(self)
| 101 | return self._tensor |
| 102 | |
| 103 | def _create_constant_tensor(self) -> Tensor: |
| 104 | if (self._value is not None and isinstance(self._value, np.ndarray) |
| 105 | and self._value.flags['C_CONTIGUOUS']): |
| 106 | lower_type = None |
| 107 | lower_shape = None |
| 108 | # workaround for reinterpreted data type |
| 109 | dtype = self._value.dtype |
| 110 | if (self.dtype == trt.fp4 or self.dtype |
| 111 | == trt.fp8) and (dtype == np.uint8 or dtype == np.int8 |
| 112 | or dtype == np.int32 or dtype == np.int64): |
| 113 | lower_type = self.dtype |
| 114 | lower_shape = self.shape |
| 115 | |
| 116 | self._value = constant(self._value, lower_type, lower_shape) |
| 117 | return self._value |
| 118 | elif self._value is None or isinstance(self._value, np.ndarray): |
| 119 | if self._dtype == trt.fp4: |
| 120 | shape = list(self._shape) |
| 121 | assert shape[ |
| 122 | -1] % 16 == 0, "For FP4, the last dimension of the shape should be multiple of 16" |
| 123 | shape[-1] = shape[-1] // 16 |
| 124 | dtype = np.int64 |
| 125 | else: |
| 126 | shape = self._shape |
| 127 | dtype = trt_dtype_to_np(self._dtype) |
| 128 | ndarray = np.empty(shape, dtype) |
| 129 | tensor = constant(ndarray, self._dtype, self._shape) |
| 130 | default_net()._register_unfilled_weights(tensor.producer.name, |
| 131 | ndarray, self._value) |
| 132 | return tensor |
| 133 | |
| 134 | def get_constant_tensor(self, network: Network) -> Tensor: |
| 135 | if self._network is None or self._network() != network: |
no test coverage detected