QConv1dUnpackWeightsInt8 Class — pytorch Architecture

Source Code

aten/src/ATen/native/quantized/qconv_unpack.cpp lines 80–119

class QConv1dUnpackWeightsInt8 final {
 public:
  static std::tuple<at::Tensor, std::optional<at::Tensor>> run(
      const c10::intrusive_ptr<ConvPackedParamsBase<2>>& packed_weight) {
    auto& ctx = at::globalContext();
    at::Tensor weight;
    std::optional<at::Tensor> bias;
#ifdef USE_FBGEMM
    if (ctx.qEngine() == at::QEngine::FBGEMM ||
        ctx.qEngine() == at::QEngine::X86) {
      std::tie(weight, bias) = packed_weight->unpack();
      weight = weight.squeeze_(quant_utils::kConv1dSqueezeDim + 2);
      return std::tuple<at::Tensor, std::optional<at::Tensor>>(weight, bias);
    }
#endif

#ifdef USE_PYTORCH_QNNPACK
    if (ctx.qEngine() == at::QEngine::QNNPACK) {
      std::tie(weight, bias) = packed_weight->unpack();
      at::Tensor new_weight = weight.clone();
      new_weight = new_weight.squeeze_(quant_utils::kConv1dSqueezeDim + 2);
      return std::tuple<at::Tensor, std::optional<at::Tensor>>(new_weight, bias);
    }
#endif

#if AT_MKLDNN_ENABLED()
    if (ctx.qEngine() == at::QEngine::ONEDNN) {
      std::tie(weight, bias) = packed_weight->unpack();
      at::Tensor new_weight = weight.clone();
      new_weight.squeeze_(quant_utils::kConv1dSqueezeDim + 2);
      return std::tuple<at::Tensor, std::optional<at::Tensor>>(new_weight, bias);
    }
#endif

    TORCH_CHECK(
        false,
        "Didn't find engine for operation quantized::conv1d_unpack ",
        toString(ctx.qEngine()));
  }
};