[co63oc]

PR Category

Execute Infrastructure

PR Types

Improvements

Description

[0-size Tensor No.57-58、60-61、63-66] Add 0-size Tensor support for fft2

修改前向和反向
paddle/phi/infermeta/unary.cc 修改等于0判断
symbolic_shape 没有对应代码，查找 FftC2cOpInferSymbolicShape FftC2rOpInferSymbolicShape FftR2cOpInferSymbolicShape

Paddle/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc

Line 1348 in 6c675da

bool FftC2rOpInferSymbolicShape(pir::Operation *op,

cpu/gpu kernel共用impl实现

PaddleAPITest 测试已修复cuda error , paddle error，存在错误为torch error
fft2

fftn

ifft2

ifftn

ihfft2

ihfftn

rfft2

rfftn

单测中取消 test_zero_point 中检查错误的判断，这样 irfft2 中 s=None 可以

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[DanielSun11]

涉及到这几个fft相关的kernel是不是input和output的shape是相同的？若是，x.numel() == 0时，out也为0 size Tensor，这时直接return就行吧？此处调用Full 是另有什么考虑吗？

[co63oc]

>>> scipy.fft.fft2(np.random.random([3, 0, 1, 2]), s=(1, 2), axes=(0, 1), norm='backward')
array([[[[0.-0.j, 0.-0.j]],

        [[0.-0.j, 0.-0.j]]]])

这样执行时会返回为0，这里不容易理解，已增加到注释

[DanielSun11]

ok,

void FFTR2CInferMeta(const MetaTensor& x,
                     const std::vector<int64_t>& axes,
                     const std::string& normalization,
                     bool forward,
                     bool onesided,
                     MetaTensor* out,
                     MetaConfig config) {
  PADDLE_ENFORCE_NOT_NULL(
      out,
      common::errors::InvalidArgument("Output of fft_r2c should not be null."));
  const phi::DDim x_dim = x.dims();

  // only ensure that fft axes' size greater than zero at runtime
  // they might be -1 to indicate unknown size ar compile time
  if (config.is_runtime) {
    for (auto axis : axes) {
      PADDLE_ENFORCE_GT(x_dim[axis],
                        0,
                        common::errors::InvalidArgument(
                            "Invalid fft n-point (%d).", x_dim[axis]));
    }
  }

  out->set_layout(x.layout());
  out->set_dtype(ToComplexType(x.dtype()));
  if (!onesided) {
    out->share_dims(x);
  } else {
    phi::DDim out_dim = x.dims();
    const int last_fft_axis = static_cast<int>(axes.back());
    const int64_t last_fft_dim_size = x_dim[last_fft_axis];
    out_dim.at(last_fft_axis) = last_fft_dim_size / 2 + 1;
    out->set_dims(out_dim);
  }
}

fft相关的kernel input和output的shape不一定相同

[DanielSun11]

LGTM