hf_qwen_convert.py

# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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'''
Convert huggingface QWen-7B-Chat model to numpy file.
Use https://huggingface.co/Qwen/Qwen-7B-Chat as demo.
'''
import argparse
import configparser
import dataclasses
import json
import os
from pathlib import Path

import torch
import torch.multiprocessing as multiprocessing
from smoothquant import capture_activation_range, smooth_gemm, smooth_gemm_mlp
from tqdm import tqdm
from transformers import AutoModelForCausalLM  # transformers-4.10.0-py3
from transformers import AutoTokenizer, GenerationConfig
# for debug
from utils.convert import split_and_save_weight

from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy

now_dir = os.path.dirname(os.path.abspath(__file__))


@dataclasses.dataclass(frozen=True)
class ProgArgs:
    out_dir: str
    in_file: str
    max_input_len: int = 2048
    tensor_parallelism: int = 1
    processes: int = 1
    calibrate_kv_cache: bool = False
    smoothquant: float = None
    model: str = "qwen"
    storage_type: str = "fp32"
    dataset_cache_dir: str = None

    @staticmethod
    def parse(args=None) -> 'ProgArgs':
        parser = argparse.ArgumentParser(
            formatter_class=argparse.RawTextHelpFormatter)
        parser.add_argument('--out-dir',
                            '-o',
                            type=str,
                            help='file name of output directory',
                            required=True)
        parser.add_argument('--in-file',
                            '-i',
                            type=str,
                            help='file name of input checkpoint file',
                            required=True)
        parser.add_argument(
            '--max_input_len',
            type=int,
            help=
            "This should be consistent with the max_input_len you used when building engine.",
            default=2048)
        parser.add_argument('--tensor-parallelism',
                            '-tp',
                            type=int,
                            help='Requested tensor parallelism for inference',
                            default=1)
        parser.add_argument(
            "--processes",
            "-p",
            type=int,
            help=
            "How many processes to spawn for conversion (default: 1). Set it to a lower value to reduce RAM usage.",
            default=1)
        parser.add_argument(
            "--calibrate-kv-cache",
            "-kv",
            action="store_true",
            help=
            "Generate scaling factors for KV cache. Used for storing KV cache in int8."
        )
        parser.add_argument(
            "--smoothquant",
            "-sq",
            type=float,
            default=None,
            help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
            " to Smoothquant the model, and output int8 weights."
            " A good first try is 0.5. Must be in [0, 1]")
        parser.add_argument(
            "--model",
            default="qwen",
            type=str,
            help="Specify GPT variants to convert checkpoints correctly",
            choices=["qwen", "gpt2", "santacoder", "starcoder"])
        parser.add_argument("--storage-type",
                            "-t",
                            type=str,
                            default="float16",
                            choices=["float32", "float16", "bfloat16"])
        parser.add_argument("--dataset-cache-dir",
                            type=str,
                            default=None,
                            help="cache dir to load the hugging face dataset")
        return ProgArgs(**vars(parser.parse_args(args)))


@torch.no_grad()
def smooth_qwen_model(model, scales, alpha, qwen_smoother):
    # Smooth the activation and weights with smoother = $\diag{s}$
    for name, module in model.named_modules():
        # if not isinstance(module, QWenBlock):
        if not str(type(module)).endswith("QWenBlock'>"):
            continue

        # qkv_proj
        layer_name = name + ".attn.c_attn"
        smoother = smooth_gemm(module.attn.c_attn.weight,
                               scales[layer_name]["x"],
                               module.ln_1.weight,
                               alpha=alpha)
        scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
        scales[layer_name]["w"] = module.attn.c_attn.weight.abs().max(dim=1)[0]

        # attention dense
        layer_name = name + ".attn.c_proj"
        smoother3 = smooth_gemm(
            module.attn.c_proj.weight,
            scales[layer_name]["x"],
            None,
            alpha=alpha,
        )
        qwen_smoother[layer_name] = smoother3.float()

        scales[layer_name]["x"] = scales[layer_name]["x"] / smoother3
        scales[layer_name]["w"] = module.attn.c_proj.weight.abs().max(dim=1)[0]

        # mlp w1 / w2, because then use some input hidden_states as input, so we need to smooth it with same scale
        mlp_w1_name = name + ".mlp.w1"
        mlp_w2_name = name + ".mlp.w2"
        smoother2 = smooth_gemm_mlp(module.mlp.w1.weight,
                                    module.mlp.w2.weight,
                                    scales[mlp_w1_name]["x"],
                                    module.ln_2.weight,
                                    alpha=alpha)
        scales[mlp_w1_name]["x"] = scales[mlp_w1_name]["x"] / smoother2
        scales[mlp_w2_name]["x"] = scales[mlp_w2_name]["x"] / smoother2
        scales[mlp_w1_name]["w"] = module.mlp.w1.weight.abs().max(dim=1)[0]
        scales[mlp_w2_name]["w"] = module.mlp.w2.weight.abs().max(dim=1)[0]

        # mlp c_proj
        layer_name = name + ".mlp.c_proj"
        smoother4 = smooth_gemm(module.mlp.c_proj.weight,
                                scales[layer_name]["x"],
                                None,
                                alpha=alpha)
        qwen_smoother[layer_name] = smoother4.float()
        scales[layer_name]["x"] = scales[layer_name]["x"] / smoother4
        scales[layer_name]["w"] = module.mlp.c_proj.weight.abs().max(dim=1)[0]


# SantaCoder separates Q projection from KV projection
def concat_qkv_weight_bias(q, hf_key, hf_model):
    kv = hf_model.state_dict()[hf_key.replace("q_attn", "kv_attn")]
    return torch.cat([q, kv], dim=-1)


# StarCoder uses nn.Linear for these following ops whose weight matrix is transposed compared to transformer.Conv1D
def transpose_weights(hf_name, param):
    weight_to_transpose = [
        "attn.c_attn", "attn.c_proj", "mlp.c_proj", "mlp.w1", "mlp.w2"
    ]
    if any([k in hf_name for k in weight_to_transpose]):
        if len(param.shape) == 2:
            param = param.transpose(0, 1)
    return param


def convert_qwen_name(orig_name):
    global_weights = {
        "transformer.wte.weight": "vocab_embedding.weight",
        "transformer.ln_f.weight": "ln_f.weight",
        "lm_head.weight": "lm_head.weight"
    }

    if orig_name in global_weights:
        return global_weights[orig_name]

    _, _, layer_id, *weight_name = orig_name.split(".")
    layer_id = int(layer_id)
    weight_name = "transformer." + ".".join(weight_name)

    per_layer_weights = {
        "transformer.ln_1.weight": "ln_1.weight",
        "transformer.ln_2.weight": "ln_2.weight",
        "transformer.attn.c_attn.weight": "attention.qkv.weight",
        "transformer.attn.c_attn.bias": "attention.qkv.bias",
        "transformer.attn.c_proj.weight": "attention.dense.weight",
        "transformer.mlp.w1.weight": "mlp.w1.weight",
        "transformer.mlp.w2.weight": "mlp.w2.weight",
        "transformer.mlp.c_proj.weight": "mlp.c_proj.weight",
    }
    return f"layers.{layer_id}.{per_layer_weights[weight_name]}"


@torch.no_grad()
def hf_qwen_converter(args: ProgArgs):
    infer_tp = args.tensor_parallelism
    multi_query_mode = True if args.model in ["santacoder", "starcoder"
                                              ] else False
    saved_dir = Path(args.out_dir) / f"{infer_tp}-gpu"
    saved_dir.mkdir(parents=True, exist_ok=True)

    # load position_embedding from rank 0
    model = AutoModelForCausalLM.from_pretrained(
        args.in_file,
        device_map=
        "auto",  # if you gpu memory is not enough, you can set device_map="cpu"
        trust_remote_code=True,
        torch_dtype=str_dtype_to_torch(args.storage_type),
    ).half()  # if you gpu memory is not enough, you can set .half() to .float()
    model.generation_config = GenerationConfig.from_pretrained(
        args.in_file, trust_remote_code=True)
    act_range = {}
    qwen_smoother = {}
    if args.smoothquant is not None or args.calibrate_kv_cache:
        os.environ["TOKENIZERS_PARALLELISM"] = os.environ.get(
            "TOKENIZERS_PARALLELISM", "false")
        from datasets import load_dataset

        # copy from summarize.py
        dataset_cnn = load_dataset("ccdv/cnn_dailymail", '3.0.0')
        dataset = dataset_cnn["test"]
        tokenizer = AutoTokenizer.from_pretrained(
            args.in_file,
            legacy=False,
            padding_side='left',
            trust_remote_code=True,
        )
        gen_config_path = os.path.join(args.in_file, 'generation_config.json')
        with open(gen_config_path, 'r') as f:
            gen_config = json.load(f)
        chat_format = gen_config['chat_format']
        tokenizer.pad_token_id = tokenizer.im_end_id
        # use this prompt to make chat model do summarize
        system_prompt = "You are a useful assistant, please directly output the corresponding summary according to the article entered by the user."
        act_range = capture_activation_range(
            model,
            tokenizer,
            dataset,
            system_prompt=system_prompt,
            chat_format=chat_format,
            max_input_len=args.max_input_len,
        )
        if args.smoothquant is not None:
            smooth_qwen_model(model, act_range, args.smoothquant, qwen_smoother)

    config = configparser.ConfigParser()
    config["qwen"] = {}
    for key in vars(args):
        config["qwen"][key] = f"{vars(args)[key]}"
    for k, v in vars(model.config).items():
        config["qwen"][k] = f"{v}"
    config["qwen"]["storage_dtype"] = args.storage_type
    config["qwen"]["multi_query_mode"] = str(multi_query_mode)
    with open(saved_dir / "config.ini", 'w') as configfile:
        config.write(configfile)

    storage_type = str_dtype_to_torch(args.storage_type)

    global_weights = ["vocab_embedding.weight", "ln_f.weight", "lm_head.weight"]

    int8_outputs = None
    if args.calibrate_kv_cache:
        int8_outputs = "kv_cache_only"
    if args.smoothquant is not None:
        int8_outputs = "all"

    starmap_args = []
    for name, param in tqdm(
            model.named_parameters(),
            desc="convert and save",
            total=len(list(model.parameters())),
            ncols=80,
    ):
        if "weight" not in name and "bias" not in name:
            continue
        converted_name = convert_qwen_name(name)
        if name.replace(".weight", "") in qwen_smoother.keys():
            smoother = qwen_smoother[name.replace(".weight", "")]
            starmap_arg = (
                0,
                saved_dir,
                infer_tp,
                f"{converted_name}.smoother".replace(".weight", ""),
                smoother,
                storage_type,
                None,
                {
                    "int8_outputs": int8_outputs,
                    "multi_query_mode": multi_query_mode,
                    "local_dim": None,
                },
            )
            if args.processes > 1:
                starmap_args.append(starmap_arg)
            else:
                split_and_save_weight(*starmap_arg)

        param = transpose_weights(name, param)
        if converted_name in global_weights:
            torch_to_numpy(param.to(storage_type).cpu()).tofile(
                saved_dir / f"{converted_name}.bin")
        else:
            if 'q_attn' in name:
                param = concat_qkv_weight_bias(param, name, model)
                converted_name = converted_name.replace("query",
                                                        "query_key_value")
            # Needed by QKV projection weight split. With multi_query_mode one does not simply take
            # out_dim and divide it by 3 to get local_dim because out_dim = local_dim + 2 * head_size
            local_dim = model.transformer.h[
                0].attn.embed_dim if multi_query_mode else None
            starmap_arg = (0, saved_dir, infer_tp, converted_name,
                           param.to(storage_type), storage_type,
                           act_range.get(name.replace(".weight", "")), {
                               "int8_outputs": int8_outputs,
                               "multi_query_mode": multi_query_mode,
                               "local_dim": local_dim
                           })
            if args.processes > 1:
                starmap_args.append(starmap_arg)
            else:
                split_and_save_weight(*starmap_arg)

    if args.processes > 1:
        starmap_args = tqdm(starmap_args, desc="saving weights")
        with multiprocessing.Pool(args.processes) as pool:
            pool.starmap(split_and_save_weight, starmap_args)


def run_conversion(args: ProgArgs):
    print("\n=============== Arguments ===============")
    for key, value in vars(args).items():
        print(f"{key}: {value}")
    print("========================================")
    hf_qwen_converter(args)


if __name__ == "__main__":
    torch.multiprocessing.set_start_method("spawn")
    run_conversion(ProgArgs.parse())