| from ggml import lib, ffi | |
| from ggml.utils import init, copy, numpy | |
| import numpy as np | |
| ctx = init(mem_size=12*1024*1024) # automatically freed when pointer is GC'd | |
| n = 256 | |
| n_threads = 4 | |
| a = lib.ggml_new_tensor_1d(ctx, lib.GGML_TYPE_Q5_K, n) | |
| b = lib.ggml_new_tensor_1d(ctx, lib.GGML_TYPE_F32, n) # can't both be quantized | |
| sum = lib.ggml_add(ctx, a, b) # all zeroes for now. Will be quantized too! | |
| # See cffi's doc on how to allocate native memory: it's very simple! | |
| # https://cffi.readthedocs.io/en/latest/ref.html#ffi-interface | |
| gf = ffi.new('struct ggml_cgraph*') | |
| lib.ggml_build_forward_expand(gf, sum) | |
| copy(np.array([i for i in range(n)], np.float32), a) | |
| copy(np.array([i*100 for i in range(n)], np.float32), b) | |
| lib.ggml_graph_compute_with_ctx(ctx, gf, n_threads) | |
| print(numpy(a, allow_copy=True)) | |
| print(numpy(b)) | |
| print(numpy(sum, allow_copy=True)) |