mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-23 00:18:20 +01:00
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
import importlib
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import platform
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from typing import Sequence
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from tqdm import tqdm
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from modules import shared
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from modules.cache_utils import process_llamacpp_cache
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imported_module = None
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def llama_cpp_lib():
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global imported_module
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# Determine the platform
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is_macos = platform.system() == 'Darwin'
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# Define the library names based on the platform
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if is_macos:
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lib_names = [
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(None, 'llama_cpp')
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]
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else:
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lib_names = [
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('cpu', 'llama_cpp'),
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('tensorcores', 'llama_cpp_cuda_tensorcores'),
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(None, 'llama_cpp_cuda'),
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(None, 'llama_cpp')
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]
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for arg, lib_name in lib_names:
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should_import = (arg is None or getattr(shared.args, arg))
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if should_import:
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if imported_module and imported_module != lib_name:
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# Conflict detected, raise an exception
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raise Exception(f"Cannot import `{lib_name}` because `{imported_module}` is already imported. Switching to a different version of llama-cpp-python currently requires a server restart.")
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try:
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return_lib = importlib.import_module(lib_name)
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imported_module = lib_name
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monkey_patch_llama_cpp_python(return_lib)
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return return_lib
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except ImportError:
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continue
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return None
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def eval_with_progress(self, tokens: Sequence[int]):
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"""
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A copy of
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https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama.py
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with tqdm to show prompt processing progress.
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"""
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assert self._ctx.ctx is not None
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assert self._batch.batch is not None
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self._ctx.kv_cache_seq_rm(-1, self.n_tokens, -1)
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if len(tokens) > 1:
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progress_bar = tqdm(range(0, len(tokens), self.n_batch), desc="Prompt evaluation", leave=False)
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else:
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progress_bar = range(0, len(tokens), self.n_batch)
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for i in progress_bar:
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batch = tokens[i : min(len(tokens), i + self.n_batch)]
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n_past = self.n_tokens
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n_tokens = len(batch)
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self._batch.set_batch(
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batch=batch, n_past=n_past, logits_all=self.context_params.logits_all
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)
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self._ctx.decode(self._batch)
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# Save tokens
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self.input_ids[n_past : n_past + n_tokens] = batch
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# Save logits
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if self.context_params.logits_all:
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rows = n_tokens
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cols = self._n_vocab
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logits = self._ctx.get_logits()[: rows * cols]
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self.scores[n_past : n_past + n_tokens, :].reshape(-1)[: :] = logits
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else:
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rows = 1
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cols = self._n_vocab
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logits = self._ctx.get_logits()[: rows * cols]
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self.scores[n_past + n_tokens - 1, :].reshape(-1)[: :] = logits
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# Update n_tokens
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self.n_tokens += n_tokens
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def monkey_patch_llama_cpp_python(lib):
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if getattr(lib.Llama, '_is_patched', False):
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# If the patch is already applied, do nothing
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return
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def my_generate(self, *args, **kwargs):
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if shared.args.streaming_llm:
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new_sequence = args[0]
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past_sequence = self._input_ids
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# Do the cache trimming for StreamingLLM
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process_llamacpp_cache(self, new_sequence, past_sequence)
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for output in self.original_generate(*args, **kwargs):
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yield output
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lib.Llama.eval = eval_with_progress
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lib.Llama.original_generate = lib.Llama.generate
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lib.Llama.generate = my_generate
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# Set the flag to indicate that the patch has been applied
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lib.Llama._is_patched = True
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