intel graphics conv2d schedule fixed for input shapes (300*300) and (512 * 512) (#1709)

topi/python/topi/intel_graphics/conv2d.py

@@ -49,7 +49,11 @@ def _alter_conv2d_layout(attrs, inputs, tinfos):
     stride = ast.literal_eval(attrs['strides'])
 
     wkl = _get_workload(data, kernel, stride, padding, data.dtype)
-    oc_bn = 16
+    oc_bn = 1
+    kernel_shape = util.get_const_tuple(kernel.shape)
+    for oc_bn in range(16, 1, -1):
+        if kernel_shape[0] % oc_bn == 0:
+            break
 
     new_attrs = {k: attrs[k] for k in attrs.keys()}
     new_attrs['kernel_layout'] = 'OIHW%do' % (oc_bn)
@@ -148,9 +152,6 @@ def _decl_cl_spatialpack_NCHWc(data, kernel, stride, padding, out_dtype='float16
     out_height = simplify((in_height - kernel_h + pad_top + pad_down) // stride_h + 1)
     out_width = simplify((in_width - kernel_w + pad_left + pad_right) // stride_w + 1)
     oshape = (batch, out_channel, out_height, out_width)
-    pad_before = [0, 0, pad_top, pad_left]
-    pad_after = [0, 0, pad_down, pad_right]
-    temp = pad(data, pad_before, pad_after, name="pad_temp")
 
     rc = tvm.reduce_axis((0, in_channel), name='rc')
     ry = tvm.reduce_axis((0, kernel_h), name='ry')
@@ -190,6 +191,10 @@ def _decl_cl_spatialpack_NCHWc(data, kernel, stride, padding, out_dtype='float16
     if not out_width % block_w == 0:
         c_w = (out_width // block_w + 1) * block_w
 
+    pad_before = [0, 0, pad_top, pad_left]
+    pad_after = [0, 0, pad_down + c_h - block_h, pad_right + c_w - block_w]
+    temp = pad(data, pad_before, pad_after, name="pad_temp")
+
     cshape = (batch, out_channel // nv, c_h, c_w, nv)
 
     conv = tvm.compute(
@@ -263,17 +268,8 @@ def _schedule_cl_spatialpack_NCHWc(s, op):
     s[conv_L].compute_at(s[conv], vci)
     i, oc, h, w, vc = s[conv_L].op.axis
     rc, ry, rx = s[conv_L].op.reduce_axis
-    if in_channel == 2048:
-        rco, rci = s[conv_L].split(rc, nparts=128)
-        s[conv_L].unroll(rci)
-        s[conv_L].reorder(i, oc, rco, rci, ry, rx, vc, h, w)
-        s[temp_W].compute_at(s[conv_L], rco)
-    else:
-        s[conv_L].reorder(i, oc, rc, ry, rx, vc, h, w)
-        s[temp_W].compute_at(s[conv_L], rc)
-    if kernel.shape[3].value != 7:
-        s[conv_L].unroll(ry)
-        s[conv_L].unroll(rx)
+    s[conv_L].reorder(i, oc, rc, ry, rx, vc, h, w)
+    s[temp_W].compute_at(s[conv_L], rc)
     if kernel.shape[3].value != 7:
         s[conv_L].unroll(ry)
         s[conv_L].unroll(rx)
@@ -396,9 +392,6 @@ def _decl_cl_spatialpack(data, kernel, stride, padding, layout, out_dtype='float
     out_height = simplify((in_height - kernel_h + pad_top + pad_down) // stride_h + 1)
     out_width = simplify((in_width - kernel_w + pad_left + pad_right) // stride_w + 1)
     oshape = (batch, out_channel, out_height, out_width)
-    pad_before = [0, 0, pad_top, pad_left]
-    pad_after = [0, 0, pad_down, pad_right]
-    temp = pad(data, pad_before, pad_after, name="pad_temp")
 
     rc = tvm.reduce_axis((0, in_channel), name='rc')
     ry = tvm.reduce_axis((0, kernel_h), name='ry')
@@ -432,13 +425,21 @@ def _decl_cl_spatialpack(data, kernel, stride, padding, layout, out_dtype='float
     c_h = out_height
     c_w = out_width
 
+    if not out_width % block_w == 0:
+        c_w = (out_width // block_w + 1) * block_w
+
     if not out_height % block_h == 0:
         c_h = (out_height // block_h + 1) * block_h
 
-    if not out_width % block_w == 0:
-        c_w = (out_width // block_w + 1) * block_w
+    pad_before = [0, 0, pad_top, pad_left]
+    pad_after = [0, 0, pad_down + c_h - block_h, pad_right + c_w - block_w]
+    temp = pad(data, pad_before, pad_after, name="pad_temp")
 
     nv = 16
+    if not num_filter % nv == 0:
+        num_filter = (num_filter // nv + 1) * nv
+        out_channel = num_filter
+
     cshape = (batch, out_channel // nv, c_h, c_w, nv)
     kvshape = (num_filter // nv, channel, kernel_h, kernel_w, nv)
 
@@ -520,14 +521,8 @@ def _schedule_cl_spatialpack(s, op):
     s[conv_L].compute_at(s[conv], vci)
     i, oc, h, w, vc = s[conv_L].op.axis
     rc, ry, rx = s[conv_L].op.reduce_axis
-    if in_channel == 2048:
-        rco, rci = s[conv_L].split(rc, nparts=128)
-        s[conv_L].unroll(rci)
-        s[conv_L].reorder(i, oc, rco, rci, ry, rx, vc, h, w)
-        s[temp_W].compute_at(s[conv_L], rco)
-    else:
-        s[conv_L].reorder(i, oc, rc, ry, rx, vc, h, w)
-        s[temp_W].compute_at(s[conv_L], rc)
+    s[conv_L].reorder(i, oc, rc, ry, rx, vc, h, w)
+    s[temp_W].compute_at(s[conv_L], rc)
     if kernel.shape[3].value != 7:
         s[conv_L].unroll(ry)
         s[conv_L].unroll(rx)

topi/tests/python/test_topi_conv2d_nchw.py

@@ -161,6 +161,10 @@ def test_conv2d_nchw():
     verify_conv2d_nchw(1, 2048,   8, 384, 1, 1, 0)
     verify_conv2d_nchw(1, 2048,   8, 448, 1, 1, 0)
     verify_conv2d_nchw(1, 2048,   8, 192, 1, 1, 0)
+    verify_conv2d_nchw(1, 1024,  19,  84, 3, 1, 1)
+    verify_conv2d_nchw(1, 2048,  10, 126, 3, 1, 1)
+    verify_conv2d_nchw(1,  512,   5, 126, 3, 1, 1)
+    verify_conv2d_nchw(1,  256,   3, 126, 3, 1, 1)
 
 
 if __name__ == "__main__":