[TOP] Initial Schedule of MobileNet on Rasp

topi/python/topi/nn/conv2d.py

@@ -21,8 +21,8 @@
 Im2ColPack = namedtuple('Im2ColPack',
                         ['vp', 'vq', 'ba', 'bc', 'unroll'])
 
-# workloads of resnet18 on imagenet
 _WORKLOADS = [
+    # workloads of resnet18 on imagenet
     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),
@@ -35,6 +35,17 @@
     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),
+    # workloads of mobile net on imagenet
+    Workload(224, 224, 3, 32, 3, 3, 1, 1, 2, 2),
+    Workload(112, 112, 32, 64, 1, 1, 0, 0, 1, 1),
+    Workload(56, 56, 64, 128, 1, 1, 0, 0, 1, 1),
+    Workload(56, 56, 128, 128, 1, 1, 0, 0, 1, 1),
+    Workload(28, 28, 128, 256, 1, 1, 0, 0, 1, 1),
+    Workload(28, 28, 256, 256, 1, 1, 0, 0, 1, 1),
+    Workload(14, 14, 256, 512, 1, 1, 0, 0, 1, 1),
+    Workload(14, 14, 512, 512, 1, 1, 0, 0, 1, 1),
+    Workload(7, 7, 512, 1024, 1, 1, 0, 0, 1, 1),
+    Workload(7, 7, 1024, 1024, 1, 1, 0, 0, 1, 1),
 ]
 
 # platform specific schedule

topi/python/topi/rasp/__init__.py

@@ -3,3 +3,4 @@
 from __future__ import absolute_import as _abs
 
 from .conv2d import *
+from .depthwise_conv2d import *

topi/python/topi/rasp/conv2d.py

@@ -23,6 +23,17 @@
     Im2ColPack(7, 4, 1, 16, True),
     Im2ColPack(7, 4, 1, 8, False),
     Im2ColPack(7, 4, 1, 16, False),
+
+    SpatialPack(2, 2, 4, 28, 1, True),
+    SpatialPack(1, 4, 8, 14, 1, False),
+    SpatialPack(1, 2, 16, 8, 1, True),
+    SpatialPack(1, 4, 8, 8, 8, True),
+    SpatialPack(2, 2, 8, 1, 1, False),
+    SpatialPack(1, 4, 8, 4, 8, False),
+    SpatialPack(2, 2, 8, 1, 4, False),
+    SpatialPack(2, 2, 8, 1, 8, False),
+    SpatialPack(1, 1, 16, 1, 4, False),
+    SpatialPack(1, 1, 4, 1, 4, True),
 ]
 
 def _schedule_conv2d(wkl):

topi/python/topi/rasp/depthwise_conv2d.py

@@ -0,0 +1,64 @@
+# pylint: disable=invalid-name,unused-variable
+"""Schedule for depthwise_conv2d with auto fusion"""
+import tvm
+from .. import tag
+
+def _schedule(s, data, data_pad, kernel, output, last):
+    A, B, C = data, kernel, output
+    A0 = data_pad
+    C0 = last
+
+    _, c, h, w = s[C].op.axis
+    dh, dw = s[C].op.reduce_axis
+
+    oh, ow, ih, iw = s[C].tile(h, w, 2, 4)
+    s[C].reorder(oh, ow, dh, dw, ih, iw)
+    s[C].unroll(ih)
+    s[C].vectorize(iw)
+
+    s[C].parallel(c)
+    s[C].pragma(c, "parallel_launch_point")
+    s[C].pragma(c, "parallel_stride_pattern")
+    s[C].pragma(c, "parallel_barrier_when_finish")
+    return s
+
+
+
+def schedule_depthwise_conv2d(outs):
+    """Schedule for depthwise_conv2d nchw forward.
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+        The computation graph description of depthwise_conv2d
+        in the format of an array of tensors.
+
+    Returns
+    -------
+    s: Schedule
+        The computation schedule for depthwise_conv2d nchw.
+    """
+    outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
+    s = tvm.create_schedule([x.op for x in outs])
+
+    def traverse(op):
+        # inline all one-to-one-mapping operators except the last stage (output)
+        if tag.is_broadcast(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)
+        # schedule depthwise_conv2d
+        if op.tag == 'depthwise_conv2d_nchw':
+            output = op.output(0)
+            kernel = op.input_tensors[1]
+            data = 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(s, data, data_pad, kernel, output, outs[0])
+
+    traverse(outs[0].op)
+    return s