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Oct 4, 2025
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Add transposed im2row #14738

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156 changes: 156 additions & 0 deletions backends/cadence/aot/ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1416,3 +1416,159 @@ def im2row_per_tensor(
torch.tensor(in_zero_point, dtype=torch.int32),
channel_last,
)


@impl(m, "transposed_im2row")
def transposed_im2row(
input_tensor: torch.Tensor,
kernel_size: tuple[int, int],
dilation: tuple[int, int],
padding: tuple[int, int],
stride: tuple[int, int],
output_padding: tuple[int, int],
in_zero_point: torch.Tensor,
channel_last: bool = False,
) -> torch.Tensor:
"""
Converts input tensor patches into im2row format for transposed convolutions.
This function extracts patches from input in a pattern suitable for transposed convolution.

Args:
- input_tensor: Input spatial tensor, NCHW or NHWC format (3D or 4D).
- kernel_size: Size of the convolution kernel.
- dilation: Dilation of the convolution kernel.
- padding: Padding to apply to the input.
- stride: Stride of the convolution.
- output_padding: Additional output padding for transposed convolution.
- in_zero_point: Zero point for input quantization (broadcastable to input).
- channel_last: If True, input is in NHWC format, else NCHW.

Returns:
- 3D tensor of shape (N, output_h * output_w, kernel_h * kernel_w * in_c)
"""
# Handle 1D convolution case by adding height dimension
if len(input_tensor.shape) == 3:
height_dim = 1 if channel_last else 2
input_tensor = input_tensor.unsqueeze(height_dim)

if in_zero_point is not None:
if in_zero_point.dtype != torch.int32:
raise ValueError("Input zero point must be an int32 tensor")

# Move to NCHW for processing if needed
if channel_last:
input_tensor = input_tensor.movedim(-1, -3).contiguous() # NHWC -> NCHW

N, C, H_in, W_in = input_tensor.shape

# Output: (N, C*H_in*W_in, H_out, W_out)
H_out = (
(H_in - 1) * stride[0]
+ kernel_size[0]
+ output_padding[0]
- 2 * padding[0]
+ dilation[0] * (kernel_size[0] - 1)
)
W_out = (
(W_in - 1) * stride[1]
+ kernel_size[1]
+ output_padding[1]
- 2 * padding[1]
+ dilation[1] * (kernel_size[1] - 1)
)

# For each input pixel, create a channel where the upsampled (transposed conv) patch is placed
# Output: (N, C*H_in*W_in, H_out, W_out)
inp_flat = input_tensor.reshape(N, C * H_in * W_in)

# Calculate output spatial size
H_out = (
(H_in - 1) * stride[0]
- 2 * padding[0]
+ dilation[0] * (kernel_size[0] - 1)
+ output_padding[0]
+ 1
)
W_out = (
(W_in - 1) * stride[1]
- 2 * padding[1]
+ dilation[1] * (kernel_size[1] - 1)
+ output_padding[1]
+ 1
)

# Compute the upsampled (top-left) position for each input pixel
h_idx = torch.arange(H_in, device=input_tensor.device)
w_idx = torch.arange(W_in, device=input_tensor.device)
grid_h, grid_w = torch.meshgrid(h_idx, w_idx, indexing="ij")
out_h_idx = grid_h * stride[0] - padding[0]
out_w_idx = grid_w * stride[1] - padding[1]

# Compute all input pixel positions (flattened)
ch_idx = torch.arange(C * H_in * W_in, device=input_tensor.device)
ij_idx = ch_idx % (H_in * W_in)
i_idx = ij_idx // W_in
j_idx = ij_idx % W_in

# For each input pixel, compute the output positions for the kernel window
kh_idx = torch.arange(kernel_size[0], device=input_tensor.device)
kw_idx = torch.arange(kernel_size[1], device=input_tensor.device)
kh_grid, kw_grid = torch.meshgrid(kh_idx, kw_idx, indexing="ij")
kh_grid = kh_grid.reshape(-1)
kw_grid = kw_grid.reshape(-1)
num_kernel = kernel_size[0] * kernel_size[1]

# Broadcast to all channels and kernel positions
ch_idx_b = ch_idx.repeat_interleave(num_kernel)
n_kernel = ch_idx.shape[0] * num_kernel

i_idx_b = i_idx.repeat_interleave(num_kernel)
j_idx_b = j_idx.repeat_interleave(num_kernel)
kh_b = kh_grid.repeat(ch_idx.shape[0])
kw_b = kw_grid.repeat(ch_idx.shape[0])

h_out = out_h_idx[i_idx_b, j_idx_b] + kh_b * dilation[0]
w_out = out_w_idx[i_idx_b, j_idx_b] + kw_b * dilation[1]

# Mask for valid output positions
valid = (h_out >= 0) & (h_out < H_out) & (w_out >= 0) & (w_out < W_out)

# Prepare indices for advanced indexing
n_idx = (
torch.arange(N, device=input_tensor.device)
.view(-1, 1)
.expand(N, n_kernel)
.reshape(-1)
)
ch_idx_full = ch_idx_b.expand(N, n_kernel).reshape(-1)
h_out_full = h_out.expand(N, n_kernel).reshape(-1)
w_out_full = w_out.expand(N, n_kernel).reshape(-1)
valid_full = valid.expand(N, n_kernel).reshape(-1)

# Gather input values for each channel
inp_vals = inp_flat[:, ch_idx_b].reshape(-1)

# Create output tensor
patches = torch.zeros((N, C * H_in * W_in, H_out, W_out), dtype=input_tensor.dtype)

# If in_zero_point is provided, fill patches with it
if in_zero_point is not None:
if in_zero_point.numel() == 1:
patches.fill_(in_zero_point.item())
else:
# Broadcast in_zero_point to (N, C, H_in, W_in)
assert in_zero_point.shape == (N,)
in_zero_point = in_zero_point.view(N, 1, 1, 1)
patches = patches + in_zero_point

# Scatter input values to output positions (only valid positions)
patches[
n_idx[valid_full],
ch_idx_full[valid_full],
h_out_full[valid_full],
w_out_full[valid_full],
] = inp_vals[valid_full]

# Optionally, flatten to (N, num_patches, patch_size) if needed
patches = patches.view(N, C * H_in * W_in, -1).transpose(1, 2).contiguous()
return patches
170 changes: 170 additions & 0 deletions backends/cadence/aot/tests/test_ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2136,3 +2136,173 @@ def test_im2row(
torch.equal(output, expected_output),
f"im2row output mismatch in {name}: got {output}, expected {expected_output}",
)

@expand(
[
(
"basic_2x2",
torch.tensor([[[[1, 2], [3, 4]]]], dtype=torch.int32),
(2, 2),
(1, 1),
(0, 0),
(1, 1),
(0, 0),
None,
False,
torch.tensor(
[
[
[1, 0, 0, 0],
[1, 2, 0, 0],
[0, 2, 0, 0],
[1, 0, 3, 0],
[1, 2, 3, 4],
[0, 2, 0, 4],
[0, 0, 3, 0],
[0, 0, 3, 4],
[0, 0, 0, 4],
]
],
dtype=torch.int32,
),
),
(
"basic_2x2_with_zero_point",
torch.tensor([[[[1, 2], [3, 4]]]], dtype=torch.int32),
(2, 2),
(1, 1),
(0, 0),
(1, 1),
(0, 0),
torch.tensor(100, dtype=torch.int32),
False,
torch.tensor(
[
[
[1, 100, 100, 100],
[1, 2, 100, 100],
[100, 2, 100, 100],
[1, 100, 3, 100],
[1, 2, 3, 4],
[100, 2, 100, 4],
[100, 100, 3, 100],
[100, 100, 3, 4],
[100, 100, 100, 4],
]
],
dtype=torch.int32,
),
),
(
"basic_2x2_with_stride_2",
torch.tensor([[[[1, 2], [3, 4]]]], dtype=torch.int32),
(2, 2), # kernel size
(1, 1), # dilation
(0, 0), # padding
(2, 2), # stride
(0, 0), # output padding
None,
False,
torch.tensor(
[
[
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 2, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 2, 0, 0],
[0, 0, 3, 0],
[0, 0, 3, 0],
[0, 0, 0, 4],
[0, 0, 0, 4],
[0, 0, 3, 0],
[0, 0, 3, 0],
[0, 0, 0, 4],
[0, 0, 0, 4],
]
],
dtype=torch.int32,
),
),
(
"batch2_with_batch2_zero_point",
torch.tensor(
[
[[[1, 2], [3, 4]]],
[[[5, 6], [7, 8]]],
],
dtype=torch.int32,
), # input: (2,1,2,2)
(2, 2), # kernel_size
(1, 1), # dilation
(0, 0), # padding
(1, 1), # stride
(0, 0), # output_padding
torch.tensor([100, 200], dtype=torch.int32), # in_zero_point per batch
False, # channel_last
torch.tensor(
[
[
[1, 100, 100, 100],
[1, 2, 100, 100],
[100, 2, 100, 100],
[1, 100, 3, 100],
[1, 2, 3, 4],
[100, 2, 100, 4],
[100, 100, 3, 100],
[100, 100, 3, 4],
[100, 100, 100, 4],
],
[
[5, 200, 200, 200],
[5, 6, 200, 200],
[200, 6, 200, 200],
[5, 200, 7, 200],
[5, 6, 7, 8],
[200, 6, 200, 8],
[200, 200, 7, 200],
[200, 200, 7, 8],
[200, 200, 200, 8],
],
],
dtype=torch.int32,
),
),
]
)
def test_transposed_im2row(
self,
name: str,
input_tensor: torch.Tensor,
kernel_size: tuple[int, int],
dilation: tuple[int, int],
padding: tuple[int, int],
stride: tuple[int, int],
output_padding: tuple[int, int],
in_zero_point: torch.Tensor | int | None,
channel_last: bool,
expected_output: torch.Tensor,
) -> None:
output = torch.ops.cadence.transposed_im2row(
input_tensor,
kernel_size,
dilation,
padding,
stride,
output_padding,
in_zero_point,
channel_last,
)

self.assertEqual(
output.shape,
expected_output.shape,
f"transposed_im2row output shape mismatch in {name}: got {output.shape}, expected {expected_output.shape}",
)
self.assertTrue(
torch.equal(output, expected_output),
f"transposed_im2row output mismatch in {name}: got {output}, expected {expected_output}",
)
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