[Frontend][TENSORFLOW] Add support for unpack with dim 0 after tensor…

…list stack (apache#8558)

* enable testcase when tensorlist stack follows by a unpack for dim 0

* address reviews and improve the docstring

python/tvm/relay/frontend/tensorflow2_ops.py

@@ -133,13 +133,21 @@ def _impl(inputs, attr, params, prelude):
             stack_func = prelude.get_global_var("tensor_array_stack", dtype_str)
             out = stack_func(inputs[0])
         else:
-            static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, input_ta_shape)
+            if "num_elements" in attr:
+                num_elements = attr["num_elements"]
+            static_tensor_array_ops = StaticTensorArrayOps(
+                prelude, dtype_str, input_ta_shape, num_elements
+            )
             static_tensor_array_ops.register()
             stack_func = prelude.get_global_var_static(
-                "tensor_array_stack", dtype_str, input_ta_shape
+                "tensor_array_stack", dtype_str, input_ta_shape, num_elements
             )
             out_tensor = stack_func(inputs[0])
-            out_shape = (Any(),) + input_ta_shape
+            out_shape = (
+                (num_elements,) + input_ta_shape
+                if num_elements and num_elements == 1
+                else (Any(),) + input_ta_shape
+            )
             static_tensor_array_ops = StaticTensorArrayOps(prelude, dtype_str, out_shape)
             static_tensor_array_ops.register()
             get_data_func = prelude.get_global_var_static("tensor_get_data", dtype_str, out_shape)

python/tvm/relay/prelude.py

@@ -73,9 +73,33 @@ def get_tensor_array_shape(expr, dtype, prelude):
     return None
 
 
-def _get_name_static(canonical, dtype, shape):
-    """Get name for static shape tensor array op corresponding
-    to the canonical name"""
+def _get_name_static(canonical, dtype, shape, batch_dim=None):
+    """Get name for static shape tensor array op
+
+    By design, static ADT tensor in TVM has type name in the format
+    of static_tensor_dim0_dim1_..._dimN_t
+    or static_tensor_batch1_dim0_dim1_..._dimN_t if tensorlist stack only have one item.
+
+    Parameters
+    ----------
+    canonical : String
+        Tensor array op name
+
+    dtype : str
+        Data type.
+
+    shape : tuple of (int, Any) or None
+        Tensor array shape
+
+    batch_dim: None or int
+        1 if tensorlist stack only have one item.
+        None by default
+
+    Returns
+    -------
+    name : String
+        The tensor array op name
+    """
     dim_names = []
     for dim in shape:
         if isinstance(dim, Any):
@@ -89,26 +113,31 @@ def _get_name_static(canonical, dtype, shape):
         shape_str = "scalar"
     if canonical == "tensor_t":
         return "static_tensor_{}_{}_t".format(dtype, shape_str)
-    return "{}_{}_{}".format(canonical, dtype, shape_str)
+    if batch_dim is None or canonical in ["tensor_constructor", "tensor_nil"]:
+        return "{}_{}_{}".format(canonical, dtype, shape_str)
+    if batch_dim != 1:
+        return "{}_{}_{}".format(canonical, dtype, shape_str)
+    return "{}_{}_batch{}_{}".format(canonical, dtype, str(batch_dim), shape_str)
 
 
 class StaticTensorArrayOps(object):
     """Contains tensor array related ops for fixed rank tensor array"""
 
-    def __init__(self, prelude, dtype, shape):
+    def __init__(self, prelude, dtype, shape, batch_dim=None):
         """Create tensor array ops registry"""
         self.prelude = prelude
         self.dtype = dtype
         self.shape = shape
+        self.batch_dim = batch_dim
         self.list, self.cons, self.nil = self.prelude.mod.get_type("List")
 
     def get_name(self, canonical):
         """Get name corresponding to the canonical name"""
-        return _get_name_static(canonical, self.dtype, self.shape)
+        return _get_name_static(canonical, self.dtype, self.shape, self.batch_dim)
 
     def get_global_var(self, canonical):
         """Get global corresponding to the canonical name"""
-        return self.prelude.get_global_var_static(canonical, self.dtype, self.shape)
+        return self.prelude.get_global_var_static(canonical, self.dtype, self.shape, self.batch_dim)
 
     def get_type(self, canonical):
         """Get type corresponding to the canonical name"""
@@ -262,9 +291,10 @@ def define_tensor_expand_dims(self):
 
         # Note: we set the added axis to be Any() instead of 1 due to
         # in stack op, we need to recursively concatenate.
+        new_axis = Any() if self.batch_dim is None or self.batch_dim != 1 else self.batch_dim
         tensor_type_var, tensor_constructor, _ = self._get_adt_by_shape(
             [
-                Any(),
+                new_axis,
             ]
             + list(self.shape)
         )
@@ -573,20 +603,27 @@ def define_tensor_array_stack(self):
         expand_dims_var = self.get_global_var("tensor_expand_dims")
 
         # Register tensor_concatenate for output_shape
+        new_axis = Any() if not self.batch_dim or self.batch_dim != 1 else self.batch_dim
         output_shape = [
-            Any(),
+            new_axis,
         ] + list(self.shape)
-
         _, _, output_ops = self._get_adt_by_shape(output_shape)
         output_ops.define_tensor_concatenate()
         concat_var = output_ops.get_global_var("tensor_concatenate")
 
         tensor_array_expand_dims = self.prelude.map(expand_dims_var, tensor_array)
-        tensors = self.prelude.foldl(
-            concat_var,
-            self.prelude.hd(tensor_array_expand_dims),
-            self.prelude.tl(tensor_array_expand_dims),
-        )
+        if self.batch_dim is not None and self.batch_dim == 1:
+            # only one element
+            tensors = self.prelude.id(
+                self.prelude.hd(tensor_array_expand_dims),
+            )
+        else:
+            tensors = self.prelude.foldl(
+                concat_var,
+                self.prelude.hd(tensor_array_expand_dims),
+                self.prelude.tl(tensor_array_expand_dims),
+            )
+
         output_tensor_type_var, _, _ = self._get_adt_by_shape(output_shape)
         self.prelude.mod[stack_var] = Function(
             [tensor_array], tensors, output_tensor_type_var(), []
@@ -599,8 +636,9 @@ def define_tensor_array_gather(self):
         helper_name = self.get_name("tensor_array_gather_helper")
         helper_var = self._create_global_var(helper_name)
 
+        new_axis = Any() if self.batch_dim is None or self.batch_dim != 1 else self.batch_dim
         output_shape = [
-            Any(),
+            new_axis,
         ] + list(self.shape)
         output_tensor_type_var, _, _ = self._get_adt_by_shape(output_shape)
         stack_var = self.get_global_var("tensor_array_stack")
@@ -668,7 +706,7 @@ def register(self):
 
     def _get_adt_by_shape(self, shape):
         """Get ADT type and constructor with given shape."""
-        adt_ops = StaticTensorArrayOps(self.prelude, self.dtype, shape)
+        adt_ops = StaticTensorArrayOps(self.prelude, self.dtype, shape, self.batch_dim)
         adt_ops.define_tensor_adt()
         tensor_type_var = adt_ops.get_type("tensor_t")
         tensor_constructor = adt_ops.get_ctor("tensor_constructor")
@@ -1482,13 +1520,13 @@ def get_tensor_ctor(self, canonical, dtype):
         ty = self.get_type("tensor_t", dtype)
         return self.get_ctor(ty.name_hint, canonical, dtype)
 
-    def get_name_static(self, canonical, dtype, shape):
+    def get_name_static(self, canonical, dtype, shape, batch_dim=None):
         """Get name corresponding to the canonical name"""
-        return _get_name_static(canonical, dtype, shape)
+        return _get_name_static(canonical, dtype, shape, batch_dim)
 
-    def get_global_var_static(self, canonical, dtype, shape):
+    def get_global_var_static(self, canonical, dtype, shape, batch_dim=None):
         """Get var corresponding to the canonical name"""
-        name = self.get_name_static(canonical, dtype, shape)
+        name = self.get_name_static(canonical, dtype, shape, batch_dim)
         return self.mod.get_global_var(name)
 
     def get_type_static(self, canonical, dtype, shape):

tests/python/frontend/tensorflow2/test_functional_models.py

@@ -484,8 +484,6 @@ def get_input(self):
                 in_tens[1] = np.zeros((3,), dtype="float32")
                 return in_tens
 
-            """2D array as input"""
-
             @tf.function(input_signature=[tf.TensorSpec(shape=(2, 3), dtype=tf.float32)])
             def func(self, x):
                 dtype = tf.float32
@@ -513,8 +511,6 @@ def get_input(self):
                 in_tens[1, :, :] = np.zeros((3, 4), dtype="float32")
                 return in_tens
 
-            """2D array as input"""
-
             @tf.function(input_signature=[tf.TensorSpec(shape=(2, 3, 4), dtype=tf.float32)])
             def func(self, x):
                 dtype = tf.float32
@@ -531,18 +527,8 @@ def func(self, x):
         run_model_graph(TensorList2D)
         run_func_graph(TensorList2D, runtime="vm")
 
-    run_test(
-        (
-            3,
-            4,
-        )
-    )
-    run_test(
-        (
-            -1,
-            -1,
-        )
-    )
+    run_test((3, 4))
+    run_test((-1, -1))
 
 
 def test_tensorlist_stack_2d():
@@ -553,8 +539,6 @@ def get_input(self):
                 in_tens[1, :, :] = np.zeros((3, 4), dtype="float32")
                 return in_tens
 
-            """2D array as input"""
-
             @tf.function(input_signature=[tf.TensorSpec(shape=(2, 3, 4), dtype=tf.float32)])
             def func(self, x):
                 dtype = tf.float32
@@ -570,18 +554,35 @@ def func(self, x):
         run_model_graph(TensorListStack2D)
         run_func_graph(TensorListStack2D, runtime="vm")
 
-    run_test(
-        (
-            3,
-            4,
-        )
-    )
-    run_test(
-        (
-            -1,
-            -1,
-        )
-    )
+    run_test((3, 4))
+    run_test((-1, -1))
+
+
+def test_tensorlist_stack_unpack():
+    def run_test(elem_shape):
+        class TensorListStack2D(tf.Module):
+            def get_input(self):
+                in_tens = np.ones((1, 3, 4), dtype="float32")
+                return in_tens
+
+            @tf.function(input_signature=[tf.TensorSpec(shape=(1, 3, 4), dtype=tf.float32)])
+            def func(self, x):
+                dtype = tf.float32
+                tl = tf.raw_ops.TensorListReserve(
+                    element_shape=elem_shape, num_elements=1, element_dtype=dtype
+                )
+                tl = tf.raw_ops.TensorListSetItem(input_handle=tl, index=0, item=x[0, :, :])
+                output = tf.raw_ops.TensorListStack(
+                    input_handle=tl, element_shape=elem_shape, element_dtype=dtype, num_elements=1
+                )
+                output = tf.raw_ops.Unpack(value=output, num=1, axis=0)
+                return output
+
+        run_model_graph(TensorListStack2D)
+        run_func_graph(TensorListStack2D, runtime="vm")
+
+    run_test((3, 4))
+    run_test((-1, -1))
 
 
 if __name__ == "__main__":