feat(mge/functional): add groups support for conv_transpose2d & 3d

GitOrigin-RevId: b75b792fb4

imperative/python/megengine/functional/nn.py

@@ -372,6 +372,7 @@ def conv_transpose2d(
     Args:
         inp: feature map of the convolution operation.
         weight: convolution kernel.
+            weight usually has shape ``(in_channels, out_channels, height, width)``.
         bias: bias added to the result of convolution (if given).
         stride: stride of the 2D convolution operation. Default: 1
         padding: size of the paddings added to the input on both sides of its
@@ -405,14 +406,12 @@ def conv_transpose2d(
         if weight.dtype != dtype:
             weight = weight.astype(dtype)
 
-    if groups != 1:
-        raise NotImplementedError("group transposed conv2d is not supported yet.")
-
     stride_h, stride_w = expand_hw(stride)
     pad_h, pad_w = expand_hw(padding)
     dilate_h, dilate_w = expand_hw(dilation)
-    compute_mode = _config._get_actual_op_param(compute_mode, _config.__compute_mode)
 
+    compute_mode = _config._get_actual_op_param(compute_mode, _config.__compute_mode)
+    sparse_type = "dense" if groups == 1 else "group"
     op = builtin.ConvolutionBackwardData(
         stride_h=stride_h,
         stride_w=stride_w,
@@ -422,6 +421,7 @@ def conv_transpose2d(
         dilate_w=dilate_w,
         strategy=get_execution_strategy(),
         compute_mode=compute_mode,
+        sparse=sparse_type,
     )
     (output,) = apply(op, weight, inp)
     if bias is not None:
@@ -447,6 +447,7 @@ def deformable_conv2d(
     Args:
         inp: input feature map.
         weight: convolution kernel.
+            weight usually has shape ``(out_channels, in_channels, height, width)``.
         offset: input offset to kernel, channel of this tensor should match the deformable settings.
         mask: input mask to kernel, channel of this tensor should match the deformable settings.
         bias: bias added to the result of convolution (if given).
@@ -551,6 +552,7 @@ def conv_transpose3d(
     stride: Union[int, Tuple[int, int, int]] = 1,
     padding: Union[int, Tuple[int, int, int]] = 0,
     dilation: Union[int, Tuple[int, int, int]] = 1,
+    groups: int = 1,
 ) -> Tensor:
     r"""3D transposed convolution operation. Only support the case that groups = 1
     and conv_mode = "cross_correlation".
@@ -581,6 +583,7 @@ def conv_transpose3d(
     if weight.dtype != dtype:
         weight = weight.astype(dtype)
 
+    sparse_type = "dense" if groups == 1 else "group"
     op = builtin.Convolution3DBackwardData(
         pad_d=pad[D],
         pad_h=pad[H],
@@ -592,6 +595,7 @@ def conv_transpose3d(
         dilate_h=dilate[H],
         dilate_w=dilate[W],
         strategy=get_execution_strategy(),
+        sparse=sparse_type,
     )
     (output,) = apply(op, weight, inp)
     if bias is not None:

imperative/python/megengine/module/conv.py

@@ -891,6 +891,7 @@ class ConvTranspose3d(_ConvNd):
         padding: Union[int, Tuple[int, int, int]] = 0,
         dilation: Union[int, Tuple[int, int, int]] = 1,
         bias: bool = True,
+        groups: int = 1,
     ):
         kernel_size = _triple_nonzero(kernel_size)
         stride = _triple_nonzero(stride)
@@ -903,7 +904,7 @@ class ConvTranspose3d(_ConvNd):
             stride=stride,
             padding=padding,
             dilation=dilation,
-            groups=1,
+            groups=groups,
             bias=bias,
         )
 
@@ -913,10 +914,21 @@ class ConvTranspose3d(_ConvNd):
         return kt * kh * kw * ic
 
     def _infer_weight_shape(self):
+        group = self.groups
         ichl = self.in_channels
         ochl = self.out_channels
         kt, kh, kw = self.kernel_size
-        return (ichl, ochl, kt, kh, kw)
+        if group == 1:
+            # Assume format is NCHW
+            return (ichl, ochl, kt, kh, kw)
+
+        assert (
+            ichl % group == 0 and ochl % group == 0
+        ), "invalid config: in_channels={} out_channels={} group={}".format(
+            ichl, ochl, group
+        )
+        # Assume format is NCHW
+        return (group, ichl // group, ochl // group, kt, kh, kw)
 
     def _infer_bias_shape(self):
         # Assume format is NCTHW

imperative/python/test/unit/functional/test_functional.py

@@ -1290,3 +1290,62 @@ def test_set_warp_perspective_config():
     expected = F.vision.warp_perspective(inp, M, (2, 2), format="NHWC")
     np.testing.assert_allclose(config_out.numpy(), expected.numpy())
     np.testing.assert_allclose(context_out.numpy(), expected.numpy())
+
+
+@pytest.mark.parametrize("stride", [(1, 1)])
+@pytest.mark.parametrize("padding", [(1, 1)])
+@pytest.mark.parametrize("dilation", [(1, 1)])
+@pytest.mark.parametrize("ksize", [(3, 3)])
+@pytest.mark.parametrize("groups", [1, 2])
+def test_local_conv2d(stride, padding, dilation, ksize, groups):
+    batch_size, in_channels, out_channels = 2, 4, 8
+    input_height, input_width = 10, 10
+    output_height = (input_height + padding[0] * 2 - ksize[0]) // stride[0] + 1
+    output_width = (input_width + padding[1] * 2 - ksize[1]) // stride[1] + 1
+
+    def local_conv2d_np(data, weight, stride, padding, dialtion):
+        # naive calculation use numpy
+        # only test output_height == input_height, output_width == input_width
+        data = np.pad(data, ((0, 0), (0, 0), (1, 1), (1, 1)))
+        expected = np.zeros(
+            (batch_size, out_channels, output_height, output_width), dtype=np.float32,
+        )
+        ic_group_size = in_channels // groups
+        oc_group_size = out_channels // groups
+        for n, oc, oh, ow in itertools.product(
+            *map(range, [batch_size, out_channels, output_height, output_width])
+        ):
+            ih, iw = oh * stride[0], ow * stride[1]
+            g_id = oc // oc_group_size
+            expected[n, oc, ih, iw] = np.sum(
+                data[
+                    n,
+                    g_id * ic_group_size : (g_id + 1) * ic_group_size,
+                    ih : ih + ksize[0],
+                    iw : iw + ksize[1],
+                ]
+                * weight[g_id, oh, ow, :, :, :, oc % oc_group_size]
+            )
+        return expected
+
+    data = np.random.rand(batch_size, in_channels, input_height, input_width).astype(
+        "float32"
+    )
+    weight = np.random.rand(
+        groups,
+        output_height,
+        output_width,
+        in_channels // groups,
+        *ksize,
+        out_channels // groups,
+    ).astype("float32")
+    output = F.local_conv2d(
+        tensor(data),
+        tensor(weight),
+        None,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+    )
+    ref = local_conv2d_np(data, weight, stride, padding, dilation)
+    np.testing.assert_almost_equal(output.numpy(), ref, 5)

imperative/python/test/unit/module/test_conv.py

@@ -42,162 +42,3 @@ def test_conv_dtype_promotion(name, reproducible):
         m = getattr(M, name)(Ci, Co, K)
     x = tensor(np.random.random(size=(N, Ci) + S).astype("float16"))
     np.testing.assert_equal(m(x).numpy(), m(x.astype("float32")).numpy())
-
-
-def test_conv_transpose2d():
-    SH, SW = 3, 1
-    PH, PW = 2, 0
-    N, IC, IH, IW = 4, 5, 8, 6
-    KH, KW = 3, 4
-    OC = 3
-    BIAS = False
-
-    def getsize(inp, kern, stride):
-        return (inp - 1) * stride + kern
-
-    OH = getsize(IH, KH, SH)
-    OW = getsize(IW, KW, SW)
-
-    inp = np.random.normal(size=(N, IC, IH, IW)).astype(np.float32)
-    out = np.zeros((N, OC, OH, OW), dtype=np.float32)
-    weight = np.random.normal(size=(IC, OC, KH, KW)).astype(np.float32)
-    bias = np.random.normal(size=(1, OC, 1, 1)).astype(np.float32)
-
-    # naive calculation use numpy
-    for n, ic, ih, iw in itertools.product(*map(range, [N, IC, IH, IW])):
-        oh, ow = ih * SH, iw * SW
-        out[n, :, oh : oh + KH, ow : ow + KW] += inp[n, ic, ih, iw] * weight[ic]
-    out = out[:, :, PH : OH - PH, PW : OW - PW]
-    if BIAS:
-        out += bias
-
-    # megengine conv_transpose2d calculation
-    conv_transpose2d = ConvTranspose2d(IC, OC, (KH, KW), (SH, SW), (PH, PW), bias=BIAS)
-    conv_transpose2d.weight = Parameter(weight, dtype=np.float32)
-    if BIAS:
-        conv_transpose2d.bias = Parameter(bias, dtype=np.float32)
-    y = conv_transpose2d(tensor(inp))
-
-    np.testing.assert_almost_equal(out, y.numpy(), 2e-6)
-
-
-def test_local_conv2d():
-    def test_func(
-        batch_size,
-        in_channels,
-        out_channels,
-        input_height,
-        input_width,
-        kernel_size,
-        stride,
-        padding,
-        dilation,
-        groups,
-    ):
-        local_conv2d = LocalConv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            input_height=input_height,
-            input_width=input_width,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-        )
-        inputs = np.random.normal(
-            size=(batch_size, in_channels, input_height, input_width)
-        ).astype(np.float32)
-        output_height = (input_height + padding * 2 - kernel_size) // stride + 1
-        output_width = (input_width + padding * 2 - kernel_size) // stride + 1
-        weights = local_conv2d.weight.numpy()
-        outputs = local_conv2d(tensor(inputs))
-        # naive calculation use numpy
-        # only test output_height == input_height, output_width == input_width
-        inputs = np.pad(inputs, ((0, 0), (0, 0), (1, 1), (1, 1)))
-        expected = np.zeros(
-            (batch_size, out_channels, output_height, output_width), dtype=np.float32,
-        )
-        ic_group_size = in_channels // groups
-        oc_group_size = out_channels // groups
-        for n, oc, oh, ow in itertools.product(
-            *map(range, [batch_size, out_channels, output_height, output_width])
-        ):
-            ih, iw = oh * stride, ow * stride
-            g_id = oc // oc_group_size
-            expected[n, oc, ih, iw] = np.sum(
-                inputs[
-                    n,
-                    g_id * ic_group_size : (g_id + 1) * ic_group_size,
-                    ih : ih + kernel_size,
-                    iw : iw + kernel_size,
-                ]
-                * weights[g_id, oh, ow, :, :, :, oc % oc_group_size]
-            )
-        np.testing.assert_almost_equal(outputs.numpy(), expected, 1e-5)
-
-    test_func(10, 4, 4, 5, 5, 3, 1, 1, 1, 1)
-    test_func(10, 32, 32, 8, 8, 3, 1, 1, 1, 2)
-    test_func(10, 32, 32, 8, 8, 3, 1, 1, 1, 4)
-
-
-def test_conv_transpose3d():
-    def getsize(inp, kernel, stride, dilate):
-        return (inp - 1) * stride + kernel * dilate - dilate + 1
-
-    def test_func(
-        N,
-        IC,
-        ID,
-        IH,
-        IW,
-        OC,
-        KD,
-        KH,
-        KW,
-        SD,
-        SH,
-        SW,
-        PD,
-        PH,
-        PW,
-        DD,
-        DH,
-        DW,
-        bias=True,
-    ):
-        conv_transpose3d = ConvTranspose3d(
-            in_channels=IC,
-            out_channels=OC,
-            kernel_size=(KD, KH, KW),
-            stride=(SD, SH, SW),
-            padding=(PD, PH, PW),
-            dilation=(DD, DH, DW),
-            bias=bias,
-        )
-
-        OD = getsize(ID, KD, SD, DD)
-        OH = getsize(IH, KH, SH, DH)
-        OW = getsize(IW, KW, SW, DW)
-
-        inp = np.random.normal(size=(N, IC, ID, IH, IW))
-        weight = np.random.normal(size=(IC, OC, KD, KH, KW))
-        out_np = np.zeros((N, OC, OD, OH, OW), dtype=np.float32)
-
-        for n, ic, idepth, ih, iw in itertools.product(
-            *map(range, [N, IC, ID, IH, IW])
-        ):
-            od, oh, ow = idepth * SD, ih * SH, iw * SW
-            out_np[n, :, od : od + KD, oh : oh + KH, ow : ow + KW] += (
-                inp[n, ic, idepth, ih, iw] * weight[ic]
-            )
-        out_np = out_np[:, :, PD : OD - PD, PH : OH - PH, PW : OW - PW]
-
-        assert conv_transpose3d.weight.numpy().shape == weight.shape
-        conv_transpose3d.weight = Parameter(weight)
-        out_meg = conv_transpose3d.forward(tensor(inp))
-
-        np.testing.assert_almost_equal(out_meg.numpy(), out_np, 1e-5)
-
-    test_func(4, 3, 8, 16, 16, 8, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1)
-    test_func(4, 8, 16, 32, 32, 16, 1, 3, 1, 2, 1, 2, 0, 1, 0, 1, 1, 1)