Fix intel conv2d auto tune (apache#5200)

* Fix x86 conv2d and depthwise conv2d auto tuning

* Fix depthwise conv2d infer layout

* Use random data instead of empty data for autotvm

* Fix pylint

* Keep empty array for now for autotvm

topi/python/topi/x86/conv2d.py

@@ -185,7 +185,19 @@ def conv2d_NCHWc(cfg, data, kernel, strides, padding, dilation, layout, out_layo
     # Pack data if raw 4-D data is provided.
     # This can only happen when autotuning.
     if len(data.shape) == 4:
-        data, kernel = _pack_data(cfg, data, kernel)
+        if autotvm.GLOBAL_SCOPE.in_tuning:
+            # Directly use modified data layout placeholder.
+            dshape = (n, in_channel // cfg["tile_ic"].size[-1],
+                      ih, iw, cfg["tile_ic"].size[-1])
+            data = tvm.te.placeholder(dshape, data.dtype, name="data")
+            kshape = (num_filter // cfg["tile_oc"].size[-1],
+                      in_channel // cfg["tile_ic"].size[-1],
+                      kernel_height, kernel_width,
+                      cfg["tile_ic"].size[-1],
+                      cfg["tile_oc"].size[-1])
+            kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel")
+        else:
+            data, kernel = _pack_data(cfg, data, kernel)
 
     return nn.conv2d_NCHWc(data,
                            kernel,

topi/python/topi/x86/conv2d_avx_1x1.py

@@ -19,7 +19,6 @@
 from __future__ import absolute_import as _abs
 import tvm
 from tvm import te
-from tvm import autotvm
 from tvm.autotvm.task.space import SplitEntity, OtherOptionEntity
 
 from ..nn.pad import pad
@@ -69,17 +68,12 @@ def _schedule_conv_NCHWc(s, cfg, data_vec, kernel_vec, conv_out, last):
     if isinstance(s[data_vec].op, tvm.te.ComputeOp) \
             and "pad" in data_vec.op.tag:
         batch, ic_chunk, ih, iw, ic_block = s[data_vec].op.axis
+        s[data_vec].vectorize(ic_block)
         parallel_axis = s[data_vec].fuse(batch, ic_chunk, ih)
         s[data_vec].parallel(parallel_axis)
         data_vec = data_vec.op.input_tensors[0]
 
-    if autotvm.GLOBAL_SCOPE.in_tuning:
-        # only in autotuning, input data of conv2d_NCHWc will be 4-D.
-        # skip this part during tuning to make records accurate.
-        # this part will be folded during Relay fold_constant pass.
-        s[data_vec].pragma(s[data_vec].op.axis[0], "debug_skip_region")
-        s[kernel_vec].pragma(s[kernel_vec].op.axis[0], "debug_skip_region")
-    elif isinstance(kernel_vec.op, tvm.te.ComputeOp) and \
+    if isinstance(kernel_vec.op, tvm.te.ComputeOp) and \
             kernel_vec.name == 'kernel_vec':
         # data and kernel are not pre-computed, schedule layout transform here.
         # this should only be used by x86 conv2d_nchw, which is for

topi/python/topi/x86/conv2d_avx_common.py

@@ -17,7 +17,6 @@
 # pylint: disable=invalid-name,unused-variable,unused-argument,invalid-name
 """Conv2D schedule on for Intel CPU"""
 import tvm
-from tvm import autotvm
 from tvm.autotvm.task.space import SplitEntity, OtherOptionEntity
 
 from ..generic import conv2d as conv2d_generic
@@ -91,17 +90,12 @@ def _schedule_conv_NCHWc(s, cfg, data_vec, kernel_vec, conv_out, last):
     if isinstance(s[data_vec].op, tvm.te.ComputeOp) \
             and "pad" in data_vec.op.tag:
         batch, ic_chunk, ih, iw, ic_block = s[data_vec].op.axis
+        s[data_vec].vectorize(ic_block)
         parallel_axis = s[data_vec].fuse(batch, ic_chunk, ih)
         s[data_vec].parallel(parallel_axis)
         data_vec = data_vec.op.input_tensors[0]
 
-    if autotvm.GLOBAL_SCOPE.in_tuning:
-        # only in autotuning, input data of conv2d_NCHWc will be 4-D.
-        # skip this part during tuning to make records accurate.
-        # this part will be folded during Relay fold_constant pass.
-        s[data_vec].pragma(s[data_vec].op.axis[0], "debug_skip_region")
-        s[kernel_vec].pragma(s[kernel_vec].op.axis[0], "debug_skip_region")
-    elif isinstance(kernel_vec.op, tvm.te.ComputeOp) and \
+    if isinstance(kernel_vec.op, tvm.te.ComputeOp) and \
             kernel_vec.name == 'kernel_vec':
         # data and kernel are not pre-computed, schedule layout transform here.
         # this should only be used by x86 conv2d_nchw, which is for

topi/python/topi/x86/depthwise_conv2d.py

@@ -43,7 +43,6 @@ def _fallback_schedule(cfg, wkl):
 
     HPAD, WPAD = wkl.hpad, wkl.wpad
     HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
     out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
 
     oc_bn = 1
@@ -148,10 +147,21 @@ def depthwise_conv2d_NCHWc(cfg, data, kernel, strides, padding, dilation,
     # Pack data if raw 4-D data is provided.
     # This can only happen when autotuning.
     if len(data.shape) == 4:
-        data, kernel = _pack_data(cfg, data, kernel)
-        _, _, _, _, in_channel_block = get_const_tuple(data.shape)
-        out_channel_chunk, _, _, _, _, out_channel_block \
-            = get_const_tuple(kernel.shape)
+        if autotvm.GLOBAL_SCOPE.in_tuning:
+            # Directly use modified data layout placeholder.
+            in_channel_block = cfg["tile_ic"].size[-1]
+            in_channel_chunk = in_channel // in_channel_block
+            out_channel_block = cfg["tile_oc"].size[-1]
+            out_channel_chunk = out_channel // out_channel_block
+            dshape = (batch, in_channel_chunk, in_height, in_width, in_channel_block)
+            data = tvm.te.placeholder(dshape, data.dtype, name="data")
+            kshape = (out_channel_chunk, 1, filter_height, filter_width, 1, out_channel_block)
+            kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel")
+        else:
+            data, kernel = _pack_data(cfg, data, kernel)
+            _, _, _, _, in_channel_block = get_const_tuple(data.shape)
+            out_channel_chunk, _, _, _, _, out_channel_block \
+                = get_const_tuple(kernel.shape)
 
     # padding stage
     DOPAD = (pad_top != 0 or pad_left != 0 or pad_down != 0 or pad_right != 0)
@@ -207,16 +217,9 @@ def _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data_vec, kernel_vec, conv_out
     if isinstance(s[data_vec].op, tvm.te.ComputeOp) \
             and "pad" in data_vec.op.tag:
         batch, ic_chunk, ih, iw, ic_block = s[data_vec].op.axis
+        s[data_vec].vectorize(ic_block)
         parallel_axis = s[data_vec].fuse(batch, ic_chunk, ih)
         s[data_vec].parallel(parallel_axis)
-        data_vec = data_vec.op.input_tensors[0]
-
-    if autotvm.GLOBAL_SCOPE.in_tuning:
-        # only in autotuning, input data of conv2d_NCHWc will be 4-D.
-        # skip this part during tuning to make recrods accurate.
-        # this part will be folded during Relay fold_constant pass.
-        s[data_vec].pragma(s[data_vec].op.axis[0], "debug_skip_region")
-        s[kernel_vec].pragma(s[kernel_vec].op.axis[0], "debug_skip_region")
 
     C, O = conv_out, output
     CC = s.cache_write(C, 'global')
@@ -264,12 +267,12 @@ def _schedule_depthwise_conv2d_NCHWc_impl(s, cfg, data_vec, kernel_vec, conv_out
 
 @depthwise_conv2d_infer_layout.register("cpu")
 def _depthwise_conv2d_infer_layout(workload, cfg):
-    _, data, kernel, strides, padding, dilation, dtype = workload
+    _, data, kernel, strides, padding, dilation, _, _, dtype = workload
     batch_size, in_channel, in_height, in_width = data[1]
     filter_channel, channel_multiplier, k_height, k_width = kernel[1]
     out_channel = filter_channel * channel_multiplier
-    out_height = (in_height + 2 * padding[0] - k_height) // strides[0] + 1
-    out_width = (in_width + 2 * padding[1] - k_width) // strides[1] + 1
+    out_height = (in_height + padding[0] + padding[2] - k_height) // strides[0] + 1
+    out_width = (in_width + padding[1] + padding[3] - k_width) // strides[1] + 1
     tile_ic, tile_oc = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
     in_shape = (batch_size, in_channel // tile_ic, in_height, in_width, tile_ic)
     in_layout = "NCHW%dc" % tile_ic