[DYNAMIC] Add Dynamic reshape to a dynamic namespace and add DynamicToStatic Pass

include/tvm/relay/attrs/transform.h

@@ -82,7 +82,7 @@ struct TransposeAttrs : public tvm::AttrsNode<TransposeAttrs> {
 
 /*! \brief Attributes used in reshape operators */
 struct ReshapeAttrs : public tvm::AttrsNode<ReshapeAttrs> {
-  Optional<Array<Integer>> newshape;
+  Array<Integer> newshape;
   bool reverse;
   TVM_DECLARE_ATTRS(ReshapeAttrs, "relay.attrs.ReshapeAttrs") {
     TVM_ATTR_FIELD(newshape).describe(

python/tvm/relay/__init__.py

@@ -45,6 +45,7 @@
 from .op import annotation
 from .op import vision
 from .op import contrib
+from .op import dyn
 from .op.reduce import *
 from .op.tensor import *
 from .op.transform import *

python/tvm/relay/_parser.py

@@ -114,7 +114,7 @@ def convert(self, v):
     def __call__(self, args, attrs, type_args):
         if attrs is None:
             attrs = {}
-        if self.operator in (op.reshape, op.strided_slice):
+        if self.operator in (op.strided_slice,):
             x = self.operator(*args)
         elif self.operator in (op.zeros, op.ones, op.full, op.broadcast_to):
             x = self.operator(*args, dtype=attrs["dtype"])

python/tvm/relay/op/_tensor_grad.py

@@ -511,7 +511,7 @@ def batch_matmul_grad(orig, grad):
 @register_gradient("reshape")
 def reshape_grad(orig, grad):
     """Gradient of reshape"""
-    return [reshape_like(grad, orig.args[0]), orig.args[1]]
+    return [reshape_like(grad, orig.args[0])]
 
 
 @register_gradient("cast")

python/tvm/relay/op/_transform.py

@@ -273,82 +273,11 @@ def _reshape_shape_func_input_shape(data_shape, newshape, ndim):
             out[infer_idx] = old_size // new_size
     return out
 
-@script
-def _reshape_shape_func_input_data(data, newshape, ndim):
-    out = output_tensor((ndim,), "int64")
-    data_shape = allocate((len(data.shape),), "int64")
-    for x in const_range(len(data.shape)):
-        data_shape[x] = int64(data.shape[x])
-    src_idx = 0
-    dst_idx = 0
-    infer_idx = -1
-    copy = False
-    skip = 0
-    for i in const_range(len(newshape)):
-        if skip > 0:
-            skip -= 1
-        elif newshape[i] > 0:
-            out[dst_idx] = int64(newshape[i])
-            src_idx += 1
-            dst_idx += 1
-        elif newshape[i] == 0:
-            out[dst_idx] = data_shape[src_idx]
-            src_idx += 1
-            dst_idx += 1
-        elif newshape[i] == -1:
-            assert infer_idx < 0, "One and only one dim can be inferred"
-            out[dst_idx] = int64(1)
-            infer_idx = i
-            dst_idx += 1
-        elif newshape[i] == -2:
-            copy = True
-        elif newshape[i] == -3:
-            assert data_shape.shape[0] - src_idx > 1, \
-                "Not enough dims in input shape for -3"
-            out[dst_idx] = data_shape[src_idx] * data_shape[src_idx+1]
-            src_idx += 2
-            dst_idx += 1
-        elif newshape[i] == -4:
-            assert len(newshape) - i > 2, "Not enough dims in new shape for -4"
-            if newshape[i+1] == -1:
-                assert newshape[i+2] != -1, "Split dims cannot both be -1."
-                out[dst_idx] = data_shape[src_idx] // int64(newshape[i+2])
-                out[dst_idx+1] = int64(newshape[i+2])
-            else:
-                out[dst_idx] = int64(newshape[i+1])
-                if newshape[i+2] == -1:
-                    out[dst_idx+1] = data_shape[src_idx] // int64(newshape[i+1])
-                else:
-                    out[dst_idx+1] = int64(newshape[i+2])
-            assert data_shape[src_idx] == out[dst_idx] * out[dst_idx+1],\
-                "Product of split dims doesn't match to input dim"
-            src_idx += 1
-            dst_idx += 2
-            skip = 2
-        else:
-            assert False, "Invalid special values in new shape"
-    if len(data_shape.shape) > 0:
-        # if data is not constant, we can then handle -1 and -2
-        if copy:
-            for i in range(src_idx, data_shape.shape[0]):
-                out[dst_idx] = data_shape[i]
-                dst_idx += 1
-        if infer_idx >= 0:
-            old_size = int64(1)
-            for i in const_range(data_shape.shape[0]):
-                old_size *= data_shape[i]
-            new_size = int64(1)
-            for i in const_range(out.shape[0]):
-                new_size *= out[i]
-            out[infer_idx] = old_size // new_size
-    return out
-
-@_reg.register_shape_func("reshape", True)
+@_reg.register_shape_func("reshape", False)
 def reshape_shape_func(attrs, inputs, out_ndims):
-    if attrs.newshape is None:
-        return [_reshape_shape_func_input_data(*inputs, out_ndims[0])]
+    newshape = get_const_tuple(attrs.newshape)
     return [_reshape_shape_func_input_shape(inputs[0],
-                                            convert(attrs.newshape),
+                                            convert(newshape),
                                             out_ndims[0])]
 
 @script

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

@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=wildcard-import, redefined-builtin, invalid-name
+"""The Relay namespace containing dynamic ops."""
+
+from . import _transform

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

@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Constructor APIs"""
+import tvm._ffi
+
+tvm._ffi._init_api("relay.op.dyn._make", __name__)

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

@@ -0,0 +1,83 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Backend compiler related feature registration"""
+# pylint: disable=invalid-name,unused-argument, len-as-condition, too-many-nested-blocks, too-many-local-variables, too-many-arguments
+from __future__ import absolute_import
+from tvm.te.hybrid import script
+from .. import op as _reg
+
+_reg.register_injective_schedule("dyn.reshape")
+
+@script
+def _reshape_shape_func_input_data(data, newshape, ndim):
+    out = output_tensor((ndim,), "int64")
+    data_shape = allocate((len(data.shape),), "int64")
+    for x in const_range(len(data.shape)):
+        data_shape[x] = int64(data.shape[x])
+    src_idx = 0
+    dst_idx = 0
+    infer_idx = -1
+    copy = False
+    skip = 0
+    for i in const_range(len(newshape)):
+        if skip > 0:
+            skip -= 1
+        elif newshape[i] > 0:
+            out[dst_idx] = int64(newshape[i])
+            src_idx += 1
+            dst_idx += 1
+        elif newshape[i] == 0:
+            out[dst_idx] = data_shape[src_idx]
+            src_idx += 1
+            dst_idx += 1
+        elif newshape[i] == -1:
+            assert infer_idx < 0, "One and only one dim can be inferred"
+            out[dst_idx] = int64(1)
+            infer_idx = i
+            src_idx += 1
+            dst_idx += 1
+        elif newshape[i] == -2:
+            assert False, "Value -2 is not valid in newshape argument of dynamic reshape"
+        elif newshape[i] == -3:
+            assert data_shape.shape[0] - src_idx > 1, \
+                "Not enough dims in input shape for -3"
+            out[dst_idx] = data_shape[src_idx] * data_shape[src_idx+1]
+            src_idx += 2
+            dst_idx += 1
+        elif newshape[i] == -4:
+            assert False, "Value -4 is not valid in newshape argument of dynamic reshape"
+        else:
+            assert False, "Invalid special values in new shape"
+    if len(data_shape.shape) > 0:
+        # if data is not constant, we can then handle -1 and -2
+        if copy:
+            for i in range(src_idx, data_shape.shape[0]):
+                out[dst_idx] = data_shape[i]
+                dst_idx += 1
+        if infer_idx >= 0:
+            old_size = int64(1)
+            for i in const_range(data_shape.shape[0]):
+                old_size *= data_shape[i]
+            new_size = int64(1)
+            for i in const_range(out.shape[0]):
+                new_size *= out[i]
+            out[infer_idx] = old_size // new_size
+    return out
+
+@_reg.register_shape_func("dyn.reshape", True)
+def dynamic_reshape_shape_func(attrs, inputs, out_ndims):
+    return [_reshape_shape_func_input_data(*inputs, out_ndims[0])]

python/tvm/relay/op/transform.py

@@ -19,7 +19,8 @@
 """Transform operators."""
 
 from . import _make
-from ..expr import TupleWrapper, const
+from .dyn import _make as _dyn_make
+from ..expr import TupleWrapper, const, Expr
 from ...tir import expr as _expr
 
 
@@ -210,8 +211,10 @@ def reshape(data, newshape):
     result : relay.Expr
         The reshaped result.
     """
+    if isinstance(newshape, Expr):
+        return _dyn_make.reshape(data, newshape)
     if isinstance(newshape, int):
-        newshape = const([newshape])
+        newshape = [newshape]
     if isinstance(newshape, (tuple, list)):
         tempshape = []
         for shape in newshape:
@@ -222,8 +225,8 @@ def reshape(data, newshape):
                     tempshape.append(int(shape))
                 except ValueError as err:
                     raise RuntimeError('Unrecognized shape type: %s' % err)
-        newshape = const(tempshape)
-    return _make.reshape(data, newshape)
+        newshape = tempshape
+    return _make.reshape(data, list(newshape))
 
 def argwhere(condition):
     """Find the indices of elements of a tensor that are

python/tvm/relay/transform/transform.py

@@ -625,6 +625,17 @@ def AnnotateTarget(targets):
     return _ffi_api.AnnotateTarget([tvm.runtime.container.String(t) for t in targets])
 
 
+def DynamicToStatic():
+    """If possible, convert tvm.relay.dynamic* ops to static versions
+
+    Returns
+    -------
+    ret : tvm.transform.Pass
+        The registered pass for dynamic->static conversion.
+    """
+    return _ffi_api.DynamicToStatic()
+
+
 def Inline():
     """Perform inlining on the given Relay IR module. The global functions that
     are marked as `inline` should be always inlined. A cost model will be

src/relay/analysis/util.cc

@@ -448,14 +448,7 @@ bool IsDataDependant(const CallNode* call) {
     return false;
   }
 
-  if (op->name == "reshape") {
-    if (const auto* attrs = call->attrs.as<ReshapeAttrs>()) {
-      if (attrs->newshape) {
-        // If newshape attribute exists, it isn't data dependant.
-        return false;
-      }
-    }
-  } else if (op->name == "topk") {
+  if (op->name == "topk") {
     if (const auto* attrs = call->attrs.as<TopKAttrs>()) {
       if (attrs->k) {
         // If k attribute exists, it isn't data dependant.