[TOPI] Add topi.target; Schedule for raspberry pi (#406)

* CPU Schedule for raspberry pi

* Update

* Update

* Add topi.target

* Refactor

* Update

* Make python3 happy

* Improve

* Improve

* Improve

* Use get_const_int

topi/python/topi/__init__.py

@@ -14,5 +14,7 @@ from .reduction import *
 from .broadcast import *
 from . import nn
 from . import cuda
+from . import rasp
+from . import target
 from . import testing
 from . import util

topi/python/topi/nn/__init__.py

@@ -4,6 +4,7 @@ from __future__ import absolute_import as _abs
 
 from .batch_norm import *
 from .convolution import *
+from .depthwise_convolution import *
 from .elemwise import *
 from .dilate import *
 from .flatten import *

topi/python/topi/nn/convolution.py

 # pylint: disable=invalid-name, unused-variable, too-many-locals
 """Convolution operators"""
 from __future__ import absolute_import as _abs
+from collections import namedtuple
 import tvm
+from .pad import pad
+from .util import get_pad_tuple
 from ..util import simplify
-from .pad import pad, _spatial2d_pad_option
+from .. import target as _target
+
+# workload description of convolution
+Workload = namedtuple('Workload',
+                      ['height', 'width', 'in_filter', 'out_filter',
+                       'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
+
+# schedule description of spatial
+SpatialPack = namedtuple('SpatialPack',
+                         ['vh', 'vw', 'vc', 'ba', 'bc', 'unroll'])
+
+# schedule description of im2col
+Im2ColPack = namedtuple('Im2ColPack',
+                        ['vp', 'vq', 'ba', 'bc', 'unroll'])
+
+# workloads of resnet18 on imagenet
+_WORKLOADS = [
+    Workload(224, 224, 3, 64, 7, 7, 3, 3, 2, 2),
+    Workload(56, 56, 64, 64, 3, 3, 1, 1, 1, 1),
+    Workload(56, 56, 64, 64, 1, 1, 0, 0, 1, 1),
+    Workload(56, 56, 64, 128, 3, 3, 1, 1, 2, 2),
+    Workload(56, 56, 64, 128, 1, 1, 0, 0, 2, 2),
+    Workload(28, 28, 128, 128, 3, 3, 1, 1, 1, 1),
+    Workload(28, 28, 128, 256, 3, 3, 1, 1, 2, 2),
+    Workload(28, 28, 128, 256, 1, 1, 0, 0, 2, 2),
+    Workload(14, 14, 256, 256, 3, 3, 1, 1, 1, 1),
+    Workload(14, 14, 256, 512, 3, 3, 1, 1, 2, 2),
+    Workload(14, 14, 256, 512, 1, 1, 0, 0, 2, 2),
+    Workload(7, 7, 512, 512, 3, 3, 1, 1, 1, 1),
+]
+
+# platform specific schedule
+_CONV_SCHEDULE = {}
+
+# platform specific declaration
+_CONV_DECLARATION = {}
+
+def convolution(data, kernel, stride, padding, layout='NCHW'):
+    """Convolution operator.
+
+    Parameters
+    ----------
+    input : tvm.Tensor
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    filter : tvm.Tensor
+        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+
+    stride : int or a list/tuple of two ints
+        stride size, or [stride_height, stride_width]
+
+    padding : int or a list/tuple of two ints
+        padding size, or [pad_height, pad_width]
+
+    layout : str
+        layout of data
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    # search platform specific declaration first
+    target = _target.current_target()
+    if target in _CONV_DECLARATION:
+        return _CONV_DECLARATION[target](data, kernel, stride, padding, layout)
+
+    # default declaration
+    if layout == 'NCHW':
+        conv2d_nchw(data, kernel, stride, padding)
+    elif layout == 'HWCN':
+        conv2d_hwcn(data, kernel, stride, padding)
+    else:
+        raise ValueError("not support this layout {} yet".format(layout))
+
+
+def _get_workload(data, kernel, stride, padding):
+    """ Get the workload structure. """
+    _, CI, IH, IW = [x.value for x in data.shape]
+    CO, _, KH, KW = [x.value for x in kernel.shape]
+    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
+    if isinstance(stride, (tuple, list)):
+        HSTR, WSTR = stride
+    else:
+        HSTR, WSTR = stride, stride
+    return Workload(IH, IW, CI, CO, KH, KW, HPAD, WPAD, HSTR, WSTR)
+
+def _get_schedule(wkl, target=None):
+    """ Get the platform specific schedule. """
+    if target is None:
+        target = _target.current_target()
+    else:
+        target = _target.Target(target)
+    assert target in _CONV_SCHEDULE, "no schedule for such target: {}".format(target)
+    return _CONV_SCHEDULE[target](wkl)
+
+
+def _spatial_pack(data, kernel, stride, padding):
+    """ Compute convolution with pack on spatial axes. """
+    assert data.shape[0].value == 1, "spatial pack convolution only support batch size=1"
+    wkl = _get_workload(data, kernel, stride, padding)
+    sch = _get_schedule(wkl)
+
+    H, W = wkl.height, wkl.width
+    CI, CO = wkl.in_filter, wkl.out_filter
+    KH, KW = wkl.hkernel, wkl.wkernel
+    HPAD, WPAD = wkl.hpad, wkl.wpad
+    HSTR, WSTR = wkl.hstride, wkl.wstride
+    HCAT, WCAT = KH-1, KW-1
+
+    VH = sch.vh
+    VW = sch.vw
+    VC = sch.vc
+    UNROLL = sch.unroll
+
+    TH = H + 2*HPAD
+    TW = W + 2*WPAD
+    OH = (H + 2*HPAD - KH) // HSTR + 1
+    OW = (W + 2*WPAD - KW) // WSTR + 1
+
+    dshape = (1, CI, H, W)
+    dpshape = (1, CI, TH, TW)
+    dvshape = (1, TH//(VH*HSTR), TW//(VW*WSTR), CI, VH*HSTR+HCAT, VW*WSTR+WCAT)
+
+    kshape = (CO, CI, KH, KW)
+    kvshape = (CO/VC, CI, KH, KW, VC)
+
+    ovshape = (1, CO // VC, OH // VH, OW // VW, VH, VW, VC)
+    oshape = (1, CO, OH, OW)
+
+    DOPAD = (HPAD != 0 and WPAD != 0)
+    if DOPAD:
+        data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
+    else:
+        data_pad = data
+
+    data_vec = tvm.compute(dvshape, lambda n, h, w, ci, vh, vw: \
+        data_pad[n][ci][h*VH*HSTR+vh][w*VW*WSTR+vw], name='data_vec')
+
+    kernel_vec = tvm.compute(kvshape, lambda co, ci, dh, dw, vc: \
+        kernel[co*VC+vc][ci][dh][dw], name='kernel_vec')
+
+    ci = tvm.reduce_axis((0, CI), name='ci')
+    dh = tvm.reduce_axis((0, KH), name='dh')
+    dw = tvm.reduce_axis((0, KW), name='dw')
+
+    conv = tvm.compute(ovshape, lambda n, co, h, w, vh, vw, vc: \
+        tvm.sum(data_vec[n, h, w, ci, vh*HSTR+dh, vw*WSTR+dw] *
+                kernel_vec[co, ci, dh, dw, vc],
+                axis=[ci, dh, dw]), name='conv')
+
+    output = tvm.compute(oshape, lambda n, co, h, w:
+                         conv[n][co//VC][h/VH][w//VW][h%VH][w%VW][co%VC],
+                         name='output_unpack', tag='spatial_conv_output')
+
+    return output
+
+
+def _im2col_pack(data, kernel, stride, padding):
+    """ Compute convolution with im2col pack layout. """
+    assert data.shape[0].value == 1, "im2col pack convolution only support batch size=1"
+    wkl = _get_workload(data, kernel, stride, padding)
+    sch = _get_schedule(wkl)
+
+    N = 1
+    H, W = wkl.height, wkl.width
+    CI = wkl.in_filter
+    CO = wkl.out_filter
+    KH, KW = wkl.hkernel, wkl.wkernel
+    HPAD, WPAD = wkl.hpad, wkl.hpad
+    HSTR, WSTR = wkl.hstride, wkl.wstride
+
+    OH = (H + 2*HPAD - KH) // HSTR + 1
+    OW = (W + 2*WPAD - KW) // WSTR + 1
+
+    P = sch.vp
+    Q = sch.vq
+    UNROLL = sch.unroll
+
+    dshape = (N, CI, H, W)
+    dpshape = (N, CI, H+2*HPAD, W+2*WPAD)
+    dcshape = (N, OH, OW, CI, KH, KW)
+    dvshape = (N, OH * OW // P, CI, KH, KW, P)
+
+    kshape = (CO, CI, KH, KW)
+    kvshape = (CO // Q, CI, KH, KW, Q)
+
+    ovshape = (N, CO // Q, OH * OW // P, P, Q)
+    oshape = (N, CO, OH, OW)
+
+    ############### declaration
+
+    DO_PAD = (wkl.hpad != 0 and wkl.wpad != 0)
+    if DO_PAD:
+        data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
+    else:
+        data_pad = data
+
+    data_col = tvm.compute(dcshape, lambda n, oh, ow, ci, hk, wk: \
+        data_pad[n][ci][oh*HSTR+hk][ow*WSTR+wk], name='data_col')
+
+    data_vec = tvm.compute(dvshape, lambda n, im, ci, hk, wk, vim: \
+        data_col[n][(im*P+vim)//OW][(im*P+vim)%OW][ci][hk][wk], name='data_vec')
+
+
+    kernel_vec = tvm.compute(kvshape, lambda co, ci, dh, dw, vc: \
+        kernel[co*Q+vc][ci][dh][dw], name='kernel_vec')
+
+    ci = tvm.reduce_axis((0, CI), name='ci')
+    hk = tvm.reduce_axis((0, KH), name='hk')
+    wk = tvm.reduce_axis((0, KW), name='wk')
+
+    conv = tvm.compute(ovshape, lambda n, co, im, vim, vco: \
+        tvm.sum(data_vec[n][im][ci][hk][wk][vim] * kernel_vec[co][ci][hk][wk][vco],
+                axis=[ci, hk, wk]), name='conv')
+
+    output = tvm.compute(oshape, lambda n, co, h, w: \
+                         conv[n][co//Q][(h*OW+w)//P][(h*OW+w)%P][co%Q],
+                         name='output_vec', tag='im2col_conv_output')
+
+    return output
 
 
 def conv2d_nchw(Input, Filter, stride, padding):
@@ -35,7 +258,7 @@ def conv2d_nchw(Input, Filter, stride, padding):
         stride_h = stride_w = stride
     else:
         stride_h, stride_w = stride
-    pad_top, pad_left, pad_down, pad_right = _spatial2d_pad_option(
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
         padding, (kernel_h, kernel_w))
     # compute the output shape
     out_channel = num_filter
@@ -86,7 +309,7 @@ def conv2d_hwcn(Input, Filter, stride, padding):
     else:
         stride_h, stride_w = stride
 
-    pad_top, pad_left, pad_down, pad_right = _spatial2d_pad_option(
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
         padding, (kernel_h, kernel_w))
     # compute the output shape
     out_channel = num_filter
@@ -106,98 +329,8 @@ def conv2d_hwcn(Input, Filter, stride, padding):
         name="Conv2dOutput", tag="conv2d_hwcn")
     return Output
 
-def depthwise_conv2d_nchw(Input, Filter, stride, padding):
-    """Depthwise convolution nchw forward operator.
-
-    Parameters
-    ----------
-    Input : tvm.Tensor
-        4-D with shape [batch, in_channel, in_height, in_width]
-
-    Filter : tvm.Tensor
-        4-D with shape [in_channel, channel_multiplier, filter_height, filter_width]
-
-    stride : tuple of two ints
-        The spatial stride along height and width
-
-    padding : int or str
-        Padding size, or ['VALID', 'SAME']
-
-    Returns
-    -------
-    Output : tvm.Tensor
-        4-D with shape [batch, out_channel, out_height, out_width]
-    """
-    batch, in_channel, in_height, in_width = Input.shape
-    filter_channel, channel_multiplier, filter_height, filter_width = Filter.shape
-    stride_h, stride_w = stride
-
-    pad_top, pad_left, pad_down, pad_right = _spatial2d_pad_option(
-        padding, (filter_height, filter_width))
-    out_channel = simplify(in_channel * channel_multiplier)
-    out_height = simplify((in_height - filter_height + pad_top + pad_down) // stride_h + 1)
-    out_width = simplify((in_width - filter_width + pad_left + pad_right) // stride_w + 1)
-
-    # padding stage
-    pad_before = [0, 0, pad_top, pad_left]
-    pad_after = [0, 0, pad_down, pad_right]
-    PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
-    # depthconv stage
-    di = tvm.reduce_axis((0, filter_height), name='di')
-    dj = tvm.reduce_axis((0, filter_width), name='dj')
-    Output = tvm.compute(
-        (batch, out_channel, out_height, out_width),
-        lambda b, c, i, j: tvm.sum(
-            (PaddedInput[b, c/channel_multiplier, i*stride_h + di, j*stride_w + dj] *
-             Filter[c/channel_multiplier, c%channel_multiplier, di, dj]),
-            axis=[di, dj]),
-        name='DepthwiseConv2d', tag="depthwise_conv2d_nchw")
-    return Output
-
-def depthwise_conv2d_nhwc(Input, Filter, stride, padding):
-    """Depthwise convolution nhwc forward operator.
-
-    Parameters
-    ----------
-    Input : tvm.Tensor
-        4-D with shape [batch, in_height, in_width, in_channel]
-
-    Filter : tvm.Tensor
-        4-D with shape [filter_height, filter_width, in_channel, channel_multiplier]
-
-    Stride : tvm.Tensor
-        1-D of size 2
-
-    padding : int or str
-        Padding size, or ['VALID', 'SAME']
-
-    Returns
-    -------
-    Output : tvm.Tensor
-        4-D with shape [batch, out_height, out_width, out_channel]
-    """
-    batch, in_height, in_width, in_channel = Input.shape
-    filter_height, filter_width, filter_channel, channel_multiplier = Filter.shape
-    stride_h, stride_w = stride
-
-    pad_top, pad_left, pad_down, pad_right = _spatial2d_pad_option(
-        padding, (filter_height, filter_width))
-    out_channel = simplify(in_channel * channel_multiplier)
-    out_height = simplify((in_height - filter_height + pad_top + pad_down) // stride_h + 1)
-    out_width = simplify((in_width - filter_width + pad_left + pad_right) // stride_w + 1)
-
-    # padding stage
-    pad_before = [0, pad_top, pad_left, 0]
-    pad_after = [0, pad_down, pad_right, 0]
-    PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
-    # depthconv stage
-    di = tvm.reduce_axis((0, filter_height), name='di')
-    dj = tvm.reduce_axis((0, filter_width), name='dj')
-    Output = tvm.compute(
-        (batch, out_height, out_width, out_channel),
-        lambda b, i, j, c: tvm.sum(
-            (PaddedInput[b, i*stride_h + di, j*stride_w + dj, c/channel_multiplier] *
-             Filter[di, dj, c/channel_multiplier, c%channel_multiplier]),
-            axis=[di, dj]),
-        name='DepthwiseConv2d', tag="depthwise_conv2d_nhwc")
-    return Output
+# map from schedule type to declaration function
+_SCH_TO_DECL_FUNC = {
+    SpatialPack: _spatial_pack,
+    Im2ColPack: _im2col_pack,
+}

topi/python/topi/nn/depthwise_convolution.py

+# pylint: disable=invalid-name, unused-variable, too-many-locals
+"""Depthwise Convolution operators"""
+from __future__ import absolute_import as _abs
+import tvm
+from .pad import pad
+from .util import get_pad_tuple
+from ..util import simplify
+
+
+def depthwise_conv2d_nchw(Input, Filter, stride, padding):
+    """Depthwise convolution nchw forward operator.
+
+    Parameters
+    ----------
+    Input : tvm.Tensor
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    Filter : tvm.Tensor
+        4-D with shape [in_channel, channel_multiplier, filter_height, filter_width]
+
+    stride : tuple of two ints
+        The spatial stride along height and width
+
+    padding : int or str
+        Padding size, or ['VALID', 'SAME']
+
+    Returns
+    -------
+    Output : tvm.Tensor
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    batch, in_channel, in_height, in_width = Input.shape
+    filter_channel, channel_multiplier, filter_height, filter_width = Filter.shape
+    stride_h, stride_w = stride
+
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
+        padding, (filter_height, filter_width))
+    out_channel = simplify(in_channel * channel_multiplier)
+    out_height = simplify((in_height - filter_height + pad_top + pad_down) // stride_h + 1)
+    out_width = simplify((in_width - filter_width + pad_left + pad_right) // stride_w + 1)
+
+    # padding stage
+    pad_before = [0, 0, pad_top, pad_left]
+    pad_after = [0, 0, pad_down, pad_right]
+    PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
+    # depthconv stage
+    di = tvm.reduce_axis((0, filter_height), name='di')
+    dj = tvm.reduce_axis((0, filter_width), name='dj')
+    Output = tvm.compute(
+        (batch, out_channel, out_height, out_width),
+        lambda b, c, i, j: tvm.sum(
+            (PaddedInput[b, c/channel_multiplier, i*stride_h + di, j*stride_w + dj] *
+             Filter[c/channel_multiplier, c%channel_multiplier, di, dj]),
+            axis=[di, dj]),
+        name='DepthwiseConv2d', tag="depthwise_conv2d_nchw")
+    return Output
+
+def depthwise_conv2d_nhwc(Input, Filter, stride, padding):
+    """Depthwise convolution nhwc forward operator.
+
+    Parameters
+    ----------
+    Input : tvm.Tensor
+        4-D with shape [batch, in_height, in_width, in_channel]
+
+    Filter : tvm.Tensor
+        4-D with shape [filter_height, filter_width, in_channel, channel_multiplier]
+
+    Stride : tvm.Tensor
+        1-D of size 2
+
+    padding : int or str
+        Padding size, or ['VALID', 'SAME']
+
+    Returns
+    -------
+    Output : tvm.Tensor
+        4-D with shape [batch, out_height, out_width, out_channel]
+    """
+    batch, in_height, in_width, in_channel = Input.shape
+    filter_height, filter_width, filter_channel, channel_multiplier = Filter.shape
+    stride_h, stride_w = stride
+
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
+        padding, (filter_height, filter_width))
+    out_channel = simplify(in_channel * channel_multiplier)
+    out_height = simplify((in_height - filter_height + pad_top + pad_down) // stride_h + 1)
+    out_width = simplify((in_width - filter_width + pad_left + pad_right) // stride_w + 1)
+
+    # padding stage
+    pad_before = [0, pad_top, pad_left, 0]
+    pad_after = [0, pad_down, pad_right, 0]
+    PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
+    # depthconv stage
+    di = tvm.reduce_axis((0, filter_height), name='di')
+    dj = tvm.reduce_axis((0, filter_width), name='dj')
+    Output = tvm.compute(
+        (batch, out_height, out_width, out_channel),
+        lambda b, i, j, c: tvm.sum(
+            (PaddedInput[b, i*stride_h + di, j*stride_w + dj, c/channel_multiplier] *
+             Filter[di, dj, c/channel_multiplier, c%channel_multiplier]),
+            axis=[di, dj]),
+        name='DepthwiseConv2d', tag="depthwise_conv2d_nhwc")
+    return Output

topi/python/topi/nn/pad.py

@@ -3,51 +3,6 @@ from __future__ import absolute_import as _abs
 import tvm
 from ..util import equal_const_int
 
-
-def _spatial2d_pad_option(padding, kernel):
-    """Common code to get the pad option
-
-    Parameters
-    ----------
-    padding : int or str
-        Padding size, or ['VALID', 'SAME']
-
-    kernel : tuple of int
-        Conv kernel size
-
-    Returns
-    -------
-    pad_top : int
-        Padding size on top
-
-    pad_left : int
-        Padding size on left
-
-    pad_down : int
-        Padding size on down.
-
-    pad_right : int
-        Padding size on right.
-    """
-    # compute the padding size
-    if isinstance(padding, (tuple, list)):
-        pad_h = padding[0] * 2
-        pad_w = padding[1] * 2
-    elif isinstance(padding, int):
-        pad_h = pad_w = padding * 2
-    elif padding == "VALID":
-        pad_h = 0
-        pad_w = 0
-    elif padding == "SAME":
-        pad_h = kernel[0] - 1
-        pad_w = kernel[1] - 1
-    else:
-        raise ValueError("Unknown padding option %s" % padding)
-    pad_top = (pad_h + 1) // 2
-    pad_left = (pad_w + 1) // 2
-    return pad_top, pad_left, pad_h - pad_top, pad_w - pad_left
-
-
 @tvm.tag_scope(tag="pad")
 def pad(data, pad_before, pad_after=None, pad_value=0.0, name="PadInput"):
     """Dilate Input with zeros.

topi/python/topi/nn/pooling.py

 """TVM operator pooling compute."""
 from __future__ import absolute_import
 import tvm
+from .pad import pad
+from .util import get_pad_tuple
 from .. import util
-from .pad import pad, _spatial2d_pad_option
 
 def max_pool(data, kernel, stride, padding):
     """Perform max pooling on the data
@@ -32,7 +33,7 @@ def max_pool(data, kernel, stride, padding):
     stride_height, stride_width = stride
     batch, channel, height, width = data.shape
 
-    pad_top, pad_left, pad_down, pad_right = _spatial2d_pad_option(
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
         padding, (kernel_height, kernel_width))
     pad_before = [0, 0, pad_top, pad_left]
     pad_after = [0, 0, pad_down, pad_right]

topi/python/topi/nn/util.py

+# pylint: disable=invalid-name, unused-variable
+"""NN operator common utilities"""
+from __future__ import absolute_import
+from ..util import get_const_int
+
+def infer_pad(data, data_pad):
+    """Infer the padding from stages in reverse.
+
+    Parameters
+    ----------
+    data : Tensor
+        data stage.
+
+    data_pad : Tensor
+        pad stage.
+
+    Returns
+    -------
+    hpad : int
+        padding size on height
+    wpad : int
+        padding size on width
+    """
+    if data_pad is None:
+        return 0, 0
+    _, _, IH, IW = data.shape
+    _, _, TH, TW = data_pad.shape
+    hpad = (TH - IH) // 2
+    wpad = (TW - IW) // 2
+    return get_const_int(hpad), get_const_int(wpad)
+
+def infer_stride(data, kernel, out):
+    """Infer the stride from stages in reverse.
+
+    Parameters
+    ----------
+    data : Tensor
+        data stage.
+
+    kernel : Tensor
+        kernel stage.
+
+    out : Tensor
+        output stage.
+
+    Returns
+    -------
+    hstride : int
+        stride size on height
+    wstride : int
+        stride size on width
+    """
+    _, _, IH, IW = data.shape
+    _, _, KH, KW = kernel.shape
+    _, _, OH, OW = out.shape
+    hstride = (IH - KH) // (OH - 1)
+    wstride = (IW - KW) // (OW - 1)
+    return get_const_int(hstride), get_const_int(wstride)
+
+
+def get_pad_tuple(padding, kernel):
+    """Common code to get the pad option
+
+    Parameters
+    ----------
+    padding : int or str
+        Padding size, or ['VALID', 'SAME']
+
+    kernel : tuple of int
+        Conv kernel size
+
+    Returns
+    -------
+    pad_top : int
+        Padding size on top
+
+    pad_left : int
+        Padding size on left
+
+    pad_down : int
+        Padding size on down.
+
+    pad_right : int
+        Padding size on right.
+    """
+    # compute the padding size
+    if isinstance(padding, (tuple, list)):
+        pad_h = padding[0] * 2
+        pad_w = padding[1] * 2
+    elif isinstance(padding, int):
+        pad_h = pad_w = padding * 2
+    elif padding == "VALID":
+        pad_h = 0
+        pad_w = 0
+    elif padding == "SAME":
+        pad_h = kernel[0] - 1
+        pad_w = kernel[1] - 1
+    else:
+        raise ValueError("Unknown padding option %s" % padding)
+    pad_top = (pad_h + 1) // 2
+    pad_left = (pad_w + 1) // 2
+    return pad_top, pad_left, pad_h - pad_top, pad_w - pad_left

topi/python/topi/rasp/__init__.py

+# pylint: disable=redefined-builtin, wildcard-import
+"""Raspberry pi specific declaration and schedules."""
+from __future__ import absolute_import as _abs
+
+from .convolution import *

topi/python/topi/rasp/convolution.py

+# pylint: disable=invalid-name,unused-variable,invalid-name
+"""Convolution schedule on raspberry pi"""
+from __future__ import absolute_import as _abs
+import tvm
+from .. import target as _target
+from ..nn.convolution import SpatialPack, Im2ColPack
+from ..nn.convolution import _CONV_DECLARATION, _CONV_SCHEDULE
+from ..nn.convolution import _WORKLOADS, _SCH_TO_DECL_FUNC
+from ..nn.convolution import _get_workload, _get_schedule
+from ..nn.util import infer_pad, infer_stride
+
+_SCHEDULES = [
+    SpatialPack(1, 8, 4, 1, 4, True),
+    SpatialPack(1, 7, 4, 2, 4, True),
+    SpatialPack(1, 4, 8, 4, 1, True),
+    SpatialPack(1, 4, 4, 1, 16, False),
+    SpatialPack(1, 4, 8, 4, 8, False),
+    SpatialPack(1, 7, 4, 3, 8, True),
+    SpatialPack(1, 2, 8, 1, 8, True),
+    SpatialPack(2, 1, 16, 1, 4, True),
+    SpatialPack(1, 7, 4, 1, 1, True),
+    Im2ColPack(7, 4, 1, 16, True),
+    Im2ColPack(7, 4, 1, 8, False),
+    Im2ColPack(7, 4, 1, 16, False),
+]
+
+def _schedule_conv2d(wkl):
+    if wkl not in _WORKLOADS:
+        raise ValueError("no schedule for such workload: {}".format(wkl))
+    idx = _WORKLOADS.index(wkl)
+    sch = _SCHEDULES[idx]
+    return sch
+
+_CONV_SCHEDULE[_target.rasp()] = _schedule_conv2d
+
+def _declaration_conv2d(data, kernel, stride, padding, layout):
+    assert layout == 'NCHW', "only support NCHW convolution on rasp"
+    assert data.shape[0].value == 1, "only support batch size=1 convolution on rasp"
+    wkl = _get_workload(data, kernel, stride, padding)
+    sch = _get_schedule(wkl)
+    return _SCH_TO_DECL_FUNC[type(sch)](data, kernel, stride, padding)
+
+_CONV_DECLARATION[_target.rasp()] = _declaration_conv2d
+
+def _schedule_spatial_conv2d(s, data, data_pad, data_vec,
+                             kernel, kernel_vec,
+                             conv_out, output, last):
+    # no stride and padding info here
+    padding = infer_pad(data, data_pad)
+    if data_pad is None:
+        stride = infer_stride(data, kernel, output)
+    else:
+        stride = infer_stride(data_pad, kernel, output)
+    wkl = _get_workload(data, kernel, stride, padding)
+    sch = _get_schedule(wkl, 'rasp')
+
+    H, W = wkl.height, wkl.width
+    CI, CO = wkl.in_filter, wkl.out_filter
+    HK, WK = wkl.hkernel, wkl.wkernel
+    HPAD, WPAD = wkl.hpad, wkl.wpad
+    HSTR, WSTR = wkl.hstride, wkl.wstride
+
+    HCAT, WCAT = HK-1, WK-1
+    DOPAD = (HPAD != 0 and WPAD != 0)
+
+    VH = sch.vh
+    VW = sch.vw
+    VC = sch.vc
+    UNROLL = sch.unroll
+
+    A, B, C = data, kernel, last
+    A0, A1 = data_pad, data_vec
+    B0 = kernel_vec
+    C0, C1 = conv_out, output
+
+    CC = s.cache_write(C0, "global")
+
+    _, co, oh, ow, vh, vw, vc = s[C0].op.axis
+    if UNROLL:
+        s[C0].unroll(vw)
+    s[C0].vectorize(vc)
+
+    s[CC].compute_at(s[C0], ow)
+    _, co, oh, ow, vh, vw, vc = s[CC].op.axis
+    ci, dh, dw = s[CC].op.reduce_axis
+    s[CC].reorder(ci, dh, vh, dw, vw, vc)
+
+    if UNROLL:
+        s[CC].unroll(vw)
+    s[CC].vectorize(vc)
+
+    ##### Schedule A
+    if DOPAD:
+        s[A0].compute_inline()
+
+    _, h, _, _, _, _ = s[A1].op.axis
+    if sch.ba == 1:
+        oaxis = h
+        paxis = h
+    else:
+        oh, ih = s[A1].split(h, sch.ba)
+        oaxis = oh
+        paxis = ih
+
+    s[A1].parallel(paxis)
+    s[A1].pragma(oaxis, "parallel_launch_point")
+    s[A1].pragma(paxis, "parallel_stride_pattern")
+    s[A1].pragma(oaxis, "parallel_barrier_when_finish")
+
+
+    ##### Schedule B
+    co, _, _, _, _ = s[B0].op.axis
+    if sch.bc == 1:
+        oaxis = co
+        paxis = co
+    else:
+        oco, ico = s[B0].split(co, sch.bc)
+        oaxis = oco
+        paxis = ico
+
+    s[B0].parallel(paxis)
+    s[B0].pragma(oaxis, "parallel_launch_point")
+    s[B0].pragma(paxis, "parallel_stride_pattern")
+    s[B0].pragma(oaxis, "parallel_barrier_when_finish")
+
+
+    ##### Schedule C
+    n, co, h, w = s[C].op.axis
+    co, vc = s[C].split(co, VC)
+    oh, ow, vh, vw = s[C].tile(h, w, VH, VW)
+    s[C].reorder(n, co, oh, ow, vh, vw, vc)
+    if C != C1:
+        s[C1].compute_inline()
+    s[C0].compute_at(s[C], ow)
+
+    if sch.bc == 1:
+        oaxis = co
+        paxis = co
+    else:
+        oco, ico = s[C].split(co, sch.bc)
+        oaxis = oco
+        paxis = ico
+
+    s[C].parallel(paxis)
+    s[C].pragma(oaxis, "parallel_launch_point")
+    s[C].pragma(paxis, "parallel_stride_pattern")
+    s[C].pragma(oaxis, "parallel_barrier_when_finish")
+
+    return s
+
+def _schedule_im2col_conv2d(s, data, data_pad, data_col, data_vec,
+                            kernel, kernel_vec,
+                            conv_out, output, last):
+    # no stride and padding info here
+    padding = infer_pad(data, data_pad)
+    if data_pad is None:
+        stride = infer_stride(data, kernel, output)
+    else:
+        stride = infer_stride(data_pad, kernel, output)
+    wkl = _get_workload(data, kernel, stride, padding)
+    sch = _get_schedule(wkl, 'rasp')
+
+    H, W = wkl.height, wkl.width
+    CI = wkl.in_filter
+    CO = wkl.out_filter
+    HK, WK = wkl.hkernel, wkl.wkernel
+    HPAD, WPAD = wkl.hpad, wkl.wpad
+    HSTR, WSTR = wkl.hstride, wkl.wstride
+
+    HCAT, WCAT = HK-1, WK-1
+    DOPAD = (HPAD != 0 and WPAD != 0)
+
+    P = sch.vp
+    Q = sch.vq
+    UNROLL = sch.unroll
+
+    A, B, C = data, kernel, last
+    A0, A1, A2 = data_pad, data_col, data_vec
+    B0 = kernel_vec
+    C0, C1 = conv_out, output
+
+    CC = s.cache_write(C0, "global")
+    AA = s.cache_read(A2, "global", [CC])
+    BB = s.cache_read(B0, "global", [CC])
+
+
+    ##### Schedule CC
+    _, co, im, vim, vco = s[C0].op.axis
+    s[C0].unroll(vim)
+    s[C0].vectorize(vco)
+
+    s[CC].compute_at(s[C0], im)
+    _, co, im, vim, vco = s[CC].op.axis
+    ci, hk, wk = s[CC].op.reduce_axis
+    s[CC].reorder(ci, hk, wk, vim, vco)
+    s[CC].unroll(vim)
+    s[CC].vectorize(vco)
+    # s[CC].unroll(ccr)
+
+    ### Schedule C
+    _, co, h, w = s[C].op.axis
+    im = s[C].fuse(h, w)
+    im, vim = s[C].split(im, P)
+    co, vco = s[C].split(co, Q)
+    s[C].reorder(co, im, vim, vco)
+
+    if sch.bc == 1:
+        oaxis = co
+        paxis = co
+    else:
+        oco, ico = s[C].split(co, sch.bc)
+        oaxis = oco
+        paxis = ico
+
+    s[C].parallel(paxis)
+    s[C].pragma(oaxis, "parallel_launch_point")
+    s[C].pragma(paxis, "parallel_stride_pattern")
+    s[C].pragma(oaxis, "parallel_barrier_when_finish")
+    if C1 != C:
+        s[C1].compute_inline()
+
+    s[C0].compute_at(s[C], paxis)
+
+    ##### Schedule A
+    if DOPAD:
+        s[A0].compute_inline()
+    s[A1].compute_inline()
+    s[AA].compute_at(s[CC], wk)
+    s[AA].unroll(AA.op.axis[4])
+
+    _, im, _, _, _, _ = s[A2].op.axis
+    if sch.ba == 1:
+        oaxis = im
+        paxis = im
+    else:
+        oim, iim = s[A2].split(im, sch.ba)
+        oaxis = oim
+        paxis = iim
+
+    s[A2].parallel(paxis)
+    s[A2].pragma(oaxis, "parallel_launch_point")
+    s[A2].pragma(paxis, "parallel_stride_pattern")
+    s[A2].pragma(oaxis, "parallel_barrier_when_finish")
+
+
+    ##### Schedule B
+    s[BB].compute_at(s[CC], wk)
+    s[BB].vectorize(BB.op.axis[4])
+
+    co, _, _, _, _ = s[B0].op.axis
+    if sch.bc == 1:
+        oaxis = co
+        paxis = co
+    else:
+        oco, ico = s[B0].split(co, sch.bc)
+        oaxis = oco
+        paxis = ico
+
+    s[B0].parallel(paxis)
+    s[B0].pragma(oaxis, "parallel_launch_point")
+    s[B0].pragma(paxis, "parallel_stride_pattern")
+    s[B0].pragma(oaxis, "parallel_barrier_when_finish")
+
+    return s
+
+def schedule_convolution(outs):
+    """Create schedule for tensors"""
+    s = tvm.create_schedule([x.op for x in outs])
+
+    def traverse(op):
+        """Traverse operators from computation graph"""
+        # inline all one-to-one-mapping operators except the last stage (output)
+        if 'ewise' in op.tag or 'bcast' in op.tag:
+            if op not in s.outputs:
+                s[op].compute_inline()
+            for tensor in op.input_tensors:
+                if tensor.op.input_tensors:
+                    traverse(tensor.op)
+        if 'spatial_conv_output' in op.tag:
+            output = op.output(0)
+            conv_out = op.input_tensors[0]
+            kernel_vec = conv_out.op.input_tensors[1]
+            kernel = kernel_vec.op.input_tensors[0]
+            data_vec = conv_out.op.input_tensors[0]
+            data = data_vec.op.input_tensors[0]
+            data_pad = None
+            if isinstance(data.op, tvm.tensor.ComputeOp) and "pad" in data.op.tag:
+                data_pad = data
+                data = data_pad.op.input_tensors[0]
+
+            _schedule_spatial_conv2d(s, data, data_pad, data_vec,
+                                     kernel, kernel_vec,
+                                     conv_out, output, outs[0])
+
+        if 'im2col_conv_output' in op.tag:
+            output = op.output(0)
+            conv_out = op.input_tensors[0]
+            kernel_vec = conv_out.op.input_tensors[1]
+            kernel = kernel_vec.op.input_tensors[0]
+            data_vec = conv_out.op.input_tensors[0]
+            data_col = data_vec.op.input_tensors[0]
+            data = data_col.op.input_tensors[0]
+            data_pad = None
+            if isinstance(data.op, tvm.tensor.ComputeOp) and "pad" in data.op.tag:
+                data_pad = data
+                data = data_pad.op.input_tensors[0]
+            _schedule_im2col_conv2d(s, data, data_pad, data_col, data_vec,
+                                    kernel, kernel_vec,
+                                    conv_out, output, outs[0])
+
+    traverse(outs[0].op)
+    return s

topi/python/topi/target.py

+"""Target management API of topi"""
+
+from __future__ import absolute_import
+
+class Target(object):
+    """A Target describes the target type on which computation should be carried on"""
+    default_target = None
+    str2type = {'x86': 1, 'cuda': 2, 'rasp': 3}
+    type2str = {1: 'x86', 2: 'cuda', 3: 'rasp'}
+    def __init__(self, target_type):
+        """Constructs a context."""
+        if isinstance(target_type, Target):
+            self.target_typeid = target_type.target_typeid
+        else:
+            self.target_typeid = Target.str2type[target_type]
+
+    @property
+    def target_type(self):
+        """Returns the target type of current target."""
+        return Target.type2str[self.target_typeid]
+
+    def __hash__(self):
+        """Compute hash value of target for dictionary lookup"""
+        return hash(self.target_typeid)
+
+    def __eq__(self, other):
+        """Compares two targets. Two targets are equal if they
+        have the same target type.
+        """
+        return isinstance(other, Target) and \
+            self.target_typeid == other.target_typeid
+
+    def __str__(self):
+        return '%s' % (self.target_type)
+
+    def __repr__(self):
+        return self.__str__()
+
+    def __enter__(self):
+        self._old_target = Target.default_target
+        Target.default_target = self
+        return self
+
+    def __exit__(self, ptype, value, trace):
+        Target.default_target = self._old_target
+
+Target.default_target = Target('x86')
+
+def x86():
+    """Returns a x86 target."""
+    return Target('x86')
+
+def cuda():
+    """Returns a cuda target."""
+    return Target('cuda')
+
+def rasp():
+    """Returns a rasp target."""
+    return Target('rasp')
+
+def current_target():
+    """Returns the current target."""
+    return Target.default_target

topi/tests/python/test_topi_convolution.py

+"""Example code to do convolution."""
+import os
+import numpy as np
+import tvm
+import topi
+from topi.util import get_const_tuple
+
+
+def verify_convolution(batch, in_size, in_channel, num_filter, kernel, stride, padding):
+    in_height = in_width = in_size
+
+    with topi.target.rasp():
+        A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
+        W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
+        B = topi.nn.convolution(A, W, stride, padding)
+    s = topi.rasp.schedule_convolution([B])
+
+    a_np = np.random.uniform(size=get_const_tuple(A.shape)).astype(A.dtype)
+    w_np = np.random.uniform(size=get_const_tuple(W.shape)).astype(W.dtype)
+    b_np = topi.testing.conv2d_nchw_python(a_np, w_np, stride, padding)
+
+    ctx = tvm.cpu(0)
+    a = tvm.nd.array(a_np, ctx)
+    w = tvm.nd.array(w_np, ctx)
+    b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
+    func = tvm.build(s, [A, W, B], "llvm")
+    func(a, w, b)
+    np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
+
+def test_convolution():
+    verify_convolution(1, 56,  64, 64,  3, 1, 1)
+
+if __name__ == "__main__":
+    test_convolution()