giuseros commented on a change in pull request #6095:
URL: https://github.com/apache/incubator-tvm/pull/6095#discussion_r459455600
##########
File path: topi/python/topi/arm_cpu/depthwise_conv2d.py
##########
@@ -181,6 +181,170 @@ def depthwise_conv2d_nchw_spatial_pack(cfg, data, kernel,
strides, padding, dila
return _decl_spatial_pack(cfg, data, kernel, strides, padding, dilation,
out_dtype, num_tile=2)
+@autotvm.register_topi_compute("depthwise_conv2d_nhwc.arm_cpu")
+def compute_depthwise_conv2d_nhwc(_, data, kernel, strides, padding, dilation,
out_dtype):
+ """TOPI compute callback for depthwise_conv2d nhwc
+
+ Parameters
+ ----------
+ cfg: ConfigEntity
+ The config for this template
+
+ data : tvm.te.Tensor
+ 4-D with shape [batch, in_height, in_width, in_channel]
+
+ kernel : tvm.te.Tensor
+ 4-D with shape [filter_height, filter_width, in_channel,
channel_multiplier]
+
+ strides : list of two ints
+ [stride_height, stride_width]
+
+ padding : list of two ints
+ [pad_height, pad_width]
+
+ dilation : list of two ints
+ [dilation_height, dilation_width]
+
+ out_dtype: str
+ The output type. This is used for mixed precision.
+
+ Returns
+ -------
+ output : tvm.te.Tensor
+ 4-D with shape [batch, out_height, out_width, out_channel]
+ """
+
+ out_dtype = out_dtype or data.dtype
+
+ N, IH, IW, IC = get_const_tuple(data.shape)
+
+ if isinstance(dilation, int):
+ dilation_h = dilation_w = dilation
+ else:
+ dilation_h, dilation_w = dilation
+
+ KH, KW, IC, channel_multiplier = get_const_tuple(kernel.shape)
+
+ 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
+
+ if pad_top or pad_left or pad_down or pad_right:
+ data_pad = nn.pad(data, [0, pad_top, pad_left, 0], [0, pad_down,
pad_right, 0],
+ name="data_pad")
+ else:
+ data_pad = data
+
+ output_shape = (N, OH, OW, IC*channel_multiplier)
+
+ idxdiv = tvm.tir.indexdiv
+ idxmod = tvm.tir.indexmod
+
+ reduce_h = te.reduce_axis((0, KH), name='reduce_h')
+ reduce_w = te.reduce_axis((0, KW), name='reduce_w')
+
+ out = te.compute(output_shape, lambda n, h, w, c:
+ te.sum(data_pad[n,
+ HSTR*h+dilation_h*reduce_h,
+ w*WSTR+reduce_w*dilation_w,
+ idxdiv(c,
channel_multiplier)].astype(out_dtype) *
+ kernel[reduce_h,
+ reduce_w,
+ idxdiv(c, channel_multiplier),
+ idxmod(c,
channel_multiplier)].astype(out_dtype),
+ axis=[reduce_h, reduce_w]),
+ name='depthwise_conv2d_nhwc_output')
+ return out
+
+@autotvm.register_topi_schedule("depthwise_conv2d_nhwc.arm_cpu")
+def schedule_depthwise_conv2d_nhwc(cfg, outs):
+ """Create the schedule for depthwise_conv2d_nchw_spatial_pack"""
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+ out = outs[0]
+
+ ##### space definition begin #####
+ n, h, w, c = s[out].op.axis
+ cfg.define_split('tile_c', c, num_outputs=2)
+ _, hi = cfg.define_split('tile_h', h, num_outputs=2)
+ _, wi = cfg.define_split('tile_w', w, num_outputs=2)
+ cfg.define_knob('locate_output', [0, 1])
+
+ # fallback support
+ if cfg.is_fallback:
+ cfg['tile_c'] = SplitEntity([-1, 8])
+ cfg['tile_h'] = SplitEntity([-1, 2])
+ cfg['tile_w'] = SplitEntity([-1, 2])
+ cfg['locate_output'] = OtherOptionEntity(1)
+ ##### space definition end #####
+
+ def schedule_conv(conv):
+ conv_data = conv.op.input_tensors[0]
+
+ n, w, h, c = conv.op.axis
+ r_h, r_w = conv.op.reduce_axis
+ ho, hi = cfg['tile_h'].apply(s, conv, h)
+ wo, wi = cfg['tile_w'].apply(s, conv, w)
+ co, ci = cfg['tile_c'].apply(s, conv, c)
+
+ if conv_data.name == "data_pad":
+ # Define a policy for padding computation
Review comment:
Done.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
