[CONV] Asymmetric padding (apache#4511)

* [CONV] Asymmetic padding

* fix lint error

* update for legalize, rocm and cudnn

* add more test cases

* change more symmetric padding

* change conv2d winograd tests according orginal cases

* remove 'alter_op_layout.h' header in bitserial.cc

include/tvm/relay/attrs/nn.h

@@ -67,7 +67,10 @@ struct Conv2DAttrs : public tvm::AttrsNode<Conv2DAttrs> {
         .describe("Specifies the strides of the convolution.");
     TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
         .describe("If padding is non-zero, then the input is implicitly zero-padded"
-                  "on both sides for padding number of points");
+                  "Padding support both symmetric and asymmetric as"
+                  "one int : same padding used on all sides"
+                  "two int : bottom, right will use same padding as top, left"
+                  "four int : padding width in the order of (top, left, bottom, right)");
     TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
         .describe("Specifies the dilation rate to use for dilated convolution.");
     TVM_ATTR_FIELD(groups).set_default(1)
@@ -138,7 +141,10 @@ struct Conv2DWinogradAttrs : public tvm::AttrsNode<Conv2DWinogradAttrs> {
         .describe("Specifies the strides of the convolution.");
     TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
         .describe("If padding is non-zero, then the input is implicitly zero-padded"
-                  "on both sides for padding number of points");
+                  "Padding support both symmetric and asymmetric as"
+                  "one int : same padding used on all sides"
+                  "two int : bottom, right will use same padding as top, left"
+                  "four int : padding width in the order of (top, left, bottom, right)");
     TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
         .describe("Specifies the dilation rate to use for dilated convolution.");
     TVM_ATTR_FIELD(groups).set_default(1)
@@ -288,10 +294,17 @@ struct Conv2DTransposeAttrs : public tvm::AttrsNode<Conv2DTransposeAttrs> {
     TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
       .describe("The strides of the convolution.");
     TVM_ATTR_FIELD(output_padding).set_default(Array<IndexExpr>({0, 0}))
-      .describe("Zero-padding added to one side of the output.");
+      .describe("Zero-padding added to one side of the output."
+                "Padding support both symmetric and asymmetric as"
+                "one int : same padding used on all sides"
+                "two int : bottom, right will use same padding as top, left"
+                "four int : padding width in the order of (top, left, bottom, right)");
     TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
       .describe("If padding is non-zero, then the input is implicitly zero-padded"
-                "on both sides for padding number of points");
+                "Padding support both symmetric and asymmetric as"
+                "one int : same padding used on all sides"
+                "two int : bottom, right will use same padding as top, left"
+                "four int : padding width in the order of (top, left, bottom, right)");
     TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
       .describe("Specifies the dilation rate to use for dilated convolution.");
     TVM_ATTR_FIELD(groups).set_default(1)
@@ -817,7 +830,10 @@ struct DeformableConv2DAttrs : public tvm::AttrsNode<DeformableConv2DAttrs> {
         .describe("Specifies the strides of the convolution.");
     TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
         .describe("If padding is non-zero, then the input is implicitly zero-padded"
-                  "on both sides for padding number of points");
+                  "Padding support both symmetric and asymmetric as"
+                  "one int : same padding used on all sides"
+                  "two int : bottom, right will use same padding as top, left"
+                  "four int : padding width in the order of (top, left, bottom, right)");
     TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
         .describe("Specifies the dilation rate to use for dilated convolution.");
     TVM_ATTR_FIELD(deformable_groups).set_default(1)

python/tvm/contrib/pickle_memoize.py

@@ -84,7 +84,7 @@ def memoize(key, save_at_exit=False):
     """
     def _register(f):
         """Registration function"""
-        allow_types = (string_types, int, float)
+        allow_types = (string_types, int, float, tuple)
         fkey = key + "." + f.__name__ + ".pkl"
         if fkey not in Cache.cache_by_key:
             Cache.cache_by_key[fkey] = Cache(fkey, save_at_exit)

python/tvm/relay/frontend/tensorflow.py

@@ -372,24 +372,7 @@ def _impl(inputs, attr, params):
             pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
             pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
 
-            if opname != 'conv_transpose':
-                if attr['data_format'] == 'NHWC':
-                    inputs_data = _op.nn.pad(data=inputs_data,
-                                             pad_width=((0, 0),
-                                                        (pad_v[0], pad_v[1]),
-                                                        (pad_h[0], pad_h[1]),
-                                                        (0, 0)))
-                else:
-                    inputs_data = _op.nn.pad(data=inputs_data,
-                                             pad_width=((0, 0),
-                                                        (0, 0),
-                                                        (pad_v[0], pad_v[1]),
-                                                        (pad_h[0], pad_h[1])))
-
-                attr['padding'] = [0, 0]
-            else:
-                attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
-
+            attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
         else:
             msg = 'Value {} in attribute "padding" of operator Conv is not ' \
                   'valid.'

src/relay/op/nn/bitserial.cc

@@ -26,6 +26,7 @@
 #include <tvm/relay/attrs/bitserial.h>
 #include <tvm/relay/op.h>
 
+#include "../op_common.h"
 #include "../../pass/infer_layout_util.h"
 
 namespace tvm {
@@ -134,10 +135,12 @@ bool BinaryConv2DRel(const Array<Type>& types, int num_inputs, const Attrs& attr
   CHECK(param->channels.defined());
   CHECK(param->kernel_size.defined());
   Array<IndexExpr> oshape({dshape_nchw[0], param->channels, 0, 0});
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
   oshape.Set(
-      2, (dshape_nchw[2] + param->padding[0] * 2 - param->kernel_size[0]) / param->strides[0] + 1);
+      2, (dshape_nchw[2] + pad_h - param->kernel_size[0]) / param->strides[0] + 1);
   oshape.Set(
-      3, (dshape_nchw[3] + param->padding[1] * 2 - param->kernel_size[1]) / param->strides[1] + 1);
+      3, (dshape_nchw[3] + pad_w - param->kernel_size[1]) / param->strides[1] + 1);
   DataType out_dtype = param->out_dtype;
   oshape = trans_in_layout.BackwardShape(oshape);
   // assign output type

src/relay/op/nn/convolution.cc

@@ -166,7 +166,6 @@ with the layer input to produce a tensor of outputs.
 .add_type_rel("Conv3D", Conv3DRel<Conv3DAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ConvInferCorrectLayout<Conv3DAttrs>);
 
-
 // relay.nn.conv2d_transpose
 TVM_REGISTER_NODE_TYPE(Conv2DTransposeAttrs);
 
@@ -250,18 +249,8 @@ bool Conv2DTransposeRel(const Array<Type>& types,
   }
   // dilation
   Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
-  auto pad_h = param->padding[0];
-  auto pad_w = param->padding[1];
-  if (param->padding.size() == 2) {
-    pad_h *= 2;
-    pad_w *= 2;
-  } else if (param->padding.size() == 4) {
-    pad_h += param->padding[2];
-    pad_w += param->padding[3];
-  } else {
-    CHECK_EQ(param->padding.size(), 4) << " Padding should be 2 or 4, but got "
-        << param->padding.size();
-  }
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
   oshape.Set(2, (param->strides[0] * (dshape_nchw[2] - 1) + dilated_ksize_y -
                  pad_h + param->output_padding[0]));
   oshape.Set(3, (param->strides[1] * (dshape_nchw[3] - 1) + dilated_ksize_x -
@@ -557,14 +546,16 @@ bool Conv2DWinogradRel(const Array<Type>& types,
   // dilation
   Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
 
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
   if (!dshape_nchw[2].as<ir::Any>()) {
-    oshape.Set(2, (dshape_nchw[2] + param->padding[0] * 2
+    oshape.Set(2, (dshape_nchw[2] + pad_h
                    - dilated_ksize_y) / param->strides[0] + 1);
   } else {
     oshape.Set(2, dshape_nchw[2]);
   }
   if (!dshape_nchw[3].as<ir::Any>()) {
-    oshape.Set(3, (dshape_nchw[3] + param->padding[1] * 2
+    oshape.Set(3, (dshape_nchw[3] + pad_w
                    - dilated_ksize_x) / param->strides[1] + 1);
   } else {
     oshape.Set(3, dshape_nchw[3]);
@@ -1015,9 +1006,11 @@ bool DeformableConv2DRel(const Array<Type>& types, int num_inputs, const Attrs&
   // dilation
   Array<IndexExpr> oshape({data->shape[0], channels, 0, 0});
 
-  oshape.Set(2, indexdiv(data->shape[2] + param->padding[0] * 2 - dilated_ksize_y,
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
+  oshape.Set(2, indexdiv(data->shape[2] + pad_h - dilated_ksize_y,
                          param->strides[0]) + 1);
-  oshape.Set(3, indexdiv(data->shape[3] + param->padding[1] * 2 - dilated_ksize_x,
+  oshape.Set(3, indexdiv(data->shape[3] + pad_w - dilated_ksize_x,
                          param->strides[1]) + 1);
   DataType out_dtype = param->out_dtype;
 

src/relay/op/nn/convolution.h

@@ -117,15 +117,17 @@ bool Conv2DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   // dilation
   Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
 
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
   if (!dshape_nchw[2].as<ir::Any>()) {
-    oshape.Set(2, indexdiv(dshape_nchw[2] + param->padding[0] * 2 - dilated_ksize_y,
+    oshape.Set(2, indexdiv(dshape_nchw[2] + pad_h - dilated_ksize_y,
                            param->strides[0]) + 1);
   } else {
     oshape.Set(2, dshape_nchw[2]);
   }
 
   if (!dshape_nchw[3].as<ir::Any>()) {
-    oshape.Set(3, indexdiv(dshape_nchw[3] + param->padding[1] * 2 - dilated_ksize_x,
+    oshape.Set(3, indexdiv(dshape_nchw[3] + pad_w - dilated_ksize_x,
                            param->strides[1]) + 1);
   } else {
     oshape.Set(3, dshape_nchw[3]);

tests/python/contrib/test_nnpack.py

@@ -17,6 +17,7 @@
 import tvm
 import numpy as np
 import scipy.signal
+from topi.nn.util import get_pad_tuple
 from tvm.contrib import nnpack
 import pytest
 
@@ -59,17 +60,9 @@ def np_conv(na, nw, padding, stride=1):
     else:
         stride_h, stride_w = stride
 
-    if isinstance(padding, int):
-        pad_h = pad_w = padding * 2
-    else:
-        pad_h, pad_w = padding
-        pad_h *= 2
-        pad_w *= 2
-
-    pad_top = int(np.ceil(float(pad_h) / 2))
-    pad_bottom = pad_h - pad_top
-    pad_left = int(np.ceil(float(pad_w) / 2))
-    pad_right = pad_w - pad_left
+    pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel_h, kernel_w))
+    pad_h = pad_top + pad_bottom
+    pad_w = pad_left + pad_right
 
     out_channel = num_filter
     out_height = (in_height - kernel_h + pad_h) // stride_h + 1
@@ -78,9 +71,9 @@ def np_conv(na, nw, padding, stride=1):
     for n in range(batch):
         for f in range(out_channel):
             for c in range(in_channel):
-                if pad_h > 0:
+                if pad_h > 0 or pad_w > 0:
                     apad = np.zeros((in_height + pad_h, in_width + pad_w))
-                    apad[pad_top:-pad_bottom, pad_left:-pad_right] = na[n, c]
+                    apad[pad_top:pad_top + in_height, pad_left:pad_left + in_width] = na[n, c]
                 else:
                     apad = na[n, c]
                 out = scipy.signal.convolve2d(

topi/python/topi/arm_cpu/conv2d.py

@@ -197,11 +197,11 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
         CO *= VC
         KH, KW = H_CAT - tile_size + 1, W_CAT - tile_size + 1
     HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
+    pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
 
     assert layout == 'NCHW'
     assert KH == 3 and KW == 3 and HSTR == 1 and WSTR == 1
-    data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
+    data_pad = pad(data, (0, 0, pt, pl), (0, 0, pb, pr), name="data_pad")
 
     idxd = tvm.indexdiv
     idxm = tvm.indexmod
@@ -214,8 +214,8 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
     K = CO
     C = CI
 
-    H = (IH + 2 * HPAD - 3) // HSTR + 1
-    W = (IW + 2 * WPAD - 3) // WSTR + 1
+    H = (IH + pt + pb - 3) // HSTR + 1
+    W = (IW + pl + pr - 3) // WSTR + 1
     nH, nW = (H + m-1) // m, (W + m-1) // m
     P = N * nH * nW
 
@@ -387,12 +387,13 @@ def conv2d_arm_cpu_winograd_nnpack(
     assert len(kernel.shape) == 4
     CO, _, KH, KW = get_const_tuple(kernel.shape)
     HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
+    pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
 
     assert layout == 'NCHW'
-    assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
-    H = (IH + 2 * HPAD - 3) // HSTR + 1
-    W = (IW + 2 * WPAD - 3) // WSTR + 1
+    assert KH == 3 and KW == 3 and pt == 1 and pb == 1 and pl == 1 and pr == 1 and HSTR == 1\
+        and WSTR == 1
+    H = (IH + pt + pb - 3) // HSTR + 1
+    W = (IW + pl + pr - 3) // WSTR + 1
 
     cfg.define_knob('winograd_nnpack_algorithm', [convolution_algorithm])
 
@@ -407,7 +408,7 @@ def conv2d_arm_cpu_winograd_nnpack(
         output = tvm.contrib.nnpack.convolution_inference_without_weight_transform(
             data, transformed_kernel,
             bias=None,
-            padding=[HPAD, HPAD, WPAD, WPAD],
+            padding=[pt, pb, pl, pr],
             stride=[HSTR, WSTR],
             algorithm=cfg['winograd_nnpack_algorithm'].val)
 
@@ -467,21 +468,22 @@ def conv2d_winograd_nnpack_ww(cfg, data, transformed_kernel, bias, strides,
     assert len(transformed_kernel.shape) == 4
     CO, _, _, _ = get_const_tuple(transformed_kernel.shape)
     HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, (3, 3))
     KH, KW = 3, 3
+    pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
 
     assert layout == 'NCHW'
-    assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
-    H = (IH + 2 * HPAD - 3) // HSTR + 1
-    W = (IW + 2 * WPAD - 3) // WSTR + 1
+    assert KH == 3 and KW == 3 and pt == 1 and pb == 1 and pl == 1 and pr == 1 and HSTR == 1\
+        and WSTR == 1
+    H = (IH + pt + pb - 3) // HSTR + 1
+    W = (IW + pl + pr - 3) // WSTR + 1
 
     assert N == 1
     with tvm.tag_scope("winograd_nnpack_conv2d_output"):
         output = tvm.contrib.nnpack.convolution_inference_without_weight_transform(
             data=data,
             transformed_kernel=transformed_kernel,
             bias=bias,
-            padding=[HPAD, HPAD, WPAD, WPAD],
+            padding=[pt, pb, pl, pr],
             stride=[HSTR, WSTR],
             algorithm=cfg['winograd_nnpack_algorithm'].val)
 

topi/python/topi/bifrost/conv2d.py

@@ -276,11 +276,11 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
         H_CAT, W_CAT, CO, CI = get_const_tuple(kernel.shape)
         KH, KW = H_CAT - tile_size + 1, W_CAT - tile_size + 1
     HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
+    pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
 
     assert layout == 'NCHW'
     assert KH == 3 and KW == 3 and HSTR == 1 and WSTR == 1
-    data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
+    data_pad = pad(data, (0, 0, pt, pl), (0, 0, pb, pr), name="data_pad")
 
     r = KW
     m = tile_size
@@ -289,8 +289,8 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
 
     K = CO
     C = CI
-    H = (IH + 2 * HPAD - 3) // HSTR + 1
-    W = (IW + 2 * WPAD - 3) // WSTR + 1
+    H = (IH + pt + pb - 3) // HSTR + 1
+    W = (IW + pl + pr - 3) // WSTR + 1
     nH, nW = (H + m-1) // m, (W + m-1) // m
     P = N * nH * nW
 

topi/python/topi/cuda/conv2d.py

@@ -21,6 +21,7 @@
 from tvm.contrib import cudnn
 
 from .. import nn, generic
+from ..nn.util import get_pad_tuple
 from ..util import get_const_tuple, traverse_inline
 
 from .conv2d_direct import schedule_direct_cuda
@@ -48,8 +49,10 @@ def conv2d_cuda(cfg, data, kernel, strides, padding, dilation, layout='NCHW', ou
     strides : int or a list/tuple of two ints
         stride size, or [stride_height, stride_width]
 
-    padding : int or a list/tuple of two ints
-        padding size, or [pad_height, pad_width]
+    padding : int or a list/tuple of 2 or 4 ints
+        padding size, or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 4 ints
 
     dilation: int or a list/tuple of two ints
         dilation size, or [dilation_height, dilation_width]
@@ -80,11 +83,13 @@ def conv2d_cuda(cfg, data, kernel, strides, padding, dilation, layout='NCHW', ou
 
         # handle dilation
         stride_h, stride_w = (strides, strides) if isinstance(strides, int) else strides
-        pad_h, pad_w = (padding, padding) if isinstance(padding, int) else padding
         dilation_h, dilation_w = (dilation, dilation) if isinstance(dilation, int) else dilation
 
-        OH = (H + 2 * pad_h - KH) // stride_h + 1
-        OW = (W + 2 * pad_w - KW) // stride_w + 1
+        if isinstance(padding, (list, tuple)) and len(padding) > 2:
+            raise ValueError("Cudnn doesn't support asymmetric padding.")
+        pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
+        OH = (H + pt + pb - KH) // stride_h + 1
+        OW = (W + pl + pr - KW) // stride_w + 1
         cfg.add_flop(2 * N * OH * OW * CO * CI * ((KH - 1) * dilation_h + 1) *\
                     ((KW - 1) * dilation_w + 1))
 
@@ -97,7 +102,7 @@ def conv2d_cuda(cfg, data, kernel, strides, padding, dilation, layout='NCHW', ou
 
         return cudnn.conv_forward(data,
                                   kernel,
-                                  [pad_h, pad_w],
+                                  [pt, pl], # cudnn padding pt, pl on both sides of input
                                   [stride_h, stride_w],
                                   [dilation_h, dilation_w],
                                   conv_mode=1,