TensorRT-LLMs/examples/baichuan/weight.py

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# 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.
import configparser
import time
from pathlib import Path
from typing import Dict, List, Optional, Union

import numpy as np
import torch

import tensorrt_llm
import tensorrt_llm.logger as logger
from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models import BaichuanForCausalLM
from tensorrt_llm.models.quantized.quant import get_dummy_quant_scales
from tensorrt_llm.quantization import QuantMode


def get_scaling_factors(
    model_path: Union[str, Path],
    num_layers: int,
    quant_mode: Optional[QuantMode] = None,
) -> Optional[Dict[str, List[int]]]:
    """ Get the scaling factors for Baichuan model

    Returns a dictionary of scaling factors for the selected layers of the
    Baichuan model.

    Args:
        model_path (str): Path to the quantized Baichuan model
        layers (list): List of layers to get the scaling factors for. If None,
            all layers are selected.

    Returns:
        dict: Dictionary of scaling factors for the selected layers of the
        Baichuan model.

        example:

        {
            'qkv_act': qkv_act_scale,
            'qkv_weights': qkv_weights_scale,
            'qkv_output' : qkv_outputs_scale,
            'dense_act': dense_act_scale,
            'dense_weights': dense_weights_scale,
            'fc_act': fc_act_scale,
            'fc_weights': fc_weights_scale,
            'gate_act': gate_act_scale,
            'gate_weights': gate_weights_scale,
            'proj_act': proj_act_scale,
            'proj_weights': proj_weights_scale,
        }
    """

    if model_path is None:
        logger.warning(f"--quantized_fp8_model_path not specified. "
                       f"Initialize quantization scales automatically.")
        return get_dummy_quant_scales(num_layers)
    weight_dict = np.load(model_path)

    # yapf: disable
    scaling_factor = {
        'qkv_act': [],
        'qkv_weights': [],
        'qkv_output': [],
        'dense_act': [],
        'dense_weights': [],
        'fc_act': [],
        'fc_weights': [],
        'gate_act': [],
        'gate_weights': [],
        'proj_act': [],
        'proj_weights': [],
    }

    for layer in range(num_layers):
        scaling_factor['qkv_act'].append(max(
            weight_dict[f'_np:layers:{layer}:attention:qkv:q:activation_scaling_factor'].item(),
            weight_dict[f'_np:layers:{layer}:attention:qkv:k:activation_scaling_factor'].item(),
            weight_dict[f'_np:layers:{layer}:attention:qkv:v:activation_scaling_factor'].item()
            ))
        scaling_factor['qkv_weights'].append(max(
            weight_dict[f'_np:layers:{layer}:attention:qkv:q:weights_scaling_factor'].item(),
            weight_dict[f'_np:layers:{layer}:attention:qkv:k:weights_scaling_factor'].item(),
            weight_dict[f'_np:layers:{layer}:attention:qkv:v:weights_scaling_factor'].item()
            ))
        if quant_mode is not None and quant_mode.has_fp8_kv_cache():
            # Not calibrarting KV cache.
            scaling_factor['qkv_output'].append(1.0)
        scaling_factor['dense_act'].append(weight_dict[f'_np:layers:{layer}:attention:dense:activation_scaling_factor'].item())
        scaling_factor['dense_weights'].append(weight_dict[f'_np:layers:{layer}:attention:dense:weights_scaling_factor'].item())
        scaling_factor['fc_act'].append(weight_dict[f'_np:layers:{layer}:mlp:fc:activation_scaling_factor'].item())
        scaling_factor['fc_weights'].append(weight_dict[f'_np:layers:{layer}:mlp:fc:weights_scaling_factor'].item())
        scaling_factor['gate_act'].append(weight_dict[f'_np:layers:{layer}:mlp:gate:activation_scaling_factor'].item())
        scaling_factor['gate_weights'].append(weight_dict[f'_np:layers:{layer}:mlp:gate:weights_scaling_factor'].item())
        scaling_factor['proj_act'].append(weight_dict[f'_np:layers:{layer}:mlp:proj:activation_scaling_factor'].item())
        scaling_factor['proj_weights'].append(weight_dict[f'_np:layers:{layer}:mlp:proj:weights_scaling_factor'].item())
    # yapf: enable
    for k, v in scaling_factor.items():
        assert len(v) == num_layers, \
        f'Expect scaling factor {k} of length {num_layers}, got {len(v)}'

    return scaling_factor


def extract_layer_idx(name):
    ss = name.split('.')
    for s in ss:
        if s.isdigit():
            return s
    return None


def split(v, tp_size, idx, dim=0):
    if tp_size == 1:
        return v
    if len(v.shape) == 1:
        return np.ascontiguousarray(np.split(v, tp_size)[idx].copy())
    else:
        return np.ascontiguousarray(np.split(v, tp_size, axis=dim)[idx].copy())


def load_from_hf_baichuan(tensorrt_llm_baichuan,
                          hf_baichuan,
                          model_version,
                          rank=0,
                          tensor_parallel=1,
                          dtype="float32"):
    assert model_version is not None
    tensorrt_llm.logger.info(
        f'Loading weights from HF Baichuan {model_version}...')
    tik = time.time()

    quant_mode = getattr(tensorrt_llm_baichuan, 'quant_mode', QuantMode(0))
    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()

    model_params = dict(hf_baichuan.named_parameters())
    for k, v in model_params.items():
        torch_dtype = str_dtype_to_torch(dtype)
        v = torch_to_numpy(v.to(torch_dtype).detach().cpu())
        if 'model.embed_tokens.weight' in k:
            tensorrt_llm_baichuan.vocab_embedding.weight.value = v
        elif 'model.norm.weight' in k:
            tensorrt_llm_baichuan.ln_f.weight.value = v
        elif 'lm_head.weight' in k:
            if model_version.startswith('v2'):
                # baichuan v2 models use NormHead
                tensorrt_llm.logger.info(
                    f'Normalizing lm_head.weight for {model_version}')
                original_v = model_params[k]
                v = torch_to_numpy(
                    torch.nn.functional.normalize(original_v).to(
                        torch_dtype).detach().cpu())
            tensorrt_llm_baichuan.lm_head.weight.value = np.ascontiguousarray(
                split(v, tensor_parallel, rank))
        else:
            layer_idx = extract_layer_idx(k)
            if layer_idx is None:
                continue
            idx = int(layer_idx)
            if idx >= tensorrt_llm_baichuan.num_layers:
                continue
            if 'input_layernorm.weight' in k:
                tensorrt_llm_baichuan.layers[
                    idx].input_layernorm.weight.value = v
            elif 'post_attention_layernorm.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].post_layernorm.weight
                dst.value = v
            elif 'self_attn.W_pack.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].attention.qkv.weight
                q_emb = v.shape[0] // 3
                model_emb = v.shape[1]
                v = v.reshape(3, q_emb, model_emb)
                split_v = split(v, tensor_parallel, rank, dim=1)
                split_v = split_v.reshape(3 * (q_emb // tensor_parallel),
                                          model_emb)
                if use_weight_only:
                    v = np.ascontiguousarray(split_v.transpose())
                    processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                        torch.tensor(v), plugin_weight_only_quant_type)
                    dst.value = processed_torch_weights.numpy()
                    scales = tensorrt_llm_baichuan.layers[
                        idx].attention.qkv.per_channel_scale
                    scales.value = torch_weight_scales.numpy()
                else:
                    dst.value = np.ascontiguousarray(split_v)
            elif 'self_attn.o_proj.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].attention.dense.weight
                split_v = split(v, tensor_parallel, rank, dim=1)
                if use_weight_only:
                    v = np.ascontiguousarray(split_v.transpose())
                    processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                        torch.tensor(v), plugin_weight_only_quant_type)
                    dst.value = processed_torch_weights.numpy()
                    scales = tensorrt_llm_baichuan.layers[
                        idx].attention.dense.per_channel_scale
                    scales.value = torch_weight_scales.numpy()
                else:
                    dst.value = np.ascontiguousarray(split_v)
            elif 'mlp.up_proj.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].mlp.gate.weight
                split_v = split(v, tensor_parallel, rank, dim=0)
                if use_weight_only:
                    v = np.ascontiguousarray(split_v.transpose())
                    processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                        torch.tensor(v), plugin_weight_only_quant_type)
                    dst.value = processed_torch_weights.numpy()
                    scales = tensorrt_llm_baichuan.layers[
                        idx].mlp.gate.per_channel_scale
                    scales.value = torch_weight_scales.numpy()
                else:
                    dst.value = np.ascontiguousarray(split_v)
            elif 'mlp.down_proj.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].mlp.proj.weight
                split_v = split(v, tensor_parallel, rank, dim=1)
                if use_weight_only:
                    v = np.ascontiguousarray(split_v.transpose())
                    processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                        torch.tensor(v), plugin_weight_only_quant_type)
                    dst.value = processed_torch_weights.numpy()
                    scales = tensorrt_llm_baichuan.layers[
                        idx].mlp.proj.per_channel_scale
                    scales.value = torch_weight_scales.numpy()
                else:
                    dst.value = np.ascontiguousarray(split_v)
            elif 'mlp.gate_proj.weight' in k:
                dst = tensorrt_llm_baichuan.layers[idx].mlp.fc.weight
                split_v = split(v, tensor_parallel, rank, dim=0)
                if use_weight_only:
                    v = np.ascontiguousarray(split_v.transpose())
                    processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                        torch.tensor(v), plugin_weight_only_quant_type)
                    dst.value = processed_torch_weights.numpy()
                    scales = tensorrt_llm_baichuan.layers[
                        idx].mlp.fc.per_channel_scale
                    scales.value = torch_weight_scales.numpy()
                else:
                    dst.value = np.ascontiguousarray(split_v)

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


def parse_bin_config(ini_file):
    baichuan_config = configparser.ConfigParser()
    baichuan_config.read(ini_file)

    n_embd = baichuan_config.getint('baichuan', 'hidden_size')
    n_head = baichuan_config.getint('baichuan', 'num_attention_heads')
    n_kv_head = n_head
    n_layer = baichuan_config.getint('baichuan', 'num_hidden_layers')
    if baichuan_config.has_option('baichuan', 'max_position_embeddings'):
        n_positions = baichuan_config.getint('baichuan',
                                             'max_position_embeddings')
    else:
        n_positions = baichuan_config.getint('baichuan', 'model_max_length')
    vocab_size = baichuan_config.getint('baichuan', 'vocab_size')
    hidden_act = baichuan_config.get('baichuan', 'hidden_act')
    inter_size = baichuan_config.getint('baichuan',
                                        'intermediate_size',
                                        fallback=None)

    if inter_size is None:
        inter_size = 4 * n_embd

    return n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, n_kv_head


def gen_suffix(rank, use_smooth_quant, quant_per_channel):
    suffix = f"{rank}.bin"
    if use_smooth_quant:
        sq_prefix = "int8."
        if quant_per_channel:
            sq_prefix += "col."
        suffix = sq_prefix + suffix
    return suffix


def load_from_binary(tensorrt_llm_baichuan: BaichuanForCausalLM,
                     dir_path,
                     model_version,
                     mapping=Mapping(),
                     fp16=False,
                     multi_query_mode=False):
    tensorrt_llm.logger.info('Loading weights from binary...')
    tik = time.time()

    quant_mode = getattr(tensorrt_llm_baichuan, 'quant_mode', QuantMode(0))

    n_embd, n_head, n_layer, n_positions, vocab_size, hidden_act, inter_size, n_kv_head = parse_bin_config(
        Path(dir_path) / 'config.ini')
    np_dtype = np.float16 if fp16 else np.float32

    def fromfile(dir_path, name, shape=None, dtype=None):
        dtype = np_dtype if dtype is None else dtype
        p = dir_path + '/' + name
        if Path(p).exists():
            t = np.fromfile(p, dtype=dtype)
            if shape is not None:
                t = t.reshape(shape)
            return t
        return None

    def set_smoothquant_scale_factors(module,
                                      pre_scale_weight,
                                      dir_path,
                                      basename,
                                      shape,
                                      per_tok_dyn,
                                      per_channel,
                                      is_qkv=False,
                                      rank=None):
        suffix = "bin"
        if per_channel:
            if rank is not None:
                suffix = f"{rank}." + suffix
            suffix = "col." + suffix

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

        if per_tok_dyn:
            if pre_scale_weight is not None:
                pre_scale_weight.value = np.array([1.0], dtype=np.float32)
            if is_qkv and not per_channel:
                t = fromfile(dir_path,
                             f"{basename}scale_w_quant_orig.{rank}.{suffix}",
                             col_shape, np.float32)
            else:
                t = fromfile(dir_path, f"{basename}scale_w_quant_orig.{suffix}",
                             col_shape, np.float32)
            module.per_channel_scale.value = t
        else:
            t = fromfile(dir_path, f"{basename}scale_x_orig_quant.bin", [1],
                         np.float32)
            pre_scale_weight.value = t
            if is_qkv:
                t = fromfile(dir_path,
                             f"{basename}scale_y_accum_quant.{rank}.{suffix}",
                             col_shape, np.float32)
            else:
                t = fromfile(dir_path,
                             f"{basename}scale_y_accum_quant.{suffix}",
                             col_shape, np.float32)
            module.per_channel_scale.value = t
            t = fromfile(dir_path, f"{basename}scale_y_quant_orig.bin", [1, 1],
                         np.float32)
            module.act_scale.value = t

    def set_smoother(module, dir_path, base_name, shape, rank):
        suffix = f"{rank}.bin"
        t = fromfile(dir_path, f"{base_name}.smoother.{suffix}", shape,
                     np.float32)
        module.smoother.value = t

    # Determine the quantization mode.
    quant_mode = getattr(tensorrt_llm_baichuan, "quant_mode", QuantMode(0))
    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
    # Do we use SmoothQuant?
    use_smooth_quant = quant_mode.has_act_and_weight_quant()
    # Do we use quantization per token?
    quant_per_token_dyn = quant_mode.has_per_token_dynamic_scaling()
    # Do we use quantization per channel?
    quant_per_channel = quant_mode.has_per_channel_scaling()

    # Do we use INT4/INT8 weight-only?
    use_weight_only = quant_mode.is_weight_only()

    # Int8 KV cache
    use_int8_kv_cache = quant_mode.has_int8_kv_cache()

    # Debug
    suffix = gen_suffix(mapping.tp_rank, use_smooth_quant, quant_per_channel)
    # The type of weights.
    w_type = np_dtype if not use_smooth_quant else np.int8

    if mapping.is_first_pp_rank():
        tensorrt_llm_baichuan.vocab_embedding.weight.value = (fromfile(
            dir_path, 'vocab_embedding.weight.bin', [vocab_size, n_embd]))

    if mapping.is_last_pp_rank():
        tensorrt_llm_baichuan.ln_f.weight.value = (fromfile(
            dir_path, 'ln_f.weight.bin'))
    # share input embedding
    lm_head_weight = fromfile(dir_path, 'lm_head.weight.bin',
                              [vocab_size, n_embd])
    if model_version.startswith('v2'):
        # baichuan v2 models use NormHead
        tensorrt_llm.logger.info(
            f'Normalizing lm_head.weight for {model_version}')
        lm_head_weight = lm_head_weight / np.linalg.norm(
            lm_head_weight, axis=1, keepdims=True)

    if vocab_size % mapping.tp_size != 0:
        # padding
        vocab_size_padded = tensorrt_llm_baichuan.lm_head.out_features * mapping.tp_size
        pad_width = vocab_size_padded - vocab_size
        lm_head_weight = np.pad(lm_head_weight, ((0, pad_width), (0, 0)),
                                'constant',
                                constant_values=0)
    if mapping.is_last_pp_rank():
        tensorrt_llm_baichuan.lm_head.weight.value = np.ascontiguousarray(
            split(lm_head_weight, mapping.tp_size, mapping.tp_rank))

    layers_range = list(
        range(mapping.pp_rank * tensorrt_llm_baichuan.num_layers,
              (mapping.pp_rank + 1) * tensorrt_llm_baichuan.num_layers, 1))

    for i in layers_range:
        n_groups = n_head // n_kv_head
        c_attn_out_dim = (
            3 * n_embd // mapping.tp_size) if not multi_query_mode else (
                n_embd // mapping.tp_size +
                (n_embd // n_head * n_groups) // mapping.tp_size * 2)
        idx = i - mapping.pp_rank * tensorrt_llm_baichuan.num_layers
        tensorrt_llm_baichuan.layers[idx].input_layernorm.weight.value = (
            fromfile(dir_path,
                     'model.layers.' + str(i) + '.input_layernorm.weight.bin'))
        t = fromfile(
            dir_path, 'model.layers.' + str(i) +
            '.attention.query_key_value.weight.' + suffix,
            [n_embd, c_attn_out_dim], w_type)
        if t is not None:
            dst = tensorrt_llm_baichuan.layers[idx].attention.qkv.weight
            if use_smooth_quant:
                dst.value = np.ascontiguousarray(np.transpose(t, [1, 0]))
                set_smoothquant_scale_factors(
                    tensorrt_llm_baichuan.layers[idx].attention.qkv,
                    tensorrt_llm_baichuan.layers[idx].input_layernorm.
                    scale_to_int,
                    dir_path,
                    'model.layers.' + str(i) + '.attention.query_key_value.',
                    [1, c_attn_out_dim],
                    quant_per_token_dyn,
                    quant_per_channel,
                    rank=mapping.tp_rank,
                    is_qkv=True)
            elif use_weight_only:
                processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                    torch.tensor(t), plugin_weight_only_quant_type)
                dst.value = processed_torch_weights.numpy()
                scales = tensorrt_llm_baichuan.layers[
                    i].attention.qkv.per_channel_scale
                scales.value = torch_weight_scales.numpy()
            else:
                dst.value = np.ascontiguousarray(np.transpose(t, [1, 0]))

        dst = tensorrt_llm_baichuan.layers[idx].attention.dense.weight
        t = fromfile(
            dir_path,
            'model.layers.' + str(i) + '.attention.dense.weight.' + suffix,
            [n_embd // mapping.tp_size, n_embd], w_type)
        if use_smooth_quant:
            dst.value = np.ascontiguousarray(np.transpose(t, [1, 0]))
            dense_scale = getattr(tensorrt_llm_baichuan.layers[idx].attention,
                                  "quantization_scaling_factor", None)
            set_smoothquant_scale_factors(
                tensorrt_llm_baichuan.layers[idx].attention.dense, dense_scale,
                dir_path, 'model.layers.' + str(i) + '.attention.dense.',
                [1, n_embd], quant_per_token_dyn, quant_per_channel)
            set_smoother(tensorrt_llm_baichuan.layers[idx].attention.dense,
                         dir_path,
                         'model.layers.' + str(i) + '.attention.dense',
                         [1, n_embd // mapping.tp_size], mapping.tp_rank)
        elif use_weight_only:
            processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                torch.tensor(t), plugin_weight_only_quant_type)
            dst.value = processed_torch_weights.numpy()
            scales = tensorrt_llm_baichuan.layers[
                i].attention.dense.per_channel_scale
            scales.value = torch_weight_scales.numpy()
        else:
            dst.value = np.ascontiguousarray(np.transpose(t, [1, 0]))

        dst = tensorrt_llm_baichuan.layers[idx].post_layernorm.weight
        dst.value = fromfile(
            dir_path, 'model.layers.' + str(i) + '.post_layernorm.weight.bin')

        t = fromfile(dir_path,
                     'model.layers.' + str(i) + '.mlp.fc.weight.' + suffix,
                     [n_embd, inter_size // mapping.tp_size], w_type)

        if use_smooth_quant:
            tensorrt_llm_baichuan.layers[
                idx].mlp.fc.weight.value = np.ascontiguousarray(
                    np.transpose(t, [1, 0]))
            set_smoothquant_scale_factors(
                tensorrt_llm_baichuan.layers[idx].mlp.fc,
                tensorrt_llm_baichuan.layers[idx].post_layernorm.scale_to_int,
                dir_path,
                'model.layers.' + str(i) + '.mlp.fc.',
                [1, inter_size // mapping.tp_size],
                quant_per_token_dyn,
                quant_per_channel,
                rank=mapping.tp_rank)
        elif use_weight_only:
            dst = tensorrt_llm_baichuan.layers[i].mlp.fc.weight
            processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                torch.tensor(t), plugin_weight_only_quant_type)
            dst.value = processed_torch_weights.numpy()
            scales = tensorrt_llm_baichuan.layers[i].mlp.fc.per_channel_scale
            scales.value = torch_weight_scales.numpy()
        else:
            tensorrt_llm_baichuan.layers[
                idx].mlp.fc.weight.value = np.ascontiguousarray(
                    np.transpose(t, [1, 0]))

        t = fromfile(dir_path,
                     'model.layers.' + str(i) + '.mlp.gate.weight.' + suffix,
                     [n_embd, inter_size // mapping.tp_size], w_type)
        if use_smooth_quant:
            tensorrt_llm_baichuan.layers[
                idx].mlp.gate.weight.value = np.ascontiguousarray(
                    np.transpose(t, [1, 0]))
            set_smoothquant_scale_factors(
                tensorrt_llm_baichuan.layers[idx].mlp.gate,
                tensorrt_llm_baichuan.layers[idx].post_layernorm.scale_to_int,
                dir_path,
                'model.layers.' + str(i) + '.mlp.gate.',
                [1, inter_size // mapping.tp_size],
                quant_per_token_dyn,
                quant_per_channel,
                rank=mapping.tp_rank)
        elif use_weight_only:
            dst = tensorrt_llm_baichuan.layers[i].mlp.gate.weight
            processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                torch.tensor(t), plugin_weight_only_quant_type)
            dst.value = processed_torch_weights.numpy()
            scales = tensorrt_llm_baichuan.layers[i].mlp.gate.per_channel_scale
            scales.value = torch_weight_scales.numpy()
        else:
            tensorrt_llm_baichuan.layers[
                idx].mlp.gate.weight.value = np.ascontiguousarray(
                    np.transpose(t, [1, 0]))

        t = fromfile(dir_path,
                     'model.layers.' + str(i) + '.mlp.proj.weight.' + suffix,
                     [inter_size // mapping.tp_size, n_embd], w_type)
        if use_smooth_quant:
            tensorrt_llm_baichuan.layers[
                idx].mlp.proj.weight.value = np.ascontiguousarray(
                    np.transpose(t, [1, 0]))
            proj_scale = getattr(tensorrt_llm_baichuan.layers[idx].mlp,
                                 "quantization_scaling_factor", None)
            set_smoothquant_scale_factors(
                tensorrt_llm_baichuan.layers[idx].mlp.proj, proj_scale,
                dir_path, 'model.layers.' + str(i) + '.mlp.proj.', [1, n_embd],
                quant_per_token_dyn, quant_per_channel)
            set_smoother(tensorrt_llm_baichuan.layers[idx].mlp.proj, dir_path,
                         'model.layers.' + str(i) + '.mlp.proj',
                         [1, inter_size // mapping.tp_size], mapping.tp_rank)
        elif use_weight_only:
            dst = tensorrt_llm_baichuan.layers[i].mlp.proj.weight
            processed_torch_weights, torch_weight_scales = torch.ops.fastertransformer.symmetric_quantize_last_axis_of_batched_matrix(
                torch.tensor(t), plugin_weight_only_quant_type)
            dst.value = processed_torch_weights.numpy()
            scales = tensorrt_llm_baichuan.layers[i].mlp.proj.per_channel_scale
            scales.value = torch_weight_scales.numpy()
        else:
            tensorrt_llm_baichuan.layers[idx].mlp.proj.weight.value = (
                np.ascontiguousarray(np.transpose(t, [1, 0])))

        if use_int8_kv_cache:
            t = fromfile(
                dir_path, 'model.layers.' + str(i) +
                '.attention.query_key_value.scale_y_quant_orig.bin', [1],
                np.float32)
            tensorrt_llm_baichuan.layers[
                idx].attention.kv_orig_quant_scale.value = 1.0 / t
            tensorrt_llm_baichuan.layers[
                idx].attention.kv_quant_orig_scale.value = t

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