Internal implementation for std (standard deviation = sqrt(variance))
| 29 | |
| 30 | // Internal implementation for std (standard deviation = sqrt(variance)) |
| 31 | inline Tensor std_impl(const Tensor& self, |
| 32 | const std::vector<int64_t>& dims_vec, |
| 33 | double correction_value, |
| 34 | bool keepdim) { |
| 35 | // Validate dimensions before processing |
| 36 | int64_t ndim = self.dim(); |
| 37 | for (int64_t d : dims_vec) { |
| 38 | int64_t dim_idx = d < 0 ? d + ndim : d; |
| 39 | if (dim_idx < 0 || dim_idx >= ndim) { |
| 40 | PD_CHECK(false, |
| 41 | "Dimension out of range (expected to be in range of [", |
| 42 | -ndim, |
| 43 | ", ", |
| 44 | ndim - 1, |
| 45 | "], but got ", |
| 46 | d, |
| 47 | ")"); |
| 48 | } |
| 49 | } |
| 50 | phi::IntArray dims_int_array(dims_vec); |
| 51 | paddle::Tensor tensor = self._PD_GetInner(); |
| 52 | |
| 53 | paddle::Tensor mean_tensor; |
| 54 | if (dims_vec.empty()) { |
| 55 | mean_tensor = paddle::experimental::mean( |
| 56 | tensor, phi::IntArray(std::vector<int64_t>{}), true); |
| 57 | } else { |
| 58 | mean_tensor = paddle::experimental::mean(tensor, dims_int_array, true); |
| 59 | } |
| 60 | |
| 61 | paddle::Tensor diff = paddle::experimental::subtract(tensor, mean_tensor); |
| 62 | paddle::Tensor diff_squared = paddle::experimental::multiply(diff, diff); |
| 63 | |
| 64 | paddle::Tensor sum_squared_diff; |
| 65 | if (dims_vec.empty()) { |
| 66 | sum_squared_diff = |
| 67 | paddle::experimental::sum(diff_squared, |
| 68 | phi::IntArray(std::vector<int64_t>{}), |
| 69 | diff_squared.dtype(), |
| 70 | keepdim); |
| 71 | } else { |
| 72 | sum_squared_diff = paddle::experimental::sum( |
| 73 | diff_squared, dims_int_array, diff_squared.dtype(), keepdim); |
| 74 | } |
| 75 | |
| 76 | int64_t n = tensor.numel(); |
| 77 | if (!dims_vec.empty()) { |
| 78 | n = 1; |
| 79 | for (int64_t d : dims_vec) { |
| 80 | int64_t dim_idx = d < 0 ? d + tensor.dims().size() : d; |
| 81 | if (dim_idx >= 0 && |
| 82 | dim_idx < static_cast<int64_t>(tensor.dims().size())) { |
| 83 | n *= tensor.dims()[dim_idx]; |
| 84 | } |
| 85 | } |
| 86 | } |
| 87 | |
| 88 | double corrected_n = static_cast<double>(n) - correction_value; |
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