Move conversion script into exllamav2 package

Author: turboderp

convert.py

@@ -1,313 +1 @@
-from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
-from exllamav2.architecture import RopeStyle
-import argparse, os, shutil
-import sys
-import json
-from conversion.tokenize import tokenize
-from conversion.measure import embeddings, measure_quant
-from conversion.quantize import quant
-from conversion.optimize import optimize
-from conversion.compile import compile_model
-from conversion.qparams import qparams_headoptions
-import torch
-
-parser = argparse.ArgumentParser(description = "Convert model to ExLlamaV2")
-parser.add_argument("-i", "--in_dir", type = str, help = "Input directory", default = "")
-parser.add_argument("-o", "--out_dir", type = str, help = "Output (working) directory")
-parser.add_argument("-res", "--resume", action = "store_true", help = "Resume job from specified output directory (without specifying other options)")
-parser.add_argument("-nr", "--no_resume", action = "store_true", help = "Do not resume an interrupted job (deletes all files in the output directory)")
-parser.add_argument("-cf", "--compile_full", type = str, help = "Output folder for compiled model with all config/tokenizer files")
-parser.add_argument("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)")
-parser.add_argument("-b", "--bits", type = float, default = 4.125, help = "Target bits per weight")
-parser.add_argument("-ss", "--shard_size", type = float, help = "Max shard size in MB (default: 8192)", default = 8192)
-parser.add_argument("-rs", "--rope_scale", type = float, help = "RoPE scaling factor")
-parser.add_argument("-ra", "--rope_alpha", type = float, help = "RoPE alpha value (NTK)")
-parser.add_argument("-hb", "--head_bits", type = int, default = 6, help = "Target bits per weight (head layer)")
-parser.add_argument("-om", "--output_measurement", type = str, help = "Only perform measurement pass, then save measurement to the specified file")
-parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
-parser.add_argument("-r", "--dataset_rows", type = int, default = 100, help = "Number of rows to apply from dataset")
-parser.add_argument("-mr", "--measurement_rows", type = int, default = 16, help = "Number of rows to apply from dataset when measuring")
-parser.add_argument("-l", "--length", type = int, default = 2048, help = "Max no. tokens per sample")
-parser.add_argument("-ml", "--measurement_length", type = int, default = 2048, help = "Max no. tokens per sample when measuring")
-parser.add_argument("-so", "--status_output", action = "store_true", help = "Include machine-parseable status updates in console output")
-parser.add_argument("-hsol", "--hidden_state_offload_layers", type = int, default = 0, help = "Number of hidden/target states to keep in VRAM. Speed-up but increases VRAM usage")
-
-args = parser.parse_args()
-
-torch.set_printoptions(precision = 7, sci_mode = False, linewidth = 200)
-
-# Check some args
-
-resuming = False
-if args.out_dir:
-    if not args.no_resume:
-        if os.path.exists(os.path.join(args.out_dir, "job_new.json")):
-            resuming = True
-else:
-    print(" ## Please specify output/working directory (-o, --out_dir)")
-    sys.exit()
-
-if not args.in_dir and not resuming:
-    print(" ## Please specify input model directory (-i, --in_dir)")
-    sys.exit()
-
-if args.length > 2048 or args.measurement_length > 2048:
-    print(" !! Warning: calibration rows > 2048 tokens may result in excessive VRAM use")
-
-if not args.head_bits in qparams_headoptions:
-    print(f" ## Error: {args.head_bits} is not a supported option for head layer bitrate")
-    sys.exit()
-
-if args.output_measurement is not None and args.compile_full is not None:
-    print(" ## Conflicting options: --output_measurement and --compile_full")
-    sys.exit()
-
-if args.bits < 2 or args.bits > 8:
-    print(f" !! Warning: target bitrate {args.bits} will likely not be attainable")
-
-if not os.path.exists(args.out_dir):
-    print(f" ## Error: Directory not found: {args.out_dir}")
-    sys.exit()
-
-# Create job
-
-def save_job():
-    global job_file, job
-    with open(job_file, "w", encoding = "utf8") as f:
-        f.write(json.dumps(job, indent = 4))
-
-job_file = os.path.join(args.out_dir, "job_new.json")
-
-if args.no_resume or not os.path.exists(job_file):
-
-    print(f" -- Beginning new job")
-    if len(os.listdir(args.out_dir)) != 0:
-        print(f" !! Warning: Output directory is not empty: {args.out_dir}")
-
-        if args.no_resume:
-            print(f" !! Cleaning output directory: {args.out_dir}")
-            for filename in os.listdir(args.out_dir):
-                file_path = os.path.join(args.out_dir, filename)
-                if os.path.isfile(file_path):
-                    os.unlink(file_path)
-                elif os.path.isdir(file_path):
-                    shutil.rmtree(file_path)
-
-output_measurement = args.output_measurement
-if output_measurement is not None:
-    if os.path.isdir(output_measurement):
-        output_measurement = os.path.join(output_measurement, "measurement.json")
-
-job = {"in_dir": args.in_dir,
-       "out_dir": args.out_dir,
-       "cal_dataset": args.cal_dataset,
-       "bits": args.bits,
-       "dataset_rows": args.dataset_rows,
-       "measurement_rows": args.measurement_rows,
-       "length": args.length,
-       "measurement_length": args.measurement_length,
-       "head_bits": args.head_bits,
-       "shard_size": args.shard_size if args.shard_size > 0 else 1024 ** 3,  # 1 PB = unlimited,
-       "compile_full": args.compile_full,
-       "rope_scale": args.rope_scale,
-       "rope_alpha": args.rope_alpha,
-       "output_measurement": output_measurement,
-       "progress": "begin"}
-
-if args.measurement is not None:
-    with open(args.measurement, "r", encoding = "utf8") as f:
-        imp_measurement = json.load(f)
-        job["measurement"] = imp_measurement["measurement"]
-        job["last_module_idx"] = imp_measurement["last_module_idx"]
-        job["reuse_measurement"] = args.measurement
-
-# Resume existing job
-
-if args.no_resume or not os.path.exists(job_file):
-    pass
-
-else:
-    print(f" -- Resuming job")
-    if args.in_dir:
-        print(f" !! Note: Overriding options with settings from existing job")
-
-    with open(job_file, "r", encoding = "utf8") as f:
-        resume_job = json.load(f)
-
-    # Override keys in existing job
-    del resume_job["out_dir"]
-
-    job.update(resume_job)
-    if "invalid" in job:
-        print(" ** Error: Corrupted job")
-        sys.exit()
-
-    if job["progress"] == "finished":
-        print(" !! Job is already finished")
-        sys.exit()
-
-# Feedback
-
-print(f" -- Input: {job['in_dir']}")
-print(f" -- Output: {job['out_dir']}")
-if job.get("cal_dataset"):
-    print(f" -- Calibration dataset: {job['cal_dataset']}, {job['dataset_rows']} / {job['measurement_rows']} rows, {job['length']} tokens per sample")
-else:
-    print(f" -- Using default calibration dataset")
-if job["output_measurement"] is None:
-    print(f" -- Target bits per weight: {job['bits']} (decoder), {job['head_bits']} (head)")
-    print(f" -- Max shard size: {job['shard_size']} MB")
-else:
-    print(f" -- Measurement will be saved to {job['output_measurement']}")
-    print(f" !! Conversion script will end after measurement pass")
-
-if job['rope_scale']: print(f" -- RoPE scale: {job['rope_scale']:.2f}")
-if job['rope_alpha']: print(f" -- RoPE alpha: {job['rope_alpha']:.2f}")
-
-# Make sure subfolders exist
-
-if job.get("compile_full"):
-    print(f" -- Full model will be compiled to: {job['compile_full']}")
-    if os.path.exists(job["compile_full"]):
-        if not os.path.isdir(job["compile_full"]):
-            print(f" ## Error: Output path {job['compile_full']} exists but is not a directory")
-            sys.exit()
-        if len(os.listdir(job["compile_full"])) > 0:
-            print(f" !! Warning: Output path {job['compile_full']} exists but is not empty")
-
-out_tensor_dir = os.path.join(job["out_dir"], "out_tensor")
-if not os.path.exists(out_tensor_dir):
-    os.makedirs(out_tensor_dir)
-
-# Create config
-
-config = ExLlamaV2Config()
-config.model_dir = job['in_dir']
-config.qkv_embed = False
-config.prepare()
-
-# Tokenizer
-
-tokenizer = ExLlamaV2Tokenizer(config)
-
-# Set scaling for input model
-
-if job["rope_scale"] is not None: config.scale_pos_emb = job["rope_scale"]
-if job["rope_alpha"] is not None: config.scale_alpha_value = job["rope_alpha"]
-
-# Create model without loading weights
-
-model = ExLlamaV2(config)
-model.load(lazy = True)
-
-# Limit context length if necessary
-
-if model.config.arch.rope_style == RopeStyle.NONE:
-    max_ctx = model.config.max_seq_len
-    if job["length"] > max_ctx:
-        print (f" !! Warning: Reducing calibration length to model max context: {max_ctx}")
-        job["length"] = max_ctx
-    if job["measurement_length"] > max_ctx:
-        print (f" !! Warning: Reducing measurement calibration length to model max context: {max_ctx}")
-        job["measurement_length"] = max_ctx
-
-# Overridable settings
-
-job["status_output"] = args.status_output
-
-# Do the things
-
-save_job()
-
-while True:
-
-    progress = job["progress"]
-
-    if progress == "begin":
-
-        if "reuse_measurement" in job:
-
-            print(f" -- Reusing measurement: {job['reuse_measurement']}")
-            job["progress"] = "optimize"
-            save_job()
-
-        else:
-
-            print(f" -- Tokenizing samples (measurement)...")
-            tokenize(job, save_job, tokenizer, measure = True)
-            job["progress"] = "initial_embeddings"
-            save_job()
-
-    if progress == "initial_embeddings":
-
-        print(f" -- Token embeddings (measurement)...")
-        embeddings(job, save_job, model)
-        job["progress"] = "measure_quant"
-        save_job()
-
-    if progress == "measure_quant":
-        print(f" -- Measuring quantization impact...")
-
-        model.unload()
-        config.max_output_len = 16
-        model = ExLlamaV2(config)
-        model.load(lazy = True)
-
-        status = measure_quant(job, save_job, model, args.hidden_state_offload_layers)  # capturing the graceful exits
-        if status == "interrupted":
-            print("Process interrupted. Exiting gracefully.")
-            save_job()
-            sys.exit(1)  
-        if job["output_measurement"] is None:
-            job["progress"] = "optimize"
-        else:
-            job["progress"] = "finished"
-        save_job()
-
-        model.unload()
-        config.max_output_len = None
-        model = ExLlamaV2(config)
-        model.load(lazy = True)
-
-    if progress == "optimize":
-
-        print(f" -- Optimizing...")
-        optimize(job, save_job, model)
-        job["progress"] = "tokens_cal"
-        save_job()
-
-    if progress == "tokens_cal":
-
-        print(f" -- Tokenizing samples...")
-        tokenize(job, save_job, tokenizer)
-        job["progress"] = "embeddings"
-        save_job()
-
-    if progress == "embeddings":
-        print(f" -- Token embeddings again...")
-        embeddings(job, save_job, model)
-        job["progress"] = "quant"
-        save_job()
-
-    if progress == "quant":
-
-        print(f" -- Quantizing...")
-        quant(job, save_job, model)
-        job["progress"] = "compile"
-        save_job()
-
-    if progress == "compile":
-
-        print(f" -- Compiling output file...")
-        compile_model(job, save_job, model)
-        job["progress"] = "finished"
-        save_job()
-
-    if progress == "finished": break
-
-print(f" -- Finished")
-
-
-
-
-
+import exllamav2.conversion.convert_exl2

conversion/adaptivegptq.py exllamav2/conversion/adaptivegptq.py

@@ -17,7 +17,7 @@
 import os, glob, shutil, json
 from safetensors import safe_open
 from safetensors.torch import save_file
-from conversion.bot_status import print_stage
+from exllamav2.conversion.bot_status import print_stage
 
 def _tsize(t):