mirror of
https://github.com/turboderp-org/exllamav2.git
synced 2026-03-15 00:07:26 +00:00
Move conversion script into exllamav2 package
This commit is contained in:
turboderp
314
convert.py
314
convert.py
@@ -1,313 +1 @@
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from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
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from exllamav2.architecture import RopeStyle
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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.measure import embeddings, measure_quant
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from conversion.quantize import 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 torch
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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("-res", "--resume", action = "store_true", help = "Resume job from specified output directory (without specifying other options)")
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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("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)")
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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("-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, help = "RoPE scaling factor")
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parser.add_argument("-ra", "--rope_alpha", type = float, help = "RoPE alpha value (NTK)")
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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("-om", "--output_measurement", type = str, help = "Only perform measurement pass, then save measurement to the specified file")
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parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
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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("-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("-so", "--status_output", action = "store_true", help = "Include machine-parseable status updates in console output")
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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")
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args = parser.parse_args()
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torch.set_printoptions(precision = 7, sci_mode = False, linewidth = 200)
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# Check some args
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resuming = False
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if args.out_dir:
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if not args.no_resume:
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if os.path.exists(os.path.join(args.out_dir, "job_new.json")):
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resuming = True
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else:
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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.in_dir and not resuming:
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print(" ## Please specify input model directory (-i, --in_dir)")
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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.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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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 not os.path.exists(args.out_dir):
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print(f" ## Error: Directory not found: {args.out_dir}")
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sys.exit()
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# Create 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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job_file = os.path.join(args.out_dir, "job_new.json")
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if args.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(args.out_dir)) != 0:
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print(f" !! Warning: Output directory is not empty: {args.out_dir}")
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if args.no_resume:
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print(f" !! Cleaning output directory: {args.out_dir}")
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for filename in os.listdir(args.out_dir):
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file_path = os.path.join(args.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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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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job = {"in_dir": args.in_dir,
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"out_dir": args.out_dir,
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"cal_dataset": args.cal_dataset,
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"bits": args.bits,
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"dataset_rows": args.dataset_rows,
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"measurement_rows": args.measurement_rows,
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"length": args.length,
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"measurement_length": args.measurement_length,
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"head_bits": args.head_bits,
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"shard_size": args.shard_size if args.shard_size > 0 else 1024 ** 3, # 1 PB = unlimited,
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"compile_full": args.compile_full,
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"rope_scale": args.rope_scale,
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"rope_alpha": args.rope_alpha,
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"output_measurement": output_measurement,
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"progress": "begin"}
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if args.measurement is not None:
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with open(args.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["reuse_measurement"] = args.measurement
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# Resume existing job
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if args.no_resume or not os.path.exists(job_file):
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pass
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else:
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print(f" -- Resuming job")
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if args.in_dir:
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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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resume_job = json.load(f)
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# Override keys in existing job
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del resume_job["out_dir"]
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job.update(resume_job)
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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 job["progress"] == "finished":
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print(" !! Job is already finished")
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sys.exit()
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# Feedback
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print(f" -- Input: {job['in_dir']}")
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print(f" -- Output: {job['out_dir']}")
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if job.get("cal_dataset"):
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print(f" -- Calibration dataset: {job['cal_dataset']}, {job['dataset_rows']} / {job['measurement_rows']} rows, {job['length']} tokens per sample")
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else:
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print(f" -- Using default calibration dataset")
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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']: print(f" -- RoPE scale: {job['rope_scale']:.2f}")
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if job['rope_alpha']: print(f" -- RoPE alpha: {job['rope_alpha']:.2f}")
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# Make sure subfolders exist
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if job.get("compile_full"):
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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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# Create config
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config = ExLlamaV2Config()
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config.model_dir = job['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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# Set scaling for input model
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if job["rope_scale"] is not None: config.scale_pos_emb = job["rope_scale"]
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if job["rope_alpha"] is not None: 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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# Limit context length if necessary
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if model.config.arch.rope_style == RopeStyle.NONE:
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max_ctx = model.config.max_seq_len
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if job["length"] > max_ctx:
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print (f" !! Warning: Reducing calibration length to model max context: {max_ctx}")
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job["length"] = max_ctx
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if job["measurement_length"] > max_ctx:
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print (f" !! Warning: Reducing measurement calibration length to model max context: {max_ctx}")
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job["measurement_length"] = max_ctx
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# Overridable settings
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job["status_output"] = args.status_output
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# Do the things
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save_job()
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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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model.unload()
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config.max_output_len = 16
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model = ExLlamaV2(config)
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model.load(lazy = True)
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status = measure_quant(job, save_job, model, args.hidden_state_offload_layers) # capturing the graceful exits
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if status == "interrupted":
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print("Process interrupted. Exiting gracefully.")
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save_job()
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sys.exit(1)
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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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model.unload()
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config.max_output_len = None
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model = ExLlamaV2(config)
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model.load(lazy = True)
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if progress == "optimize":
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print(f" -- Optimizing...")
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optimize(job, save_job, model)
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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")
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import exllamav2.conversion.convert_exl2
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@@ -17,7 +17,7 @@ import torch
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import os, glob, shutil, json
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from safetensors import safe_open
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from safetensors.torch import save_file
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from conversion.bot_status import print_stage
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from exllamav2.conversion.bot_status import print_stage
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def _tsize(t):
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313
exllamav2/conversion/convert_exl2.py
Normal file
313
exllamav2/conversion/convert_exl2.py
Normal file
@@ -0,0 +1,313 @@
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from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
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from exllamav2.architecture import RopeStyle
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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 exllamav2.conversion.tokenize import tokenize
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from exllamav2.conversion.measure import embeddings, measure_quant
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from exllamav2.conversion.quantize import quant
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from exllamav2.conversion.optimize import optimize
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from exllamav2.conversion.compile import compile_model
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from exllamav2.conversion.qparams import qparams_headoptions
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import torch
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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("-res", "--resume", action = "store_true", help = "Resume job from specified output directory (without specifying other options)")
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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("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)")
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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("-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, help = "RoPE scaling factor")
|
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parser.add_argument("-ra", "--rope_alpha", type = float, help = "RoPE alpha value (NTK)")
|
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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("-om", "--output_measurement", type = str, help = "Only perform measurement pass, then save measurement to the specified file")
|
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parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
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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("-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("-so", "--status_output", action = "store_true", help = "Include machine-parseable status updates in console output")
|
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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")
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args = parser.parse_args()
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torch.set_printoptions(precision = 7, sci_mode = False, linewidth = 200)
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# Check some args
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resuming = False
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if args.out_dir:
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if not args.no_resume:
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if os.path.exists(os.path.join(args.out_dir, "job_new.json")):
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resuming = True
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else:
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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.in_dir and not resuming:
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print(" ## Please specify input model directory (-i, --in_dir)")
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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.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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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 not os.path.exists(args.out_dir):
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print(f" ## Error: Directory not found: {args.out_dir}")
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sys.exit()
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# Create 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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job_file = os.path.join(args.out_dir, "job_new.json")
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if args.no_resume or not os.path.exists(job_file):
|
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|
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print(f" -- Beginning new job")
|
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if len(os.listdir(args.out_dir)) != 0:
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print(f" !! Warning: Output directory is not empty: {args.out_dir}")
|
||||
|
||||
if args.no_resume:
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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")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -13,14 +13,14 @@ from exllamav2.model import \
|
||||
|
||||
from safetensors import safe_open
|
||||
from safetensors.torch import save_file
|
||||
from conversion.qparams import QParams, qparams_headoptions, qparams_attn, qparams_mlp, get_qparams_reduced
|
||||
from conversion.adaptivegptq import AdaptiveGPTQ
|
||||
from exllamav2.conversion.qparams import QParams, qparams_headoptions, qparams_attn, qparams_mlp, get_qparams_reduced
|
||||
from exllamav2.conversion.adaptivegptq import AdaptiveGPTQ
|
||||
import torch
|
||||
from torch import nn
|
||||
import os, time, math, json
|
||||
import torch.nn.functional as F
|
||||
import gc
|
||||
from conversion.bot_status import print_stage
|
||||
from exllamav2.conversion.bot_status import print_stage
|
||||
|
||||
# graceful exiting
|
||||
import signal
|
||||
@@ -1,9 +1,9 @@
|
||||
from conversion.qparams import QParams
|
||||
from exllamav2.conversion.qparams import QParams
|
||||
from exllamav2.ext import exllamav2_ext as ext_c, none_tensor
|
||||
import math
|
||||
import itertools
|
||||
import time
|
||||
from conversion.bot_status import print_stage
|
||||
from exllamav2.conversion.bot_status import print_stage
|
||||
|
||||
def optimize(job, save_fn, model):
|
||||
|
||||
@@ -13,14 +13,14 @@ from exllamav2.model import \
|
||||
|
||||
from safetensors import safe_open
|
||||
from safetensors.torch import save_file
|
||||
from conversion.qparams import QParams, qparams_headoptions, qparams_attn, qparams_mlp, get_qparams_reduced
|
||||
from conversion.adaptivegptq import AdaptiveGPTQ
|
||||
from exllamav2.conversion.qparams import QParams, qparams_headoptions, qparams_attn, qparams_mlp, get_qparams_reduced
|
||||
from exllamav2.conversion.adaptivegptq import AdaptiveGPTQ
|
||||
import torch
|
||||
from torch import nn
|
||||
import os, time, math, json
|
||||
import torch.nn.functional as F
|
||||
import gc
|
||||
from conversion.bot_status import print_stage
|
||||
from exllamav2.conversion.bot_status import print_stage
|
||||
|
||||
def list_live_tensors():
|
||||
|
||||
@@ -3,7 +3,7 @@ import pandas, fastparquet
|
||||
import os
|
||||
from safetensors.torch import save_file
|
||||
import random
|
||||
from conversion.bot_status import print_stage
|
||||
from exllamav2.conversion.bot_status import print_stage
|
||||
|
||||
def get_tokens(num_rows, length, filename, tokenizer):
|
||||
|
||||
Reference in New Issue
Block a user