TensorRT-LLMs/examples/llama/convert_checkpoint.py

import argparse
import copy
import functools
import json
import os
import time
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, wait
from pathlib import Path
from typing import List

import numpy as np
import safetensors
import torch
import torch.nn as nn
from datasets import load_dataset
from safetensors import safe_open
from tqdm import tqdm
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
from transformers.pytorch_utils import Conv1D

import tensorrt_llm
from tensorrt_llm._utils import str_dtype_to_torch
from tensorrt_llm.layers import MoeConfig
from tensorrt_llm.logger import logger
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models.llama.utils import (iterate_shard_files,
                                             load_state_dict,
                                             retrieved_layer_index_from_name)
from tensorrt_llm.models.llama.weight import (load_from_awq_llama,
                                              load_from_fp8_llama,
                                              load_from_meta_llama)
from tensorrt_llm.models.modeling_utils import PretrainedConfig
from tensorrt_llm.runtime.lora_manager import LoraConfig

try:
    from transformers import MixtralForCausalLM
except ImportError:
    MixtralForCausalLM = None

try:
    from transformers import LlavaConfig, LlavaForConditionalGeneration
except ImportError:
    pass


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str, default=None)
    parser.add_argument('--meta_ckpt_dir', type=str, default=None)

    parser.add_argument('--tp_size',
                        type=int,
                        default=1,
                        help='N-way tensor parallelism size')
    parser.add_argument('--pp_size',
                        type=int,
                        default=1,
                        help='N-way pipeline parallelism size')
    parser.add_argument('--dtype',
                        type=str,
                        default='float16',
                        choices=['float32', 'bfloat16', 'float16'])
    parser.add_argument('--vocab_size', type=int, default=32000)
    parser.add_argument('--n_positions', type=int, default=2048)
    parser.add_argument('--n_layer', type=int, default=32)
    parser.add_argument('--n_head', type=int, default=32)
    parser.add_argument('--n_kv_head', type=int, default=None)
    parser.add_argument('--n_embd', type=int, default=4096)
    parser.add_argument('--inter_size', type=int, default=11008)
    parser.add_argument('--rms_norm_eps', type=float, default=1e-06)

    parser.add_argument(
        '--use_weight_only',
        default=False,
        action="store_true",
        help='Quantize weights for the various GEMMs to INT4/INT8.'
        'See --weight_only_precision to set the precision')
    parser.add_argument(
        '--disable_weight_only_quant_plugin',
        default=False,
        action="store_true",
        help=
        'By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
        'You must also use --use_weight_only for that argument to have an impact.'
    )
    parser.add_argument(
        '--weight_only_precision',
        const='int8',
        type=str,
        nargs='?',
        default='int8',
        choices=['int8', 'int4', 'int4_awq', 'int4_gptq'],
        help=
        'Define the precision for the weights when using weight-only quantization.'
        'You must also use --use_weight_only for that argument to have an impact.'
    )
    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(
        '--per_channel',
        action="store_true",
        default=False,
        help=
        'By default, we use a single static scaling factor for the GEMM\'s result. '
        'per_channel instead uses a different static scaling factor for each channel. '
        'The latter is usually more accurate, but a little slower.')
    parser.add_argument(
        '--per_token',
        action="store_true",
        default=False,
        help=
        'By default, we use a single static scaling factor to scale activations in the int8 range. '
        'per_token chooses at run time, and for each token, a custom scaling factor. '
        'The latter is usually more accurate, but a little slower.')
    parser.add_argument(
        '--int8_kv_cache',
        default=False,
        action="store_true",
        help=
        'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
    )
    parser.add_argument(
        '--ammo_quant_ckpt_path',
        type=str,
        default=None,
        help='Path of a quantized model checkpoint in .npz format')

    parser.add_argument(
        '--per_group',
        default=False,
        action="store_true",
        help=
        'By default, we use a single static scaling factor to scale weights in the int4 range. '
        'per_group chooses at run time, and for each group, a custom scaling factor. '
        'The flag is built for GPTQ/AWQ quantization.')
    parser.add_argument(
        '--quantize_lm_head',
        default=False,
        action="store_true",
        help='Quantize lm_head weights as well when using int4_awq.')
    parser.add_argument(
        '--enable_fp8',
        default=False,
        action='store_true',
        help='Use FP8 Linear layer for Attention QKV/Dense and MLP.')
    parser.add_argument(
        '--fp8_kv_cache',
        default=False,
        action="store_true",
        help='By default, we use dtype for KV cache. fp8_kv_cache chooses int8 '
        'quantization for KV')
    parser.add_argument('--load_by_shard',
                        action='store_true',
                        help='Load a pretrained model shard-by-shard.')
    parser.add_argument('--hidden_act', type=str, default='silu')

    parser.add_argument('--rotary_base', type=float, default=10000.0)
    parser.add_argument('--rotary_scaling', nargs=2, type=str, default=None)

    parser.add_argument('--group_size',
                        type=int,
                        default=128,
                        help='Group size used in GPTQ/AWQ quantization.')

    parser.add_argument("--storage-type",
                        "-t",
                        type=str,
                        default="fp32",
                        choices=["fp32", "fp16"])
    parser.add_argument("--dataset-cache-dir",
                        type=str,
                        default=None,
                        help="cache dir to load the hugging face dataset")
    parser.add_argument("--load-model-on-cpu", action="store_true")
    parser.add_argument("--convert-model-on-cpu", action="store_true")
    parser.add_argument(
        '--use_parallel_embedding',
        action="store_true",
        default=False,
        help=
        'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled'
    )
    parser.add_argument(
        '--embedding_sharding_dim',
        type=int,
        default=0,
        choices=[0, 1],
        help=
        'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). '
        'To shard it along hidden dimension, set embedding_sharding_dim=1'
        'Note: embedding sharing is only enabled when embedding_sharding_dim = 0'
    )
    parser.add_argument(
        '--use_embedding_sharing',
        action="store_true",
        default=False,
        help=
        'Try to reduce the engine size by sharing the embedding lookup table between two layers.'
        'Note: the flag might not take effect when the criteria are not met.')
    parser.add_argument('--use_prompt_tuning',
                        action="store_true",
                        default=False)
    parser.add_argument('--output_dir',
                        type=str,
                        default='tllm_checkpoint',
                        help='The path to save the TensorRT-LLM checkpoint')
    parser.add_argument(
        '--workers',
        type=int,
        default=1,
        help='The number of workers for converting checkpoint in parallel')
    parser.add_argument(
        '--moe_num_experts',
        default=0,
        type=int,
        help='Specify the number of experts to use for MOE layers')
    parser.add_argument(
        '--moe_top_k',
        default=0,
        type=int,
        help=
        'Specify the top_k value to use for MOE layers. Default to 1 if --moe_num_experts is set'
    )
    parser.add_argument(
        '--moe_tp_mode',
        default=MoeConfig.ParallelismMode.TENSOR_PARALLEL,
        type=int,
        help=
        'Controls how to distribute experts in TP. Check layers/moe.py for accepted values',
    )
    parser.add_argument(
        '--moe_renorm_mode',
        default=MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE,
        type=int,
        help=
        'Controls renormalization after gate logits. Check layers/moe.py for accepted values',
    )
    parser.add_argument(
        '--use_fused_mlp',
        default=False,
        action='store_true',
        help=
        'Enable horizontal fusion in GatedMLP, reduces layer input traffic and potentially improves performance. '
        'For FP8 PTQ, the downside is slight reduction of accuracy because one of the quantization scaling factors are discarded '
        '(0.45734 vs 0.45755 for LLaMA-v2 7B using ammo/examples/hf/instruct_eval/mmlu.py).'
    )
    parser.add_argument('--enable_pos_shift',
                        default=False,
                        action='store_true',
                        help='Enable position shift for streamingllm method')
    parser.add_argument(
        '--dense_context_fmha',
        default=False,
        action='store_true',
        help=
        'Enable dense fmha in context phase, otherwise sliding window attention.'
        'If dense_context_fmha=False, the sliding window size is the max attention window size.'
    )
    parser.add_argument('--hf_lora_dir', type=str, default=None)
    parser.add_argument(
        '--lora_target_modules',
        nargs='+',
        default=None,
        choices=[
            "attn_qkv",
            "attn_q",
            "attn_k",
            "attn_v",
            "attn_dense",
            "mlp_h_to_4h",
            "mlp_gate",
            "mlp_4h_to_h",
        ],
        help=
        "Add lora in which modules. Only be activated when use_lora_plugin is enabled."
    )
    parser.add_argument(
        '--max_lora_rank',
        type=int,
        default=64,
        help='maximum lora rank for different lora modules. '
        'It is used to compute the workspace size of lora plugin.')
    args = parser.parse_args()
    return args


def generate_int8(weights, act_range, is_qkv=False, multi_query_mode=False):
    """
     This function has two purposes:
      - compute quantized weights, scaled either per-tensor or per-column
      - compute scaling factors

      Depending on the GEMM API (CUTLASS/CUBLAS) the required scaling factors differ.
      CUTLASS uses two sets of scaling factors. One for the activation X, one for the weight W.
      CUBLAS only has one (we can't do per-row scaling). So we must provide pre-multiplied scaling factor.

      Here is the list of what we need (T means per-tensor, C per-column):
        - scale_x_orig_quant puts fp activation into the quantized range (i.e. [-128, 127], for int8). Used before the GEMM. (T)
        - scale_y_quant_orig puts quantized activation into the fp range. Used if the GEMM outputs int8. (T)
        - scale_w_quant_orig puts weights from quant range to fp range (used with CUTLASS) (T, C)
        - scale_y_accum_quant puts the GEMM result (XW) from accumulation range (int32)
          to quant range (int8) (used for CUBLAS) (T, C)

      Note that we don't do anything special about row-parallel GEMM. Theoretically, we could have per-GPU scaling factors too,
      but then the model would change depending on the number of GPUs used.

      For QKV projection, the behavior is special. Even if we have a single matrix to perform QKV projection, we consider it
      as three different matrices: Q, K, and V. So per-tensor actually means one scaling factor for each Q, K and V.
      For our GEMM implementation to respect this behavior, we use per-column mode and replicate values along columns.
    """
    weights = weights.detach().cpu().numpy()

    # compute weight scaling factors for fp->int8 and int8->fp
    if is_qkv and not multi_query_mode:
        scale_w_orig_quant_t = 127. / act_range["w"].reshape(3, -1).max(
            dim=-1, keepdims=True)[0].cpu().numpy()
        scale_w_orig_quant_c = 127. / act_range["w"].reshape(3,
                                                             -1).cpu().numpy()
    elif is_qkv and multi_query_mode:
        hidden_dim = weights.shape[0]
        local_dim = act_range["w"].shape[0]
        kv_dim = (local_dim - hidden_dim) // 2
        scale_w_q = act_range["w"][0:hidden_dim]
        scale_w_k = act_range["w"][hidden_dim:hidden_dim + kv_dim]
        scale_w_v = act_range["w"][-kv_dim:]

        scale_w_qkv_t = torch.concat([
            scale_w_q.max(dim=0, keepdim=True)[0],
            scale_w_k.max(dim=0, keepdim=True)[0],
            scale_w_v.max(dim=0, keepdim=True)[0]
        ])

        scale_w_orig_quant_t = 127. / scale_w_qkv_t.cpu().numpy()
        scale_w_orig_quant_c = 127. / act_range["w"].cpu().numpy()
    else:
        scale_w_orig_quant_t = 127. / act_range["w"].max().cpu().numpy()
        scale_w_orig_quant_c = 127. / act_range["w"].cpu().numpy()
    scale_w_quant_orig_t = 1.0 / scale_w_orig_quant_t
    scale_w_quant_orig_c = 1.0 / scale_w_orig_quant_c

    scale_w_orig_quant_c = scale_w_orig_quant_c.astype(np.float32)
    scale_w_orig_quant_t = scale_w_orig_quant_t.astype(np.float32)

    # compute the rest of needed scaling factors
    scale_x_orig_quant_t = np.array(127. / act_range["x"].max().item())
    scale_y_orig_quant_t = np.array(127. / act_range["y"].max().item())
    scale_y_quant_orig_t = np.array(act_range["y"].max().item() / 127.)
    scale_y_accum_quant_t = scale_y_orig_quant_t / (scale_x_orig_quant_t *
                                                    scale_w_orig_quant_t)
    scale_y_accum_quant_c = scale_y_orig_quant_t / (scale_x_orig_quant_t *
                                                    scale_w_orig_quant_c)
    if is_qkv and not multi_query_mode:
        scale_y_accum_quant_t = np.broadcast_to(scale_y_accum_quant_t,
                                                scale_w_orig_quant_c.shape)
        scale_w_quant_orig_t = np.broadcast_to(scale_w_quant_orig_t,
                                               scale_w_orig_quant_c.shape)
    if is_qkv and multi_query_mode:
        scale_q_y_accum_t = np.broadcast_to(scale_y_accum_quant_t[0],
                                            scale_w_q.shape)
        scale_k_y_accum_t = np.broadcast_to(scale_y_accum_quant_t[1],
                                            scale_w_k.shape)
        scale_v_y_accum_t = np.broadcast_to(scale_y_accum_quant_t[2],
                                            scale_w_v.shape)
        scale_y_accum_quant_t = np.concatenate(
            [scale_q_y_accum_t, scale_k_y_accum_t, scale_v_y_accum_t])
        scale_w_quant_orig_t = np.concatenate([
            np.broadcast_to(scale_w_quant_orig_t[0], scale_w_q.shape),
            np.broadcast_to(scale_w_quant_orig_t[1], scale_w_k.shape),
            np.broadcast_to(scale_w_quant_orig_t[2], scale_w_v.shape)
        ])

    to_i8 = lambda x: x.round().clip(-127, 127).astype(np.int8)

    if is_qkv and multi_query_mode:
        weight_int8 = to_i8(weights / scale_w_quant_orig_t)
    else:
        weight_int8 = to_i8(weights * scale_w_orig_quant_t)
    return {
        "weight.int8": weight_int8,
        "weight.int8.col": to_i8(weights * scale_w_orig_quant_c),
        "scale_x_orig_quant": scale_x_orig_quant_t.astype(np.float32),
        "scale_w_quant_orig": scale_w_quant_orig_t.astype(np.float32),
        "scale_w_quant_orig.col": scale_w_quant_orig_c.astype(np.float32),
        "scale_y_accum_quant": scale_y_accum_quant_t.astype(np.float32),
        "scale_y_accum_quant.col": scale_y_accum_quant_c.astype(np.float32),
        "scale_y_quant_orig": scale_y_quant_orig_t.astype(np.float32),
    }


@torch.no_grad()
def apply_smoothing(scales,
                    gemm_weights,
                    layernorm_weights=None,
                    layernorm_bias=None,
                    dtype=torch.float32,
                    layernorm_1p=False):
    if not isinstance(gemm_weights, list):
        gemm_weights = [gemm_weights]

    if layernorm_weights is not None:
        assert layernorm_weights.numel() == scales.numel()
        layernorm_weights.div_(scales).to(dtype)
    if layernorm_bias is not None:
        assert layernorm_bias.numel() == scales.numel()
        layernorm_bias.div_(scales).to(dtype)
    if layernorm_1p:
        layernorm_weights += (1 / scales) - 1

    for gemm in gemm_weights:
        gemm.mul_(scales.view(1, -1)).to(dtype)


@torch.no_grad()
def smooth_gemm(gemm_weights,
                act_scales,
                layernorm_weights=None,
                layernorm_bias=None,
                alpha=0.5,
                weight_scales=None):
    if not isinstance(gemm_weights, list):
        gemm_weights = [gemm_weights]
    orig_dtype = gemm_weights[0].dtype

    for gemm in gemm_weights:
        # gemm_weights are expected to be transposed
        assert gemm.shape[1] == act_scales.numel()

    if weight_scales is None:
        weight_scales = torch.cat(
            [gemm.abs().max(dim=0, keepdim=True)[0] for gemm in gemm_weights],
            dim=0)
        weight_scales = weight_scales.max(dim=0)[0]
    weight_scales.to(float).clamp(min=1e-5)
    scales = (act_scales.to(gemm_weights[0].device).to(float).pow(alpha) /
              weight_scales.pow(1 - alpha)).clamp(min=1e-5)

    apply_smoothing(scales, gemm_weights, layernorm_weights, layernorm_bias,
                    orig_dtype)

    return scales


@torch.no_grad()
def smooth_gemm_fc1_gate(fc1_weights,
                         gate_weights,
                         act_scales,
                         layernorm_weights=None,
                         layernorm_bias=None,
                         alpha=0.5,
                         weight_scales=None):
    gemm_weights = []
    if not isinstance(fc1_weights, list):
        fc1_weights = [fc1_weights]
    if not isinstance(gate_weights, list):
        gate_weights = [gate_weights]

    for i in range(len(fc1_weights)):
        gemm_weight = torch.cat([fc1_weights[i], gate_weights[i]], dim=0)
        gemm_weights.append(gemm_weight)

    orig_dtype = gemm_weights[0].dtype

    for gemm in gemm_weights:
        # gemm_weights are expected to be transposed
        assert gemm.shape[1] == act_scales.numel()

    if weight_scales is None:
        weight_scales = torch.cat(
            [gemm.abs().max(dim=0, keepdim=True)[0] for gemm in gemm_weights],
            dim=0)
        weight_scales = weight_scales.max(dim=0)[0]
    weight_scales.to(float).clamp(min=1e-5)
    scales = (act_scales.to(gemm_weights[0].device).to(float).pow(alpha) /
              weight_scales.pow(1 - alpha)).clamp(min=1e-5)

    apply_smoothing(scales, fc1_weights + gate_weights, layernorm_weights,
                    layernorm_bias, orig_dtype)

    return scales


@torch.no_grad()
def smooth_llama_model(model, scales, alpha, llama_qkv_para, llama_smoother):
    # Smooth the activation and weights with smoother = $\diag{s}$
    for name, module in model.named_modules():
        if not isinstance(module, LlamaDecoderLayer):
            continue
        # qkv_proj
        layer_name_q = name + ".self_attn.q_proj"
        layer_name_k = name + ".self_attn.k_proj"
        layer_name_v = name + ".self_attn.v_proj"
        layer_name_qkv = name + ".self_attn.qkv_proj"

        weight = torch.cat([
            module.self_attn.q_proj.weight, module.self_attn.k_proj.weight,
            module.self_attn.v_proj.weight
        ],
                           dim=0)

        smoother = smooth_gemm(weight, scales[layer_name_q]["x"],
                               module.input_layernorm.weight, None, alpha)

        scales[layer_name_qkv]["x"] = scales[layer_name_q]["x"] / smoother
        scales[layer_name_qkv]["w"] = weight.abs().max(dim=1)[0]
        scales[layer_name_qkv]["y"] = torch.cat([
            scales[layer_name_q]["y"], scales[layer_name_k]["y"],
            scales[layer_name_v]["y"]
        ],
                                                dim=0)

        # see transpose_weights function
        llama_qkv_para[layer_name_qkv] = weight.transpose(0, 1)

        # =================================================================
        layer_name = name + ".self_attn.o_proj"
        smoother = smooth_gemm(module.self_attn.o_proj.weight,
                               scales[layer_name]["x"], None, None, alpha)
        llama_smoother[layer_name] = smoother.float()

        scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
        scales[layer_name]["w"] = module.self_attn.o_proj.weight.abs().max(
            dim=1)[0]

        # ==================================================================
        fc1_layer_name = name + ".mlp.gate_proj"
        gate_layer_name = name + ".mlp.up_proj"

        smoother = smooth_gemm_fc1_gate(module.mlp.gate_proj.weight,
                                        module.mlp.up_proj.weight,
                                        scales[fc1_layer_name]["x"],
                                        module.post_attention_layernorm.weight,
                                        None, alpha)

        scales[fc1_layer_name]["x"] = scales[fc1_layer_name]["x"] / smoother
        scales[fc1_layer_name]["w"] = module.mlp.gate_proj.weight.abs().max(
            dim=1)[0]

        scales[gate_layer_name]["x"] = scales[gate_layer_name]["x"] / smoother
        scales[gate_layer_name]["w"] = module.mlp.up_proj.weight.abs().max(
            dim=1)[0]

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


@torch.no_grad()
def capture_activation_range(model,
                             tokenizer,
                             dataset,
                             num_samples=512,
                             seq_len=512):
    model.eval()
    device = next(model.parameters()).device
    act_scales = defaultdict(lambda: {"x": None, "y": None, "w": None})

    tokenizer.pad_token = tokenizer.eos_token

    def stat_tensor(name, tensor, act_scales, key):
        hidden_dim = tensor.shape[-1]
        tensor = tensor.view(-1, hidden_dim).abs().detach()
        comming_max = torch.max(tensor, dim=0)[0].float()

        if act_scales[name][key] is None:
            act_scales[name][key] = comming_max
        else:
            act_scales[name][key] = torch.max(act_scales[name][key],
                                              comming_max)

    def stat_input_hook(m, x, y, name):
        if isinstance(x, tuple):
            x = x[0]
        stat_tensor(name, x, act_scales, "x")
        stat_tensor(name, y, act_scales, "y")

        if act_scales[name]["w"] is None:
            act_scales[name]["w"] = m.weight.abs().clip(1e-8,
                                                        None).max(dim=1)[0]

    hooks = []
    for name, m in model.named_modules():
        if isinstance(m, nn.Linear) or isinstance(m, Conv1D):
            hooks.append(
                m.register_forward_hook(
                    functools.partial(stat_input_hook, name=name)))

    for i in tqdm(range(num_samples), desc="calibrating model"):
        datapoint = dataset['train'][i:i + 1]
        line = copy.copy(datapoint['article'])
        line[0] = line[0] + ' TL;DR: '
        line[0] = line[0].strip()
        line[0] = line[0].replace(" n't", "n't")
        input_ids = tokenizer(line,
                              return_tensors="pt",
                              max_length=seq_len,
                              padding=True,
                              truncation=True).input_ids.to(device)
        model(input_ids)
    for h in hooks:
        h.remove()
    return act_scales


def split(v, tp_size, idx, dim=0):
    if tp_size == 1:
        return v
    if len(v.shape) == 1:
        return torch.chunk(v, tp_size)[idx].contiguous()
    else:
        return torch.chunk(v, tp_size, dim=dim)[idx].contiguous()


def split_qkv_tp(v, n_head, n_hidden, tensor_parallel, rank):
    """
    Splits the QKV matrix according to tensor parallelism
    """
    v = v.reshape(3, n_hidden, n_hidden)
    split_v = split(v, tensor_parallel, rank, dim=1)
    split_v = split_v.reshape(3 * (n_hidden // tensor_parallel), n_hidden)
    return split_v.contiguous()


def split_qkv_bias_tp(v, n_head, n_hidden, tensor_parallel, rank):
    """
    Splits the QKV bias according to tensor parallelism
    """
    v = v.reshape(3, n_hidden)
    split_v = split(v, tensor_parallel, rank, dim=1)
    split_v = split_v.reshape(3 * (n_hidden // tensor_parallel))
    return split_v.contiguous()


def split_matrix_tp(v, tensor_parallel, rank, dim):
    return split(v, tensor_parallel, rank, dim=dim)


def get_weight(config, prefix, dtype):
    if config[prefix + '.weight'].dtype != dtype:
        config[prefix + '.weight'].data = config[prefix + '.weight'].to(dtype)
    return config[prefix + '.weight']


def get_bias(config, prefix, dtype):
    if config[prefix + '.bias'].dtype != dtype:
        config[prefix + '.bias'].data = config[prefix + '.bias'].to(dtype)
    return config[prefix + '.bias']


def get_weight_and_bias(config, prefix, dtype):
    return get_weight(config, prefix, dtype), get_bias(config, prefix, dtype)


def get_tllm_linear_weight(weight,
                           prefix,
                           bias=None,
                           use_weight_only=False,
                           plugin_weight_only_quant_type=torch.int8,
                           dtype='float32',
                           use_gemm_woq_plugin=True,
                           postfix='weight'):
    results = {}
    if use_weight_only:
        v = weight.t().contiguous()
        processed_torch_weights, torch_weight_scales = \
            torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                v.cpu(), plugin_weight_only_quant_type)
        if not use_gemm_woq_plugin:
            results[prefix + postfix] = v.to(dtype)
        else:
            results[prefix + postfix] = processed_torch_weights
        results[prefix + 'per_channel_scale'] = torch_weight_scales
    else:
        results[prefix + postfix] = weight.contiguous()

    if bias is not None:
        results[prefix + 'bias'] = bias

    return results


def dup_kv_weight(v, num_head, tp_size):
    assert tp_size % num_head == 0
    reps = tp_size // num_head
    head_size = v.shape[0] // num_head
    v = v.reshape(num_head, head_size,
                  -1)[:, None, :, :].expand(num_head, reps, head_size,
                                            v.shape[1])
    return v.reshape(num_head * reps * head_size, -1).clone().detach()


def get_tllm_linear_sq_weight(vals,
                              prefix,
                              shape,
                              tensor_parallel,
                              is_qkv=False,
                              per_token=False,
                              per_channel=False,
                              last_prefix=None,
                              bias=None,
                              smoother_value=None,
                              smoother_shape=None,
                              rank=0,
                              cat_dim=0,
                              multi_query_mode=False):
    results = {}

    def multi_query_split(data, local_dim, head_size, tp_size, cur_rank):
        q, k, v = np.split(data, [local_dim, local_dim + head_size], axis=-1)
        q_split = np.split(q, tp_size, axis=-1)
        k_split = np.split(k, tp_size, axis=-1)
        v_split = np.split(v, tp_size, axis=-1)
        return [
            np.concatenate((q_split[ii], k_split[ii], v_split[ii]), axis=-1)
            for ii in range(tp_size)
        ][cur_rank]

    col_shape = shape if (is_qkv or per_channel) else [1, 1]

    if per_token:
        original_weights = vals["weight.int8.col"]

        local_dim = original_weights.shape[0]
        head_size = (original_weights.shape[1] - local_dim) // 2
        if multi_query_mode:
            cur_weights = multi_query_split(original_weights, local_dim,
                                            head_size, tensor_parallel, rank)
        else:
            cur_weights = np.split(original_weights,
                                   tensor_parallel,
                                   axis=cat_dim)[rank]
        if is_qkv:
            hidden_dim = cur_weights.shape[0]
            cur_weights = cur_weights.reshape(hidden_dim, -1)
        results[prefix +
                'weight'] = torch.from_numpy(cur_weights).t().contiguous()
        if smoother_value is None:
            results[last_prefix] = torch.from_numpy(
                np.array([1.0], dtype=np.float32))

        if smoother_value is None:
            if multi_query_mode:
                cur_per_channel_value = multi_query_split(
                    vals["scale_w_quant_orig.col"], local_dim, head_size,
                    tensor_parallel, rank)
            else:
                cur_per_channel_value = np.split(vals["scale_w_quant_orig.col"],
                                                 tensor_parallel,
                                                 axis=cat_dim)[rank]
        else:
            cur_per_channel_value = vals["scale_w_quant_orig.col"]
        results[prefix + 'per_channel_scale'] = torch.from_numpy(
            np.array(cur_per_channel_value,
                     dtype=np.float32).reshape(col_shape)).contiguous()
    else:
        original_weights = np.array(vals["weight.int8"])
        cur_weights = np.split(original_weights, tensor_parallel,
                               axis=cat_dim)[rank]

        if is_qkv:
            hidden_dim = cur_weights.shape[0]
            cur_weights = cur_weights.reshape(hidden_dim, -1)
        results[prefix +
                'weight'] = torch.from_numpy(cur_weights).t().contiguous()
        # 'weight'] = torch.from_numpy(cur_weights).t().contiguous()

        cur_per_channel_value = vals["scale_y_accum_quant"]

        results[prefix + 'per_channel_scale'] = torch.from_numpy(
            np.array([cur_per_channel_value],
                     dtype=np.float32).reshape(col_shape)).contiguous()

        results[last_prefix] = torch.from_numpy(
            np.array([vals['scale_x_orig_quant']],
                     dtype=np.float32)).contiguous()

        results[prefix + 'act_scale'] = torch.from_numpy(
            np.array([[vals["scale_y_quant_orig"]]],
                     dtype=np.float32)).contiguous()

    if smoother_value is not None:
        cur_smoother_value = np.split(smoother_value,
                                      tensor_parallel,
                                      axis=cat_dim)[rank]
        results[prefix + 'smoother'] = cur_smoother_value.reshape(
            smoother_shape).contiguous().to(torch.float32)

    if bias is not None:
        results[prefix + 'bias'] = bias

    return results


def load_from_gptq_llama(quant_ckpt_path,
                         hf_config=None,
                         mapping=Mapping(),
                         dtype="float16",
                         bin_model_dir=None):
    tensorrt_llm.logger.info(
        'Loading weights from groupwise GPTQ LLaMA safetensors...')
    weights = {}
    tik = time.time()

    gptq_llama = safe_open(quant_ckpt_path, framework="pt", device=0)
    gptq_prefix = "model."
    gptq_suffix_list = [".qweight", ".qzeros", ".scales"]
    gptq_key_list = [
        "embed_tokens.weight",  # vocab_embedding
        "lm_head.weight",  # lm_head
        "norm.weight",  # ln_f
        "self_attn.",  # attention.qkv
        "_proj",  # qkv suffix
        "self_attn.o_proj",  # attention.dense
        "mlp.up_proj",  # mlp.gate
        "mlp.down_proj",  # mlp.proj
        "mlp.gate_proj",  # mlp.fc
        "input_layernorm.weight",  # input_layernorm
        "post_attention_layernorm.weight",  # post_layernorm
    ]
    split_sym = "."

    packer = torch.ops.trtllm.pack_int8_tensor_to_packed_int4
    preprocessor = torch.ops.trtllm.preprocess_weights_for_mixed_gemm
    torch_dtype = str_dtype_to_torch(dtype)

    def load(key, no_prefix=0):
        if no_prefix:
            return gptq_llama.get_tensor(key)
        else:
            return gptq_llama.get_tensor(gptq_prefix + key)

    def torch_split(v, dim):
        if v.shape[dim] % mapping.tp_size != 0:
            tensorrt_llm.logger.error(
                "Current weight shape is invalid for mapping.tp_size=" +
                str(mapping.tp_size))
            assert False, "Invalid TP size"
        return v.split(v.shape[dim] // mapping.tp_size,
                       dim=dim)[mapping.tp_rank]

    def unpack_int32_into_int8(w_packed):
        # Unpack inputs packed in int32/float32 into uint4 and store them in int8 format
        w_packed_int4x2 = w_packed.contiguous().view(torch.uint8)
        w_unpacked = torch.zeros(w_packed_int4x2.shape[0],
                                 w_packed_int4x2.shape[1] * 2,
                                 dtype=torch.int8)
        w_unpacked[:, ::2] = w_packed_int4x2 % 16
        w_unpacked[:, 1::2] = w_packed_int4x2 // 16
        return w_unpacked.contiguous()

    def process_and_assign_weight(v: List[torch.Tensor],
                                  tllm_prex: str,
                                  tp_dim: int = -1):
        if tp_dim == -1:
            qweight_int32, qzeros_int32, scales_fp16 = [
                item.cpu() for item in v
            ]
        else:
            qweight_int32, qzeros_int32, scales_fp16 = [
                torch_split(item, tp_dim).cpu() for item in v
            ]

        USE_UINT4_INPUT = 1  # Set to true if checkpoint store UINT4 weights
        USE_GPTQ_FOR_LLAMA = 1  # GPTQ-for-LLaMA added 1 to zeros

        qweight_unpacked_int8 = unpack_int32_into_int8(
            qweight_int32.T).T.contiguous() - 8
        qweight_interleaved = preprocessor(packer(qweight_unpacked_int8),
                                           torch.quint4x2).view(torch.float16)
        # zeros = zeros * scales
        qzeros_unpacked_int32 = unpack_int32_into_int8(qzeros_int32)
        if not USE_UINT4_INPUT:
            # Correcting UINT4 values back to INT4 order
            mask_negative = qzeros_unpacked_int32[qzeros_unpacked_int32 < 0]
            mask_positive = qzeros_unpacked_int32[qzeros_unpacked_int32 >= 0]
            qzeros_unpacked_int32 = qzeros_unpacked_int32 + 16 * mask_negative - 16 * mask_positive
        zeros_x_scales_fp16 = (-qzeros_unpacked_int32 + 8 * USE_UINT4_INPUT -
                               USE_GPTQ_FOR_LLAMA) * scales_fp16
        zeros_x_scales_fp16 = zeros_x_scales_fp16.half()

        results = {
            f'{tllm_prex}.weight': qweight_interleaved,
            f'{tllm_prex}.weights_scaling_factor': scales_fp16,
            f'{tllm_prex}.zero': zeros_x_scales_fp16,
        }
        return results

    # Load weights from GPTQ checkpoint into TRT-LLM module
    # 1. vocab_embedding
    v = load(gptq_key_list[0])
    if mapping.is_first_pp_rank():
        # tensorrt_llm_llama.vocab_embedding.weight.value = v.to(
        #     torch_dtype).cpu().numpy()
        weights['transformer.vocab_embedding.weight'] = v.to(torch_dtype)
    # 2. lm_head
    v = load(gptq_key_list[1], "no_prefix")
    if mapping.is_last_pp_rank():
        # tensorrt_llm_llama.lm_head.weight.value = torch_split(
        #     v, 0).to(torch_dtype).cpu().numpy()
        weights['lm_head.weight'] = torch_split(v, 0).to(torch_dtype)

    # 3. ln_f
    v = load(gptq_key_list[2])
    if mapping.is_last_pp_rank():
        # tensorrt_llm_llama.ln_f.weight.value = v.to(torch_dtype).cpu().numpy()
        weights['transformer.ln_f.weight'] = v.to(torch_dtype)
    # 4. Weights inside each layer
    num_hidden_layers = hf_config.num_hidden_layers
    layers_per_pipeline_stage = num_hidden_layers // mapping.pp_size
    layers_range = list(
        range(mapping.pp_rank * layers_per_pipeline_stage,
              (mapping.pp_rank + 1) * layers_per_pipeline_stage, 1))

    for l in layers_range:
        layer_idx = l - mapping.pp_rank * layers_per_pipeline_stage
        prefix = "layers" + split_sym + str(layer_idx) + split_sym
        tensorrt_llm.logger.info(f'Process weights in layer: {layer_idx}')
        # layer = tensorrt_llm_llama.layers[layer_idx]
        tllm_prex = f'transformer.layers.{l-layers_range[0]}'
        # 4.1 attention.qkv
        qkv_weight_list = []
        for suf in gptq_suffix_list:
            qkv_list = []
            for comp in ["q", "k", "v"]:
                comp_part = load(prefix + gptq_key_list[3] + comp +
                                 gptq_key_list[4] + suf)
                comp_part = torch_split(comp_part, 1)
                qkv_list.append(comp_part)
            qkv_weight_list.append(torch.cat(qkv_list, dim=1))

        # process_and_assign_weight(layer.attention.qkv, qkv_weight_list)
        weights.update(
            process_and_assign_weight(qkv_weight_list,
                                      f'{tllm_prex}.attention.qkv'))
        # 4.2 attention.dense
        v = [load(prefix + gptq_key_list[5] + suf) for suf in gptq_suffix_list]
        # process_and_assign_weight(layer.attention.dense, v, 0)
        weights.update(
            process_and_assign_weight(v,
                                      f'{tllm_prex}.attention.dense',
                                      tp_dim=0))
        # 4.3 mlp.gate
        v = [load(prefix + gptq_key_list[6] + suf) for suf in gptq_suffix_list]
        # process_and_assign_weight(layer.mlp.gate, v, 1)
        weights.update(
            process_and_assign_weight(v, f'{tllm_prex}.mlp.gate', tp_dim=1))
        # 4.4 mlp.proj
        v = [load(prefix + gptq_key_list[7] + suf) for suf in gptq_suffix_list]
        # process_and_assign_weight(layer.mlp.proj, v, 0)
        weights.update(
            process_and_assign_weight(v, f'{tllm_prex}.mlp.proj', tp_dim=0))
        # 4.5 mlp.fc
        v = [load(prefix + gptq_key_list[8] + suf) for suf in gptq_suffix_list]
        # process_and_assign_weight(layer.mlp.fc, v, 1)
        weights.update(
            process_and_assign_weight(v, f'{tllm_prex}.mlp.fc', tp_dim=1))
        # 4.6 input_layernorm
        v = load(prefix + gptq_key_list[9])
        # layer.input_layernorm.weight.value = v.to(torch_dtype).cpu().numpy()
        weights[f'{tllm_prex}.input_layernorm.weight'] = v.to(torch_dtype)

        # 4.7 post_layernorm
        v = load(prefix + gptq_key_list[10])
        # layer.post_layernorm.weight.value = v.to(torch_dtype).cpu().numpy()
        weights[f'{tllm_prex}.post_layernorm.weight'] = v.to(torch_dtype)

    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    tensorrt_llm.logger.info(f'Weights loaded. Total time: {t}')

    return weights


class QkvWeightHelper:
    """ A helper utility for loading QKV weights from sharded files. """

    def __init__(self, config: PretrainedConfig):
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_kv_heads = config.num_key_value_heads
        self.tp_size = config.mapping.tp_size
        self.tp_rank = config.mapping.tp_rank
        self.is_mha = self.num_heads == self.num_kv_heads
        self._qkv_weights = {}

    @staticmethod
    def is_qkv_weight(name):
        for k in ['q_proj', 'k_proj', 'v_proj']:
            if 'self_attn' in name and k in name:
                return True
        return False

    def add_weight(self, i: int, name: str, weight: torch.Tensor):
        if 'q_proj' in name:
            tag = 'q'
        elif 'k_proj' in name:
            tag = 'k'
        elif 'v_proj' in name:
            tag = 'v'
        else:
            raise ValueError(f'Got an unexpected parameter of name {name}')
        if i not in self._qkv_weights:
            self._qkv_weights[i] = {}
        self._qkv_weights[i][tag] = weight

    def is_qkv_prepared(self, layer_id):
        if layer_id not in self._qkv_weights:
            return False
        weights = self._qkv_weights[layer_id]
        return 'q' in weights and 'k' in weights and 'v' in weights

    def split_qkv_weights(self, layer_id):
        if not self.is_qkv_prepared(layer_id):
            return None
        weights = self._qkv_weights.pop(layer_id)  # to prevent memory leak.
        q, k, v = (torch.tensor(weights[t]) for t in ['q', 'k', 'v'])

        if not self.is_mha:
            head_size = self.hidden_size // self.num_heads
            if self.num_kv_heads < self.tp_size:
                # duplicate the KV heads up to tensor_parallel
                k = dup_kv_weight(k, self.num_kv_heads, self.tp_size)
                v = dup_kv_weight(v, self.num_kv_heads, self.tp_size)
            assert k.shape[0] % (self.tp_size * head_size) == 0
            assert v.shape[0] % (self.tp_size * head_size) == 0
            wq = split(q, self.tp_size, self.tp_rank)
            wk = split(k, self.tp_size, self.tp_rank)
            wv = split(v, self.tp_size, self.tp_rank)
            fused_qkv = torch.cat((wq, wk, wv), dim=0)
        else:
            qkv = torch.cat([q, k, v], dim=0)
            qkv = qkv.reshape(3, q.shape[0], q.shape[1])
            fused_qkv = split(qkv, self.tp_size, self.tp_rank, dim=1)
            fused_qkv = fused_qkv.reshape(3 * (q.shape[0] // self.tp_size),
                                          q.shape[1])
        return fused_qkv


def load_from_hf_checkpoint(model_dir,
                            mapping=Mapping(),
                            config=None,
                            lora_config=LoraConfig()):
    tensorrt_llm.logger.info('Loading weights from HF LLaMA...')
    tik = time.time()
    weights = {}
    dtype = config.dtype
    if isinstance(dtype, str):
        dtype = str_dtype_to_torch(dtype)

    moe_config = MoeConfig(config.moe_num_experts, config.moe_top_k,
                           config.moe_tp_mode, config.moe_normalization_mode)
    assert not moe_config.has_moe(), "MoE does not support sharded load"

    model_dir = Path(model_dir)

    from transformers import AutoConfig
    hf_config = AutoConfig.from_pretrained(model_dir)

    quant_mode = config.quant_mode
    if quant_mode.is_int8_weight_only():
        plugin_weight_only_quant_type = torch.int8
    elif quant_mode.is_int4_weight_only():
        plugin_weight_only_quant_type = torch.quint4x2
    use_weight_only = quant_mode.is_weight_only()

    layers_range = mapping.pp_layers(config.num_hidden_layers)

    qkv_weight_helper = QkvWeightHelper(config)

    for model_file in iterate_shard_files(model_dir,
                                          rank=mapping.tp_rank,
                                          progress_bar=False):
        tensorrt_llm.logger.debug(f'Loading file {str(model_file)}...')
        model_params = load_state_dict(model_file, dtype=dtype)
        for name, param in model_params.items():
            tensorrt_llm.logger.debug(f'Converting weight {name}...')
            i = retrieved_layer_index_from_name(name)
            if i is None:
                layer = None
            else:
                if i not in layers_range:
                    continue
            tllm_prex = f'transformer.layers.{i}.'

            if 'model.embed_tokens.weight' in name:
                if lora_config.is_valid and lora_config.embedding_weight is not None:
                    param = lora_config.embedding_weight.to(dtype)
                if hf_config.tie_word_embeddings:
                    # lm_head.weight has the same weights as embedding
                    if mapping.is_last_pp_rank():
                        weights['lm_head.weight'] = split(
                            param, mapping.tp_size, mapping.tp_rank)
                if config.use_parallel_embedding:
                    param = split(param, mapping.tp_size, mapping.tp_rank,
                                  config.embedding_sharding_dim)
                if mapping.is_first_pp_rank():
                    weights['transformer.vocab_embedding.weight'] = param
            elif 'model.norm.weight' in name:
                if mapping.is_last_pp_rank():
                    weights['transformer.ln_f.weight'] = param
            elif 'lm_head.weight' in name:
                if lora_config.is_valid and lora_config.lm_head_weight is not None:
                    param = lora_config.lm_head_weight.to(dtype)
                if mapping.is_last_pp_rank():
                    weights['lm_head.weight'] = split(param, mapping.tp_size,
                                                      mapping.tp_rank)
            elif 'input_layernorm.weight' in name:
                weights[tllm_prex + 'input_layernorm.weight'] = param
            elif 'post_attention_layernorm.weight' in name:
                weights[tllm_prex + 'post_layernorm.weight'] = param
            elif qkv_weight_helper.is_qkv_weight(name):
                qkv_weight_helper.add_weight(i, name, param)
                if not qkv_weight_helper.is_qkv_prepared(i):
                    continue
                split_v = qkv_weight_helper.split_qkv_weights(i)
                if use_weight_only:
                    param = split_v.transpose()
                    processed_torch_weights, torch_weight_scales = \
                        torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                            param, plugin_weight_only_quant_type)
                    weights[tllm_prex +
                            'attention.qkv.weight'] = processed_torch_weights
                    weights[
                        tllm_prex +
                        'attention.qkv.per_channel_scale'] = torch_weight_scales
                else:
                    weights[tllm_prex + 'attention.qkv.weight'] = split_v
            elif 'self_attn.o_proj.weight' in name:
                split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=1)
                if use_weight_only:
                    processed_torch_weights, torch_weight_scales = \
                        torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                            split_v.transpose(), plugin_weight_only_quant_type)
                    weights[tllm_prex +
                            'attention.dense.weight'] = processed_torch_weights
                    weights[
                        tllm_prex +
                        'attention.dense.per_channel_scale'] = torch_weight_scales
                else:
                    weights[tllm_prex + 'attention.dense.weight'] = split_v
            elif 'mlp.up_proj.weight' in name:
                split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=0)
                if use_weight_only:
                    processed_torch_weights, torch_weight_scales = \
                        torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                            split_v.transpose(), plugin_weight_only_quant_type)
                    weights[tllm_prex +
                            'mlp.gate.weight'] = processed_torch_weights
                    weights[tllm_prex +
                            'mlp.gate.per_channel_scale'] = torch_weight_scales
                else:
                    weights[tllm_prex + 'mlp.gate.weight'] = split_v
            elif 'mlp.down_proj.weight' in name:
                split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=1)
                if use_weight_only:
                    processed_torch_weights, torch_weight_scales = \
                        torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                            split_v.transpose(), plugin_weight_only_quant_type)
                    weights[tllm_prex +
                            'mlp.proj.weight'] = processed_torch_weights
                    weights[tllm_prex +
                            'mlp.proj.per_channel_scale'] = torch_weight_scales
                else:
                    weights[tllm_prex + 'mlp.proj.weight'] = split_v

            elif 'mlp.gate_proj.weight' in name:
                split_v = split(param, mapping.tp_size, mapping.tp_rank, dim=0)
                if use_weight_only:
                    processed_torch_weights, torch_weight_scales = \
                        torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
                            split_v.transpose(), plugin_weight_only_quant_type)
                    layer.mlp.fc.weight.value = processed_torch_weights
                    layer.mlp.fc.per_channel_scale.value = torch_weight_scales
                    weights[tllm_prex +
                            'mlp.fc.weight'] = processed_torch_weights
                    weights[tllm_prex +
                            'mlp.fc.per_channel_scale'] = torch_weight_scales
                else:
                    weights[tllm_prex + 'mlp.fc.weight'] = split_v

        del model_params
    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    tensorrt_llm.logger.info(f'Weights loaded. Total time: {t}')
    return weights


def convert_hf_llama(hf_model,
                     mapping,
                     rank=0,
                     dtype='float32',
                     use_parallel_embedding=False,
                     sharding_dim=0,
                     use_weight_only=False,
                     share_embedding_table=False,
                     use_gemm_woq_plugin=False,
                     plugin_weight_only_quant_type=torch.int8,
                     use_smooth_quant=False,
                     per_channel=False,
                     per_token=False,
                     int8_kv_cache=False,
                     act_range=[],
                     qkv_para=[],
                     smoother=[],
                     moe_config=None,
                     lora_config=None):

    weights = {}
    tik = time.time()
    tensor_parallel = mapping.tp_size
    model_params = dict(hf_model.named_parameters())
    dtype = getattr(torch, dtype)
    num_attention_heads = hf_model.config.num_attention_heads
    hidden_size = hf_model.config.hidden_size
    intermediate_size = hf_model.config.intermediate_size
    num_key_value_heads = hf_model.config.num_key_value_heads
    mha_mode = (num_key_value_heads == num_attention_heads)

    layers_per_pipeline_stage = hf_model.config.num_hidden_layers // mapping.pp_size
    layers_range = list(
        range(mapping.pp_rank * layers_per_pipeline_stage,
              (mapping.pp_rank + 1) * layers_per_pipeline_stage, 1))

    if moe_config and moe_config.has_moe():
        rank_experts = list(range(moe_config.num_experts))
        if moe_config.tp_mode == moe_config.ParallelismMode.EXPERT_PARALLEL:
            rank_experts = mapping.ep_experts(moe_config.num_experts)

        for l in range(hf_model.config.num_hidden_layers):
            for suffix in ["w1", "w2", "w3"]:
                model_params[f'model.layers.{l}.block_sparse_moe.experts.{suffix}.weight'] = \
                            torch.stack(list(model_params[f'model.layers.{l}.block_sparse_moe.experts.{expert}.{suffix}.weight']
                                        for expert in rank_experts))
            w3 = model_params[
                f'model.layers.{l}.block_sparse_moe.experts.w3.weight']
            w2 = model_params[
                f'model.layers.{l}.block_sparse_moe.experts.w2.weight']
            w1 = model_params[
                f'model.layers.{l}.block_sparse_moe.experts.w1.weight']
            if moe_config.tp_mode == moe_config.ParallelismMode.TENSOR_PARALLEL:
                w3 = split(w3, mapping.tp_size, mapping.tp_rank, dim=1)
                w2 = split(w2, mapping.tp_size, mapping.tp_rank, dim=2)
                w1 = split(w1, mapping.tp_size, mapping.tp_rank, dim=1)
            # concat w3 and w1 for gated expert
            model_params[f'model.layers.{l}.block_sparse_moe.experts.w3w1.weight'] = \
                torch.concat([w3, w1], dim=-2)
            model_params[
                f'model.layers.{l}.block_sparse_moe.experts.w2.weight'] = w2

    for l in range(hf_model.config.num_hidden_layers):
        if l not in layers_range:
            continue
        prefix = f'model.layers.{l}.'
        idx = int(l) - mapping.pp_rank * layers_per_pipeline_stage
        tllm_prex = f'transformer.layers.{idx}.'
        q_weight = get_weight(model_params, prefix + 'self_attn.q_proj', dtype)
        k_weight = get_weight(model_params, prefix + 'self_attn.k_proj', dtype)
        v_weight = get_weight(model_params, prefix + 'self_attn.v_proj', dtype)

        if not mha_mode:
            head_size = hidden_size // num_attention_heads
            if num_key_value_heads < tensor_parallel:
                # duplicate the KV heads up to tensor_parallel
                k_weight = dup_kv_weight(k_weight, num_key_value_heads,
                                         tensor_parallel)
                v_weight = dup_kv_weight(v_weight, num_key_value_heads,
                                         tensor_parallel)
            assert (k_weight.shape[0] % (mapping.tp_size * head_size)) == 0
            assert (v_weight.shape[0] % (mapping.tp_size * head_size)) == 0

            wq = split(q_weight, mapping.tp_size, mapping.tp_rank)
            wk = split(k_weight, mapping.tp_size, mapping.tp_rank)
            wv = split(v_weight, mapping.tp_size, mapping.tp_rank)

            split_v = torch.concat((wq, wk, wv))

        else:
            qkv_weight = torch.cat([q_weight, k_weight, v_weight], dim=0)

            split_v = split_qkv_tp(qkv_weight, num_attention_heads, hidden_size,
                                   tensor_parallel, mapping.tp_rank)
        if use_smooth_quant:
            qkv_weight = qkv_para[prefix + 'self_attn.qkv_proj']

            if not mha_mode:
                hidden_size = qkv_weight.shape[0]
                local_dim = hidden_size
                head_size = (qkv_weight.shape[-1] - local_dim) // 2
                qkv_weight = qkv_weight.reshape(hidden_size,
                                                local_dim + 2 * head_size)
            else:
                qkv_weight = qkv_weight.reshape(hidden_size, 3, hidden_size)

            int8_weights = generate_int8(qkv_weight,
                                         act_range.get(prefix +
                                                       'self_attn.qkv_proj'),
                                         is_qkv=True,
                                         multi_query_mode=bool(not mha_mode))

            weights.update(
                get_tllm_linear_sq_weight(
                    int8_weights,
                    tllm_prex + 'attention.qkv.', [
                        1, 3 * hidden_size // tensor_parallel
                        if mha_mode else hidden_size // tensor_parallel +
                        (hidden_size // num_key_value_heads) //
                        tensor_parallel * 2
                    ],
                    tensor_parallel,
                    is_qkv=True,
                    per_token=per_token,
                    per_channel=per_channel,
                    last_prefix=tllm_prex + 'input_layernorm.scale_to_int',
                    smoother_value=None,
                    smoother_shape=None,
                    rank=mapping.tp_rank,
                    cat_dim=-1,
                    multi_query_mode=bool(not mha_mode)))
        else:
            weights.update(
                get_tllm_linear_weight(split_v, tllm_prex + 'attention.qkv.',
                                       None, use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

        if int8_kv_cache:
            qkv_y = torch.cat([
                act_range.get(prefix + 'self_attn.q_proj')["y"],
                act_range.get(prefix + 'self_attn.k_proj')["y"],
                act_range.get(prefix + 'self_attn.v_proj')["y"]
            ],
                              dim=0)

            int8_kv_scales = qkv_y.max() / 127.

            kv_cache_weights = {}

            kv_cache_weights[
                tllm_prex +
                'attention.kv_cache_scaling_factor'] = int8_kv_scales.reshape(
                    [1])

            weights.update(kv_cache_weights)

        attn_dense_weight = get_weight(model_params,
                                       prefix + 'self_attn.o_proj', dtype)
        split_v = split_matrix_tp(attn_dense_weight,
                                  tensor_parallel,
                                  mapping.tp_rank,
                                  dim=1)
        if use_smooth_quant:
            attn_dense_weight = attn_dense_weight.t()
            int8_weights = generate_int8(
                attn_dense_weight, act_range.get(prefix + 'self_attn.o_proj'))
            weights.update(
                get_tllm_linear_sq_weight(
                    int8_weights,
                    tllm_prex + 'attention.dense.', [1, hidden_size],
                    tensor_parallel,
                    is_qkv=False,
                    per_token=per_token,
                    per_channel=per_channel,
                    last_prefix=tllm_prex +
                    'attention.quantization_scaling_factor',
                    smoother_value=smoother[(prefix + 'self_attn.o_proj')],
                    smoother_shape=[1, hidden_size // tensor_parallel],
                    rank=mapping.tp_rank,
                    cat_dim=0))
        else:
            weights.update(
                get_tllm_linear_weight(split_v, tllm_prex + 'attention.dense.',
                                       None, use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

        if moe_config and moe_config.has_moe():
            ## block_sparse_moe.experts.w2.weight
            moe_experts_w2_weights = get_weight(
                model_params, prefix + 'block_sparse_moe.experts.w2', dtype)
            weights.update(
                get_tllm_linear_weight(moe_experts_w2_weights,
                                       tllm_prex + 'mlp.experts_weight_2',
                                       None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type,
                                       dtype,
                                       use_gemm_woq_plugin,
                                       postfix=''))
            ##block_sparse_moe.experts.w3w1.weight
            moe_experts_w3w1_weights = get_weight(
                model_params, prefix + 'block_sparse_moe.experts.w3w1', dtype)
            weights.update(
                get_tllm_linear_weight(moe_experts_w3w1_weights,
                                       tllm_prex + 'mlp.experts_weight_1',
                                       None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type,
                                       dtype,
                                       use_gemm_woq_plugin,
                                       postfix=''))

            moe_experts_gate_weights = get_weight(
                model_params, prefix + 'block_sparse_moe.gate', dtype)
            v = split(moe_experts_gate_weights,
                      mapping.tp_size,
                      mapping.tp_rank,
                      dim=-1)

            weights.update(
                get_tllm_linear_weight(v.to(torch.float32),
                                       tllm_prex + 'mlp.router.', None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))
        else:
            mlp_gate_weight = get_weight(model_params, prefix + 'mlp.up_proj',
                                         dtype)
            split_v = split_matrix_tp(mlp_gate_weight,
                                      tensor_parallel,
                                      mapping.tp_rank,
                                      dim=0)
            if use_smooth_quant:
                mlp_gate_weight = mlp_gate_weight.t()
                int8_weights = generate_int8(
                    mlp_gate_weight, act_range.get(prefix + 'mlp.up_proj'))

                weights.update(
                    get_tllm_linear_sq_weight(
                        int8_weights,
                        tllm_prex + 'mlp.gate.',
                        [1, intermediate_size // tensor_parallel],
                        tensor_parallel,
                        is_qkv=False,
                        per_token=per_token,
                        per_channel=per_channel,
                        last_prefix=tllm_prex + 'post_layernorm.scale_to_int',
                        smoother_value=None,
                        smoother_shape=None,
                        rank=mapping.tp_rank,
                        cat_dim=-1))
            else:
                weights.update(
                    get_tllm_linear_weight(split_v, tllm_prex + 'mlp.gate.',
                                           None, use_weight_only,
                                           plugin_weight_only_quant_type, dtype,
                                           use_gemm_woq_plugin))

            mlp_fc_weight = get_weight(model_params, prefix + 'mlp.gate_proj',
                                       dtype)
            split_v = split_matrix_tp(mlp_fc_weight,
                                      tensor_parallel,
                                      mapping.tp_rank,
                                      dim=0)

            if use_smooth_quant:
                mlp_fc_weight = mlp_fc_weight.t()  #verified
                int8_weights = generate_int8(
                    mlp_fc_weight, act_range.get(prefix + 'mlp.gate_proj'))
                weights.update(
                    get_tllm_linear_sq_weight(
                        int8_weights,
                        tllm_prex + 'mlp.fc.',
                        [1, intermediate_size // tensor_parallel],
                        tensor_parallel,
                        is_qkv=False,
                        per_token=per_token,
                        per_channel=per_channel,
                        last_prefix=tllm_prex + 'post_layernorm.scale_to_int',
                        smoother_value=None,
                        smoother_shape=None,
                        rank=mapping.tp_rank,
                        cat_dim=-1))
            else:
                weights.update(
                    get_tllm_linear_weight(split_v, tllm_prex + 'mlp.fc.', None,
                                           use_weight_only,
                                           plugin_weight_only_quant_type, dtype,
                                           use_gemm_woq_plugin))

            mlp_proj_weight = get_weight(model_params, prefix + 'mlp.down_proj',
                                         dtype)
            split_v = split_matrix_tp(mlp_proj_weight,
                                      tensor_parallel,
                                      mapping.tp_rank,
                                      dim=1)

            if use_smooth_quant:
                mlp_proj_weight = mlp_proj_weight.t()
                int8_weights = generate_int8(
                    mlp_proj_weight, act_range.get(prefix + 'mlp.down_proj'))
                weights.update(
                    get_tllm_linear_sq_weight(
                        int8_weights,
                        tllm_prex + 'mlp.proj.', [1, hidden_size],
                        tensor_parallel,
                        is_qkv=False,
                        per_token=per_token,
                        per_channel=per_channel,
                        last_prefix=tllm_prex +
                        'mlp.quantization_scaling_factor',
                        smoother_value=smoother[prefix + 'mlp.down_proj'],
                        smoother_shape=[
                            1, intermediate_size // tensor_parallel
                        ],
                        rank=mapping.tp_rank,
                        cat_dim=0))
            else:
                weights.update(
                    get_tllm_linear_weight(split_v, tllm_prex + 'mlp.proj.',
                                           None, use_weight_only,
                                           plugin_weight_only_quant_type, dtype,
                                           use_gemm_woq_plugin))

        # Layer norms do not use tensor parallelism
        input_ln_weight = get_weight(model_params, prefix + 'input_layernorm',
                                     dtype)
        weights[tllm_prex + 'input_layernorm.weight'] = input_ln_weight

        post_ln_weight = get_weight(model_params,
                                    prefix + 'post_attention_layernorm', dtype)
        weights[tllm_prex + 'post_layernorm.weight'] = post_ln_weight

    v = get_weight(model_params, 'model.embed_tokens', dtype)
    if lora_config.is_valid and lora_config.embedding_weight is not None:
        v = lora_config.embedding_weight
    if hf_model.config.tie_word_embeddings:
        # lm_head.weight has the same weights as embedding
        if mapping.is_last_pp_rank():
            weights['lm_head.weight'] = split(v, mapping.tp_size,
                                              mapping.tp_rank)

    if use_parallel_embedding:
        v = split_matrix_tp(v,
                            mapping.tp_size,
                            mapping.tp_rank,
                            dim=sharding_dim)

    if mapping.is_first_pp_rank():
        weights['transformer.vocab_embedding.weight'] = v

    # if not use_parallel_embedding:
    #     weights['transformer.vocab_embedding.weight'] = embed_w
    # else:
    #     assert hf_model.config.vocab_size % tensor_parallel == 0
    #     weights['transformer.vocab_embedding.weight'] = split_matrix_tp(
    #         embed_w, tensor_parallel, rank

    lm_head_weights = get_weight(model_params, 'lm_head', dtype)

    if mapping.is_last_pp_rank():

        if lora_config.is_valid and lora_config.lm_head_weight is not None:

            lm_head_weights = lora_config.lm_head_weight

        weights['lm_head.weight'] = split_matrix_tp(lm_head_weights,
                                                    tensor_parallel,
                                                    mapping.tp_rank,
                                                    dim=0)
        ln_f_w = get_weight(model_params, 'model.norm', dtype)
        weights['transformer.ln_f.weight'] = ln_f_w

    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    print(f'Weights loaded. Total time: {t}')
    return weights


def main():
    # TODO(qijun): Currently, the convert script depends on a torch op:
    # torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix,
    # which is included in tensorrt_llm Python package. Otherwise, the convert
    # script does not need to import tensorrt_llm. Will remove it after reimplementing
    # the op with PyTorch.
    print(tensorrt_llm.__version__)
    args = parse_arguments()
    world_size = args.tp_size * args.pp_size

    tik = time.time()

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    hf_config = None
    if args.model_dir is not None:
        hf_config = LlamaConfig.from_pretrained(args.model_dir)
        if hf_config.model_type == "llava":
            # LLaVA = Vision model + Llama LLM
            # We load a llava config and use its' text config as llama config
            hf_config = LlavaConfig.from_pretrained(args.model_dir).text_config
            hf_config.model_type = "llava"  # Replace llama with llava

        args.model_type = hf_config.model_type
        args.n_head = hf_config.num_attention_heads
        args.inter_size = hf_config.intermediate_size
        args.n_layer = hf_config.num_hidden_layers
        args.n_embd = hf_config.hidden_size
        args.n_kv_head = hf_config.num_key_value_heads
        args.rms_norm_eps = hf_config.rms_norm_eps
        args.vocab_size = hf_config.vocab_size
        args.n_positions = hf_config.max_position_embeddings
        if hf_config.model_type == "mixtral":
            # HF LLaMA-type models are implicitly using gated activation.
            # With our MoE implementation, we must make it explicit
            args.hidden_act = "swiglu"
            args.moe_num_experts = getattr(hf_config, "num_local_experts",
                                           args.moe_num_experts)
            args.moe_top_k = getattr(hf_config, "num_experts_per_tok",
                                     args.moe_top_k)
            args.rotary_base = getattr(hf_config, "rope_theta",
                                       args.rotary_base)
    elif args.meta_ckpt_dir is not None:
        with open(Path(args.meta_ckpt_dir, "params.json")) as fp:
            meta_config: dict = json.load(fp)
        args.n_embd = meta_config["dim"]
        args.n_head = meta_config["n_heads"]
        args.n_layer = meta_config["n_layers"]
        args.n_kv_head = meta_config.get("n_kv_heads", args.n_head)

        if "hidden_dim" in meta_config:
            args.inter_size = meta_config["hidden_dim"]
        else:
            args.multiple_of = meta_config.get("multiple_of", 1)
            n_embd = int(4 * args.n_embd * 2 / 3)
            args.ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1)
            args.inter_size = args.multiple_of * (
                (int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1)
                // args.multiple_of)
        args.rms_norm_eps = meta_config["norm_eps"]
        args.moe_num_experts = meta_config.get("moe", {}).get("num_experts", 0)
        args.moe_top_k = meta_config.get("moe", {}).get("num_experts_per_tok",
                                                        0)
    else:
        args.n_kv_head = args.n_kv_head or args.n_head

    if args.moe_num_experts and args.moe_top_k == 0:
        args.moe_top_k = 1
    args.moe_config = MoeConfig(args.moe_num_experts, args.moe_top_k,
                                args.moe_tp_mode,
                                args.moe_renorm_mode).validate()

    if args.rotary_scaling is not None:
        # assert args.use_gpt_attention_plugin, "RoPE scaling is only supported through GPT attention plugin."
        rotary_scaling = {
            "type": args.rotary_scaling[0],
            "factor": float(args.rotary_scaling[1])
        }
        assert rotary_scaling["type"] in ["linear", "dynamic"]
        assert rotary_scaling["factor"] > 1.0
        args.rotary_scaling = rotary_scaling

    hf_modules_to_trtllm_modules = {
        "q_proj": "attn_q",
        "k_proj": "attn_k",
        "v_proj": "attn_v",
        "o_proj": "attn_dense",
        "gate_proj": "mlp_h_to_4h",
        "down_proj": "mlp_4h_to_h",
        "up_proj": "mlp_gate"
    }  # lora modules on llama

    trtllm_modules_to_hf_modules = {
        "attn_q": "q_proj",
        "attn_k": "k_proj",
        "attn_v": "v_proj",
        "attn_dense": "o_proj",
        "mlp_h_to_4h": "gate_proj",
        "mlp_4h_to_h": "down_proj",
        "mlp_gate": "up_proj",
    }

    lora_config = LoraConfig.from_hf(args.hf_lora_dir,
                                     hf_modules_to_trtllm_modules,
                                     trtllm_modules_to_hf_modules)

    if lora_config.is_valid and lora_config.vocab_size != 0:
        if args.lora_target_modules is None:
            args.lora_target_modules = lora_config.lora_target_modules

        # the lora checkpoint might finetune the embedding
        if lora_config.vocab_size != 0:
            args.vocab_size = lora_config.vocab_size

    args.lora_config = lora_config

    config = {
        'architecture': hf_config.architectures[0]
        if hf_config is not None else "LlamaForCausalLM",
        'dtype': args.dtype,
        'logits_dtype': 'float32',
        'num_hidden_layers': args.n_layer,
        'num_attention_heads': args.n_head,
        'hidden_size': args.n_embd,
        'intermediate_size': args.inter_size,
        'num_key_value_heads': args.n_kv_head,
        'vocab_size': args.vocab_size,
        'position_embedding_type': 'rope_gpt_neox',
        'max_position_embeddings': args.n_positions,
        'hidden_act': args.hidden_act,
        'rotary_base': args.rotary_base,
        'rotary_scaling': args.rotary_scaling,
        'norm_epsilon': args.rms_norm_eps,
        'quantization': {
            'quant_algo': None,
            'kv_cache_quant_algo': None,
            "sq_use_plugin": True,
        },
        'mapping': {
            'world_size': world_size,
            'tp_size': args.tp_size,
            'pp_size': args.pp_size,
        },
        "moe_config": {
            "num_experts": args.moe_num_experts,
            "top_k": args.moe_top_k,
            "tp_mode": args.moe_tp_mode,
            "normalization_mode": args.moe_renorm_mode
        },
        'use_parallel_embedding': args.use_parallel_embedding,
        'embedding_sharding_dim': args.embedding_sharding_dim,
        'share_embedding_table': args.use_embedding_sharing,
        'use_prompt_tuning': args.use_prompt_tuning,
        'moe_num_experts': args.moe_num_experts,
        'moe_top_k': args.moe_top_k,
        'moe_tp_mode': args.moe_tp_mode,
        'moe_normalization_mode': args.moe_renorm_mode,
        'use_fused_mlp': args.use_fused_mlp,
        'enable_pos_shift': args.enable_pos_shift,
        'dense_context_fmha': args.dense_context_fmha,
        'max_lora_rank': args.max_lora_rank,
        'lora_target_modules': args.lora_target_modules,
        'hf_modules_to_trtllm_modules':
        args.lora_config.hf_modules_to_trtllm_modules,
        'trtllm_modules_to_hf_modules':
        args.lora_config.trtllm_modules_to_hf_modules,
        'disable_weight_only_quant_plugin':
        args.disable_weight_only_quant_plugin
    }

    if args.use_weight_only:
        if args.weight_only_precision == 'int8':
            config['quantization']['quant_algo'] = 'W8A16'
        elif args.weight_only_precision == 'int4':
            config['quantization']['quant_algo'] = 'W4A16'
    elif args.smoothquant:
        if args.per_channel:
            if args.per_token:
                config['quantization'][
                    'quant_algo'] = 'W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN'
            else:
                config['quantization'][
                    'quant_algo'] = 'W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN'
        else:
            if args.per_token:
                config['quantization'][
                    'quant_algo'] = 'W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN'
            else:
                config['quantization'][
                    'quant_algo'] = 'W8A8_SQ_PER_TENSOR_PLUGIN'
    elif args.enable_fp8:
        config['quantization']['quant_algo'] = 'FP8'
    if args.int8_kv_cache:
        config['quantization']['kv_cache_quant_algo'] = 'INT8'
    elif args.fp8_kv_cache:
        config['quantization']['kv_cache_quant_algo'] = 'FP8'

    if args.weight_only_precision == 'int4_awq':
        exclude_modules = ['lm_head'] if not args.quantize_lm_head else []

        config['quantization'].update({
            "group_size": args.group_size,
            'has_zero_point': False,
            'pre_quant_scale': True,
            'exclude_modules': exclude_modules,
            'quant_algo': 'W4A16_AWQ'
        })
    elif args.weight_only_precision == 'int4_gptq':
        config['quantization'].update({
            "group_size": args.group_size,
            "has_zero_point": True,
            "pre_quant_scale": False,
            'quant_algo': 'W4A16_GPTQ'
        })

    with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
        json.dump(config, f, indent=4)

    if args.model_dir is None and args.meta_ckpt_dir is None:
        return

    if args.weight_only_precision == 'int8':
        plugin_weight_only_quant_type = torch.int8
    elif args.weight_only_precision == 'int4':
        plugin_weight_only_quant_type = torch.quint4x2

    act_range = {}
    llama_qkv_para = {}
    # smoother for inputs of self_attn.o_proj and mlp.down_proj
    llama_smoother = {}
    model = None
    if args.model_dir is not None:

        if args.model_type == "llava":
            hf_llava = LlavaForConditionalGeneration.from_pretrained(
                args.model_dir, torch_dtype="auto")
            model = hf_llava.language_model
        else:
            hf_model = LlamaForCausalLM if args.model_type != "mixtral" else MixtralForCausalLM
            model = hf_model.from_pretrained(
                args.model_dir,
                # device_map={
                #     "model": "cpu",
                #     "lm_head": "cpu",
                #     "embed_tokens": "cpu",
                #     "layers": "cpu",
                #     "norm": "cpu",
                # },  # Load to CPU memory
                device_map='auto',
                torch_dtype='auto',
            )

        if args.smoothquant is not None or args.int8_kv_cache:
            os.environ["TOKENIZERS_PARALLELISM"] = os.environ.get(
                "TOKENIZERS_PARALLELISM", "false")
            if args.load_model_on_cpu:
                logger.warning(
                    "Note that running capture_activation_range on cpu would be very small."
                )
            dataset = load_dataset("ccdv/cnn_dailymail",
                                   '3.0.0',
                                   cache_dir=args.dataset_cache_dir)

            act_range = capture_activation_range(
                model,
                LlamaTokenizer.from_pretrained(args.model_dir,
                                               padding_side='left'), dataset)
            if args.smoothquant is not None:
                smooth_llama_model(model, act_range, args.smoothquant,
                                   llama_qkv_para, llama_smoother)
    convert_args = {
        'hf_model': model,
        'act_range': act_range,
        'llama_qkv_para': llama_qkv_para,
        'llama_smoother': llama_smoother,
    }

    def covert_and_save(rank, convert_args):
        mapping = Mapping(world_size=world_size,
                          rank=rank,
                          tp_size=args.tp_size,
                          pp_size=args.pp_size)

        if args.use_weight_only and args.weight_only_precision in ('int4_awq',
                                                                   'int4_gptq'):
            if args.weight_only_precision == 'int4_awq':
                weights = load_from_awq_llama(
                    args.ammo_quant_ckpt_path,
                    hf_config=hf_config,
                    quantize_lm_head=args.quantize_lm_head,
                    mapping=mapping,
                    dtype=args.dtype)

            elif args.weight_only_precision == 'int4_gptq':
                weights = load_from_gptq_llama(args.ammo_quant_ckpt_path,
                                               hf_config,
                                               mapping,
                                               dtype=args.dtype)

        elif args.meta_ckpt_dir is not None:
            weights = load_from_meta_llama(
                args.meta_ckpt_dir, mapping,
                PretrainedConfig.from_dict(copy.deepcopy(config)))

        else:
            if args.load_by_shard:
                weights = load_from_hf_checkpoint(
                    args.model_dir, mapping,
                    PretrainedConfig.from_dict(copy.deepcopy(config)),
                    args.lora_config)

            else:
                weights = convert_hf_llama(
                    convert_args['hf_model'],
                    mapping,
                    rank,
                    dtype=args.dtype,
                    use_weight_only=args.use_weight_only,
                    use_gemm_woq_plugin=not args.
                    disable_weight_only_quant_plugin,
                    plugin_weight_only_quant_type=plugin_weight_only_quant_type,
                    use_parallel_embedding=args.use_parallel_embedding,
                    sharding_dim=args.embedding_sharding_dim,
                    share_embedding_table=args.use_embedding_sharing,
                    use_smooth_quant=args.smoothquant,
                    per_channel=args.per_channel,
                    per_token=args.per_token,
                    int8_kv_cache=args.int8_kv_cache,
                    act_range=convert_args['act_range'],
                    qkv_para=convert_args['llama_qkv_para'],
                    smoother=convert_args['llama_smoother'],
                    moe_config=args.moe_config,
                    lora_config=args.lora_config)

                if args.enable_fp8 or args.fp8_kv_cache:
                    scales = load_from_fp8_llama(args.ammo_quant_ckpt_path,
                                                 hf_config, mapping,
                                                 args.fp8_kv_cache)
                    weights.update(scales)

        if args.use_fused_mlp:
            for l in range(args.n_layer):
                tllm_prex = f'transformer.layers.{l}.'
                weights[tllm_prex + 'mlp.fused_fc.weight'] = torch.cat(
                    (weights[tllm_prex + 'mlp.gate.weight'],
                     weights[tllm_prex + 'mlp.fc.weight']),
                    dim=0)
                if tllm_prex + 'mlp.gate.bias' in weights and tllm_prex + 'mlp.fc.bias' in weights:
                    weights[tllm_prex + 'mlp.fused_fc.bias'] = torch.cat(
                        (weights[tllm_prex + 'mlp.gate.bias'],
                         weights[tllm_prex + 'mlp.fc.bias']),
                        dim=0)
                if args.enable_fp8 or args.fp8_kv_cache:
                    weights[tllm_prex +
                            'mlp.fused_fc.weights_scaling_factor'] = torch.cat(
                                (weights[tllm_prex +
                                         'mlp.gate.weights_scaling_factor'],
                                 weights[tllm_prex +
                                         'mlp.fc.weights_scaling_factor']),
                                dim=0)
                    weights[
                        tllm_prex +
                        'mlp.fused_fc.activation_scaling_factor'] = torch.cat(
                            (weights[tllm_prex +
                                     'mlp.gate.activation_scaling_factor'],
                             weights[tllm_prex +
                                     'mlp.fc.activation_scaling_factor']),
                            dim=0)

        safetensors.torch.save_file(
            weights, os.path.join(args.output_dir, f'rank{rank}.safetensors'))

    if args.workers == 1:

        for rank in range(world_size):
            covert_and_save(rank, convert_args)
    else:
        with ThreadPoolExecutor(max_workers=args.workers) as p:
            futures = [
                p.submit(covert_and_save, rank, convert_args)
                for rank in range(world_size)
            ]
            wait(futures)

    tok = time.time()
    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
    print(f'Total time of converting checkpoints: {t}')


if __name__ == '__main__':
    main()