| 385 | } |
| 386 | |
| 387 | torch::Tensor code2x8_matmat( |
| 388 | const torch::Tensor& input, |
| 389 | const torch::Tensor& codes, |
| 390 | const torch::Tensor& codebooks, |
| 391 | const torch::Tensor& scales, |
| 392 | const std::optional<torch::Tensor>& bias |
| 393 | ) { |
| 394 | bool use_bfloat16 = check_use_bfloat16(input); |
| 395 | auto input_sizes = input.sizes(); |
| 396 | auto out_features = codes.size(0) * codebooks.size(2); |
| 397 | auto flat_input = input.reshape({-1, input.size(-1)}); |
| 398 | auto flat_output = torch::empty({flat_input.size(0), out_features}, |
| 399 | torch::TensorOptions() |
| 400 | .dtype(input.dtype()) |
| 401 | .device(input.device()) |
| 402 | ); |
| 403 | |
| 404 | for (int i = 0; i < flat_input.size(0); ++i) { |
| 405 | auto input_vec = flat_input.index({i}); |
| 406 | auto output_vec = flat_output.index({i}); |
| 407 | code2x8_matvec( |
| 408 | codes.squeeze(2), |
| 409 | input_vec, |
| 410 | output_vec, |
| 411 | codebooks, |
| 412 | use_bfloat16 |
| 413 | ); |
| 414 | } |
| 415 | return scale_bias_unflatten_output( |
| 416 | flat_output, |
| 417 | scales, |
| 418 | bias, |
| 419 | input_sizes |
| 420 | ); |
| 421 | } |
| 422 | |
| 423 | torch::Tensor code2x8_dequant( |
| 424 | const torch::Tensor& codes, |
nothing calls this directly
no test coverage detected