List of operations supported by QuantizedCPU backend

Hi, I was trying to quantize Vision Transformer from timm as follows:

class QuantAttention(nn.Module):

    def __init__(
        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        # quantization stubs
        self.quant = QuantStub()
        self.dequant = DeQuantStub()

    def forward(self, x):
        B, N, C = x.shape
        x = self.quant(x)
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        q, k = self.q_norm(q), self.k_norm(k)
        q = q * self.scale

        attn = q @ k.transpose(-2, -1)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.dequant(x)
        x = self.proj_drop(x)
        return x

However, some operations, in particular:

  • q = q * self.scale - multiplication by scalar
  • attn = q @ k.transpose(-2, -1) batched matmul
    are not supported by QuantizedCPU and the error message claims the following:
    NotImplementedError: Could not run 'aten::bmm.out' with arguments from the 'QuantizedCPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build).

The quantization pipeline is the following:

quant_attn.qconfig ='x86')
quant_attn =
for _ in range(10):
    inputs = torch.randn(1, 16, 128, device=device)
quant_attn =

The message appears on the call:

inputs = torch.randn(1, 16, 128, device=device)
outputs = quant_attn(inputs)

Where can one find a list of supported operations to better understand the limitations of the current Quantization API?


  • torch 2.1.0+cu118
  • torchvision 0.16.0+cu118

here is a somewhat outdated support matrix: Quantization — PyTorch master documentation

but yes, quantized bmm is not supported. but you can produce a representation for quantized bmm: (dq → bmm → q) in the new quantization flow and lower it to some backend that does support this op.

Thanks for your answer and help!