mirror of
https://github.com/turboderp-org/exllamav2.git
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6bfcefe940
The default encoding on linux is utf8, but Windows uses cp1252 which isn't compatible with some unicode characters. Signed-off-by: kingbri <bdashore3@proton.me>
313 lines
11 KiB
Python
313 lines
11 KiB
Python
from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
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import argparse, os, shutil
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import sys
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import json
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from conversion.tokenize import tokenize
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from conversion.quantize import embeddings, measure_quant, quant
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from conversion.optimize import optimize
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from conversion.compile import compile_model
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from conversion.qparams import qparams_headoptions
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# import tracemalloc
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# tracemalloc.start()
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parser = argparse.ArgumentParser(description = "Convert model to ExLlamaV2")
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parser.add_argument("-i", "--in_dir", type = str, help = "Input directory", default = "")
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parser.add_argument("-o", "--out_dir", type = str, help = "Output (working) directory")
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parser.add_argument("-nr", "--no_resume", action = "store_true", help = "Do not resume an interrupted job (deletes all files in the output directory)")
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parser.add_argument("-cf", "--compile_full", type = str, help = "Output folder for compiled model with all config/tokenizer files")
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parser.add_argument("-om", "--output_measurement", type = str, help = "Only perform measurement pass, then save measurement to the specified file")
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parser.add_argument("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)", default = "")
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parser.add_argument("-r", "--dataset_rows", type = int, default = 100, help = "Number of rows to apply from dataset")
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parser.add_argument("-mr", "--measurement_rows", type = int, default = 16, help = "Number of rows to apply from dataset when measuring")
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parser.add_argument("-gr", "--gpu_rows", type = int, default = 0, help = "Threshold for paging hidden state to CPU")
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parser.add_argument("-l", "--length", type = int, default = 2048, help = "Max no. tokens per sample")
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parser.add_argument("-ml", "--measurement_length", type = int, default = 2048, help = "Max no. tokens per sample when measuring")
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parser.add_argument("-b", "--bits", type = float, default = 4.125, help = "Target bits per weight")
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parser.add_argument("-hb", "--head_bits", type = int, default = 6, help = "Target bits per weight (head layer)")
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parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
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parser.add_argument("-ss", "--shard_size", type = float, help = "Max shard size in MB (default: 8192)", default = 8192)
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parser.add_argument("-rs", "--rope_scale", type = float, default = 1.0, help = "RoPE scaling factor")
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parser.add_argument("-ra", "--rope_alpha", type = float, default = 1.0, help = "RoPE alpha value (NTK)")
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args = parser.parse_args()
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# Check some args
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if not args.in_dir:
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print(" ## Please specify input model directory (-i, --in_dir)")
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sys.exit()
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if not args.out_dir:
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print(" ## Please specify output/working directory (-o, --out_dir)")
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sys.exit()
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if not args.cal_dataset:
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print(" ## Please specify dataset Parquet file (-c, --cal_dataset)")
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sys.exit()
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if args.length > 2048 or args.measurement_length > 2048:
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print(" !! Warning: calibration rows > 2048 tokens may result in excessive VRAM use")
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if not args.head_bits in qparams_headoptions:
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print(f" ## Error: {args.head_bits} is not a supported option for head layer bitrate")
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sys.exit()
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if args.bits < 2 or args.bits > 8:
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print(f" !! Warning: target bitrate {args.bits} will likely not be attainable")
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if args.output_measurement is not None and args.compile_full is not None:
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print(" ## Conflicting options: --output_measurement and --compile_full")
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sys.exit()
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# Arguments
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in_dir = None if args.in_dir == "" else os.path.abspath(args.in_dir)
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out_dir = os.path.abspath(args.out_dir)
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cal_dataset = None if args.cal_dataset == "" else os.path.abspath(args.cal_dataset)
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dataset_rows = args.dataset_rows
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measurement_rows = args.measurement_rows
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gpu_rows = args.gpu_rows
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length = args.length
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measurement_length = args.measurement_length
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bits = args.bits
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head_bits = args.head_bits
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reuse_measurement = args.measurement
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shard_size = args.shard_size if args.shard_size > 0 else 1024 ** 3 # 1 PB = unlimited
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no_resume = args.no_resume
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output_measurement = args.output_measurement
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if output_measurement is not None:
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if os.path.isdir(output_measurement):
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output_measurement = os.path.join(output_measurement, "measurement.json")
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rope_scale = args.rope_scale
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rope_alpha = args.rope_alpha
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compile_full = args.compile_full
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if not os.path.exists(out_dir):
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print(f" ## Error: Directory not found: {out_dir}")
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sys.exit()
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# Create config
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config = ExLlamaV2Config()
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config.model_dir = in_dir
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config.qkv_embed = False
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config.prepare()
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# Tokenizer
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tokenizer = ExLlamaV2Tokenizer(config)
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# Job file
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job_file = os.path.join(out_dir, "job.json")
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# Create new job
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def save_job():
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global job_file, job
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with open(job_file, "w", encoding = "utf8") as f:
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f.write(json.dumps(job, indent = 4))
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if no_resume or not os.path.exists(job_file):
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print(f" -- Beginning new job")
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if len(os.listdir(out_dir)) != 0:
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print(f" !! Warning: Output directory is not empty: {out_dir}")
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if no_resume:
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print(f" !! Cleaning output directory: {out_dir}")
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for filename in os.listdir(out_dir):
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file_path = os.path.join(out_dir, filename)
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if os.path.isfile(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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if in_dir is None:
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print(f" ## Error: No input directory specified")
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sys.exit()
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if cal_dataset is None:
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print(f" ## Error: No calibration dataset specified")
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sys.exit()
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job = { "in_dir": in_dir,
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"out_dir": out_dir,
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"cal_dataset": cal_dataset,
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"dataset_rows": dataset_rows,
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"measurement_rows": measurement_rows,
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"gpu_rows": gpu_rows,
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"length": length,
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"measurement_length": measurement_length,
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"bits": bits,
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"head_bits": head_bits,
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"progress": "begin",
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"shard_size": shard_size,
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"output_measurement": output_measurement,
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"compile_full": compile_full,
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"rope_scale": rope_scale,
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"rope_alpha": rope_alpha
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}
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if reuse_measurement is not None:
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with open(reuse_measurement, "r", encoding = "utf8") as f:
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imp_measurement = json.load(f)
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job["measurement"] = imp_measurement["measurement"]
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job["last_module_idx"] = imp_measurement["last_module_idx"]
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job["base_perplexity"] = imp_measurement["base_perplexity"]
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job["reuse_measurement"] = reuse_measurement
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save_job()
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# Resume existing job
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else:
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print(f" -- Resuming job")
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print(f" !! Note: Overriding options with settings from existing job")
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with open(job_file, "r", encoding = "utf8") as f:
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job = json.load(f)
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if "invalid" in job:
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print(" ** Error: Corrupted job")
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sys.exit()
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if "shard_size" not in job: job["shard_size"] = shard_size
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if "output_measurement" not in job: job["output_measurement"] = output_measurement
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if "compile_full" not in job: job["compile_full"] = compile_full
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job["out_dir"] = out_dir
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# Feedback
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print(f" -- Input: {job['in_dir']}")
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print(f" -- Output: {out_dir}")
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print(f" -- Calibration dataset: {job['cal_dataset']}, {job['dataset_rows']} / {job['measurement_rows']} ({job['gpu_rows']}) rows, {job['length']} tokens per sample")
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if job["output_measurement"] is None:
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print(f" -- Target bits per weight: {job['bits']} (decoder), {job['head_bits']} (head)")
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print(f" -- Max shard size: {job['shard_size']} MB")
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else:
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print(f" -- Measurement will be saved to {job['output_measurement']}")
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print(f" !! Conversion script will end after measurement pass")
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if job["rope_scale"] is not None:
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print(f" -- RoPE scale: {job['rope_scale']:.2f}")
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if job["rope_alpha"] is not None:
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print(f" -- RoPE alpha: {job['rope_alpha']:.2f}")
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# Make sure subfolders exist
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if job["compile_full"] is not None:
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print(f" -- Full model will be compiled to: {job['compile_full']}")
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if os.path.exists(job["compile_full"]):
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if not os.path.isdir(job["compile_full"]):
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print(f" ## Error: Output path {job['compile_full']} exists but is not a directory")
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sys.exit()
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if len(os.listdir(job["compile_full"])) > 0:
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print(f" !! Warning: Output path {job['compile_full']} exists but is not empty")
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out_tensor_dir = os.path.join(job["out_dir"], "out_tensor")
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if not os.path.exists(out_tensor_dir):
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os.makedirs(out_tensor_dir)
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# Allocate space for hidden state
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max_l = max(job["measurement_length"], job["length"])
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config.max_input_len = max_l
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config.max_attention_size = max_l ** 2
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# Set scaling for input model
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if job["rope_scale"] is not None:
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config.scale_pos_emb = job["rope_scale"]
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if job["rope_alpha"] is not None:
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config.scale_alpha_value = job["rope_alpha"]
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# Create model without loading weights
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model = ExLlamaV2(config)
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model.load(lazy = True)
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# Do the things
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while True:
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progress = job["progress"]
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if progress == "begin":
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if "reuse_measurement" in job:
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print(f" -- Reusing measurement: {job['reuse_measurement']}")
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job["progress"] = "optimize"
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save_job()
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else:
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print(f" -- Tokenizing samples (measurement)...")
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tokenize(job, save_job, tokenizer, measure = True)
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job["progress"] = "initial_embeddings"
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save_job()
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if progress == "initial_embeddings":
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print(f" -- Token embeddings (measurement)...")
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embeddings(job, save_job, model)
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job["progress"] = "measure_quant"
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save_job()
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if progress == "measure_quant":
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print(f" -- Measuring quantization impact...")
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measure_quant(job, save_job, model)
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if job["output_measurement"] is None:
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job["progress"] = "optimize"
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else:
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job["progress"] = "finished"
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save_job()
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if progress == "optimize":
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print(f" -- Optimizing...")
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optimize(job, save_job)
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job["progress"] = "tokens_cal"
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save_job()
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if progress == "tokens_cal":
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print(f" -- Tokenizing samples...")
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tokenize(job, save_job, tokenizer)
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job["progress"] = "embeddings"
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save_job()
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if progress == "embeddings":
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print(f" -- Token embeddings again...")
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embeddings(job, save_job, model)
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job["progress"] = "quant"
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save_job()
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if progress == "quant":
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print(f" -- Quantizing...")
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quant(job, save_job, model)
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job["progress"] = "compile"
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save_job()
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if progress == "compile":
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print(f" -- Compiling output file...")
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compile_model(job, save_job, model)
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job["progress"] = "finished"
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save_job()
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if progress == "finished": break
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print(f" -- Finished") |