mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-21 23:57:58 +01:00
116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
import importlib
|
|
import platform
|
|
from typing import Sequence
|
|
|
|
from tqdm import tqdm
|
|
|
|
from modules import shared
|
|
from modules.cache_utils import process_llamacpp_cache
|
|
|
|
|
|
imported_module = None
|
|
|
|
|
|
def llama_cpp_lib():
|
|
global imported_module
|
|
|
|
# Determine the platform
|
|
is_macos = platform.system() == 'Darwin'
|
|
|
|
# Define the library names based on the platform
|
|
if is_macos:
|
|
lib_names = [
|
|
(None, 'llama_cpp')
|
|
]
|
|
else:
|
|
lib_names = [
|
|
('cpu', 'llama_cpp'),
|
|
('tensorcores', 'llama_cpp_cuda_tensorcores'),
|
|
(None, 'llama_cpp_cuda'),
|
|
(None, 'llama_cpp')
|
|
]
|
|
|
|
for arg, lib_name in lib_names:
|
|
should_import = (arg is None or getattr(shared.args, arg))
|
|
|
|
if should_import:
|
|
if imported_module and imported_module != lib_name:
|
|
# Conflict detected, raise an exception
|
|
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.")
|
|
|
|
try:
|
|
return_lib = importlib.import_module(lib_name)
|
|
imported_module = lib_name
|
|
monkey_patch_llama_cpp_python(return_lib)
|
|
return return_lib
|
|
except ImportError:
|
|
continue
|
|
|
|
return None
|
|
|
|
|
|
def eval_with_progress(self, tokens: Sequence[int]):
|
|
"""
|
|
A copy of
|
|
|
|
https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama.py
|
|
|
|
with tqdm to show prompt processing progress.
|
|
"""
|
|
assert self._ctx.ctx is not None
|
|
assert self._batch.batch is not None
|
|
self._ctx.kv_cache_seq_rm(-1, self.n_tokens, -1)
|
|
|
|
if len(tokens) > self.n_batch:
|
|
progress_bar = tqdm(range(0, len(tokens), self.n_batch), desc="Prompt evaluation", leave=False)
|
|
else:
|
|
progress_bar = range(0, len(tokens), self.n_batch)
|
|
|
|
for i in progress_bar:
|
|
batch = tokens[i : min(len(tokens), i + self.n_batch)]
|
|
n_past = self.n_tokens
|
|
n_tokens = len(batch)
|
|
self._batch.set_batch(
|
|
batch=batch, n_past=n_past, logits_all=self.context_params.logits_all
|
|
)
|
|
self._ctx.decode(self._batch)
|
|
# Save tokens
|
|
self.input_ids[n_past : n_past + n_tokens] = batch
|
|
# Save logits
|
|
if self.context_params.logits_all:
|
|
rows = n_tokens
|
|
cols = self._n_vocab
|
|
logits = self._ctx.get_logits()[: rows * cols]
|
|
self.scores[n_past : n_past + n_tokens, :].reshape(-1)[: :] = logits
|
|
else:
|
|
rows = 1
|
|
cols = self._n_vocab
|
|
logits = self._ctx.get_logits()[: rows * cols]
|
|
self.scores[n_past + n_tokens - 1, :].reshape(-1)[: :] = logits
|
|
# Update n_tokens
|
|
self.n_tokens += n_tokens
|
|
|
|
|
|
def monkey_patch_llama_cpp_python(lib):
|
|
if getattr(lib.Llama, '_is_patched', False):
|
|
# If the patch is already applied, do nothing
|
|
return
|
|
|
|
def my_generate(self, *args, **kwargs):
|
|
if shared.args.streaming_llm:
|
|
new_sequence = args[0]
|
|
past_sequence = self._input_ids
|
|
|
|
# Do the cache trimming for StreamingLLM
|
|
process_llamacpp_cache(self, new_sequence, past_sequence)
|
|
|
|
for output in self.original_generate(*args, **kwargs):
|
|
yield output
|
|
|
|
lib.Llama.eval = eval_with_progress
|
|
lib.Llama.original_generate = lib.Llama.generate
|
|
lib.Llama.generate = my_generate
|
|
|
|
# Set the flag to indicate that the patch has been applied
|
|
lib.Llama._is_patched = True
|