lib.linux-x86_64-cpython-310/tensorrt_llm/models/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");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.import functools

import copy
import functools
import json
import os
import time
from collections import defaultdict
from typing import List, Optional

import numpy as np
import safetensors
import torch
import torch.nn as nn
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from transformers.pytorch_utils import Conv1D

from ..._utils import pad_vocab_size, str_dtype_to_torch
from ...logger import logger
from ...mapping import Mapping
from ...quantization import QuantAlgo
from ..convert_utils import load_calib_dataset
from .config import QWenConfig
from .utils import get_qwen_key_list, make_context


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_qwen_model(model, scales, alpha, qwen_qkv_para, qwen_smoother):
    # Smooth the activation and weights with smoother = $\diag{s}$
    for name, module in model.named_modules():
        if not module._get_name() == "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,
                               None, alpha)

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

        # see transpose_weights function
        qwen_qkv_para[layer_name] = module.attn.c_attn.weight.transpose(0, 1)

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

        scales[layer_name]["x"] = scales[layer_name]["x"] / smoother
        scales[layer_name]["w"] = module.attn.c_proj.weight.abs().max(dim=1)[0]
        # ==================================================================
        fc1_layer_name = name + ".mlp.w1"
        gate_layer_name = name + ".mlp.w2"

        smoother = smooth_gemm_fc1_gate(module.mlp.w1.weight,
                                        module.mlp.w2.weight,
                                        scales[fc1_layer_name]["x"],
                                        module.ln_2.weight, None, alpha)

        scales[fc1_layer_name]["x"] = scales[fc1_layer_name]["x"] / smoother
        scales[fc1_layer_name]["w"] = module.mlp.w1.weight.abs().max(dim=1)[0]

        scales[gate_layer_name]["x"] = scales[gate_layer_name]["x"] / smoother
        scales[gate_layer_name]["w"] = module.mlp.w2.weight.abs().max(dim=1)[0]

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


@torch.no_grad()
def smooth_qwen2_model(model, scales, alpha, qwen_qkv_para, qwen_smoother):
    # Smooth the activation and weights with smoother = $\diag{s}$
    from transformers.models.qwen2.modeling_qwen2 import Qwen2DecoderLayer
    for name, module in model.named_modules():
        if not isinstance(module, Qwen2DecoderLayer):
            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
        qwen_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)
        qwen_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)
        qwen_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,
                             qwen_type,
                             tokenizer,
                             dataset,
                             system_prompt,
                             chat_format,
                             num_samples=512,
                             seq_len=512):
    model.eval()
    device = next(model.parameters()).device
    act_scales = defaultdict(lambda: {"x": None, "y": None, "w": None})

    if qwen_type == 'qwen':
        tokenizer.pad_token_id = tokenizer.im_end_id
    else:
        tokenizer.pad_token_id = tokenizer.eos_token_id

    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"):
        line = dataset[i]
        line = line + ' TL;DR: '
        line = line.strip()
        line = line.replace(" n't", "n't")
        if qwen_type == 'qwen':
            _, input_id_list = make_context(tokenizer=tokenizer,
                                            query=line,
                                            history=[],
                                            system=system_prompt,
                                            chat_format=chat_format,
                                            max_input_length=seq_len)
            line_encoded = torch.from_numpy(
                np.array(input_id_list,
                         dtype=np.int32)).type(torch.int32).unsqueeze(0)
            line_encoded = line_encoded.to(device)
        else:
            line_encoded = tokenizer(line,
                                     return_tensors="pt",
                                     max_length=seq_len,
                                     padding=True,
                                     truncation=True).input_ids.to(device)
        model(line_encoded)
    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'].detach()


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'].detach()


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',
                           quant_scale_name=None):
    results = {}
    if use_weight_only:
        if weight.dim() > 2:
            v = weight.transpose(1, 2).contiguous().clone()
        else:
            v = weight.t().contiguous().clone()
        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
        if quant_scale_name is not None:
            results[quant_scale_name] = torch_weight_scales
        else:
            results[prefix + 'per_channel_scale'] = torch_weight_scales
    else:
        results[prefix + postfix] = weight.clone()

    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:
        if per_channel:
            original_weights = np.array(vals["weight.int8.col"])
        else:
            original_weights = np.array(vals["weight.int8"])
        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().clone().contiguous()
        if smoother_value is None:
            results[last_prefix] = torch.from_numpy(
                np.array([1.0], dtype=np.float32))

        if per_channel:
            cur_per_channel_value = vals["scale_w_quant_orig.col"]
            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"]
            if is_qkv:
                if multi_query_mode:
                    cur_per_channel_value = multi_query_split(
                        vals["scale_w_quant_orig"], local_dim, head_size,
                        tensor_parallel, rank)
                else:
                    cur_per_channel_value = np.split(vals["scale_w_quant_orig"],
                                                     tensor_parallel,
                                                     axis=cat_dim)[rank]

        results[prefix + 'per_channel_scale'] = torch.from_numpy(
            np.array(cur_per_channel_value,
                     dtype=np.float32).reshape(col_shape)).contiguous()
    else:
        if per_channel:
            original_weights = np.array(vals["weight.int8.col"])
        else:
            original_weights = np.array(vals["weight.int8"])
        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().clone().contiguous()

        if per_channel:
            cur_per_channel_value = vals["scale_y_accum_quant.col"]
            if smoother_value is None:
                if multi_query_mode:
                    cur_per_channel_value = multi_query_split(
                        vals["scale_y_accum_quant.col"], local_dim, head_size,
                        tensor_parallel, rank)
                else:
                    cur_per_channel_value = np.split(
                        vals["scale_y_accum_quant.col"],
                        tensor_parallel,
                        axis=cat_dim)[rank]
        else:
            cur_per_channel_value = vals["scale_y_accum_quant"]
            # QKV is always per_channel
            if is_qkv:
                if multi_query_mode:
                    cur_per_channel_value = multi_query_split(
                        vals["scale_y_accum_quant"], local_dim, head_size,
                        tensor_parallel, rank)
                else:
                    cur_per_channel_value = np.split(
                        vals["scale_y_accum_quant"],
                        tensor_parallel,
                        axis=cat_dim)[rank]

        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_hf_qwen(model_dir: str, load_model_on_cpu: bool = False):
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained(
        model_dir,
        device_map='auto' if not load_model_on_cpu else 'cpu',
        torch_dtype='auto',
        trust_remote_code=True)
    return model


def convert_hf_qwen(hf_model,
                    qwen_type,
                    mapping: Mapping,
                    vocab_size=32000,
                    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):
    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
    head_size = hidden_size // num_attention_heads
    if qwen_type == 'qwen':
        intermediate_size = hf_model.config.intermediate_size // 2  # Qwen version 1 has actual intermediate_size one half of what's in hf_config
    else:
        intermediate_size = hf_model.config.intermediate_size
    num_key_value_heads = hf_model.config.num_key_value_heads if hasattr(
        hf_model.config, "num_key_value_heads") else num_attention_heads
    mha_mode = (num_key_value_heads == num_attention_heads)
    layers_range = mapping.pp_layers(hf_model.config.num_hidden_layers)

    layer_prefix = "transformer.h." if qwen_type == 'qwen' else "model.layers."
    key_list = get_qwen_key_list(qwen_type)

    for l in layers_range:
        prefix = layer_prefix + f'{l}.'
        tllm_prex = f'transformer.layers.{l - layers_range[0]}.'
        if qwen_type == 'qwen':
            qkv_weight, qkv_bias = get_weight_and_bias(model_params,
                                                       prefix + key_list[0],
                                                       dtype)
            qkv_w = split_qkv_tp(qkv_weight, num_attention_heads, hidden_size,
                                 tensor_parallel, mapping.tp_rank)
            qkv_b = split_qkv_bias_tp(qkv_bias, num_attention_heads,
                                      hidden_size, tensor_parallel,
                                      mapping.tp_rank)
        else:
            q_weight, q_bias = get_weight_and_bias(
                model_params, prefix + key_list[0] + 'q_proj', dtype)
            k_weight, k_bias = get_weight_and_bias(
                model_params, prefix + key_list[0] + 'k_proj', dtype)
            v_weight, v_bias = get_weight_and_bias(
                model_params, prefix + key_list[0] + 'v_proj', dtype)
            if not mha_mode:
                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)
                    k_bias = dup_kv_weight(k_bias, num_key_value_heads,
                                           tensor_parallel)
                    v_bias = dup_kv_weight(v_bias, 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
                assert (k_bias.shape[0] % (mapping.tp_size * head_size)) == 0
                assert (v_bias.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)

                bq = split(q_bias, mapping.tp_size, mapping.tp_rank)
                bk = split(k_bias, mapping.tp_size, mapping.tp_rank)
                bv = split(v_bias, mapping.tp_size, mapping.tp_rank)

                qkv_w = torch.concat((wq, wk, wv))
                qkv_b = torch.concat((bq, bk, bv))
            else:
                qkv_weight = torch.cat([q_weight, k_weight, v_weight], dim=0)
                qkv_bias = torch.cat([q_bias, k_bias, v_bias], dim=0)

                qkv_w = split_qkv_tp(qkv_weight, num_attention_heads,
                                     hidden_size, tensor_parallel,
                                     mapping.tp_rank)
                qkv_b = split_qkv_bias_tp(qkv_bias, num_attention_heads,
                                          hidden_size, tensor_parallel,
                                          mapping.tp_rank)

        if use_smooth_quant:
            qkv_proj_key = key_list[
                0] if qwen_type == 'qwen' else 'self_attn.qkv_proj'
            qkv_weight = qkv_para[prefix + qkv_proj_key]
            qkv_out_dim = qkv_weight.shape[1]

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

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

            weights.update(
                get_tllm_linear_sq_weight(int8_weights,
                                          tllm_prex + 'attention.qkv.',
                                          [1, qkv_out_dim // tensor_parallel],
                                          tensor_parallel,
                                          is_qkv=True,
                                          per_token=per_token,
                                          per_channel=per_channel,
                                          last_prefix=tllm_prex +
                                          'input_layernorm.scale_to_int',
                                          bias=qkv_b,
                                          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(qkv_w, tllm_prex + 'attention.qkv.',
                                       qkv_b, use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

        if int8_kv_cache:
            if qwen_type == 'qwen':
                qkv_y = act_range.get(prefix + key_list[0])["y"]
            else:
                qkv_y = torch.cat([
                    act_range.get(prefix + key_list[0] + 'q_proj')["y"],
                    act_range.get(prefix + key_list[0] + 'k_proj')["y"],
                    act_range.get(prefix + key_list[0] + '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 + key_list[1],
                                       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 + key_list[1]))
            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 + key_list[1])],
                    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 qwen_type == "qwen2_moe" and moe_config and moe_config.has_moe():

            # shared_expert for qwen2_moe
            shared_expert_up_proj = model_params[
                f'model.layers.{l}.mlp.shared_expert.up_proj.weight']
            shared_expert_down_proj = model_params[
                f'model.layers.{l}.mlp.shared_expert.down_proj.weight']
            shared_expert_gate = model_params[
                f'model.layers.{l}.mlp.shared_expert.gate_proj.weight']
            shared_expert_up_proj = split(shared_expert_up_proj,
                                          mapping.tp_size,
                                          mapping.tp_rank,
                                          dim=0)
            shared_expert_down_proj = split(shared_expert_down_proj,
                                            mapping.tp_size,
                                            mapping.tp_rank,
                                            dim=1)
            shared_expert_gate = split(shared_expert_gate,
                                       mapping.tp_size,
                                       mapping.tp_rank,
                                       dim=0)
            shared_expert_gate_up_proj = torch.concat(
                [shared_expert_up_proj, shared_expert_gate], dim=-2).to(dtype)

            ## mlp.shared_expert.gate_up_proj.weight
            weights.update(
                get_tllm_linear_weight(shared_expert_gate_up_proj,
                                       tllm_prex + 'shared_expert.fc.', None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

            ## mlp.shared_expert.down_proj.weight
            weights.update(
                get_tllm_linear_weight(shared_expert_down_proj.to(dtype),
                                       tllm_prex + 'shared_expert.proj.', None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

            moe_shared_expert_gate_weights = get_weight(
                model_params, prefix + 'mlp.shared_expert_gate', dtype)
            weights.update(
                get_tllm_linear_weight(
                    moe_shared_expert_gate_weights,
                    tllm_prex + 'shared_expert_gate.',
                    None,
                    False,  # Router should never be quantized
                    plugin_weight_only_quant_type,
                    dtype,
                    use_gemm_woq_plugin))

            ## fine-grained experts
            rank_experts = list(range(moe_config.num_experts))
            if mapping.has_moe_ep():
                rank_experts = mapping.ep_experts(moe_config.num_experts)
            for suffix in ["gate_proj", "down_proj", "up_proj"]:
                model_params[f'model.layers.{l}.mlp.experts.{suffix}.weight'] = \
                            torch.stack([model_params[f'model.layers.{l}.mlp.experts.{expert}.{suffix}.weight'].detach()
                                        for expert in rank_experts])
            w3 = model_params[f'model.layers.{l}.mlp.experts.up_proj.weight']
            w2 = model_params[f'model.layers.{l}.mlp.experts.down_proj.weight']
            w1 = model_params[f'model.layers.{l}.mlp.experts.gate_proj.weight']
            if mapping.has_moe_tp():
                w3 = split(w3, mapping.moe_tp_size, mapping.moe_tp_rank, dim=1)
                w2 = split(w2, mapping.moe_tp_size, mapping.moe_tp_rank, dim=2)
                w1 = split(w1, mapping.moe_tp_size, mapping.moe_tp_rank, dim=1)

            moe_experts_w3w1_weights = torch.concat([w3, w1], dim=-2).to(dtype)

            ## mlp.experts.w2.weight
            weights.update(
                get_tllm_linear_weight(w2.to(dtype), tllm_prex + 'mlp.proj.',
                                       None, use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

            ## mlp.experts.w3w1.weight
            weights.update(
                get_tllm_linear_weight(moe_experts_w3w1_weights,
                                       tllm_prex + 'mlp.fc.', None,
                                       use_weight_only,
                                       plugin_weight_only_quant_type, dtype,
                                       use_gemm_woq_plugin))

            moe_experts_gate_weights = get_weight(model_params,
                                                  prefix + 'mlp.gate',
                                                  torch.float32)
            weights.update(
                get_tllm_linear_weight(
                    moe_experts_gate_weights,
                    tllm_prex + 'mlp.router.',
                    None,
                    False,  # Router should never be quantized
                    plugin_weight_only_quant_type,
                    dtype,
                    use_gemm_woq_plugin))
        else:
            mlp_gate_weight = get_weight(model_params, prefix + key_list[2],
                                         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 + key_list[2]))

                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 + key_list[3],
                                       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 + key_list[3]))
                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 + key_list[4],
                                         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 + key_list[4]))
                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 + key_list[4]],
                        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 + key_list[5], dtype)
        weights[tllm_prex + 'input_layernorm.weight'] = input_ln_weight

        post_ln_weight = get_weight(model_params, prefix + key_list[6], dtype)
        weights[tllm_prex + 'post_layernorm.weight'] = post_ln_weight

    v = get_weight(model_params, key_list[7], dtype)

    if mapping.is_last_pp_rank():
        if hf_model.config.tie_word_embeddings:
            # lm_head.weight has the same weights as embedding
            lm_head_weights = v
        else:
            lm_head_weights = get_weight(model_params, 'lm_head', dtype)

        if vocab_size % mapping.tp_size != 0:
            # padding
            vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
            pad_width = vocab_size_padded - vocab_size

            lm_head_weights = torch.from_numpy(
                np.pad(lm_head_weights.detach().cpu().numpy(),
                       ((0, pad_width), (0, 0)),
                       'constant',
                       constant_values=0))
        weights['lm_head.weight'] = split_matrix_tp(lm_head_weights,
                                                    tensor_parallel,
                                                    mapping.tp_rank,
                                                    dim=0)

    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 mapping.is_last_pp_rank():
        ln_f_w = get_weight(model_params, key_list[8], 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 smooth_quant(model,
                 qwen_type,
                 model_dir,
                 calib_dataset='cnn_dailymail',
                 smoothquant: Optional[float] = None):
    assert model is not None
    act_range = {}
    qwen_qkv_para = {}
    # smoother for inputs of self_attn.o_proj and mlp.down_proj
    qwen_smoother = {}

    os.environ["TOKENIZERS_PARALLELISM"] = os.environ.get(
        "TOKENIZERS_PARALLELISM", "false")
    tokenizer = AutoTokenizer.from_pretrained(model_dir,
                                              trust_remote_code=True,
                                              use_fast=False,
                                              padding_side='left')
    dataset = load_calib_dataset(calib_dataset)
    system_prompt = "You are a useful assistant, please directly output the corresponding summary according to the article entered by the user."
    gen_config_path = os.path.join(model_dir, 'generation_config.json')
    with open(gen_config_path, 'r') as f:
        gen_config = json.load(f)
    chat_format = getattr(gen_config, 'chat_format', 'chatml')
    act_range = capture_activation_range(model, qwen_type, tokenizer, dataset,
                                         system_prompt, chat_format)
    if smoothquant is not None:
        if qwen_type == 'qwen':
            smooth_qwen_model(model, act_range, smoothquant, qwen_qkv_para,
                              qwen_smoother)
        else:
            smooth_qwen2_model(model, act_range, smoothquant, qwen_qkv_para,
                               qwen_smoother)
    return act_range, qwen_qkv_para, qwen_smoother


def quantize(hf_model_dir: str,
             output_dir: str,
             config: QWenConfig,
             calib_dataset='cnn_dailymail'):
    '''
        Quantize the save the model as TRT-LLM checkpoint to output_dir
    '''
    #TODO: currently only smooth quant and kv cache quantization are supported, needs to support mode quant algorithm calling modelopt

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

    mapping = config.mapping
    assert mapping.rank == -1, "You shall call quantize only once in one rank, assert rank==-1 for precaution"
    quant_config = config.quantization

    use_smooth_quant = quant_config.use_plugin_sq
    int8_kv_cache = quant_config.kv_cache_quant_algo == "INT8"

    assert use_smooth_quant or int8_kv_cache, "Call from_hugging_face when there is no quantization"
    if use_smooth_quant:
        assert quant_config.smoothquant_val is not None, "A smooth value must be specified when using smooth quant"

    assert hf_model_dir is not None
    ## only load and call smooth quant routine once for all ranks
    hf_config = AutoConfig.from_pretrained(hf_model_dir, trust_remote_code=True)
    hf_model = AutoModelForCausalLM.from_pretrained(
        hf_model_dir,
        device_map='auto',
        torch_dtype='auto' if not use_smooth_quant else torch.float16,
        trust_remote_code=True).half()

    act_range, qkv_para, smoother = smooth_quant(hf_model, config.qwen_type,
                                                 hf_model_dir, calib_dataset,
                                                 quant_config.smoothquant_val)

    for rank in range(mapping.world_size):
        # To avoid changing the mapping arg in-place, also the given mapping from caller is rank agnostic, since quantize is called from only one rank
        config = copy.deepcopy(config)
        config.set_rank(rank)
        weights = load_weights_from_hf_model(hf_model,
                                             config=config,
                                             act_range=act_range,
                                             qkv_para=qkv_para,
                                             smoother=smoother)
        safetensors.torch.save_file(
            weights, os.path.join(output_dir, f'rank{rank}.safetensors'))
        del weights


def load_weights_from_hf_model(hf_model,
                               config: QWenConfig,
                               act_range: Optional[dict] = None,
                               qkv_para: Optional[dict] = None,
                               smoother: Optional[dict] = None):
    #TODO: simplify the parameters here

    assert hf_model is not None
    plugin_weight_only_quant_type = None  # the value does not matter when use_weight_only is False
    quant_algo = config.quantization.quant_algo
    if quant_algo == QuantAlgo.W8A16:
        plugin_weight_only_quant_type = torch.int8
    elif quant_algo == QuantAlgo.W4A16:
        plugin_weight_only_quant_type = torch.quint4x2
    else:
        plugin_weight_only_quant_type = None
    use_gemm_woq_plugin = (not config.disable_weight_only_quant_plugin)

    mapping = config.mapping
    moe_config = config.moe

    use_weight_only = quant_algo in [QuantAlgo.W8A16, QuantAlgo.W4A16]
    use_smooth_quant = config.quantization.use_plugin_sq
    per_channel = use_smooth_quant and 'PER_CHANNEL' in quant_algo
    per_token = use_smooth_quant and 'PER_TOKEN' in quant_algo
    int8_kv_cache = config.quantization.kv_cache_quant_algo == QuantAlgo.INT8
    qwen_type = config.qwen_type
    weights = convert_hf_qwen(
        hf_model,
        qwen_type,
        mapping,
        vocab_size=config.vocab_size,
        dtype=config.dtype,
        use_weight_only=use_weight_only,
        use_gemm_woq_plugin=use_gemm_woq_plugin,
        plugin_weight_only_quant_type=plugin_weight_only_quant_type,
        use_parallel_embedding=config.use_parallel_embedding,
        sharding_dim=config.embedding_sharding_dim,
        share_embedding_table=config.share_embedding_table,
        use_smooth_quant=use_smooth_quant,
        per_channel=per_channel,
        per_token=per_token,
        int8_kv_cache=int8_kv_cache,
        act_range=act_range,
        qkv_para=qkv_para,
        smoother=smoother,
        moe_config=moe_config)
    return weights


def load_weights_from_hf_gptq_model(hf_model, config: QWenConfig):
    logger.info("loading weights from groupwise GPTQ QWen safetensors...")
    weights = {}
    tik = time.time()

    qwen_type = config.qwen_type
    num_hidden_layers = config.num_hidden_layers
    mapping = config.mapping
    dtype = config.dtype

    model_params = {k: v for k, v in hf_model.state_dict().items()}
    torch.cuda.empty_cache()
    valid_types = ('qwen', 'qwen2')
    assert qwen_type in valid_types, f"Unsupported Qwen type: {qwen_type}, only {valid_types} are supported for GPTQ."
    layer_prefix = "transformer.h." if qwen_type == 'qwen' else "model.layers."
    key_list = get_qwen_key_list(qwen_type)

    def torch_split(v, dim):
        if v.shape[dim] % mapping.tp_size != 0:
            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_QWEN = 1  # GPTQ-for-QWEN 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,
                                           torch.float16).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_QWEN) * 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

    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)

    # Load weights from GPTQ checkpoint into TRT-LLM module
    # 1. vocab_embedding
    v = model_params[key_list[7] + '.weight']
    if mapping.is_first_pp_rank():
        weights['transformer.vocab_embedding.weight'] = v.to(torch_dtype)

    # 2. ln_f
    v = model_params[key_list[8] + '.weight']
    if mapping.is_last_pp_rank():
        weights['transformer.ln_f.weight'] = v.to(torch_dtype)

    # 3. lm_head
    v = model_params['lm_head.weight']
    if mapping.is_last_pp_rank():
        weights['lm_head.weight'] = torch_split(v, 0).to(torch_dtype)

    # 4. Weights inside each layer
    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))
    suffixs = [".qweight", ".qzeros", ".scales"]

    for l in tqdm(layers_range, desc="loading weight in each layer..."):
        layer_idx = l - mapping.pp_rank * layers_per_pipeline_stage
        prefix = layer_prefix + str(layer_idx) + "."
        tllm_prex = f'transformer.layers.{l-layers_range[0]}'
        # 4.1 attention.qkv
        qkv_weight_list = []
        if qwen_type == 'qwen':
            for suf in suffixs:
                qkv_part = model_params[prefix + key_list[0] + suf]
                q_emb = qkv_part.shape[1] // 3
                model_emb = qkv_part.shape[0]
                qkv_part = qkv_part.reshape(model_emb, 3, q_emb)
                qkv_part = torch_split(qkv_part, 2)
                qkv_part = qkv_part.reshape(model_emb,
                                            3 * (q_emb // mapping.tp_size))
                qkv_weight_list.append(qkv_part)
        else:
            for suf in suffixs:
                qkv_list = []
                for comp in ["q_proj", "k_proj", "v_proj"]:
                    comp_part = model_params[prefix + key_list[0] + comp + suf]
                    comp_part = torch_split(comp_part, 1)
                    qkv_list.append(comp_part)
                qkv_weight_list.append(torch.cat(qkv_list, dim=1))
        weights.update(
            process_and_assign_weight(qkv_weight_list,
                                      f'{tllm_prex}.attention.qkv'))
        # 4.2 attention.bias
        suf = ".bias"
        if qwen_type == 'qwen':
            qkv_bias = model_params[prefix + key_list[0] +
                                    suf].to(torch_dtype).cpu().contiguous()
            q_emb = qkv_bias.shape[0] // 3
            qkv_bias = qkv_bias.reshape(3, q_emb)
            split_v = split(qkv_bias, mapping.tp_size, mapping.rank, dim=1)
            qkv_bias = split_v.reshape(3 * (q_emb // mapping.tp_size))
        else:
            qkv_bias_list = []
            for comp in ["q_proj", "k_proj", "v_proj"]:
                comp_part = model_params[prefix + key_list[0] + comp + suf].to(
                    torch_dtype).cpu().contiguous()
                comp_part = torch_split(comp_part, dim=0)
                qkv_bias_list.append(comp_part)
            qkv_bias = torch.cat(qkv_bias_list, dim=0)
        weights[tllm_prex + ".attention.qkv.bias"] = qkv_bias
        # 4.3 attention.dense
        qkv_dense_list = []
        for suf in suffixs:
            qkv_dense_part = model_params[prefix + key_list[1] + suf]
            qkv_dense_list.append(qkv_dense_part)
        weights.update(
            process_and_assign_weight(qkv_dense_list,
                                      f'{tllm_prex}.attention.dense',
                                      tp_dim=0))
        # 4.4 mlp.gate
        mlp_gate_list = []
        for suf in suffixs:
            mlp_gate_part = model_params[prefix + key_list[2] + suf]
            mlp_gate_list.append(mlp_gate_part)
        weights.update(
            process_and_assign_weight(mlp_gate_list,
                                      f'{tllm_prex}.mlp.gate',
                                      tp_dim=1))
        # 4.5 mlp.fc
        mlp_fc_list = []
        for suf in suffixs:
            mlp_fc_part = model_params[prefix + key_list[3] + suf]
            mlp_fc_list.append(mlp_fc_part)
        weights.update(
            process_and_assign_weight(mlp_fc_list,
                                      f'{tllm_prex}.mlp.fc',
                                      tp_dim=1))
        # 4.6 mlp.proj
        mlp_proj_list = []
        for suf in suffixs:
            mlp_proj_part = model_params[prefix + key_list[4] + suf]
            mlp_proj_list.append(mlp_proj_part)
        weights.update(
            process_and_assign_weight(mlp_proj_list,
                                      f'{tllm_prex}.mlp.proj',
                                      tp_dim=0))
        # 4.7 input_layernorm
        v = model_params[prefix + key_list[5] + '.weight']
        weights[f'{tllm_prex}.input_layernorm.weight'] = v.to(torch_dtype)
        # 4.8 post_layernorm
        v = model_params[prefix + key_list[6] + '.weight']
        weights[f'{tllm_prex}.post_layernorm.weight'] = v.to(torch_dtype)

    tok = time.time()
    t = time.strftime("%H:%M:%S", time.gmtime(tok - tik))
    logger.info(f"weights loaded. total time: {t}")

    return weights