[Relay][Frontend][TF] Add tensor array ops

python/tvm/relay/frontend/tensorflow.py

@@ -25,6 +25,9 @@
 import numpy as np
 
 import tvm
+
+from tvm.relay.prelude import Prelude
+
 from .. import analysis
 from .. import expr as _expr
 from .. import op as _op
@@ -505,6 +508,61 @@ def _impl(inputs, attr, params):
         return _op.concatenate(inputs_reshaped, axis)
     return _impl
 
+def _tensor_array():
+    def _impl(inputs, attr, params, prelude):
+        return prelude.tensor_array(_op.take(inputs[0], tvm.relay.const(0)))
+    return _impl
+
+def _tensor_array_scatter():
+    def _impl(inputs, attr, params, prelude):
+        values = None
+        if len(inputs[2].type_annotation.shape) == 1:
+            pass
+        elif len(inputs[2].type_annotation.shape) == 2:
+            values = prelude.tensor_array_unstack_tensor2(inputs[2])
+
+        return prelude.tensor_array_scatter(inputs[0], inputs[1], values)
+    return _impl
+
+def _tensor_array_gather():
+    def _impl(inputs, attr, params, prelude):
+        return prelude.tensor_array_gather(inputs[2], inputs[1])
+    return _impl
+
+def _tensor_array_size():
+    def _impl(inputs, attr, params, prelude):
+        return prelude.tensor_array_size(inputs[0])
+    return _impl
+
+def _tensor_array_write():
+    def _impl(inputs, attr, params, prelude):
+        if len(inputs[2].type_annotation.shape) == 2:
+            v = prelude.tensor2(inputs[2])
+        elif len(inputs[2].type_annotation.shape) == 3:
+            v = prelude.tensor3(inputs[2])
+        return prelude.tensor_array_write(inputs[3], _op.take(inputs[1], tvm.relay.const(0)), v)
+    return _impl
+
+def _tensor_array_read():
+    def _impl(inputs, attr, params, prelude):
+        return prelude.tensor_array_read(inputs[2], _op.take(inputs[1], tvm.relay.const(0)))
+    return _impl
+
+def _tensor_array_split():
+    def _impl(inputs, attr, params, prelude):
+        if len(inputs[1].type_annotation.shape) == 2:
+            v = prelude.tensor2(inputs[1])
+        elif len(inputs[1].type_annotation.shape) == 3:
+            v = prelude.tensor3(inputs[1])
+        lengths = _op.cast(inputs[2], 'int32')
+        return prelude.tensor_array_split(inputs[0], v, lengths)
+    return _impl
+
+def _tensor_array_concat():
+    def _impl(inputs, attr, params, prelude):
+        return prelude.tensor_array_concat(inputs[1])
+    return _impl
+
 def _tile():
     def _impl(inputs, attr, params):
         reps = _get_list_param(params, inputs.pop())
@@ -1302,6 +1360,14 @@ def _impl(inputs, attr, params):
     'NotEqual'                          : _broadcast('not_equal'),
     'OneHot'                            : _one_hot(),
     'Pack'                              : _pack(),
+    'TensorArrayV3'                     : _tensor_array(),
+    'TensorArrayScatterV3'              : _tensor_array_scatter(),
+    'TensorArrayGatherV3'               : _tensor_array_gather(),
+    'TensorArraySizeV3'                 : _tensor_array_size(),
+    'TensorArrayWriteV3'                : _tensor_array_write(),
+    'TensorArrayReadV3'                 : _tensor_array_read(),
+    'TensorArraySplitV3'                : _tensor_array_split(),
+    'TensorArrayConcatV3'               : _tensor_array_concat(),
     'Pad'                               : _pad('Pad'),
     'PadV2'                             : _pad('PadV2'),
     'Pow'                               : _elemwise('power'),
@@ -1847,6 +1913,7 @@ def __init__(self):
         self._loops = {}
         self._branches = {}
         self._mod = _module.Module({})
+        self._prelude = Prelude(self._mod)
 
     def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
         """Construct relay nodes from tensorflow graph definition - GraphDef.
@@ -2322,7 +2389,11 @@ def _convert_operator(self, op_name, inputs, attrs,
         if op_name in identity_list:
             sym = get_relay_op(op_name)(*inputs, **attrs)
         elif op_name in convert_map:
-            sym = convert_map[op_name](inputs, attrs, self._params)
+            if 'TensorArray' in op_name:
+                sym = convert_map[op_name](inputs, attrs, self._params, self._prelude)
+            else:
+                sym = convert_map[op_name](inputs, attrs, self._params)
+
         elif op_name in convert_map_rnn:
             sym = self._convert_rnn_operator(op_name, inputs, attrs,
                                              self._params, graph,

python/tvm/relay/op/_tensor.py

@@ -107,6 +107,29 @@ def clip_compute(attrs, inputs, output_type, target):
 
 register_schedule("clip", schedule_elemwise)
 
+@script
+def _cast_shape_function(x):
+    out_ndim = len(x)
+    out = output_tensor((out_ndim,), "int64")
+    for i in const_range(out_ndim):
+        out[i] = x[i]
+    return out
+
+def cast_shape_func(attrs, inputs, out_ndims):
+    return [_cast_shape_function(*inputs)]
+
+@script
+def _expand_dims_shape_func(x):
+    ndim = len(x.shape)
+    out = output_tensor((ndim+1,), "int64")
+    out[0] = int64(1)
+    for i in const_range(0, ndim):
+        out[i+1] = int64(x.shape[i])
+    return out
+
+def expand_dims_shape_func(attrs, inputs, out_ndims):
+    return [_expand_dims_shape_func(*inputs)]
+
 # shape func
 @script
 def _broadcast_shape_func(x, y, ndim):
@@ -139,6 +162,9 @@ def _broadcast_shape_func(x, y, ndim):
 def broadcast_shape_func(attrs, inputs, out_ndims):
     return [_broadcast_shape_func(*inputs, out_ndims[0])]
 
+register_shape_func("expand_dims", False, expand_dims_shape_func)
+register_shape_func("cast", False, cast_shape_func)
+
 register_shape_func("add", False, broadcast_shape_func)
 register_shape_func("subtract", False, broadcast_shape_func)
 register_shape_func("multiply", False, broadcast_shape_func)