From ab028cb878412400b41b68b68460adeea6cb6fd8 Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Nov 2023 20:13:57 -0400 Subject: [PATCH] Migrate inference to llama_batch and llama_decode api (#795) * Add low-level batching notebook * fix: tokenization of special characters: (#850) It should behave like llama.cpp, where most out of the box usages treat special characters accordingly * Update CHANGELOG * Cleanup * Fix runner label * Update notebook * Use llama_decode and batch api * Support logits_all parameter --------- Co-authored-by: Antoine Lizee --- examples/notebooks/Batching.ipynb | 723 ++++++++++++++++++++++++++++++ llama_cpp/llama.py | 34 +- tests/test_llama.py | 4 +- 3 files changed, 753 insertions(+), 8 deletions(-) create mode 100644 examples/notebooks/Batching.ipynb diff --git a/examples/notebooks/Batching.ipynb b/examples/notebooks/Batching.ipynb new file mode 100644 index 0000000..687316b --- /dev/null +++ b/examples/notebooks/Batching.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import llama_cpp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no\n", + "ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes\n", + "ggml_init_cublas: found 1 CUDA devices:\n", + " Device 0: NVIDIA GeForce RTX 2060, compute capability 7.5\n" + ] + } + ], + "source": [ + "llama_cpp.llama_backend_init(numa=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "llama_model_loader: loaded meta data with 16 key-value pairs and 291 tensors from ../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf (version GGUF V2)\n", + "llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32000, 1, 1 ]\n", + "llama_model_loader: - tensor 1: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 2: output.weight q6_K [ 4096, 32000, 1, 1 ]\n", + "llama_model_loader: - tensor 3: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 4: blk.0.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 5: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 6: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 7: blk.0.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 8: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 9: blk.0.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 10: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 11: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 12: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 13: blk.1.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 14: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 15: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 16: blk.1.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 17: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 18: blk.1.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 19: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 20: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 21: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 22: blk.2.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 23: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - 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tensor 276: blk.30.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 277: blk.30.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 278: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 279: blk.30.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 280: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 281: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 282: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 283: blk.31.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 284: blk.31.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", + "llama_model_loader: - tensor 285: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 286: blk.31.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 287: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", + "llama_model_loader: - tensor 288: blk.31.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", + "llama_model_loader: - tensor 289: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - tensor 290: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", + "llama_model_loader: - kv 0: general.architecture str \n", + "llama_model_loader: - kv 1: general.name str \n", + "llama_model_loader: - kv 2: llama.context_length u32 \n", + "llama_model_loader: - kv 3: llama.embedding_length u32 \n", + "llama_model_loader: - kv 4: llama.block_count u32 \n", + "llama_model_loader: - kv 5: llama.feed_forward_length u32 \n", + "llama_model_loader: - kv 6: llama.rope.dimension_count u32 \n", + "llama_model_loader: - kv 7: llama.attention.head_count u32 \n", + "llama_model_loader: - kv 8: llama.attention.head_count_kv u32 \n", + "llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 \n", + "llama_model_loader: - kv 10: general.file_type u32 \n", + "llama_model_loader: - kv 11: tokenizer.ggml.model str \n", + "llama_model_loader: - kv 12: tokenizer.ggml.tokens arr \n", + "llama_model_loader: - kv 13: tokenizer.ggml.scores arr \n", + "llama_model_loader: - kv 14: tokenizer.ggml.token_type arr \n", + "llama_model_loader: - kv 15: general.quantization_version u32 \n", + "llama_model_loader: - type f32: 65 tensors\n", + "llama_model_loader: - type q4_K: 193 tensors\n", + "llama_model_loader: - type q6_K: 33 tensors\n", + "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n", + "llm_load_print_meta: format = GGUF V2\n", + "llm_load_print_meta: arch = llama\n", + "llm_load_print_meta: vocab type = SPM\n", + "llm_load_print_meta: n_vocab = 32000\n", + "llm_load_print_meta: n_merges = 0\n", + "llm_load_print_meta: n_ctx_train = 4096\n", + "llm_load_print_meta: n_embd = 4096\n", + "llm_load_print_meta: n_head = 32\n", + "llm_load_print_meta: n_head_kv = 8\n", + "llm_load_print_meta: n_layer = 32\n", + "llm_load_print_meta: n_rot = 128\n", + "llm_load_print_meta: n_gqa = 4\n", + "llm_load_print_meta: f_norm_eps = 0.0e+00\n", + "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n", + "llm_load_print_meta: f_clamp_kqv = 0.0e+00\n", + "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n", + "llm_load_print_meta: n_ff = 14336\n", + "llm_load_print_meta: freq_base_train = 10000.0\n", + "llm_load_print_meta: freq_scale_train = 1\n", + "llm_load_print_meta: model type = 7B\n", + "llm_load_print_meta: model ftype = mostly Q4_K - Medium\n", + "llm_load_print_meta: model params = 7.24 B\n", + "llm_load_print_meta: model size = 4.07 GiB (4.83 BPW) \n", + "llm_load_print_meta: general.name = LLaMA v2\n", + "llm_load_print_meta: BOS token = 1 ''\n", + "llm_load_print_meta: EOS token = 2 ''\n", + "llm_load_print_meta: UNK token = 0 ''\n", + "llm_load_print_meta: LF token = 13 '<0x0A>'\n", + "llm_load_tensors: ggml ctx size = 0.10 MB\n", + "llm_load_tensors: using CUDA for GPU acceleration\n", + "llm_load_tensors: mem required = 70.41 MB\n", + "llm_load_tensors: offloading 32 repeating layers to GPU\n", + "llm_load_tensors: offloading non-repeating layers to GPU\n", + "llm_load_tensors: offloaded 35/35 layers to GPU\n", + "llm_load_tensors: VRAM used: 4095.05 MB\n", + ".................................................................................................\n" + ] + } + ], + "source": [ + "params = llama_cpp.llama_model_default_params()\n", + "params.n_gpu_layers = 35\n", + "model = llama_cpp.llama_load_model_from_file(b\"../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf\", params=params) # Update this to whatever" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1014, 2936, 9060, 285, 1142]\n", + "58\n" + ] + } + ], + "source": [ + "n_ctx = 512\n", + "n_len = 32\n", + "n_parallel = 2\n", + "prompt = b\"The quick brown fox\"\n", + "\n", + "tokens = (llama_cpp.llama_token * n_ctx)()\n", + "tokens_len = llama_cpp.llama_tokenize(model, prompt, len(prompt), tokens, len(tokens), True, True)\n", + "print(tokens[:tokens_len])\n", + "\n", + "n_kv_req = tokens_len + (n_len - tokens_len) * n_parallel\n", + "print(n_kv_req)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "llama_new_context_with_model: n_ctx = 58\n", + "llama_new_context_with_model: freq_base = 10000.0\n", + "llama_new_context_with_model: freq_scale = 1\n", + "llama_kv_cache_init: offloading v cache to GPU\n", + "llama_kv_cache_init: offloading k cache to GPU\n", + "llama_kv_cache_init: VRAM kv self = 7.25 MB\n", + "llama_new_context_with_model: kv self size = 7.25 MB\n", + "llama_build_graph: non-view tensors processed: 740/740\n", + "llama_new_context_with_model: compute buffer total size = 10.63 MB\n", + "llama_new_context_with_model: VRAM scratch buffer: 4.51 MB\n", + "llama_new_context_with_model: total VRAM used: 4106.81 MB (model: 4095.05 MB, context: 11.76 MB)\n" + ] + } + ], + "source": [ + "\n", + "ctx_params = llama_cpp.llama_context_default_params()\n", + "ctx_params.seed = 1234\n", + "ctx_params.n_ctx = n_kv_req\n", + "ctx_params.n_batch = max(n_len, n_parallel)\n", + "ctx_params.n_threads = 1\n", + "ctx_params.n_threads_batch = 1\n", + "ctx = llama_cpp.llama_new_context_with_model(model, ctx_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "n_ctx = llama_cpp.llama_n_ctx(ctx)\n", + "batch = llama_cpp.llama_batch_init(max(tokens_len, n_parallel), 0, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import ctypes\n", + "\n", + "batch.n_tokens = tokens_len\n", + "for i in range(tokens_len):\n", + " batch.token[i] = tokens[i]\n", + " batch.pos[i] = i\n", + " batch.seq_id[i][0] = 0\n", + " batch.n_seq_id[i] = 1\n", + " batch.logits[i] = False\n", + "\n", + "batch.logits[batch.n_tokens - 1] = True\n", + "\n", + "if llama_cpp.llama_decode(ctx, batch) != 0:\n", + " print(\"Error decoding\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(n_parallel):\n", + " llama_cpp.llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "[' j', ' jumped']\n", + "8\n", + "[' jumps', ' jumped over']\n", + "9\n", + "[' jumps over', ' jumped over the']\n", + "10\n", + "[' jumps over the', ' jumped over the lazy']\n", + "11\n", + "[' jumps over the lazy', ' jumped over the lazy dog']\n", + "12\n", + "[' jumps over the lazy dog', ' jumped over the lazy dog.']\n", + "13\n", + "[' jumps over the lazy dog.', ' jumped over the lazy dog.\\n']\n", + "14\n", + "[' jumps over the lazy dog.\\n', ' jumped over the lazy dog.\\n\\n']\n", + "15\n", + "[' jumps over the lazy dog.\\n\\n', ' jumped over the lazy dog.\\n\\nThe']\n", + "16\n", + "[' jumps over the lazy dog.\\n\\nI', ' jumped over the lazy dog.\\n\\nThe quick']\n", + "17\n", + "[' jumps over the lazy dog.\\n\\nI’', ' jumped over the lazy dog.\\n\\nThe quick brown']\n", + "18\n", + "[' jumps over the lazy dog.\\n\\nI’m', ' jumped over the lazy dog.\\n\\nThe quick brown f']\n", + "19\n", + "[' jumps over the lazy dog.\\n\\nI’m not', ' jumped over the lazy dog.\\n\\nThe quick brown fox']\n", + "20\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped']\n", + "21\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over']\n", + "22\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the']\n", + "23\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy']\n", + "24\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog']\n", + "25\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.']\n", + "26\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n']\n", + "27\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\n']\n", + "28\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe']\n", + "29\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick']\n", + "30\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown']\n", + "31\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown f']\n", + "32\n", + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n" + ] + } + ], + "source": [ + "import ctypes\n", + "\n", + "streams = [\"\"] * n_parallel\n", + "i_batch = [batch.n_tokens - 1] * n_parallel\n", + "\n", + "n_cur = batch.n_tokens\n", + "n_decode = 0\n", + "\n", + "while n_cur <= n_len:\n", + " batch.n_tokens = 0\n", + " for i in range(n_parallel):\n", + " if i_batch[i] < 0:\n", + " continue\n", + " \n", + " n_vocab = llama_cpp.llama_n_vocab(model)\n", + " logits = llama_cpp.llama_get_logits_ith(ctx, i_batch[i])\n", + "\n", + " candidates = (llama_cpp.llama_token_data * n_vocab)()\n", + "\n", + " for token_id in range(n_vocab):\n", + " candidates[token_id].id = token_id\n", + " candidates[token_id].logit = logits[token_id]\n", + " candidates[token_id].p = 0.0\n", + "\n", + " candidates_p = llama_cpp.llama_token_data_array(candidates, len(candidates), False)\n", + "\n", + " top_k = 40\n", + " top_p = 0.9\n", + " temp = 0.4\n", + "\n", + " llama_cpp.llama_sample_top_k(ctx, ctypes.byref(candidates_p), top_k, 1)\n", + " llama_cpp.llama_sample_top_p(ctx, ctypes.byref(candidates_p), top_p, 1)\n", + " llama_cpp.llama_sample_temp (ctx, ctypes.byref(candidates_p), temp)\n", + " \n", + " new_token_id = llama_cpp.llama_sample_token(ctx, ctypes.byref(candidates_p))\n", + "\n", + " if new_token_id == llama_cpp.llama_token_eos(ctx) or n_cur == n_len:\n", + " i_batch[i] = -1\n", + " continue\n", + "\n", + " buf = (ctypes.c_char * 32)()\n", + " outlen = llama_cpp.llama_token_to_piece(model, new_token_id, buf, len(buf))\n", + " streams[i] += bytes(buf[:outlen]).decode(\"utf-8\")\n", + "\n", + " batch.token[batch.n_tokens] = new_token_id\n", + " batch.pos[batch.n_tokens] = n_cur\n", + " batch.seq_id[batch.n_tokens][0] = i\n", + " batch.n_seq_id[batch.n_tokens] = 1\n", + " batch.logits[batch.n_tokens] = True\n", + "\n", + " i_batch[i] = batch.n_tokens\n", + " batch.n_tokens += 1\n", + " n_decode += 1\n", + " \n", + " if batch.n_tokens == 0:\n", + " break\n", + "\n", + " n_cur += 1\n", + "\n", + " if llama_cpp.llama_decode(ctx, batch) != 0:\n", + " print(\"Error decoding\", flush=True)\n", + " break\n", + " print(n_cur)\n", + " print(streams)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n" + ] + } + ], + "source": [ + "print(streams)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "llama_cpp.llama_batch_free(batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "llama_cpp.llama_free(ctx)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "llama_cpp.llama_free_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "llama_cpp.llama_backend_free()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5+" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 705a4b2..e9b1999 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -402,6 +402,16 @@ class Llama: assert self.ctx is not None + if verbose: + self.batch = llama_cpp.llama_batch_init( + self.n_batch, 0, 1 + ) + else: + with suppress_stdout_stderr(): + self.batch = llama_cpp.llama_batch_init( + self.n_batch, 0, 1 + ) + if self.lora_path: if llama_cpp.llama_model_apply_lora_from_file( self.model, @@ -554,19 +564,27 @@ class Llama: tokens: The list of tokens to evaluate. """ assert self.ctx is not None + assert self.batch is not None n_ctx = self._n_ctx for i in range(0, len(tokens), self.n_batch): batch = tokens[i : min(len(tokens), i + self.n_batch)] n_past = min(n_ctx - len(batch), len(self._input_ids)) n_tokens = len(batch) - return_code = llama_cpp.llama_eval( + llama_cpp.llama_kv_cache_seq_rm(self.ctx, -1, n_past, -1) + self.batch.n_tokens = n_tokens + for i in range(n_tokens): + self.batch.token[i] = batch[i] + self.batch.pos[i] = n_past + i + self.batch.seq_id[i][0] = 0 + self.batch.n_seq_id[i] = 1 + self.batch.logits[i] = True if self.context_params.logits_all else False + self.batch.logits[n_tokens - 1] = True + return_code = llama_cpp.llama_decode( ctx=self.ctx, - tokens=(llama_cpp.llama_token * len(batch))(*batch), - n_tokens=n_tokens, - n_past=n_past, + batch=self.batch, ) if return_code != 0: - raise RuntimeError(f"llama_eval returned {return_code}") + raise RuntimeError(f"llama_decode returned {return_code}") # Save tokens self.input_ids[self.n_tokens : self.n_tokens + n_tokens] = batch # Save logits @@ -1662,7 +1680,11 @@ class Llama: ) return self._convert_completion_to_chat(completion_or_chunks, stream=stream) # type: ignore - def _free_model(self, *, _lfree_model=llama_cpp._lib.llama_free_model, _free=llama_cpp._lib.llama_free): + def _free_model(self, *, _lbatch_free=llama_cpp._lib.llama_batch_free, _lfree_model=llama_cpp._lib.llama_free_model, _free=llama_cpp._lib.llama_free): + batch = getattr(self, 'batch', None) + if batch is not None: + _lbatch_free(batch) + self.batch = None model = getattr(self, 'model', None) if model is not None: _lfree_model(model) diff --git a/tests/test_llama.py b/tests/test_llama.py index 330b69b..54f4bd6 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -48,7 +48,7 @@ def test_llama_patch(monkeypatch): *[llama_cpp.c_float(0) for _ in range(n_vocab)] ) - monkeypatch.setattr("llama_cpp.llama_cpp.llama_eval", mock_eval) + monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_eval) monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits) output_text = " jumps over the lazy dog." @@ -138,7 +138,7 @@ def test_utf8(monkeypatch): *[llama_cpp.c_float(0) for _ in range(n_vocab)] ) - monkeypatch.setattr("llama_cpp.llama_cpp.llama_eval", mock_eval) + monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_eval) monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits) output_text = "😀"