype="int32"))
+                             begin,
+                             end)
 
 
 def _mx_crop_like(inputs, attrs):
@@ -566,9 +566,9 @@ def _mx_crop_like(inputs, attrs):
         return _op.slice_like(*inputs, **new_attrs)
     expr = _infer_type(inputs[1])
     like_shape = expr.checked_type.shape
-    new_attrs['begin'] = _expr.const([0, 0, offset[0], offset[1]], dtype="int32")
-    new_attrs['end'] = _expr.const([like_shape[0], like_shape[1], offset[0]+like_shape[2],
-                                    offset[1]+like_shape[3]], dtype="int32")
+    new_attrs['begin'] = [0, 0, offset[0], offset[1]]
+    new_attrs['end'] = [like_shape[0], like_shape[1], offset[0]+like_shape[2],
+                        offset[1]+like_shape[3]]
     return _op.strided_slice(inputs[0], **new_attrs)
 
 

python/tvm/relay/frontend/onnx.py

@@ -1048,8 +1048,8 @@ def _impl_v1(cls, inputs, attr, params):
         end = list(attr['ends'])
 
         return _op.strided_slice(inputs[0],
-                                 begin=_expr.const(begin, dtype="int64"),
-                                 end=_expr.const(end, dtype="int64"))
+                                 begin=begin,
+                                 end=end)
 
     @classmethod
     def _impl_v10(cls, inputs, attr, params):
@@ -1069,8 +1069,8 @@ def _impl_v10(cls, inputs, attr, params):
                 attrs['starts'] = new_starts
                 attrs['ends'] = new_ends
         return _op.strided_slice(inputs[0],
-                                 begin=_expr.const(attrs['starts'], dtype="int64"),
-                                 end=_expr.const(attrs['ends'], dtype="int64"))
+                                 begin=list(attrs['starts']),
+                                 end=list(attrs['ends']))
 
 
 class Gather(OnnxOpConverter):

python/tvm/relay/frontend/pytorch.py

@@ -282,9 +282,9 @@ def _impl(inputs, input_types):
 
         strides.append(int(inputs[4]))
         return _op.transform.strided_slice(data,
-                                           begin=_expr.const(begin),
-                                           end=_expr.const(end),
-                                           strides=_expr.const(strides),
+                                           begin=begin,
+                                           end=end,
+                                           strides=strides,
                                            slice_mode="end")
     return _impl
 
@@ -1346,9 +1346,9 @@ def _impl(inputs, input_types):
             stride = [1] * len(shape)
 
             chunk_out = _op.transform.strided_slice(data,
-                                                    begin=_expr.const(begin),
-                                                    end=_expr.const(end),
-                                                    strides=_expr.const(stride))
+                                                    begin=begin,
+                                                    end=end,
+                                                    strides=stride)
             chunks.append(chunk_out)
 
         if dim % num_chunks:
@@ -1359,9 +1359,9 @@ def _impl(inputs, input_types):
             stride = [1] * len(shape)
 
             chunk_out = _op.transform.strided_slice(data,
-                                                    begin=_expr.const(begin),
-                                                    end=_expr.const(end),
-                                                    strides=_expr.const(stride))
+                                                    begin=begin,
+                                                    end=end,
+                                                    strides=stride)
             chunks.append(chunk_out)
 
         return chunks

python/tvm/relay/op/_tensor_grad.py

@@ -407,9 +407,9 @@ def conv2d_grad(orig, grad):
     assert padded_weight_grad_w >= filter_w
     if padded_weight_grad_h > filter_h or padded_weight_grad_w > filter_w:
         backward_weight = strided_slice(backward_weight,
-                                        begin=const([0, 0, 0, 0], dtype="int64"),
-                                        end=const([out_channel, in_channel // attrs.groups,
-                                                   filter_h, filter_w], dtype="int64"))
+                                        begin=[0, 0, 0, 0],
+                                        end=[out_channel, in_channel // attrs.groups,
+                                             filter_h, filter_w])
 
     return [backward_data, backward_weight]
 

python/tvm/relay/op/_transform.py

@@ -126,33 +126,6 @@ def arange_shape_func(attrs, inputs, _):
     """
     return [_arange_shape_func(*inputs)]
 
-@script
-def _strided_slice_shape_func_input_data(data, begin, end, strides,
-                                         slice_mode):
-    ndim = len(data.shape)
-    out = output_tensor((ndim,), "int64")
-    for i in const_range(ndim):
-        cbegin = 0
-        cend = data.shape[i]
-        cstride = 1
-        if strides.shape[0] > i:
-            cstride = strides[i]
-        if begin.shape[0] > i:
-            cbegin = begin[i]
-        if end.shape[0] <= i:
-            cend = data.shape[i]
-        elif slice_mode != 0:
-            cstride = 1
-            if end[i] < 0:
-                cend = data.shape[i]
-            else:
-                cend = cbegin + end[i]
-        else:
-            cend = end[i]
-        assert cstride != 0, "Strides can't be zero."
-        out[i] = int64(ceil_div((int64(cend) - int64(cbegin)), int64(cstride)))
-    return out
-
 @script
 def _strided_slice_shape_func_input_shape(data_shape, begin, end, strides, slice_mode):
     ndim = data_shape.shape[0]
@@ -165,6 +138,8 @@ def _strided_slice_shape_func_input_shape(data_shape, begin, end, strides, slice
             cstride = int64(strides[i])
         if len(begin) > i:
             cbegin = int64(begin[i])
+            if cbegin < 0:
+                cbegin += int64(data_shape[i])
         if len(end) <= i:
             cend = int64(data_shape[i])
         elif slice_mode != 0:
@@ -174,23 +149,32 @@ def _strided_slice_shape_func_input_shape(data_shape, begin, end, strides, slice
             else:
                 cend = cbegin + int64(end[i])
         else:
-            cend = int64(end[i])
+            if end[i] > data_shape[i]:
+                cend = int64(data_shape[i])
+            else:
+                cend = int64(end[i])
+                if cend < 0:
+                    cend += int64(data_shape[i])
         assert cstride != 0, "Strides can't be zero."
-        out[i] = int64(ceil_div((int64(cend) - int64(cbegin)), int64(cstride)))
+        if cstride < 0:
+            slice_range = cbegin - cend
+            step = -cstride
+        else:
+            slice_range = cend - cbegin
+            step = cstride
+
+        out[i] = int64(ceil_div(slice_range, step))
     return out
 
 
-@_reg.register_shape_func("strided_slice", True)
+@_reg.register_shape_func("strided_slice", False)
 def strided_slice_shape_func(attrs, inputs, _):
     """
     Shape func for strided_slice
     """
     slice_mode = convert(0 if attrs.slice_mode == "end" else 1)
-    # data independent if begin, end and strides exist
-    if attrs.begin and attrs.end and attrs.strides:
-        return [_strided_slice_shape_func_input_shape(inputs[0], attrs.begin, attrs.end,
-                                                      attrs.strides, slice_mode)]
-    return [_strided_slice_shape_func_input_data(*inputs, slice_mode)]
+    return [_strided_slice_shape_func_input_shape(inputs[0], attrs.begin, attrs.end,
+                                                  attrs.strides, slice_mode)]
 
 @script
 def _concatenate_shape_func(inputs, axis):

python/tvm/relay/op/dyn/_transform.py

@@ -27,6 +27,7 @@
 _reg.register_broadcast_schedule("dyn.tile")
 _reg.register_injective_schedule("dyn.one_hot")
 _reg.register_injective_schedule("dyn.full")
+_reg.register_injective_schedule("dyn.strided_slice")
 
 @script
 def _reshape_shape_func_input_data(data, newshape, ndim):
@@ -145,3 +146,53 @@ def one_hot_shape_func(attrs, inputs, _):
     """
     axis = len(inputs[0].shape) if attrs.axis == -1 else attrs.axis
     return [_onehot_shape_func(inputs[0].shape, inputs[3], convert(axis))]
+
+
+@script
+def _strided_slice_shape_func_input_data(data, begin, end, strides,
+                                         slice_mode):
+    ndim = len(data.shape)
+    out = output_tensor((ndim,), "int64")
+    for i in const_range(ndim):
+        cbegin = int64(0)
+        cend = int64(data.shape[i])
+        cstride = int64(1)
+        if strides.shape[0] > i:
+            cstride = int64(strides[i])
+        if begin.shape[0] > i:
+            cbegin = int64(begin[i])
+            if cbegin < 0:
+                cbegin += int64(data.shape[i])
+        if end.shape[0] <= i:
+            cend = int64(data.shape[i])
+        elif slice_mode != 0:
+            cstride = int64(1)
+            if end[i] < 0:
+                cend = int64(data.shape[i])
+            else:
+                cend = cbegin + int64(end[i])
+        else:
+            if end[i] > data.shape[i]:
+                cend = int64(data.shape[i])
+            else:
+                cend = int64(end[i])
+                if cend < 0:
+                    cend += int64(data.shape[i])
+        assert cstride != 0, "Strides can't be zero."
+        if cstride < 0:
+            slice_range = cbegin - cend
+            step = -cstride
+        else:
+            slice_range = cend - cbegin
+            step = cstride
+
+        out[i] = int64(ceil_div(slice_range, step))
+    return out
+
+@_reg.register_shape_func("dyn.strided_slice", True)
+def strided_slice_shape_func(attrs, inputs, _):
+    """
+    Shape func for strided_slice
+    """
+    slice_mode = convert(0 if attrs.slice_mode == "end" else 1)
+    return [_strided_slice_shape_func_input_data(*inputs, slice_mode)]

python/tvm/relay/op/transform.py

@@ -20,6 +20,7 @@
 
 from . import _make
 from .dyn import _make as _dyn_make
+from .tensor import shape_of
 from ..expr import TupleWrapper, const, Expr, Tuple
 from ...tir import expr as _expr
 
@@ -827,13 +828,17 @@ def strided_slice(data, begin, end, strides=None, slice_mode="end"):
     ret : relay.Expr
         The computed result.
     """
-    strides = strides or const([1], dtype="int32")
-    if isinstance(begin, (tuple, list)):
-        begin = const(list(begin))
-    if isinstance(end, (tuple, list)):
-        end = const(list(end))
-    if isinstance(strides, (tuple, list)):
-        strides = const(list(strides))
+    strides = strides or [1]
+    if (isinstance(begin, Expr) or isinstance(end, Expr) or isinstance(strides, Expr)):
+        if isinstance(begin, (tuple, list)):
+            begin = const(list(begin))
+        if isinstance(end, (tuple, list)):
+            end = const(list(end))
+        if isinstance(strides, (tuple, list)):
+            strides = const(list(strides))
+        normalized_begin = _make.where(begin < cast_like(const(0), begin),
+                                       begin + cast_like(shape_of(data), begin), begin)
+        return _dyn_make.strided_slice(data, normalized_begin, end, strides, slice_mode)
     return _make.strided_slice(data, begin, end, strides, slice_mode)
 
 

python/tvm/topi/cuda/conv2d_alter_op.py

@@ -276,8 +276,8 @@ def _conv2d_legalize(attrs, inputs, arg_types):
                 new_attrs['channels'] = new_out_channel
                 out = tvm.relay.nn.conv2d(data, kernel, **new_attrs)
                 original_out_shape = [x.value for x in output_tensor.shape]
-                out = relay.strided_slice(out, begin=relay.const([0, 0, 0, 0]),
-                                          end=relay.const(original_out_shape))
+                out = relay.strided_slice(out, begin=[0, 0, 0, 0],
+                                          end=original_out_shape)
             else:
                 out = relay.nn.conv2d(data, kernel, **new_attrs)
             return out

python/tvm/topi/x86/conv2d_alter_op.py

@@ -313,8 +313,8 @@ def _conv2d_legalize(attrs, inputs, arg_types):
             out = tvm.relay.nn.conv2d(data, kernel, **new_attrs)
             original_out_shape = [x.value for x in output_tensor.shape]
             out = relay.strided_slice(out,
-                                      begin=relay.const([0, 0, 0, 0], "int32"),
-                                      end=relay.const(original_out_shape, "int32"))
+                                      begin=[0, 0, 0, 0],
+                                      end=original_out_shape)
         else:
             out = relay.nn.conv2d(data, kernel, **new_attrs)