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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"import math\n",
"import multiprocessing\n",
"import json\n",
"import os\n",
"import re\n",
"import subprocess\n",
"import yaml"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Define base model name and default values for parameters\n",
"path_to_llamacpp = '/Users/macdev/Downloads/build/bin'\n",
"base_model_name = 'salamandra-2b-instruct'\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def extract_from_config(config_file):\n",
" \"\"\"Extract parameters like context size, rope frequency base, and other sampling settings from a config JSON file.\"\"\"\n",
" with open(config_file, 'r') as file:\n",
" config_data = json.load(file)\n",
"\n",
" # Extract parameters if present\n",
" params = {}\n",
" params['ctx_size'] = config_data.get(\"max_position_embeddings\") # Context size\n",
" params['rope_freq_base'] = config_data.get(\"rope_theta\") # RoPE frequency base\n",
" params['rope_scaling'] = config_data.get(\"rope_scaling\") # RoPE scaling factor\n",
" params['rope_scaling_type'] = config_data.get(\"rope_scaling_type\") # RoPE scaling type\n",
" params['torch_dtype'] = config_data.get(\"torch_dtype\") # Torch data type\n",
" params['top_p'] = config_data.get(\"sampling.top_p\") # Top-p sampling\n",
" params['temp'] = config_data.get(\"sampling.temperature\") # Sampling temperature\n",
" params['repeat_penalty'] = config_data.get(\"sampling.repeat_penalty\") # Repetition penalty\n",
" params['repeat_last_n'] = config_data.get(\"sampling.repeat_last_n\") # Last N tokens for repetition penalty\n",
" params['min_p'] = config_data.get(\"sampling.min_p\") # Minimum probability sampling\n",
" params['top_k'] = config_data.get(\"sampling.top_k\") # Top-k sampling\n",
" params['presence_penalty'] = config_data.get(\"sampling.presence_penalty\") # Presence penalty for repeat tokens\n",
" params['frequency_penalty'] = config_data.get(\"sampling.frequency_penalty\") # Frequency penalty for repeat tokens\n",
" params['mirostat'] = config_data.get(\"sampling.mirostat\") # Mirostat sampling\n",
" params['mirostat_lr'] = config_data.get(\"sampling.mirostat_lr\") # Mirostat learning rate\n",
" params['mirostat_ent'] = config_data.get(\"sampling.mirostat_ent\") # Mirostat entropy target\n",
" params['tfs'] = config_data.get(\"sampling.tfs\") # Tail free sampling\n",
" params['typical'] = config_data.get(\"sampling.typical\") # Locally typical sampling\n",
"\n",
" return params\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"unquantized = defaultdict(lambda: \"fp16\")\n",
"unquantized[\"float32\"] = \"fp32\"\n",
"unquantized[\"float16\"] = \"fp16\"\n",
"unquantized[\"bfloat16\"] = \"bf16\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def extract_from_generation_config(generation_config_file):\n",
" \"\"\"Extract generation-specific parameters relevant to llama-perplexity if available.\"\"\"\n",
" with open(generation_config_file, 'r') as file:\n",
" generation_data = json.load(file)\n",
" \n",
" # Extract and map only parameters useful for llama-perplexity\n",
" params = {}\n",
" params['top_p'] = generation_data.get(\"top_p\") # Top-p sampling\n",
" params['temp'] = generation_data.get(\"temperature\") # Sampling temperature\n",
" params['repeat_penalty'] = generation_data.get(\"repetition_penalty\") # Repetition penalty\n",
" params['repeat_last_n'] = generation_data.get(\"repeat_last_n\") # Last N tokens for repetition penalty\n",
" params['top_k'] = generation_data.get(\"top_k\") # Top-k sampling (if present)\n",
" params['presence_penalty'] = generation_data.get(\"presence_penalty\") # Presence penalty\n",
" params['frequency_penalty'] = generation_data.get(\"frequency_penalty\")# Frequency penalty\n",
"\n",
" # Remove None values to avoid overwriting defaults\n",
" params = {key: value for key, value in params.items() if value is not None}\n",
"\n",
" return params\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def get_parameters(use_temp=False):\n",
" \"\"\"Retrieve parameters from the configuration files or use defaults, preferring generation_config if available.\"\"\"\n",
" # Initialize default parameters\n",
" config_params = dict()\n",
"\n",
" # Extract parameters from config.json, if available\n",
" try:\n",
" config_params.update(extract_from_config('config.json'))\n",
" except FileNotFoundError:\n",
" print(\"config.json not found. Using default values.\")\n",
"\n",
" # Extract parameters from generation_config.json, if available and prefer these values\n",
" try:\n",
" gen_params = extract_from_generation_config('generation_config.json')\n",
" # Update config_params with values from gen_params, if they are not None\n",
" for key, value in gen_params.items():\n",
" if value is not None:\n",
" config_params[key] = value\n",
" except FileNotFoundError:\n",
" print(\"generation_config.json not found. Using default generation values.\")\n",
"\n",
" # Ensure that temperature ('temp') is never used\n",
" if 'temp' in config_params and use_temp is False:\n",
" config_params['temp'] = 0 # Set temperature to 0\n",
"\n",
" return config_params\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'ctx_size': 8192, 'rope_freq_base': 10000.0, 'rope_scaling': None, 'rope_scaling_type': None, 'torch_dtype': 'bfloat16', 'top_p': None, 'temp': 0, 'repeat_penalty': 1.2, 'repeat_last_n': None, 'min_p': None, 'top_k': None, 'presence_penalty': None, 'frequency_penalty': None, 'mirostat': None, 'mirostat_lr': None, 'mirostat_ent': None, 'tfs': None, 'typical': None}\n"
]
}
],
"source": [
"# Extract configuration parameters\n",
"config_params = get_parameters()\n",
"print(config_params)\n",
"\n",
"base_precision = unquantized[config_params[\"torch_dtype\"]]\n",
"\n",
"base_model = f'{base_model_name}_{base_precision}.gguf'\n",
"base_perplexity_file = f\"perplexity_{base_precision}.txt\"\n",
"\n",
"threads = max(multiprocessing.cpu_count() - 1, 1)\n",
"batch_size = 512\n",
"ubatch_size = 128\n",
"dataset_file = \"imatrix/oscar/imatrix-dataset.txt\" \n",
"ppl_file = \"ppl_test_data.txt\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quantization types: ['IQ2_S', 'IQ2_M', 'IQ3_M', 'IQ4_NL', 'IQ4_XS', 'Q3_K_L', 'Q3_K_M', 'Q4_K_M', 'Q4_K_S', 'Q5_K_M', 'Q5_K_S', 'Q6_K', 'Q8_0']\n"
]
}
],
"source": [
"# Load YAML file and extract quantization types\n",
"yaml_file = 'quantizations.yaml'\n",
"with open(yaml_file, 'r') as file:\n",
" data = yaml.safe_load(file)\n",
"\n",
"# Extract the list of quantization types\n",
"quantization_types = data['quantizations']\n",
"print(\"Quantization types: \", quantization_types)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Quantization parameters\n",
"use_leave_output_tensor = True # Set to False if you don't want to use --leave-output-tensor\n",
"\n",
"# Optional importance matrix path (set to None if you don't want to include --imatrix)\n",
"imatrix_path = \"imatrix/oscar/imatrix.dat\" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def quantize_model(\n",
" quantization_type, \n",
" base_model, \n",
" base_model_name, \n",
" path_to_llamacpp=\"\",\n",
" imatrix_path=None, \n",
" use_leave_output_tensor=True,\n",
" output_dir=\".\"\n",
"):\n",
" \"\"\"\n",
" Quantize the base model into the specified quantization type.\n",
"\n",
" Parameters:\n",
" - quantization_type (str): The type of quantization (e.g., \"Q4_0\", \"Q5_K_M\").\n",
" - base_model (str): Path to the base model file (e.g., \"salamandra-2b_bf16.gguf\").\n",
" - base_model_name (str): The base name of the model (e.g., \"salamandra-2b\").\n",
" - path_to_llamacpp (str): Path to the llama-quantize binary.\n",
" - imatrix_path (str, optional): Path to the importance matrix file. Default is None.\n",
" - use_leave_output_tensor (bool): Whether to include the --leave-output-tensor flag. Default is True.\n",
" - output_dir (str): Directory where the quantized models and logs will be saved. Default is current directory.\n",
"\n",
" Returns:\n",
" - None\n",
" \"\"\"\n",
" # Construct the output model path\n",
" output_model = os.path.join(output_dir, f\"{base_model_name}_{quantization_type}.gguf\")\n",
"\n",
" # Check if the quantized model already exists\n",
" if os.path.exists(output_model):\n",
" print(f\"Quantized model {output_model} already exists. Skipping quantization.\")\n",
" return\n",
"\n",
" # Build the llama-quantize command\n",
" command_parts = [\n",
" os.path.join(path_to_llamacpp, \"llama-quantize\")\n",
" ]\n",
"\n",
" # Conditionally add the --imatrix argument if the path is provided\n",
" if imatrix_path:\n",
" command_parts.append(f\"--imatrix {imatrix_path}\")\n",
"\n",
" # Conditionally add the --leave-output-tensor argument based on the external boolean\n",
" if use_leave_output_tensor:\n",
" command_parts.append(\"--leave-output-tensor\")\n",
"\n",
" # Add base model, output model, and quantization type\n",
" command_parts.extend([\n",
" f\"{base_model}\",\n",
" f\"\\\"{output_model}\\\"\",\n",
" f\"{quantization_type}\"\n",
" ])\n",
"\n",
" # Redirect output to a log file for each quantization type\n",
" log_file = os.path.join(output_dir, f\"{quantization_type}_log.txt\")\n",
" command_parts.append(f\"> \\\"{log_file}\\\" 2>&1\")\n",
"\n",
" # Join the command parts into a single command string\n",
" quantize_command = \" \".join(command_parts)\n",
"\n",
" # Run the quantization command\n",
" print(f\"Quantizing model to {quantization_type} format with command: {quantize_command}\")\n",
" result = subprocess.run(quantize_command, shell=True, text=True)\n",
" if result.returncode != 0:\n",
" print(f\"Error during quantization to {quantization_type}. Check {log_file} for details.\")\n",
" else:\n",
" print(f\"Successfully quantized model to {quantization_type} and saved as {output_model}.\")\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def run_command(command):\n",
" \"\"\"Function to run a command and capture output\"\"\"\n",
" print(f\"Running command: {command}\")\n",
" result = subprocess.run(command, shell=True, capture_output=True, text=True)\n",
" if result.returncode != 0:\n",
" print(f\"Error executing command: {result.stderr}\")\n",
" return result.stdout\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def extract_perplexity(output):\n",
" \"\"\"extract perplexity from the output\"\"\"\n",
" match = re.search(r\"Final estimate: PPL = ([\\d.]+)\", output)\n",
" if match:\n",
" return float(match.group(1))\n",
" return None\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def build_command(model, output_file, ppl_file, config_params, threads=8, batch_size=512, ubatch_size=128):\n",
" \"\"\"Build the perplexity command based on the provided parameters.\"\"\"\n",
" command_parts = [\n",
" \"/Users/macdev/Downloads/build/bin/llama-perplexity\",\n",
" f\"-m {model}\",\n",
" f\"-f {ppl_file}\",\n",
" \"--perplexity\",\n",
" ]\n",
"\n",
" # Add parameters only if they are set in config_params\n",
" if config_params.get('ctx_size') is not None:\n",
" command_parts.append(f\"--ctx-size {config_params['ctx_size']}\")\n",
" if config_params.get('rope_freq_base') is not None:\n",
" command_parts.append(f\"--rope-freq-base {config_params['rope_freq_base']}\")\n",
" if config_params.get('rope_freq_scale') is not None:\n",
" command_parts.append(f\"--rope-freq-scale {config_params['rope_freq_scale']}\")\n",
" if config_params.get('rope_scaling_type') is not None:\n",
" command_parts.append(f\"--rope-scaling {config_params['rope_scaling_type']}\")\n",
"\n",
" # Add sampling-related parameters if they are set\n",
" if config_params.get('top_p') is not None:\n",
" command_parts.append(f\"--top-p {config_params['top_p']}\")\n",
" if config_params.get('repeat_penalty') is not None:\n",
" command_parts.append(f\"--repeat-penalty {config_params['repeat_penalty']}\")\n",
" if config_params.get('repeat_last_n') is not None:\n",
" command_parts.append(f\"--repeat-last-n {config_params['repeat_last_n']}\")\n",
"\n",
" # Do not include `temp` as it's set to 0 in `get_parameters` if `use_temp` is False\n",
" # Only add if temp is non-zero (if `use_temp` is True in get_parameters)\n",
" if config_params.get('temp') is not None and config_params['temp'] != 0:\n",
" command_parts.append(f\"--temp {config_params['temp']}\")\n",
"\n",
" # Add fixed parameters for threads and batch sizes\n",
" command_parts.extend([\n",
" f\"--threads {threads}\",\n",
" f\"--batch-size {batch_size}\",\n",
" f\"--ubatch-size {ubatch_size}\",\n",
" ])\n",
"\n",
" # Redirect output to file\n",
" command = \" \".join(command_parts) + f\" > {output_file} 2>&1\"\n",
" return command\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Measure perplexity for the base model\n",
"if os.path.exists(f'perplexity_{base_precision}.txt'):\n",
" with open(base_perplexity_file, 'r') as file:\n",
" base_output = file.read()\n",
"else:\n",
" base_command = build_command(base_model, base_perplexity_file, ppl_file, config_params=config_params, threads=threads, batch_size=batch_size, ubatch_size= ubatch_size)\n",
" base_output = run_command(base_command)\n",
"base_perplexity = extract_perplexity(base_output)\n",
"calculated_perplexity_recently = False # This will be set to True later"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quantizing model to IQ2_S format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_IQ2_S.gguf\" IQ2_S > \"./IQ2_S_log.txt\" 2>&1\n",
"Successfully quantized model to IQ2_S and saved as ./salamandra-2b-instruct_IQ2_S.gguf.\n",
"Quantizing model to IQ2_M format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_IQ2_M.gguf\" IQ2_M > \"./IQ2_M_log.txt\" 2>&1\n",
"Successfully quantized model to IQ2_M and saved as ./salamandra-2b-instruct_IQ2_M.gguf.\n",
"Quantizing model to IQ3_M format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_IQ3_M.gguf\" IQ3_M > \"./IQ3_M_log.txt\" 2>&1\n",
"Successfully quantized model to IQ3_M and saved as ./salamandra-2b-instruct_IQ3_M.gguf.\n",
"Quantizing model to IQ4_NL format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_IQ4_NL.gguf\" IQ4_NL > \"./IQ4_NL_log.txt\" 2>&1\n",
"Successfully quantized model to IQ4_NL and saved as ./salamandra-2b-instruct_IQ4_NL.gguf.\n",
"Quantizing model to IQ4_XS format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_IQ4_XS.gguf\" IQ4_XS > \"./IQ4_XS_log.txt\" 2>&1\n",
"Successfully quantized model to IQ4_XS and saved as ./salamandra-2b-instruct_IQ4_XS.gguf.\n",
"Quantizing model to Q3_K_L format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q3_K_L.gguf\" Q3_K_L > \"./Q3_K_L_log.txt\" 2>&1\n",
"Successfully quantized model to Q3_K_L and saved as ./salamandra-2b-instruct_Q3_K_L.gguf.\n",
"Quantizing model to Q3_K_M format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q3_K_M.gguf\" Q3_K_M > \"./Q3_K_M_log.txt\" 2>&1\n",
"Successfully quantized model to Q3_K_M and saved as ./salamandra-2b-instruct_Q3_K_M.gguf.\n",
"Quantizing model to Q4_K_M format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q4_K_M.gguf\" Q4_K_M > \"./Q4_K_M_log.txt\" 2>&1\n",
"Successfully quantized model to Q4_K_M and saved as ./salamandra-2b-instruct_Q4_K_M.gguf.\n",
"Quantizing model to Q4_K_S format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q4_K_S.gguf\" Q4_K_S > \"./Q4_K_S_log.txt\" 2>&1\n",
"Successfully quantized model to Q4_K_S and saved as ./salamandra-2b-instruct_Q4_K_S.gguf.\n",
"Quantizing model to Q5_K_M format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q5_K_M.gguf\" Q5_K_M > \"./Q5_K_M_log.txt\" 2>&1\n",
"Successfully quantized model to Q5_K_M and saved as ./salamandra-2b-instruct_Q5_K_M.gguf.\n",
"Quantizing model to Q5_K_S format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q5_K_S.gguf\" Q5_K_S > \"./Q5_K_S_log.txt\" 2>&1\n",
"Successfully quantized model to Q5_K_S and saved as ./salamandra-2b-instruct_Q5_K_S.gguf.\n",
"Quantizing model to Q6_K format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q6_K.gguf\" Q6_K > \"./Q6_K_log.txt\" 2>&1\n",
"Successfully quantized model to Q6_K and saved as ./salamandra-2b-instruct_Q6_K.gguf.\n",
"Quantizing model to Q8_0 format with command: /Users/macdev/Downloads/build/bin/llama-quantize --imatrix imatrix/oscar/imatrix.dat --leave-output-tensor salamandra-2b-instruct_bf16.gguf \"./salamandra-2b-instruct_Q8_0.gguf\" Q8_0 > \"./Q8_0_log.txt\" 2>&1\n",
"Successfully quantized model to Q8_0 and saved as ./salamandra-2b-instruct_Q8_0.gguf.\n"
]
}
],
"source": [
"# Quantize the models\n",
"for quant in quantization_types:\n",
" quantize_model(\n",
" quantization_type=quant,\n",
" base_model=base_model,\n",
" base_model_name=base_model_name,\n",
" path_to_llamacpp=path_to_llamacpp,\n",
" imatrix_path=imatrix_path,\n",
" use_leave_output_tensor=use_leave_output_tensor,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_IQ2_M.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_IQ2_M.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_IQ3_M.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_IQ3_M.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_IQ4_NL.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_IQ4_NL.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_IQ4_XS.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_IQ4_XS.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q3_K_L.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q3_K_L.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q3_K_M.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q3_K_M.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q4_K_M.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q4_K_M.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q4_K_S.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q4_K_S.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q5_K_M.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q5_K_M.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q5_K_S.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q5_K_S.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q6_K.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q6_K.txt 2>&1\n",
"Running command: /Users/macdev/Downloads/build/bin/llama-perplexity -m salamandra-2b-instruct_Q8_0.gguf -f ppl_test_data.txt --perplexity --ctx-size 8192 --rope-freq-base 10000.0 --repeat-penalty 1.2 --threads 15 --batch-size 512 --ubatch-size 128 > perplexity_Q8_0.txt 2>&1\n"
]
}
],
"source": [
"# Measure perplexity for each quantized model\n",
"perplexity_results = dict()\n",
"perplexity_results[base_precision] = base_perplexity\n",
"for quant in quantization_types:\n",
" calculated_perplexity_recently = True\n",
" \n",
" model = f\"{base_model_name}_{quant}.gguf\"\n",
" output_file = f\"perplexity_{quant}.txt\"\n",
"\n",
" command = build_command(model, output_file, ppl_file, config_params=config_params, threads=threads, batch_size=batch_size, ubatch_size= ubatch_size)\n",
" output = run_command(command)\n",
"\n",
" perplexity = extract_perplexity(output)\n",
" perplexity_results[quant] = perplexity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load previous measurements if we didnt just measure perplexity for each quantized model\n",
"if not calculated_perplexity_recently:\n",
" perplexity_results = dict()\n",
" perplexity_results[base_precision] = base_perplexity\n",
"\n",
" for quant in quantization_types:\n",
" output_file = f\"perplexity_{quant}.txt\"\n",
" try:\n",
" with open(output_file, 'r') as file:\n",
" output = file.read()\n",
" perplexity = extract_perplexity(output)\n",
" except FileNotFoundError:\n",
" print(f\"Output file {output_file} not found.\")\n",
" perplexity = None\n",
"\n",
" perplexity_results[quant] = perplexity\n",
"\n",
" # Calculate ln(PPL(Q)/PPL(fp16)) and generate the table\n",
" print(\"\\nPerplexity Comparison Table:\")\n",
" print(f\"{'Quantization Type':<20} {'PPL(Q)':<10} {'ln(PPL(Q)/PPL(fp16))':<25}\")\n",
" print(\"=\" * 55)\n",
" for quant, ppl in perplexity_results.items():\n",
" if ppl and base_perplexity:\n",
" ln_ratio = round(math.log(ppl / base_perplexity), 6)\n",
" print(f\"{quant:<20} {ppl:<10} {ln_ratio:<25}\")\n",
"\n",
" print(perplexity_results)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate ln(PPL(Q)/PPL(fp16)) and generate the table\n",
"print(\"\\nPerplexity Comparison Table:\")\n",
"print(f\"{'Quantization Type':<20} {'PPL(Q)':<10} {'ln(PPL(Q)/PPL(fp16))':<25}\")\n",
"print(\"=\" * 55)\n",
"for quant, ppl in perplexity_results.items():\n",
" if ppl and base_perplexity:\n",
" ln_ratio = round(math.log(ppl / base_perplexity), 6)\n",
" print(f\"{quant:<20} {ppl:<10} {ln_ratio:<25}\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Perplexity Comparison Table:\n",
"Quantization Type PPL(Q) ln(PPL(Q)/PPL(fp16)) \n",
"=======================================================\n",
"bf16 15.3799 0.0 \n",
"IQ2_S 25.3893 0.501266 \n",
"IQ2_M 21.6684 0.342794 \n",
"IQ3_M 16.774 0.086769 \n",
"IQ4_NL 15.9602 0.037037 \n",
"IQ4_XS 15.9591 0.036968 \n",
"Q3_K_L 16.5067 0.070705 \n",
"Q3_K_M 16.8567 0.091687 \n",
"Q4_K_M 15.8651 0.03106 \n",
"Q4_K_S 15.9346 0.035431 \n",
"Q5_K_M 15.4746 0.006139 \n",
"Q5_K_S 15.4901 0.00714 \n",
"Q6_K 15.3961 0.001053 \n",
"Q8_0 15.3831 0.000208 \n",
"{'bf16': 15.3799, 'IQ2_S': 25.3893, 'IQ2_M': 21.6684, 'IQ3_M': 16.774, 'IQ4_NL': 15.9602, 'IQ4_XS': 15.9591, 'Q3_K_L': 16.5067, 'Q3_K_M': 16.8567, 'Q4_K_M': 15.8651, 'Q4_K_S': 15.9346, 'Q5_K_M': 15.4746, 'Q5_K_S': 15.4901, 'Q6_K': 15.3961, 'Q8_0': 15.3831}\n"
]
}
],
"source": [
"perplexity_results = dict()\n",
"perplexity_results[base_precision] = base_perplexity\n",
"\n",
"for quant in quantization_types:\n",
" output_file = f\"perplexity_{quant}.txt\"\n",
" try:\n",
" with open(output_file, 'r') as file:\n",
" output = file.read()\n",
" perplexity = extract_perplexity(output)\n",
" except FileNotFoundError:\n",
" print(f\"Output file {output_file} not found.\")\n",
" perplexity = None\n",
"\n",
" perplexity_results[quant] = perplexity\n",
"\n",
"# Calculate ln(PPL(Q)/PPL(fp16)) and generate the table\n",
"print(\"\\nPerplexity Comparison Table:\")\n",
"print(f\"{'Quantization Type':<20} {'PPL(Q)':<10} {'ln(PPL(Q)/PPL(fp16))':<25}\")\n",
"print(\"=\" * 55)\n",
"for quant, ppl in perplexity_results.items():\n",
" if ppl and base_perplexity:\n",
" ln_ratio = round(math.log(ppl / base_perplexity), 6)\n",
" print(f\"{quant:<20} {ppl:<10} {ln_ratio:<25}\")\n",
"\n",
"print(perplexity_results)\n"
]
}
],
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|