[Relay] Add conv2d_backward_weight op (without topi)

python/tvm/relay/op/_tensor_grad.py

@@ -52,7 +52,6 @@
     reshape_like,
     strided_slice,
     take,
-    tile,
     transpose,
     where,
     repeat,
@@ -399,15 +398,14 @@ def conv2d_grad(orig, grad):
     data_shape = get_const_tuple(data.checked_type.shape)
     weight_shape = get_const_tuple(weight.checked_type.shape)
     _, _, grad_h, grad_w = get_const_tuple(orig.checked_type.shape)
-    batch, in_channel, in_h, in_w = data_shape
-    out_channel, _, filter_h, filter_w = weight_shape
+    _, _, in_h, in_w = data_shape
+    _, _, filter_h, filter_w = weight_shape
 
     # infer output_padding
     fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(
         get_const_tuple(attrs.padding), (filter_h, filter_w)
     )
     stride_h, stride_w = get_const_tuple(attrs.strides)
-    dilation_h, dilation_w = get_const_tuple(attrs.dilation)
     out_h = (grad_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h
     out_w = (grad_w - 1) * stride_w - fpad_left - fpad_right + filter_w
     output_padding = (in_h - out_h, in_w - out_w)
@@ -425,46 +423,21 @@ def conv2d_grad(orig, grad):
         groups=attrs.groups,
         output_padding=output_padding,
     )
-    grad = tile(grad, [1, in_channel // attrs.groups, 1, 1])
-    grad = reshape(grad, [-1, 1, 0, 0])  # batch * oc * ic // groups, 1, oh, ow
-    data = reshape(data, [1, -1, 0, 0])  # 1, batch * ic, ih, iw
 
-    backward_weight = _nn.conv2d(
-        data,
+    backward_weight = _nn.conv2d_backward_weight(
         grad,
-        strides=attrs.dilation,
+        data,
+        strides=attrs.strides,
         padding=attrs.padding,
-        dilation=attrs.strides,
-        groups=in_channel * batch,
-    )
-    # infer shape of backward_weight
-    padded_weight_grad_h = (
-        in_h - (grad_h - 1) * stride_h - 1 + fpad_top + fpad_bottom
-    ) // dilation_h + 1
-    padded_weight_grad_w = (
-        in_w - (grad_w - 1) * stride_w - 1 + fpad_left + fpad_right
-    ) // dilation_w + 1
-    backward_weight = reshape(
-        backward_weight,
-        [
-            batch,
-            in_channel // attrs.groups,
-            out_channel,
-            padded_weight_grad_h,
-            padded_weight_grad_w,
-        ],
+        dilation=attrs.dilation,
+        groups=attrs.groups,
+        channels=attrs.channels,
+        kernel_size=(filter_h, filter_w),
+        grad_layout=attrs.out_layout if attrs.out_layout else attrs.data_layout,
+        data_layout=attrs.data_layout,
+        kernel_layout=attrs.kernel_layout,
+        out_dtype=attrs.out_dtype,
     )
-    backward_weight = _sum(backward_weight, axis=0)
-    backward_weight = transpose(backward_weight, [1, 0, 2, 3])
-
-    assert padded_weight_grad_h >= filter_h
-    assert padded_weight_grad_w >= filter_w
-    if padded_weight_grad_h > filter_h or padded_weight_grad_w > filter_w:
-        backward_weight = strided_slice(
-            backward_weight,
-            begin=[0, 0, 0, 0],
-            end=[out_channel, in_channel // attrs.groups, filter_h, filter_w],
-        )
 
     return [backward_data, backward_weight]
 

python/tvm/relay/op/nn/_nn.py

@@ -23,6 +23,7 @@
 from tvm.runtime import convert
 from tvm.te.hybrid import script
 from tvm.topi.utils import get_const_tuple
+from tvm.topi.nn.utils import get_pad_tuple
 
 from ....ir import container
 from ....tir import expr
@@ -1061,6 +1062,83 @@ def compute_space_to_depth(attrs, inputs, out_dtype):
 reg.register_injective_schedule("nn.batch_to_space_nd")
 
 
+@reg.register_legalize("nn.conv2d_backward_weight")
+def legalize_conv2d_backward_weight(attrs, inputs, types):
+    """Legalize conv2d_backward_weight op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current op
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    grad, data = inputs
+    data_shape = get_const_tuple(data.checked_type.shape)
+    weight_shape = get_const_tuple(types[2].shape)
+    _, out_channel, grad_h, grad_w = get_const_tuple(grad.checked_type.shape)
+    batch, in_channel, in_h, in_w = data_shape
+    _, _, filter_h, filter_w = weight_shape
+    fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(
+        get_const_tuple(attrs.padding), (filter_h, filter_w)
+    )
+    stride_h, stride_w = get_const_tuple(attrs.strides)
+    dilation_h, dilation_w = get_const_tuple(attrs.dilation)
+
+    grad = relay.tile(grad, [1, in_channel // attrs.groups, 1, 1])
+    grad = relay.reshape(grad, [-1, 1, 0, 0])  # batch * oc * ic // groups, 1, oh, ow
+    data = relay.reshape(data, [1, -1, 0, 0])  # 1, batch * ic, ih, iw
+
+    backward_weight = relay.nn.conv2d(
+        data,
+        grad,
+        strides=attrs.dilation,
+        padding=attrs.padding,
+        dilation=attrs.strides,
+        groups=in_channel * batch,
+    )
+
+    # infer shape of backward_weight
+    padded_weight_grad_h = (
+        in_h - (grad_h - 1) * stride_h - 1 + fpad_top + fpad_bottom
+    ) // dilation_h + 1
+    padded_weight_grad_w = (
+        in_w - (grad_w - 1) * stride_w - 1 + fpad_left + fpad_right
+    ) // dilation_w + 1
+
+    backward_weight = relay.reshape(
+        backward_weight,
+        [
+            batch,
+            in_channel // attrs.groups,
+            out_channel,
+            padded_weight_grad_h,
+            padded_weight_grad_w,
+        ],
+    )
+    backward_weight = relay.sum(backward_weight, axis=0)
+    backward_weight = relay.transpose(backward_weight, [1, 0, 2, 3])
+
+    assert padded_weight_grad_h >= filter_h
+    assert padded_weight_grad_w >= filter_w
+
+    if padded_weight_grad_h > filter_h or padded_weight_grad_w > filter_w:
+        backward_weight = relay.strided_slice(
+            backward_weight,
+            begin=[0, 0, 0, 0],
+            end=[out_channel, in_channel // attrs.groups, filter_h, filter_w],
+        )
+
+    return backward_weight
+
+
 #####################
 #  Shape functions  #
 #####################

python/tvm/relay/op/nn/nn.py

@@ -3770,3 +3770,54 @@ def batch_to_space_nd(data, block_shape, crops):
     """
 
     return _make.batch_to_space_nd(data, block_shape, crops)
+
+
+def conv2d_backward_weight(
+    grad,
+    data,
+    strides=(1, 1),
+    padding=(0, 0),
+    dilation=(1, 1),
+    groups=1,
+    channels=None,
+    kernel_size=None,
+    grad_layout="NCHW",
+    data_layout="NCHW",
+    kernel_layout="OIHW",
+    out_dtype="",
+):
+    r"""The gradient of conv2d with respect to weight.
+
+    This operator takes the output gradient `grad` and convolves it with `data` as
+    the convolution kernel, to produce the gradient with respect to weight.
+
+    Note that the parameter `kernel_size` is the spatial size of the corresponding
+    forward convolution kernel, not that of `data`. `grad_layout` and
+    `kernel_layout` are the layouts of `grad` and the weight gradient respectively.
+
+    Other parameters are the same as the conv2d op. See its documentation for more
+    details.
+
+    """
+    if isinstance(kernel_size, int):
+        kernel_size = (kernel_size, kernel_size)
+    if isinstance(strides, int):
+        strides = (strides, strides)
+    if isinstance(dilation, int):
+        dilation = (dilation, dilation)
+    padding = get_pad_tuple2d(padding)
+
+    return _make.conv2d_backward_weight(
+        grad,
+        data,
+        strides,
+        padding,
+        dilation,
+        groups,
+        channels,
+        kernel_size,
+        grad_layout,
+        data_layout,
+        kernel_layout,
+        out_dtype,
+    )

python/tvm/relay/testing/__init__.py

@@ -127,6 +127,7 @@ def check_grad(
 
     fwd_func = run_infer_type(func)
     bwd_func = run_infer_type(gradient(fwd_func, mode=mode))
+    bwd_func = run_opt_pass(bwd_func, relay.transform.Legalize())
 
     if scale is None:
         scale = 10 * eps

python/tvm/topi/testing/__init__.py

@@ -75,3 +75,4 @@
 from .nll_loss import nll_loss
 from .dense import dense
 from .searchsorted import searchsorted_ref
+from .conv2d_backcward_weight_python import conv2d_backward_weight_nchw_python

python/tvm/topi/testing/conv2d_backcward_weight_python.py

@@ -0,0 +1,76 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-nested-blocks
+"""Gradient of conv2d with respect to weight in python"""
+import numpy as np
+
+
+# Reference: cutlass/tools/util/include/cutlass/util/reference/host/convolution.h
+def conv2d_backward_weight_nchw_python(dy_np, x_np, kernel_size, stride, padding):
+    """Gradient of the conv2d op with respect to weight, in NCHW layout.
+
+    Parameters
+    ----------
+    dy_np : numpy.ndarray
+        4-D with shape [batch, in_channel, out_height, out_width]
+
+    x_np : numpy.ndarray
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    kernel_size : tuple of two ints
+        Height and width of the weight
+
+    stride : tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    padding : tuple of two ints
+        Spatial padding, or [pad_h, pad_w]
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+
+    """
+    N, C, H, W = x_np.shape
+    _, K, P, Q = dy_np.shape
+    R, S = kernel_size
+    pad_h, pad_w = padding
+    stride_h, stride_w = stride
+    dw = np.zeros((K, C, R, S)).astype(dy_np.dtype)
+
+    for k in range(K):
+        for r in range(R):
+            for s in range(S):
+                for c in range(C):
+                    acc = 0
+                    for n in range(N):
+                        for p in range(P):
+                            for q in range(Q):
+                                coord = (n, c, p * stride_h - pad_h + r, q * stride_w - pad_w + s)
+
+                                if (
+                                    coord[2] < H
+                                    and coord[2] >= 0
+                                    and coord[3] < W
+                                    and coord[3] >= 0
+                                ):
+                                    acc += dy_np[n, k, p, q] * x_np[coord]
+
+                    dw[k, c, r, s] = acc
+
+    return dw