[TOPI] add reshape, concatenate, split (#481)

* [TOPI]add reshape

* fix problems

* fix lint

* try to add concatenate

* fix lint and error

* fix doc

* fix error

* try to add split

* fix lint

* fix error

* fix lint

topi/python/topi/broadcast.py

@@ -80,7 +80,7 @@ def _get_binary_op_bcast_shape(lhs_shape, rhs_shape):
 
 
 
-@tvm.tag_scope(tag="broadcast_to")
+@tvm.tag_scope(tag=tag.BROADCAST)
 def broadcast_to(data, shape):
     """Broadcast the src to the target shape
 
@@ -97,20 +97,20 @@ def broadcast_to(data, shape):
     -------
     ret : tvm.Tensor
     """
-    def _bcast_to_arg_eval(data, bcast_info, *args):
+    def _bcast_to_arg_eval(data, bcast_info, *indices):
         indices_tuple = []
-        for i in range(len(args)):
+        for i, ind in enumerate(indices):
             if bcast_info[i] == 0:
-                indices_tuple.append(args[i])
+                indices_tuple.append(ind)
             elif bcast_info[i] == 1:
                 indices_tuple.append(0)
         return data[tuple(indices_tuple)]
     original_shape = data.shape
     bcast_info = _get_bcast_info(original_shape=original_shape, target_shape=shape)
-    ret = tvm.compute([tvm.convert(ele) for ele in shape],
-                      lambda *args: _bcast_to_arg_eval(data,
+    ret = tvm.compute(shape,
+                      lambda *indices: _bcast_to_arg_eval(data,
                                                           bcast_info,
-                                                       *args), name=data.name + "_broadcast")
+                                                          *indices), name=data.name + "_broadcast")
     return ret
 
 
@@ -131,25 +131,25 @@ def broadcast_binary_op(lhs, rhs, func, name="bop"):
     -------
     ret : tvm.Tensor
     """
-    def _inner_arg_eval(lhs, rhs, lhs_bcast_info, rhs_bcast_info, func, *args):
+    def _inner_arg_eval(lhs, rhs, lhs_bcast_info, rhs_bcast_info, func, *indices):
         lhs_indices = []
         rhs_indices = []
-        for i in range(len(args)):
+        for i, ind in enumerate(indices):
             if lhs_bcast_info[i] == 0:
-                lhs_indices.append(args[i])
+                lhs_indices.append(ind)
             elif lhs_bcast_info[i] == 1:
                 lhs_indices.append(0)
             if rhs_bcast_info[i] == 0:
-                rhs_indices.append(args[i])
+                rhs_indices.append(ind)
             elif rhs_bcast_info[i] == 1:
                 rhs_indices.append(0)
         return func(lhs[tuple(lhs_indices)], rhs[tuple(rhs_indices)])
     ret_shape = _get_binary_op_bcast_shape(get_const_tuple(lhs.shape), get_const_tuple(rhs.shape))
     lhs_bcast_info = _get_bcast_info(original_shape=lhs.shape, target_shape=ret_shape)
     rhs_bcast_info = _get_bcast_info(original_shape=rhs.shape, target_shape=ret_shape)
-    ret = tvm.compute([tvm.convert(ele) for ele in ret_shape],
-                      lambda *args: _inner_arg_eval(lhs, rhs, lhs_bcast_info, rhs_bcast_info,
-                                                    func, *args),
+    ret = tvm.compute(ret_shape,
+                      lambda *indices: _inner_arg_eval(lhs, rhs, lhs_bcast_info, rhs_bcast_info,
+                                                       func, *indices),
                       name=lhs.name + "_" + rhs.name + "_" + name)
     return ret
 

topi/python/topi/cuda/injective.py

@@ -30,7 +30,9 @@ def schedule_injective(outs):
     s = tvm.create_schedule([x.op for x in outs])
 
     tvm.schedule.AutoInlineInjective(s)
-    return _schedule_injective(outs[0].op, s)
+    for out in outs:
+        _schedule_injective(out.op, s)
+    return s
 
 schedule_elemwise = schedule_injective
 schedule_broadcast = schedule_injective

topi/python/topi/transform.py

+# pylint: disable=invalid-name,consider-using-enumerate
 """Injective transformation operators"""
 from __future__ import absolute_import as _abs
 import tvm
 from . import tag
+from .util import ravel_index, unravel_index, get_const_int
 
 @tvm.tag_scope(tag=tag.BROADCAST)
 def expand_dims(a, axis, num_newaxis=1):
@@ -52,3 +54,109 @@ def transpose(a, axes=None):
             idx[k] = indices[i]
         return a(*idx)
     return tvm.compute(new_shape, _compute)
+
+
+@tvm.tag_scope(tag=tag.INJECTIVE)
+def reshape(a, newshape):
+    """Reshape the array
+
+    Parameters
+    ----------
+    a : tvm.Tensor
+        The tensor to be reshaped
+    newshape : tuple of ints
+        The new shape
+
+    Returns
+    -------
+    ret : tvm.Tensor
+    """
+    ndim = len(a.shape)
+    a_shape = [a.shape[i] for i in range(ndim)]
+    return tvm.compute(newshape,
+                       lambda *indices: a(*unravel_index(ravel_index(indices, newshape), a_shape)))
+
+
+@tvm.tag_scope(tag=tag.INJECTIVE)
+def concatenate(a_tuple, axis=0):
+    """Join a sequence of arrays along an existing axis.
+
+    Parameters
+    ----------
+    a_tuple : tuple of tvm.Tensor
+        The arrays to concatenate
+
+    axis : int, optional
+        The axis along which the arrays will be joined. Default is 0.
+
+    Returns
+    -------
+    ret : tvm.Tensor
+    """
+    assert isinstance(a_tuple, (list, tuple))
+    if axis < 0:
+        axis += len(a_tuple[0].shape)
+    assert axis < len(a_tuple[0].shape)
+    axis_sizes = [a_tuple[i].shape[axis] for i in range(len(a_tuple))]
+    out_shape = [a_tuple[0].shape[i] for i in range(0, axis)] + [sum(axis_sizes)]\
+                + [a_tuple[0].shape[i] for i in range(axis + 1, len(a_tuple[0].shape))]
+
+    def _compute(*indices):
+        ret = a_tuple[0](*indices)
+        ind = indices[axis]
+        for i in range(len(a_tuple) - 1):
+            ind -= axis_sizes[i]
+            ret = tvm.select(ind >= 0,
+                             a_tuple[i + 1](*(indices[0:axis] + (ind,) + indices[axis + 1:])),
+                             ret)
+        return ret
+    return tvm.compute(out_shape, _compute)
+
+
+@tvm.tag_scope(tag=tag.INJECTIVE)
+def split(ary, indices_or_sections, axis=0):
+    """Split an array into multiple sub-arrays.
+
+    Parameters
+    ----------
+    ary : tvm.Tensor
+
+    indices_or_sections : int or 1-D array
+
+    axis : int
+
+    Returns
+    -------
+    ret : tuple of tvm.Tensor
+    """
+    def _compute(begin, *indices):
+        real_indices = indices[:axis] + (indices[axis] + begin, ) + indices[axis + 1:]
+        return ary(*real_indices)
+
+    if axis < 0:
+        axis += len(ary.shape)
+    src_axis_size = get_const_int(ary.shape[axis])
+    if isinstance(indices_or_sections, int):
+        assert indices_or_sections > 0
+        assert src_axis_size % indices_or_sections == 0
+        seg_size = src_axis_size // indices_or_sections
+        begin_ids = [seg_size * i for i in range(indices_or_sections)]
+    elif isinstance(indices_or_sections, (tuple, list)):
+        assert tuple(indices_or_sections) == tuple(sorted(indices_or_sections)),\
+            "Should be sorted, recieved %s" %str(indices_or_sections)
+        begin_ids = [0] + list(indices_or_sections)
+    else:
+        raise NotImplementedError
+    out_shapes = []
+    for i in range(len(begin_ids)):
+        if i == len(begin_ids) - 1:
+            out_axis_size = src_axis_size - begin_ids[i]
+        else:
+            out_axis_size = begin_ids[i + 1] - begin_ids[i]
+        out_shapes.append([ary.shape[i] for i in range(axis)] + [out_axis_size] +\
+                          [ary.shape[i] for i in range(axis + 1, len(ary.shape))])
+    # pylint: disable=cell-var-from-loop
+    return [tvm.compute(out_shape,
+                        lambda *indices: _compute(begin_id, *indices), name="s%d" %i)
+            for i, (out_shape, begin_id) in enumerate(zip(out_shapes, begin_ids))]
+    # pylint: enable=cell-var-from-loop

topi/python/topi/util.py

@@ -79,3 +79,52 @@ def simplify(expr):
         The simplified output
     """
     return tvm.ir_pass.Simplify(expr) if isinstance(expr, tvm.expr.Expr) else expr
+
+
+def ravel_index(indices, shape):
+    """Flatten the index tuple to 1D
+
+    Parameters
+    ----------
+    indices : tuple of int or tvm.expr.IntImm
+        The input coordinates
+
+    shape : tuple of int
+        Shape of the tensor.
+
+    Returns
+    -------
+    idx : int or Expr
+        The index after flattening
+    """
+    idx = None
+    for i, (shape_val, ind) in enumerate(zip(shape, indices)):
+        if i != 0:
+            idx = idx * shape_val + ind
+        else:
+            idx = ind
+    return idx
+
+
+def unravel_index(idx, shape):
+    """Convert the flattened ind to the coordinate array
+
+    Parameters
+    ----------
+    idx : int or tvm.expr.IntImm
+        The 1D index
+
+    shape : tuple of int
+        Shape of the tensor
+
+    Returns
+    -------
+    indices : tuple of int or tvm.expr.IntImm
+        Corresponding coordinate of the 1D index
+    """
+    indices = []
+    for i in range(len(shape) - 1, -1, -1):
+        indices.append(idx % shape[i])
+        idx = idx // shape[i]
+    indices = indices[::-1]
+    return indices

topi/tests/python/test_topi_transform.py

@@ -47,6 +47,74 @@ def verify_tranpose(in_shape, axes):
     check_device("metal")
 
 
+def verify_reshape(src_shape, dst_shape):
+    A = tvm.placeholder(shape=src_shape, name="A")
+    B = topi.reshape(A, dst_shape)
+    s = topi.cuda.schedule_injective(B)
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
+        foo = tvm.build(s, [A, B], device, name="reshape")
+        data_npy = np.random.normal(size=src_shape).astype(A.dtype)
+        out_npy = np.reshape(data_npy, newshape=dst_shape)
+        data_nd = tvm.nd.array(data_npy, ctx)
+        out_nd = tvm.nd.empty(dst_shape, ctx=ctx, dtype=B.dtype)
+        foo(data_nd, out_nd)
+        np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
+
+    check_device("cuda")
+    check_device("opencl")
+    check_device("metal")
+
+
+def verify_concatenate(shapes, axis):
+    tensor_l = []
+    for i, shape in enumerate(shapes):
+        tensor_l.append(tvm.placeholder(shape, name="A" + str(i)))
+    out_tensor = topi.concatenate(a_tuple=tensor_l, axis=axis)
+    s = topi.cuda.schedule_injective(out_tensor)
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
+        foo = tvm.build(s, tensor_l + [out_tensor], device, name="concatenate")
+        data_npys = [np.random.normal(size=shape).astype(tensor_l[0].dtype) for shape in shapes]
+        out_npy = np.concatenate(data_npys, axis=axis)
+        data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys]
+        out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=out_tensor.dtype)
+        foo(*(data_nds + [out_nd]))
+        np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
+
+    check_device("cuda")
+    check_device("opencl")
+    check_device("metal")
+
+
+def verify_split(src_shape, indices_or_sections, axis):
+    A = tvm.placeholder(shape=src_shape, name="A")
+    tensor_l = topi.split(A, indices_or_sections, axis=axis)
+    s = topi.cuda.schedule_injective(tensor_l)
+    def check_device(device):
+        if not tvm.module.enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
+        foo = tvm.build(s, [A] + tensor_l, device, name="split")
+        data_npy = np.random.normal(size=src_shape).astype(A.dtype)
+        out_npys = np.split(data_npy, indices_or_sections, axis=axis)
+        data_nd = tvm.nd.array(data_npy, ctx)
+        out_nds = [tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=tensor_l[0].dtype) for out_npy in out_npys]
+        foo(*([data_nd] + out_nds))
+        for out_nd, out_npy in zip(out_nds, out_npys):
+            np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
+
+    check_device("cuda")
+    check_device("opencl")
+    check_device("metal")
+
 def test_expand_dims():
     verify_expand_dims((3, 10), (3, 10, 1, 1), 2, 2)
     verify_expand_dims((3, 10), (1, 3, 10), -3, 1)
@@ -58,6 +126,32 @@ def test_tranpose():
     verify_tranpose((3, 10), None)
 
 
+def test_reshape():
+    verify_reshape((1, 2, 3, 4), (2, 3, 4))
+    verify_reshape((4, 2, 3, 4), (2, 4, 12))
+    verify_reshape((4, 2, 3, 4), (2, 48))
+    verify_reshape((16, ), (2, 2, 2, 2))
+
+
+def test_concatenate():
+    verify_concatenate([(2, 3, 4), (2, 2, 4), (2, 5, 4)], 1)
+    verify_concatenate([(1, 2, 4), (1, 2, 3), (1, 2, 7), (1, 2, 8), (1, 2, 1)], -1)
+    verify_concatenate([(5, 6, 7, 3),
+                        (16, 6, 7, 3),
+                        (12, 6, 7, 3),
+                        (8, 6, 7, 3),
+                        (2, 6, 7, 3)], 0)
+
+
+def test_split():
+    verify_split((2, 12, 3), 3, 1)
+    verify_split((2, 12, 3), [2, 4], 1)
+    verify_split((10, 12, 24), [5, 7, 9], -1)
+
 if __name__ == "__main__":
     test_tranpose()
     test_expand_dims()
+    test_reshape()
+    test_concatenate()
+    test_split()
+