llama.cpp: add a progress bar for prompt evaluation

modules/llama_cpp_python_hijack.py

@@ -0,0 +1,63 @@
+from typing import Sequence
+
+from tqdm import tqdm
+
+try:
+    import llama_cpp
+except:
+    llama_cpp = None
+
+try:
+    import llama_cpp_cuda
+except:
+    llama_cpp_cuda = None
+
+try:
+    import llama_cpp_cuda_tensorcores
+except:
+    llama_cpp_cuda_tensorcores = 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) > 1:
+        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
+        rows = n_tokens
+        cols = self._n_vocab
+        offset = (
+            0 if self.context_params.logits_all else n_tokens - 1
+        )  # NOTE: Only save the last token logits if logits_all is False
+        self.scores[n_past + offset : n_past + n_tokens, :].reshape(-1)[
+            :
+        ] = self._ctx.get_logits()[offset * cols : rows * cols]
+        # Update n_tokens
+        self.n_tokens += n_tokens
+
+
+for lib in [llama_cpp, llama_cpp_cuda, llama_cpp_cuda_tensorcores]:
+    if lib is not None:
+        lib.Llama.eval = eval_with_progress

modules/llamacpp_hf.py

@@ -7,7 +7,7 @@ from torch.nn import CrossEntropyLoss
 from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
-from modules import RoPE, shared
+from modules import RoPE, llama_cpp_python_hijack, shared
 from modules.logging_colors import logger
 
 try:

modules/llamacpp_model.py

@@ -4,7 +4,7 @@ from functools import partial
 import numpy as np
 import torch
 
-from modules import RoPE, shared
+from modules import RoPE, llama_cpp_python_hijack, shared
 from modules.callbacks import Iteratorize
 from modules.logging_colors import logger
 from modules.text_generation import get_max_prompt_length