[RELAY][DYN] Dynamic UpSampling3D Op (apache#6353)

* frontend and start of cpp impl

* upsampling3d typerel and makefunc

* impl upsampling3d dynamic to static pass

* passes test_dyn_upsampling3d_infer_type_const

* fix bugs and improve doc for resize and upsampling

* code cleanup

* make tests more complex

* code cleanup, fix test_dyn_upsampling3d_run

* fix typo

* ci not working

python/tvm/relay/op/dyn/nn/_nn.py

@@ -38,7 +38,21 @@ def compute_upsampling(attrs, inputs, out_dtype):
     return [topi.nn.upsampling(data, scale_h, scale_w, layout,
                                method, align_corners, out_dtype.shape)]
 
+# upsampling3d
+@register_compute("dyn.nn.upsampling3d")
+def compute_upsampling3d(attrs, inputs, out_dtype):
+    data = inputs[0]
+    scale_d = inputs[1]
+    scale_h = inputs[2]
+    scale_w = inputs[3]
+    layout = attrs.layout
+    method = attrs.method
+    coordinate_transformation_mode = attrs.coordinate_transformation_mode
+    return [topi.nn.upsampling3d(data, scale_d, scale_h, scale_w, layout, method,\
+        coordinate_transformation_mode, out_dtype.shape)]
+
 register_injective_schedule("dyn.nn.upsampling")
+register_injective_schedule("dyn.nn.upsampling3d")
 register_broadcast_schedule("dyn.nn.pad")
 
 #####################
@@ -47,12 +61,12 @@ def compute_upsampling(attrs, inputs, out_dtype):
 
 # upsampling
 @script
-def _upsampling_shape_func(dshape, scale_h, scale_w, height_axis, width_axis, channel_axis):
+def _upsampling_shape_func(dshape, scale_h, scale_w, height_axis, width_axis):
     out = output_tensor((4,), "int64")
-    out[0] = int64(dshape[0])
+    for i in const_range(4):
+        out[i] = int64(dshape[i])
     out[height_axis] = int64(round(dshape[height_axis] * scale_h[0]))
     out[width_axis] = int64(round(dshape[width_axis] * scale_w[0]))
-    out[channel_axis] = int64(dshape[channel_axis])
     return out
 
 @register_shape_func("dyn.nn.upsampling", True)
@@ -65,11 +79,39 @@ def upsampling_shape_func(attrs, inputs, _):
             height_axis = i
         if letter == "W":
             width_axis = i
-        if letter == "C":
-            channel_axis = i
     return [_upsampling_shape_func(inputs[0].shape, inputs[1], inputs[2],
-                                   convert(height_axis), convert(width_axis),
-                                   convert(channel_axis))]
+                                   convert(height_axis), convert(width_axis))]
+
+# upsampling3d
+@script
+def _upsampling3d_shape_func(dshape, scale_d, scale_h, scale_w,
+                             depth_axis, height_axis, width_axis):
+    out = output_tensor((5,), "int64")
+    for i in const_range(5):
+        out[i] = int64(dshape[i])
+    out[depth_axis] = int64(round(dshape[depth_axis] * scale_d[0]))
+    out[height_axis] = int64(round(dshape[height_axis] * scale_h[0]))
+    out[width_axis] = int64(round(dshape[width_axis] * scale_w[0]))
+    return out
+
+
+@register_shape_func("dyn.nn.upsampling3d", True)
+def upsampling3d_shape_func(attrs, inputs, _):
+    """Shape function for upsampling. Supports NCHW and NHWC layouts."""
+    layout = attrs.layout
+    depth_axis = height_axis = width_axis = 1
+    for i, letter in enumerate(layout):
+        if letter == "D":
+            depth_axis = i
+        if letter == "H":
+            height_axis = i
+        if letter == "W":
+            width_axis = i
+    return [_upsampling3d_shape_func(inputs[0].shape, inputs[1], inputs[2],
+                                     inputs[3], convert(depth_axis),
+                                     convert(height_axis),
+                                     convert(width_axis))]
+
 # pad
 @script
 def _dyn_pad_shape_func(data, pad_width):

python/tvm/relay/op/nn/nn.py

@@ -1229,6 +1229,15 @@ def upsampling3d(data,
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(scale_d, Expr) or isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
+        if not isinstance(scale_d, Expr):
+            scale_d = const(scale_d, "float64")
+        if not isinstance(scale_h, Expr):
+            scale_h = const(scale_h, "float64")
+        if not isinstance(scale_w, Expr):
+            scale_w = const(scale_w, "float64")
+        return _dyn_make.upsampling3d(data, scale_d, scale_h, scale_w, layout, method,
+                                      coordinate_transformation_mode)
     return _make.upsampling3d(data, scale_d, scale_h, scale_w, layout, method,
                               coordinate_transformation_mode)
 

python/tvm/topi/image/resize.py

@@ -519,8 +519,9 @@ def resize(data, size, layout="NCHW", method="bilinear",
     out_dtype: string, optional
         Type to return. If left None will be same as input type.
 
-    output_shape: optional
+    output_shape: tvm.tir.container.Array, optional
         Shape to return. If left None will be inferred
+        (If shape is determined dynamically, pass out_dtype.shape as output_shape)
 
     Returns
     -------
@@ -680,17 +681,22 @@ def resize3d(data, size, layout="NCDHW", method="nearest_neighbor",
         inputs is a 5-D tensor with shape
         [batch, channel, in_depth, in_height, in_width]
         or  [batch, in_depth, in_height, in_width, channel]
+
     size: Tuple
         Output resolution scale to
+
     layout: string, optional
         "NCDHW", "NDHWC", or "NCDHWc".
+
     coordinate_transformation_mode: string, optional
         Describes how to transform the coordinate in the resized tensor
         to the coordinate in the original tensor.
         Refer to the ONNX Resize operator specification for details.
+
         Available options are "half_pixel", "align_corners" and "asymmetric".
     method: {"trilinear", "nearest_neighbor"}
         Method to be used for resizing.
+
     out_dtype: string, optional
         Type to return. If left None will be same as input type.
 

python/tvm/topi/nn/upsampling.py

@@ -44,6 +44,10 @@ def upsampling(data, scale_h, scale_w, layout="NCHW", method='nearest_neighbor',
     method : {"bilinear", "nearest_neighbor", "bicubic"}
         Method to be used for upsampling.
 
+    output_shape: tvm.tir.container.Array, optional
+        Shape to return. If left None will be inferred
+        (If shape is determined dynamically, pass out_dtype.shape as output_shape)
+
     Returns
     -------
     output : tvm.te.Tensor
@@ -79,7 +83,7 @@ def upsampling(data, scale_h, scale_w, layout="NCHW", method='nearest_neighbor',
 
 
 def upsampling3d(data, scale_d, scale_h, scale_w, layout="NCDHW", method='nearest_neighbor',
-                 coordinate_transformation_mode="half_pixel"):
+                 coordinate_transformation_mode="half_pixel", output_shape=None):
     """Perform upsampling on the data.
        Nearest neighbor and bilinear upsampling are supported.
 
@@ -111,6 +115,10 @@ def upsampling3d(data, scale_d, scale_h, scale_w, layout="NCDHW", method='neares
         Refer to the ONNX Resize operator specification for details.
         Available options are "half_pixel", "align_corners" and "asymmetric".
 
+    output_shape: tvm.tir.container.Array, optional
+        Shape to return. If left None will be inferred
+        (If shape is determined dynamically, pass out_dtype.shape as output_shape)
+
     Returns
     -------
     output : tvm.te.Tensor
@@ -119,15 +127,30 @@ def upsampling3d(data, scale_d, scale_h, scale_w, layout="NCDHW", method='neares
     """
     base_layout = layout[0:5]
     if base_layout == "NCDHW":
-        out_shape = (simplify(topi.cast(te.round(data.shape[2] * scale_d), data.shape[2].dtype)),
-                     simplify(topi.cast(te.round(data.shape[3] * scale_h), data.shape[3].dtype)),
-                     simplify(topi.cast(te.round(data.shape[4] * scale_w), data.shape[4].dtype)))
+        if not output_shape: # static case
+            scaled_d = data.shape[2] * scale_d
+            scaled_h = data.shape[3] * scale_h
+            scaled_w = data.shape[4] * scale_w
+            resize_shape = (simplify(topi.cast(te.round(scaled_d), data.shape[2].dtype)),
+                            simplify(topi.cast(te.round(scaled_h), data.shape[3].dtype)),
+                            simplify(topi.cast(te.round(scaled_w), data.shape[4].dtype)))
+        else: # dynamic case -- don't need to scale; already done in shape func
+            resize_shape = (simplify(topi.cast(te.round(output_shape[2]), data.shape[2].dtype)),
+                            simplify(topi.cast(te.round(output_shape[3]), data.shape[3].dtype)),
+                            simplify(topi.cast(te.round(output_shape[4]), data.shape[4].dtype)))
     elif layout == "NDHWC":
-        out_shape = (simplify(topi.cast(te.round(data.shape[1] * scale_d), data.shape[1].dtype)),
-                     simplify(topi.cast(te.round(data.shape[2] * scale_h), data.shape[2].dtype)),
-                     simplify(topi.cast(te.round(data.shape[3] * scale_w), data.shape[3].dtype)))
-
+        if not output_shape: # static case
+            scaled_d = data.shape[1] * scale_d
+            scaled_h = data.shape[2] * scale_h
+            scaled_w = data.shape[3] * scale_w
+            resize_shape = (simplify(topi.cast(te.round(scaled_d), data.shape[1].dtype)),
+                            simplify(topi.cast(te.round(scaled_h), data.shape[2].dtype)),
+                            simplify(topi.cast(te.round(scaled_w), data.shape[3].dtype)))
+        else: # dynamic case
+            resize_shape = (simplify(topi.cast(te.round(output_shape[1]), data.shape[1].dtype)),
+                            simplify(topi.cast(te.round(output_shape[2]), data.shape[2].dtype)),
+                            simplify(topi.cast(te.round(output_shape[3]), data.shape[3].dtype)))
     else:
         raise ValueError("not support this layout {} yet".format(layout))
-    return topi.image.resize3d(data, out_shape, layout=layout, method=method,
+    return topi.image.resize3d(data, resize_shape, layout=layout, method=method,
                                coordinate_transformation_mode=coordinate_transformation_mode)

src/relay/op/dyn/nn/upsampling.cc

@@ -95,16 +95,16 @@ RELAY_REGISTER_OP("dyn.nn.upsampling")
             (batch_size, channels, in_height, in_width) for NCHW
             (batch_size, in_height, in_width, channels) for NHWC
 
-- **scale_h**: scale_h is an integer of the amount to scale height by
+- **scale_h**: scale_h is a double of the amount to scale height by
 
-- **scale_w**: scale_w is an integer of the amount to scale width by
+- **scale_w**: scale_w is a double of the amount to scale width by
 
 - **out**: Output is 4D array of shape
            for layout NCHW
-           (batch_size, channels, in_height*scale, in_width*scale)
+           (batch_size, channels, in_height*scale_h, in_width*scale_w)
 
            for layout NHWC
-           (batch_size, in_height*scale, in_width*scale, channels)
+           (batch_size, in_height*scale_h, in_width*scale_w, channels)
 
 )code" TVM_ADD_FILELINE)
     .set_attrs_type<UpSamplingAttrs>()
@@ -118,6 +118,84 @@ RELAY_REGISTER_OP("dyn.nn.upsampling")
                                    UpsamplingInferCorrectLayout<UpSamplingAttrs>)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
+// UpSampling3D
+bool UpSampling3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                     const TypeReporter& reporter) {
+  // types = [data_type, scale_d_type, scale_h_type, scale_w_type, ret_type]
+  CHECK_EQ(types.size(), 5);
+  const auto* data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) return false;
+
+  static const Layout kNCDHW("NCDHW");
+
+  const UpSampling3DAttrs* param = attrs.as<UpSampling3DAttrs>();
+  CHECK(param != nullptr);
+  const Layout in_layout(param->layout);
+
+  auto layout_converter = tir::BijectiveLayout(in_layout, kNCDHW);
+  CHECK(layout_converter.defined())
+      << "UpSampling3D only support input layouts that are convertible from NCDHW."
+      << " But got " << in_layout;
+
+  auto ncdhw_oshape = layout_converter.ForwardShape(data->shape);
+
+  ncdhw_oshape.Set(2, Any());
+  ncdhw_oshape.Set(3, Any());
+  ncdhw_oshape.Set(4, Any());
+
+  auto oshape = layout_converter.BackwardShape(ncdhw_oshape);
+
+  reporter->Assign(types[4], TensorType(oshape, data->dtype));
+  return true;
+}
+
+Expr MakeUpSampling3D(Expr data, Expr scale_d, Expr scale_h, Expr scale_w, String layout,
+                      String method, String coordinate_transformation_mode) {
+  auto attrs = make_object<UpSampling3DAttrs>();
+  attrs->layout = std::move(layout);
+  attrs->method = std::move(method);
+  attrs->coordinate_transformation_mode = coordinate_transformation_mode;
+
+  static const Op& op = Op::Get("dyn.nn.upsampling3d");
+  return Call(op, {data, scale_d, scale_h, scale_w}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.dyn.nn._make.upsampling3d").set_body_typed(MakeUpSampling3D);
+
+RELAY_REGISTER_OP("dyn.nn.upsampling3d")
+    .describe(R"code(Perform upsampling on input array with nearest neighbour or
+bilinear interpolation.
+
+- **data**: data is 5D array of shape
+            (batch_size, channels, in_depth, in_height, in_width) for NCDHW
+            (batch_size, in_depth, in_height, in_width, channels) for NDHWC
+
+- **scale_d**: scale_d is a double of the amount to scale depth by
+
+- **scale_h**: scale_h is a double of the amount to scale height by
+
+- **scale_w**: scale_w is a double of the amount to scale width by
+
+- **out**: Output is 5D array of shape
+           for layout NCDHW
+           (batch_size, channels, in_depth*scale_d, in_height*scale_h, in_width*scale_w)
+
+           for layout NDHWC
+           (batch_size, in_depth*scale_d, in_height*scale_h, in_width*scale_w, channels)
+
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<UpSampling3DAttrs>()
+    .set_num_inputs(4)
+    .add_argument("data", "Tensor", "The input tensor.")
+    .add_argument("scale_d", "double", "The scale for the depth.")
+    .add_argument("scale_h", "double", "The scale for the height.")
+    .add_argument("scale_w", "double", "The scale for the width.")
+    .set_support_level(2)
+    .add_type_rel("DynamicUpSampling3D", UpSampling3DRel)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
+                                   UpsamplingInferCorrectLayout<UpSampling3DAttrs>)
+    .set_attr<TOpPattern>("TOpPattern", kInjective);
+
 }  // namespace dyn
 }  // namespace relay
 }  // namespace tvm

src/relay/op/make_op.h

@@ -77,6 +77,9 @@ Expr MakeTopK(Expr data, int k, int axis, String ret_type, bool is_ascend, DataT
 Expr MakeUpSampling(Expr data, double scale_h, double scale_w, String layout, String method,
                     bool align_corners);
 
+Expr MakeUpSampling3D(Expr data, double scale_d, double scale_h, double scale_w, String layout,
+                      String method, String coordinate_transformation_mode);
+
 Expr MakeVariance(Expr data, Expr mean, Array<Integer> axis, bool keepdims, bool exclude,
                   bool unbiased);
 

src/relay/transforms/dynamic_to_static.cc

@@ -139,6 +139,25 @@ class DynamicToStaticMutator : public MixedModeMutator {
            }
            return Expr(nullptr);
          }},
+        {Op::Get("dyn.nn.upsampling3d"),
+         [](const CallNode* call_node) {
+           const ConstantNode* scale_d = call_node->args[1].as<ConstantNode>();
+           const ConstantNode* scale_h = call_node->args[2].as<ConstantNode>();
+           const ConstantNode* scale_w = call_node->args[3].as<ConstantNode>();
+           if (scale_d && scale_h && scale_w) {
+             CHECK_EQ(scale_d->data->ndim, 0);
+             CHECK_EQ(scale_h->data->ndim, 0);
+             CHECK_EQ(scale_w->data->ndim, 0);
+             const UpSampling3DAttrs* param = call_node->attrs.as<UpSampling3DAttrs>();
+             CHECK(param);
+
+             return MakeUpSampling3D(call_node->args[0], ToScalar(scale_d->data),
+                                     ToScalar(scale_h->data), ToScalar(scale_w->data),
+                                     param->layout, param->method,
+                                     param->coordinate_transformation_mode);
+           }
+           return Expr(nullptr);
+         }},
         {Op::Get("dyn.nn.pad"),
          [](const CallNode* call_node) {
            const ConstantNode* pad_width = call_node->args[1].as<ConstantNode>();