baalajimaestro/llama.cpp
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llama.cpp/examples/notebooks/Batching.ipynb
Andrei ab028cb878
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 <antoine.lizee@gmail.com>
2023-11-02 20:13:57 -04:00

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{
"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: - tensor 24: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 25: blk.2.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 26: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 27: blk.2.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 28: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 29: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 30: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 31: blk.3.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 32: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 33: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 34: blk.3.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 35: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 36: blk.3.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 37: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 38: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 39: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 40: blk.4.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 41: blk.4.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 42: blk.4.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 43: blk.4.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 44: blk.4.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 45: blk.4.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 46: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 47: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 48: blk.5.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 49: blk.5.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 50: blk.5.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 51: blk.5.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 52: blk.5.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 53: blk.5.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 54: blk.5.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 55: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 56: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 57: blk.6.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 58: blk.6.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 59: blk.6.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 60: blk.6.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 61: blk.6.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 62: blk.6.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 63: blk.6.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 64: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 65: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 66: blk.7.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 67: blk.7.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 68: blk.7.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 69: blk.7.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 70: blk.7.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 71: blk.7.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 72: blk.7.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 73: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 74: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 75: blk.8.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 76: blk.8.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 77: blk.8.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 78: blk.8.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 79: blk.8.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 80: blk.8.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 81: blk.8.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 82: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 83: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 84: blk.9.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 85: blk.9.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 86: blk.9.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 87: blk.9.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 88: blk.9.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 89: blk.9.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 90: blk.9.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 91: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 92: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 93: blk.10.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 94: blk.10.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 95: blk.10.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 96: blk.10.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 97: blk.10.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 98: blk.10.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 99: blk.10.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 100: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 101: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 102: blk.11.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 103: blk.11.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 104: blk.11.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 105: blk.11.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 106: blk.11.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 107: blk.11.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 109: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 110: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 111: blk.12.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 112: blk.12.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 113: blk.12.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 114: blk.12.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 115: blk.12.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 116: blk.12.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 119: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 120: blk.13.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 121: blk.13.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 122: blk.13.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 123: blk.13.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 124: blk.13.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 125: blk.13.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 127: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 128: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 132: blk.14.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 133: blk.14.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 134: blk.14.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 135: blk.14.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 136: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 138: blk.15.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 139: blk.15.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 140: blk.15.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 141: blk.15.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 142: blk.15.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
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"llama_model_loader: - tensor 177: blk.19.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 178: blk.19.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 179: blk.19.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 180: blk.19.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 181: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 218: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 222: blk.24.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 223: blk.24.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
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"llama_model_loader: - tensor 227: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 228: blk.25.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 232: blk.25.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
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"llama_model_loader: - tensor 235: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 236: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 237: blk.26.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 240: blk.26.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 241: blk.26.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 242: blk.26.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 244: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 245: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 246: blk.27.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 250: blk.27.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
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"llama_model_loader: - tensor 253: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 254: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 255: blk.28.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 258: blk.28.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 259: blk.28.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 260: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 262: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 263: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 264: blk.29.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 265: blk.29.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 266: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 267: blk.29.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 268: blk.29.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 269: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 270: blk.29.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 271: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 272: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 273: blk.30.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 274: blk.30.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 275: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - 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 '<s>'\n",
"llm_load_print_meta: EOS token = 2 '</s>'\n",
"llm_load_print_meta: UNK token = 0 '<unk>'\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
}
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