huggingface/candle · error

metal col2im1d {dtype:?} not implemented

Error message

metal col2im1d {dtype:?} not implemented

What it means

In the col2im-based conv_transpose1d path, Metal dispatches a col2im1d kernel by dtype; only F32, F16, BF16, U32, U8 have kernels. Any other dtype bails here, meaning transposed convolution cannot run for that dtype via this path.

Source

Thrown at candle-core/src/metal_backend/mod.rs:1025

                    "convtr1d: shape mismatch on c_in {:?} {:?}",
                    layout.shape(),
                    k_layout.shape()
                )
            }
            let buffer = self
                .device
                .new_buffer_builder()
                .with_size_for(dst_el, self.dtype)
                .with_label("conv_transpose1d")
                .build()?;

            let name = match self.dtype {
                DType::F32 => "col2im1d_f32",
                DType::F16 => "col2im1d_f16",
                DType::BF16 => "col2im1d_bf16",
                DType::U32 => "col2im1d_u32",
                DType::U8 => "col2im1d_u8",
                dtype => crate::bail!("metal col2im1d {dtype:?} not implemented"),
            };
            let col = {
                // This merges the last two dimensions of the kernel together.
                let kernel_l_mm = Layout::new(
                    (b_size, c_in, k_size * c_out).into(),
                    vec![0, k_size * c_out, 1],
                    k_layout.start_offset(),
                );
                self.matmul(
                    k,
                    (b_size, l_in, c_out * k_size, c_in),
                    &layout.transpose(1, 2)?,
                    &kernel_l_mm,
                )?
            };
            // It is important for the command encoder to be obtained *after* the matmul
            // kernel has run, otherwise we might use a command-buffer that has been committed
            // already resulting in the following error.

View on GitHub (pinned to d5fee525bf)

Solutions

1. Cast input and kernel to F32/F16/BF16 before conv_transpose1d
2. Cast back to the original dtype after the op if needed
3. Run conv_transpose1d on CPU for that tensor

Example fix

// before
let y = x_i64.conv_transpose1d(&k, padding, output_padding, stride, dilation, groups)?;
// after
let y = x_i64.to_dtype(DType::F32)?.conv_transpose1d(&k.to_dtype(DType::F32)?, padding, output_padding, stride, dilation, groups)?;

if !matches!(x.dtype(), DType::F32 | DType::F16 | DType::BF16 | DType::U32 | DType::U8) {
    x = x.to_dtype(DType::F32)?;
    kernel = kernel.to_dtype(DType::F32)?;
}

match x.conv_transpose1d(&k, p, op, s, d, g) {
    Ok(y) => y,
    Err(e) if e.to_string().contains("col2im1d") => {
        x.to_dtype(DType::F32)?.conv_transpose1d(&k.to_dtype(DType::F32)?, p, op, s, d, g)
    }
    Err(e) => return Err(e.into()),
}

When it happens

Trigger: Calling conv_transpose1d on Metal (with USE_COL2IM_CONV1D_TR enabled and col2im applicable) on an I64 or otherwise unsupported dtype tensor.

Common situations: Integer activations flowing into a transposed conv; dtype mismatch from a quantization pipeline; models ported with weights in i64.

Related errors

• Metal contiguous unary {name} {dtype:?} not implemented
• Metal strided unary {name} {dtype:?} not implemented
• Metal where_cond {left:?} {right:?} not implemented
• Metal conv1d {dtype:?} not implemented
• convtr1d: shape mismatch on c_in {:?} {:?}

AI-assisted analysis of huggingface/candle@d5fee525bf (2026-09-02). Data as JSON: /api/errors/e5cbc383ddde27cd. Report an issue: GitHub.