add support for mixtral

.gitignore

@@ -158,3 +158,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+**/runpod

examples/Alpaca_Mixtral.ipynb

@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4b2a910-40ce-48f9-91b6-11d5eec547f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/workspace/unsloth')\n",
+    "from unsloth.models.mixtral import FastMixtralModel\n",
+    "from unsloth import FastLanguageModel\n",
+    "import torch\n",
+    "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
+    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
+    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
+    "\n",
+    "model, tokenizer = FastMixtralModel.from_pretrained(\n",
+    "    model_name = \"mistralai/Mixtral-8x7B-v0.1\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    dtype = dtype,\n",
+    "    load_in_4bit = load_in_4bit,\n",
+    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77182aa0-762f-4e80-bdf8-8af785fc6f97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = FastMixtralModel.get_peft_model(\n",
+    "    model,\n",
+    "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
+    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n",
+    "                      # \"gate\", \"w1\", \"w2\", \"w3\"],\n",
+    "    lora_alpha = 16,\n",
+    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
+    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
+    "    use_gradient_checkpointing = True,\n",
+    "    random_state = 3407,\n",
+    "    use_rslora = False,  # We support rank stabilized LoRA\n",
+    "    loftq_config = None, # And LoftQ\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4847ef2c-cdf5-4905-899d-9fc331fde245",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
+    "\n",
+    "### Instruction:\n",
+    "{}\n",
+    "\n",
+    "### Input:\n",
+    "{}\n",
+    "\n",
+    "### Response:\n",
+    "{}\"\"\"\n",
+    "\n",
+    "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
+    "def formatting_prompts_func(examples):\n",
+    "    instructions = examples[\"instruction\"]\n",
+    "    inputs       = examples[\"input\"]\n",
+    "    outputs      = examples[\"output\"]\n",
+    "    texts = []\n",
+    "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
+    "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
+    "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
+    "        texts.append(text)\n",
+    "    return { \"text\" : texts, }\n",
+    "pass\n",
+    "\n",
+    "from datasets import load_dataset\n",
+    "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
+    "dataset = dataset.map(formatting_prompts_func, batched = True,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "508de7d8-64be-407d-8899-f8c737ed3650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "trainer = SFTTrainer(\n",
+    "    model = model,\n",
+    "    tokenizer = tokenizer,\n",
+    "    train_dataset = dataset,\n",
+    "    dataset_text_field = \"text\",\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    dataset_num_proc = 2,\n",
+    "    packing = False, # Can make training 5x faster for short sequences.\n",
+    "    args = TrainingArguments(\n",
+    "        per_device_train_batch_size = 2,\n",
+    "        gradient_accumulation_steps = 4,\n",
+    "        warmup_steps = 5,\n",
+    "        max_steps = 60,\n",
+    "        learning_rate = 2e-4,\n",
+    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
+    "        bf16 = torch.cuda.is_bf16_supported(),\n",
+    "        logging_steps = 1,\n",
+    "        optim = \"adamw_8bit\",\n",
+    "        weight_decay = 0.01,\n",
+    "        lr_scheduler_type = \"linear\",\n",
+    "        seed = 3407,\n",
+    "        output_dir = \"outputs\",\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e33a6e5-a8b9-402c-8419-10c3e969a561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title Show current memory stats\n",
+    "gpu_stats = torch.cuda.get_device_properties(0)\n",
+    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
+    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
+    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
+    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9406a6c0-ce05-4c1b-bbb1-60ed2b3a7418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer_stats = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47f6cd90-4b6a-4165-a934-ee8d630d1f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title Show final memory and time stats\n",
+    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
+    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
+    "used_percentage = round(used_memory         /max_memory*100, 3)\n",
+    "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
+    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
+    "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
+    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
+    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
+    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
+    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e13e692-e4f7-46d1-9df0-870695fd7e9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# alpaca_prompt = Copied from above\n",
+    "FastMixtralModel.for_inference(model) # Enable native 2x faster inference\n",
+    "inputs = tokenizer(\n",
+    "[\n",
+    "    alpaca_prompt.format(\n",
+    "        \"Continue the fibonnaci sequence.\", # instruction\n",
+    "        \"1, 1, 2, 3, 5, 8\", # input\n",
+    "        \"\", # output - leave this blank for generation!\n",
+    "    )\n",
+    "], return_tensors = \"pt\").to(\"cuda\")\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
+    "tokenizer.batch_decode(outputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09978b57-d549-4888-aaba-459abb683545",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}