diff --git a/examples/python/llama.cpp/llama.cpp_in_Spark_NLP_AutoGGUFModel_Embeddings.ipynb b/examples/python/llama.cpp/llama.cpp_in_Spark_NLP_AutoGGUFModel_Embeddings.ipynb new file mode 100644 index 00000000000000..c8da8aa1525bc3 --- /dev/null +++ b/examples/python/llama.cpp/llama.cpp_in_Spark_NLP_AutoGGUFModel_Embeddings.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/llama.cpp/llama.cpp_in_Spark_NLP_AutoGGUFModel_Embeddings.ipynb)\n", + "\n", + "# Import llama.cpp 🦙 models into Spark NLP 🚀\n", + "\n", + "Let's keep in mind a few things before we start 😊\n", + "\n", + "- Support for llama.cpp embeddings was introduced in `Spark NLP 5.5.1`, enabling quantized LLM inference on a wide range of devices. Please make sure you have upgraded to the latest Spark NLP release.\n", + "- You need to use your own `.gguf` model files, which also include the models from the [Hugging Face Models](https://huggingface.co/models?library=gguf)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download a GGUF Model\n", + "\n", + "Lets download a GGUF model to test it out. For this, we will use [bartowski/Phi-3.5-mini-instruct-GGUF](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF). It is a 3.8B parameter model which also is available in 4-bit quantization. \n", + "\n", + "We can download the model by selecting the q4 GGUF file from the \"Files and versions\" tab.\n", + "\n", + "Once downloaded, we can directly import this model into Spark NLP!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EXPORT_PATH = \"Phi-3.5-mini-instruct-Q4_K_M.gguf\"\n", + "! wget \"https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF/resolve/main/Phi-3.5-mini-instruct-Q4_K_M.gguf?download=true\" -O {EXPORT_PATH}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and Save AutGGUF models in Spark NLP\n", + "\n", + "- Let's install and setup Spark NLP (if running it Google Colab)\n", + "- This part is pretty easy via our simple script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Only execute this if you are on Google Colab\n", + "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start Spark with Spark NLP included via our simple `start()` function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sparknlp\n", + "\n", + "# let's start Spark with Spark NLP with GPU enabled. If you don't have GPUs available remove this parameter.\n", + "spark = sparknlp.start(gpu=True)\n", + "print(sparknlp.version())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Let's use the `loadSavedModel` functon in `AutoGGUFModel`\n", + "- Most params will be set automatically. They can also be set later after loading the model in `AutoGGUFModel` during runtime, so don't worry about setting them now.\n", + "- `loadSavedModel` accepts two params, first is the path to the exported model. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n", + "- We can set the model to embedding mode with `setEmbedding`. Afterwards the model will return the embeddings in the Annotations.\n", + "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jsl-llama: Extracted 'libjllama.so' to '/tmp/libjllama.so'\n" + ] + } + ], + "source": [ + "from sparknlp.annotator import *\n", + "\n", + "# All these params should be identical to the original ONNX model\n", + "autoGGUFModel = (\n", + " AutoGGUFModel.loadSavedModel(EXPORT_PATH, spark)\n", + " .setInputCols(\"document\")\n", + " .setOutputCol(\"embeddings\")\n", + " .setBatchSize(4)\n", + " .setEmbedding(True)\n", + " .setNGpuLayers(99)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoGGUFModel.write().overwrite().save(f\"Phi-3.5-mini-instruct-Q4_K_M_spark_nlp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's clean up stuff we don't need anymore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!rm -rf {EXPORT_PATH}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome 😎 !\n", + "\n", + "This is your GGUF model from loaded and saved by Spark NLP 🚀" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 2337164\n", + "drwxr-xr-x 2 root root 4096 Oct 19 11:45 metadata\n", + "-rwxrwxr-x 1 root root 2393232672 Oct 19 11:45 Phi-3.5-mini-instruct-Q4_K_M.gguf\n" + ] + } + ], + "source": [ + "! ls -l Phi-3.5-mini-instruct-Q4_K_M_spark_nlp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny GGUF model 😊" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24/10/19 11:52:55 WARN DAGScheduler: Broadcasting large task binary with size 1042.2 KiB\n", + "24/10/19 11:52:57 WARN DAGScheduler: Broadcasting large task binary with size 1042.2 KiB\n", + "24/10/19 11:52:57 WARN DAGScheduler: Broadcasting large task binary with size 1042.2 KiB\n", + "llama_model_loader: loaded meta data with 40 key-value pairs and 197 tensors from /tmp/spark-0375a9c3-860e-4fd1-aff8-49597303b20f/userFiles-b664e666-88c2-4f1b-bf41-3ee678d08309/Phi-3.5-mini-instruct-Q4_K_M.gguf (version GGUF V3 (latest))\n", + "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", + "llama_model_loader: - kv 0: general.architecture str = phi3\n", + "llama_model_loader: - kv 1: general.type str = model\n", + "llama_model_loader: - kv 2: general.name str = Phi 3.5 Mini Instruct\n", + "llama_model_loader: - kv 3: general.finetune str = instruct\n", + "llama_model_loader: - kv 4: general.basename str = Phi-3.5\n", + "llama_model_loader: - kv 5: general.size_label str = mini\n", + "llama_model_loader: - kv 6: general.license str = mit\n", + "llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/microsoft/Phi-...\n", + "llama_model_loader: - kv 8: general.tags arr[str,3] = [\"nlp\", \"code\", \"text-generation\"]\n", + "llama_model_loader: - kv 9: general.languages arr[str,1] = [\"multilingual\"]\n", + "llama_model_loader: - kv 10: phi3.context_length u32 = 131072\n", + "llama_model_loader: - kv 11: phi3.rope.scaling.original_context_length u32 = 4096\n", + "llama_model_loader: - kv 12: phi3.embedding_length u32 = 3072\n", + "llama_model_loader: - kv 13: phi3.feed_forward_length u32 = 8192\n", + "llama_model_loader: - kv 14: phi3.block_count u32 = 32\n", + "llama_model_loader: - kv 15: phi3.attention.head_count u32 = 32\n", + "llama_model_loader: - kv 16: phi3.attention.head_count_kv u32 = 32\n", + "llama_model_loader: - kv 17: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010\n", + "llama_model_loader: - kv 18: phi3.rope.dimension_count u32 = 96\n", + "llama_model_loader: - kv 19: phi3.rope.freq_base f32 = 10000.000000\n", + "llama_model_loader: - kv 20: general.file_type u32 = 15\n", + "llama_model_loader: - kv 21: phi3.attention.sliding_window u32 = 262144\n", + "llama_model_loader: - kv 22: phi3.rope.scaling.attn_factor f32 = 1.190238\n", + "llama_model_loader: - kv 23: tokenizer.ggml.model str = llama\n", + "llama_model_loader: - kv 24: tokenizer.ggml.pre str = default\n", + "llama_model_loader: - kv 25: tokenizer.ggml.tokens arr[str,32064] = [\"\", \"\", \"\", \"<0x00>\", \"<...\n", + "llama_model_loader: - kv 26: tokenizer.ggml.scores arr[f32,32064] = [-1000.000000, -1000.000000, -1000.00...\n", + "llama_model_loader: - kv 27: tokenizer.ggml.token_type arr[i32,32064] = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n", + "llama_model_loader: - kv 28: tokenizer.ggml.bos_token_id u32 = 1\n", + "llama_model_loader: - kv 29: tokenizer.ggml.eos_token_id u32 = 32000\n", + "llama_model_loader: - kv 30: tokenizer.ggml.unknown_token_id u32 = 0\n", + "llama_model_loader: - kv 31: tokenizer.ggml.padding_token_id u32 = 32000\n", + "llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = false\n", + "llama_model_loader: - kv 33: tokenizer.ggml.add_eos_token bool = false\n", + "llama_model_loader: - kv 34: tokenizer.chat_template str = {% for message in messages %}{% if me...\n", + "llama_model_loader: - kv 35: general.quantization_version u32 = 2\n", + "llama_model_loader: - kv 36: quantize.imatrix.file str = /models_out/Phi-3.5-mini-instruct-GGU...\n", + "llama_model_loader: - kv 37: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt\n", + "llama_model_loader: - kv 38: quantize.imatrix.entries_count i32 = 128\n", + "llama_model_loader: - kv 39: quantize.imatrix.chunks_count i32 = 151\n", + "llama_model_loader: - type f32: 67 tensors\n", + "llama_model_loader: - type q4_K: 81 tensors\n", + "llama_model_loader: - type q5_K: 32 tensors\n", + "llama_model_loader: - type q6_K: 17 tensors\n", + "llm_load_vocab: special tokens cache size = 14\n", + "llm_load_vocab: token to piece cache size = 0.1685 MB\n", + "llm_load_print_meta: format = GGUF V3 (latest)\n", + "llm_load_print_meta: arch = phi3\n", + "llm_load_print_meta: vocab type = SPM\n", + "llm_load_print_meta: n_vocab = 32064\n", + "llm_load_print_meta: n_merges = 0\n", + "llm_load_print_meta: vocab_only = 0\n", + "llm_load_print_meta: n_ctx_train = 131072\n", + "llm_load_print_meta: n_embd = 3072\n", + "llm_load_print_meta: n_layer = 32\n", + "llm_load_print_meta: n_head = 32\n", + "llm_load_print_meta: n_head_kv = 32\n", + "llm_load_print_meta: n_rot = 96\n", + "llm_load_print_meta: n_swa = 262144\n", + "llm_load_print_meta: n_embd_head_k = 96\n", + "llm_load_print_meta: n_embd_head_v = 96\n", + "llm_load_print_meta: n_gqa = 1\n", + "llm_load_print_meta: n_embd_k_gqa = 3072\n", + "llm_load_print_meta: n_embd_v_gqa = 3072\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: f_logit_scale = 0.0e+00\n", + "llm_load_print_meta: n_ff = 8192\n", + "llm_load_print_meta: n_expert = 0\n", + "llm_load_print_meta: n_expert_used = 0\n", + "llm_load_print_meta: causal attn = 1\n", + "llm_load_print_meta: pooling type = 0\n", + "llm_load_print_meta: rope type = 2\n", + "llm_load_print_meta: rope scaling = linear\n", + "llm_load_print_meta: freq_base_train = 10000.0\n", + "llm_load_print_meta: freq_scale_train = 1\n", + "llm_load_print_meta: n_ctx_orig_yarn = 4096\n", + "llm_load_print_meta: rope_finetuned = unknown\n", + "llm_load_print_meta: ssm_d_conv = 0\n", + "llm_load_print_meta: ssm_d_inner = 0\n", + "llm_load_print_meta: ssm_d_state = 0\n", + "llm_load_print_meta: ssm_dt_rank = 0\n", + "llm_load_print_meta: model type = 3B\n", + "llm_load_print_meta: model ftype = Q4_K - Medium\n", + "llm_load_print_meta: model params = 3.82 B\n", + "llm_load_print_meta: model size = 2.23 GiB (5.01 BPW) \n", + "llm_load_print_meta: general.name = Phi 3.5 Mini Instruct\n", + "llm_load_print_meta: BOS token = 1 ''\n", + "llm_load_print_meta: EOS token = 32000 '<|endoftext|>'\n", + "llm_load_print_meta: UNK token = 0 ''\n", + "llm_load_print_meta: PAD token = 32000 '<|endoftext|>'\n", + "llm_load_print_meta: LF token = 13 '<0x0A>'\n", + "llm_load_print_meta: EOT token = 32007 '<|end|>'\n", + "llm_load_print_meta: max token length = 48\n", + "llm_load_tensors: ggml ctx size = 0.10 MiB\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WARN] Not compiled with GPU offload support, --n-gpu-layers option will be ignored. See main README.md for information on enabling GPU BLAS support n_gpu_layers=-1\n", + "[INFO] build info build=3534 commit=\"641f5dd2\"\n", + "[INFO] system info n_threads=6 n_threads_batch=-1 total_threads=6 system_info=\"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "llm_load_tensors: CPU buffer size = 2281.66 MiB\n", + "............................................................................................\n", + "llama_new_context_with_model: n_ctx = 4096\n", + "llama_new_context_with_model: n_batch = 512\n", + "llama_new_context_with_model: n_ubatch = 512\n", + "llama_new_context_with_model: flash_attn = 0\n", + "llama_new_context_with_model: freq_base = 10000.0\n", + "llama_new_context_with_model: freq_scale = 1\n", + "llama_kv_cache_init: CPU KV buffer size = 1536.00 MiB (0 + 1) / 1]\n", + "llama_new_context_with_model: KV self size = 1536.00 MiB, K (f16): 768.00 MiB, V (f16): 768.00 MiB\n", + "llama_new_context_with_model: CPU output buffer size = 0.06 MiB\n", + "llama_new_context_with_model: CPU compute buffer size = 300.01 MiB\n", + "llama_new_context_with_model: graph nodes = 1286\n", + "llama_new_context_with_model: graph splits = 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] initializing slots n_slots=4\n", + "[INFO] new slot id_slot=0 n_ctx_slot=1024\n", + "[INFO] new slot id_slot=1 n_ctx_slot=1024\n", + "[INFO] new slot id_slot=2 n_ctx_slot=1024\n", + "[INFO] new slot id_slot=3 n_ctx_slot=1024\n", + "[INFO] model loaded\n", + "[INFO] chat template chat_example=\"<|system|>\\nYou are a helpful assistant<|end|>\\n<|user|>\\nHello<|end|>\\n<|assistant|>\\nHi there<|end|>\\n<|user|>\\nHow are you?<|end|>\\n<|assistant|>\\n\" built_in=true\n", + "[INFO] all slots are idle\n", + "[INFO] slot is processing task id_slot=0 id_task=0\n", + "[INFO] kv cache rm [p0, end) id_slot=0 id_task=0 p0=0\n", + "[INFO] slot released id_slot=0 id_task=0 n_ctx=4096 n_past=5 n_system_tokens=0 n_cache_tokens=0 truncated=false\n", + "[INFO] all slots are idle\n", + 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+ "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "import sparknlp\n", + "from sparknlp.base import *\n", + "from sparknlp.annotator import *\n", + "from pyspark.ml import Pipeline\n", + "\n", + "document_assembler = DocumentAssembler().setInputCol(\"text\").setOutputCol(\"document\")\n", + "\n", + "autoGGUFModel = AutoGGUFModel.load(\"Phi-3.5-mini-instruct-Q4_K_M_spark_nlp\")\n", + "\n", + "pipeline = Pipeline().setStages([document_assembler, autoGGUFModel])\n", + "\n", + "data = spark.createDataFrame([[\"This is a sentence.\"]]).toDF(\"text\")\n", + "\n", + "result = pipeline.fit(data).transform(data)\n", + "result.select(\"embeddings.embeddings\").show(truncate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! You can now go wild and use hundreds of GGUF models from HuggingFace 🤗 in Spark NLP 🚀\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "sparknlp_dev", + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}