103 lines
3 KiB
Python
103 lines
3 KiB
Python
import llama_cpp
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import multiprocessing
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import llama_cpp
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N_THREADS = multiprocessing.cpu_count()
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prompt = b"\n\n### Instruction:\nWhat is the capital of France?\n\n### Response:\n"
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lparams = llama_cpp.llama_context_default_params()
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ctx = llama_cpp.llama_init_from_file(b"../models/7B/ggml-model.bin", lparams)
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# determine the required inference memory per token:
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tmp = [0, 1, 2, 3]
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llama_cpp.llama_eval(ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, N_THREADS)
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n_past = 0
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prompt = b" " + prompt
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embd_inp = (llama_cpp.llama_token * (len(prompt) + 1))()
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n_of_tok = llama_cpp.llama_tokenize(ctx, prompt, embd_inp, len(embd_inp), True)
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embd_inp = embd_inp[:n_of_tok]
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n_ctx = llama_cpp.llama_n_ctx(ctx)
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n_predict = 20
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n_predict = min(n_predict, n_ctx - len(embd_inp))
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input_consumed = 0
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input_noecho = False
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remaining_tokens = n_predict
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embd = []
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last_n_size = 64
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last_n_tokens_data = [0] * last_n_size
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n_batch = 24
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last_n_repeat = 64
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repeat_penalty = 1
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frequency_penalty = 0.0
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presence_penalty = 0.0
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while remaining_tokens > 0:
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if len(embd) > 0:
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llama_cpp.llama_eval(
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ctx, (llama_cpp.c_int * len(embd))(*embd), len(embd), n_past, N_THREADS
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)
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n_past += len(embd)
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embd = []
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if len(embd_inp) <= input_consumed:
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logits = llama_cpp.llama_get_logits(ctx)
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n_vocab = llama_cpp.llama_n_vocab(ctx)
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_arr = (llama_cpp.llama_token_data * n_vocab)(*[
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llama_cpp.llama_token_data(token_id, logits[token_id], 0.0)
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for token_id in range(n_vocab)
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])
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candidates_p = llama_cpp.ctypes.pointer(llama_cpp.llama_token_data_array(_arr, len(_arr), False))
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_arr = (llama_cpp.c_int * len(last_n_tokens_data))(*last_n_tokens_data)
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llama_cpp.llama_sample_repetition_penalty(ctx, candidates_p,
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_arr,
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last_n_repeat, repeat_penalty)
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llama_cpp.llama_sample_frequency_and_presence_penalties(ctx, candidates_p,
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_arr,
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last_n_repeat, frequency_penalty, presence_penalty)
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llama_cpp.llama_sample_top_k(ctx, candidates_p, 40)
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llama_cpp.llama_sample_top_p(ctx, candidates_p, 0.8)
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llama_cpp.llama_sample_temperature(ctx, candidates_p, 0.2)
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id = llama_cpp.llama_sample_token(ctx, candidates_p)
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last_n_tokens_data = last_n_tokens_data[1:] + [id]
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embd.append(id)
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input_noecho = False
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remaining_tokens -= 1
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else:
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while len(embd_inp) > input_consumed:
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embd.append(embd_inp[input_consumed])
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last_n_tokens_data = last_n_tokens_data[1:] + [embd_inp[input_consumed]]
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input_consumed += 1
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if len(embd) >= n_batch:
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break
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if not input_noecho:
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for id in embd:
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print(
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llama_cpp.llama_token_to_str(ctx, id).decode("utf-8", errors="ignore"),
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end="",
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flush=True,
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)
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if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos():
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break
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print()
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llama_cpp.llama_print_timings(ctx)
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llama_cpp.llama_free(ctx)
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