jackwish commented on a change in pull request #3859: [TOPI][AutoTVM] NHWC
conv2d templates for ARM
URL: https://github.com/apache/incubator-tvm/pull/3859#discussion_r349421208
##########
File path: topi/python/topi/arm_cpu/conv2d_spatial_pack.py
##########
@@ -196,3 +196,160 @@ def schedule_conv2d_spatial_pack_nchw(cfg, s, data_vec,
kernel_vec,
s[kernel_vec].parallel(co)
return s
+
+def conv2d_spatial_pack_nhwc(cfg, data, kernel, strides, padding, dilation,
out_dtype):
+ """Spatial pack compute for Conv2d NHWC"""
+ out_dtype = out_dtype or data.dtype
+
+ N, IH, IW, IC = get_const_tuple(data.shape)
+ assert len(kernel.shape) == 4, "AlterOpLayout not enabled for NHWC yet"
+ KH, KW, _, OC = get_const_tuple(kernel.shape)
+
+ if isinstance(dilation, int):
+ dilation_h = dilation_w = dilation
+ else:
+ dilation_h, dilation_w = dilation
+
+ dilated_kernel_h = (KH - 1) * dilation_h + 1
+ dilated_kernel_w = (KW - 1) * dilation_w + 1
+
+ pad_top, pad_left, pad_down, pad_right = \
+ get_pad_tuple(padding, (dilated_kernel_h, dilated_kernel_w))
+ HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides,
strides)
+
+ OH = (IH + pad_top + pad_down - dilated_kernel_h) // HSTR + 1
+ OW = (IW + pad_left + pad_right - dilated_kernel_w) // WSTR + 1
+ data_pad = nn.pad(data, [0, pad_top, pad_left, 0], [0, pad_down,
pad_right, 0])
+
+ # ==================== define configuration space ====================
+ n, oc, oh, ow = cfg.axis(N), cfg.axis(OC), cfg.axis(OH), cfg.axis(OW)
+ ic, kh, kw = cfg.reduce_axis(IC), cfg.reduce_axis(KH), cfg.reduce_axis(KW)
+
+ oco, oci = cfg.define_split('tile_co', oc, num_outputs=2)
+ oho, ohi = cfg.define_split('tile_oh', oh, num_outputs=2)
+ owo, owi = cfg.define_split('tile_ow', ow, num_outputs=2)
+
+ cfg.define_reorder('reorder_conv',
+ [n, oho, owo, oco, kh, kw, ic, ohi, owi, oci],
+ policy='candidate', candidate=[
+ [n, oho, owo, oco, kh, kw, ic, ohi, owi, oci],
+ [n, oho, owo, oco, ohi, kh, kw, ic, owi, oci],
+ [n, oho, owo, oco, ohi, kh, kw, owi, ic, oci],
+ [n, oho, owo, ohi, oco, kh, kw, owi, ic, oci]])
+
+ cfg.define_annotate("ann_reduce", [kh, kw], policy='try_unroll')
+ cfg.define_annotate("ann_spatial", [ohi, owi, oci],
policy='try_unroll_vec')
+ # ====================================================================
+
+ OCI = cfg['tile_co'].size[-1]
+ OHI = cfg['tile_oh'].size[-1]
+ OWI = cfg['tile_ow'].size[-1]
+ OCO = OC // OCI
+ OHO = OH // OHI
+ OWO = OW // OWI
+
+ kvshape = (OCO, KH, KW, IC, OCI)
+ ovshape = (N, OHO, OWO, OCO, OHI, OWI, OCI)
+ oshape = (N, OH, OW, OC)
+
+ if dilation_h != 1 or dilation_w != 1:
+ # undilate input data
+ dvshape = (N, OHO, OWO, KH, KW, IC, OHI, OWI)
+ data_vec = tvm.compute(dvshape, lambda n, oho, owo, kh, kw, ic, ohi,
owi:
+ data_pad[n][(oho*OHI+ohi)*HSTR+kh*dilation_h]
+ [(owo*OWI+owi)*WSTR+kw*dilation_w][ic],
+ name='data_vec_undilated')
+ else:
+ dvshape = (N, OHO, OWO, KH + (OHI-1)*HSTR, KW + (OWI-1)*WSTR, IC)
+ data_vec = tvm.compute(dvshape, lambda n, oho, owo, ohi, owi, ic:
+
data_pad[n][oho*OHI*HSTR+ohi][owo*OWI*WSTR+owi][ic],
+ name='data_vec')
+ kernel_vec = tvm.compute(kvshape, lambda oco, kh, kw, ic, oci: \
+ kernel[kh][kw][ic][oco*OCI+oci],
+ name='kernel_vec')
+
+ ic = tvm.reduce_axis((0, IC), name='ic')
+ kh = tvm.reduce_axis((0, KH), name='kh')
+ kw = tvm.reduce_axis((0, KW), name='kw')
+
+ if dilation_h != 1 or dilation_w != 1:
+ conv = tvm.compute(ovshape, lambda n, oho, owo, oco, ohi, owi, oci: \
+ tvm.sum(data_vec[n, oho, owo, kh, kw, ohi, owi,
ic].astype(out_dtype) *
+ kernel_vec[oco, kh, kw, ic, oci].astype(out_dtype),
+ axis=[ic, kh, kw]), name='conv')
+ else:
+ conv = tvm.compute(ovshape, lambda n, oho, owo, oco, ohi, owi, oci: \
+ tvm.sum(data_vec[n, oho, owo, ohi*HSTR+kh, owi*WSTR+kw,
ic].astype(out_dtype) *
+ kernel_vec[oco, kh, kw, ic, oci].astype(out_dtype),
+ axis=[ic, kh, kw]), name='conv')
+
+ output = tvm.compute(oshape, lambda n, oho, owo, oc:
+
conv[n][oho//OHI][owo//OWI][oc//OCI][oho%OHI][owo%OWI][oc%OCI],
+ name='output_unpack', tag='spatial_conv_output_NHWC')
+ return output
+
+def schedule_conv2d_spatial_pack_nhwc(cfg, s, op, output):
+ """Spatial Pack schedule for Conv2d NHWC"""
+ unpack = op.output(0)
+ conv = unpack.op.input_tensors[0]
+ data_vec = conv.op.input_tensors[0]
+ kernel_vec = conv.op.input_tensors[1]
+ data_pad = data_vec.op.input_tensors[0]
+ OHI = cfg['tile_oh'].size[-1]
+ OWI = cfg['tile_ow'].size[-1]
+ OCI = cfg['tile_co'].size[-1]
+
+ # schedule unpack/output
+ if output != unpack:
+ s[unpack].compute_inline()
+ n, oh, ow, oc = s[output].op.axis
+ oco, oci = cfg['tile_co'].apply(s, output, oc)
+ oho, ohi = cfg['tile_oh'].apply(s, output, oh)
+ owo, owi = cfg['tile_ow'].apply(s, output, ow)
+ s[output].reorder(n, oho, owo, oco, ohi, owi, oci)
+ cfg['ann_spatial'].apply(s, output, [ohi, owi, oci], axis_lens=[OHI, OWI,
OCI],
+ max_unroll=32, cfg=cfg)
+ cfg.define_knob('compat', [0, 1, 2])
+ if cfg['compat'].val < 2:
+ compat_axis = [owo, oco][cfg['compat'].val]
+ s[conv].compute_at(s[output], compat_axis)
+ paxis = s[output].fuse(n, oho)
+ s[output].parallel(paxis)
+
+ # schedule conv
+ n, oho, owo, oco, ohi, owi, oci = s[conv].op.axis
+ ic, kh, kw = s[conv].op.reduce_axis
+ cfg['reorder_conv'].apply(s, conv, [n, oho, owo, oco, kh, kw, ohi, owi,
ic, oci])
+ cfg['ann_reduce'].apply(s, conv, [kh, kw],
+ axis_lens=[get_const_int(kh.dom.extent),
+ get_const_int(kw.dom.extent)],
+ max_unroll=32,
+ cfg=cfg)
+ cfg['ann_spatial'].apply(s, conv, [ohi, owi, oci], axis_lens=[OHI, OWI,
OCI],
+ max_unroll=32, cfg=cfg)
+ if cfg['compat'].val < 2:
+ compat_axis = [owo, oco][cfg['compat'].val]
+ s[kernel_vec].compute_at(s[conv], compat_axis)
+ s[data_vec].compute_at(s[conv], compat_axis)
+
+ # schedule kernel pack
+ oco, kh, kw, ic, oci = kernel_vec.op.axis
+ s[kernel_vec].vectorize(oci)
+ s[kernel_vec].unroll(ic)
+ if cfg['compat'].val == 2:
+ s[kernel_vec].parallel(oco)
Review comment:
Attaching `data_vec` and `kernel_vec` compute to `conv` when
`cfg['compat'].val < 2`, in which case parallel them will lead to *nested
parallel* error - `conv` has already been parallelized.
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