TensorRT-LLMs/tensorrt_llm/models/llama/model.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 tempfile
from pathlib import Path
from typing import List, Optional

import tensorrt as trt

from ... import profiler
from ..._common import default_net
from ..._utils import pad_vocab_size, str_dtype_to_trt
from ...functional import (RotaryScalingType, Tensor, gather_last_token_logits,
                           recv, send)
from ...layers import (MOE, Attention, AttentionMaskType, AttentionParams,
                       ColumnLinear, Embedding, FusedGatedMLP, GatedMLP,
                       KeyValueCacheParams, LoraParams, MoeConfig,
                       PositionEmbeddingType, PromptTuningEmbedding, RmsNorm)
from ...mapping import Mapping
from ...models.quantized.quant import quantize_model
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ...runtime.lora_manager import LoraConfig
from ...top_model_mixin import TopModelMixin
from ..generation_mixin import GenerationMixin
from ..modeling_utils import PretrainedConfig
from .weight import get_scaling_factors, load_from_awq_llama, load_from_hf_llama


class LLaMADecoderLayer(Module):

    def __init__(
            self,
            layer_id,
            hidden_size,
            num_attention_heads,
            num_kv_heads=None,
            max_position_embeddings=2048,
            dtype=None,
            attention_mask_type=AttentionMaskType.causal,
            hidden_act='silu',
            position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
            rotary_base=10000.0,
            rotary_scaling=None,
            mlp_hidden_size=None,
            tp_group=None,
            tp_size=1,
            tp_rank=0,
            use_auto_parallel=False,
            quant_mode=QuantMode(0),
            rms_norm_eps=1e-06,
            attn_bias=False,
            mlp_bias=False,
            use_fused_mlp=False,
            enable_pos_shift=False,
            dense_context_fmha=False,
            moe_config: MoeConfig = MoeConfig(),
            max_lora_rank=None,
    ):
        super().__init__()
        self._layer_id = layer_id  # useful for debugging
        # used for quantizing model
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_kv_heads = num_kv_heads
        self.max_position_embeddings = max_position_embeddings
        self.dtype = dtype
        self.hidden_act = hidden_act
        self.tp_group = tp_group
        self.tp_size = tp_size
        self.mlp_hidden_size = mlp_hidden_size
        self.attention_mask_type = attention_mask_type
        self.position_embedding_type = position_embedding_type
        self.input_layernorm = RmsNorm(normalized_shape=hidden_size,
                                       eps=rms_norm_eps,
                                       dtype=dtype)
        self.mlp_bias = mlp_bias
        self.attention = Attention(
            hidden_size,
            num_attention_heads,
            num_kv_heads,
            max_position_embeddings,
            dtype=dtype,
            attention_mask_type=AttentionMaskType.causal,
            bias=attn_bias,
            position_embedding_type=position_embedding_type,
            rotary_embedding_base=rotary_base,
            rotary_embedding_scaling=rotary_scaling,
            tp_group=tp_group,
            tp_size=tp_size,
            use_auto_parallel=use_auto_parallel,
            quant_mode=quant_mode,
            enable_pos_shift=enable_pos_shift,
            dense_context_fmha=dense_context_fmha,
            max_lora_rank=max_lora_rank,
        )
        if not mlp_hidden_size:
            self.mlp_hidden_size = hidden_size * 4

        ClsMLP = GatedMLP
        mlp_kwargs = {}
        if moe_config.has_moe():
            ClsMLP = MOE
            mlp_kwargs = {
                "moe_config": moe_config,
                "tp_rank": tp_rank,
            }
        elif use_fused_mlp:
            ClsMLP = FusedGatedMLP
        self.mlp = ClsMLP(hidden_size=hidden_size,
                          ffn_hidden_size=self.mlp_hidden_size,
                          hidden_act=hidden_act,
                          dtype=dtype,
                          bias=mlp_bias,
                          tp_group=tp_group,
                          tp_size=tp_size,
                          quant_mode=quant_mode,
                          max_lora_rank=max_lora_rank,
                          **mlp_kwargs)
        self.post_layernorm = RmsNorm(normalized_shape=hidden_size,
                                      eps=rms_norm_eps,
                                      dtype=dtype)

    def forward(
            self,
            hidden_states,
            attention_mask=None,
            medusa_packed_mask=None,  # For Medusa support
            medusa_position_offsets=None,
            use_cache=False,
            kv_cache_params=None,
            attention_params=None,
            lora_layer_params=None):
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        if self._layer_id == 0:
            self.register_network_output(f"norm0", hidden_states)

        attention_output = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            medusa_packed_mask=medusa_packed_mask,  # For Medusa support
            medusa_position_offsets=medusa_position_offsets,
            use_cache=use_cache,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
            lora_layer_params=lora_layer_params)

        if use_cache:
            attention_output, presents = attention_output
        if self._layer_id == 0:
            self.register_network_output(f"attn", attention_output)

        hidden_states = residual + attention_output

        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)
        if self._layer_id == 0:
            self.register_network_output(f"norm1", hidden_states)

        hidden_states = self.mlp(hidden_states,
                                 lora_layer_params=lora_layer_params)
        if self._layer_id == 0:
            self.register_network_output(f"mlp", hidden_states)

        hidden_states = residual + hidden_states
        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class LLaMAModel(Module):

    def __init__(self,
                 num_layers,
                 num_heads,
                 num_kv_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype,
                 mlp_hidden_size=None,
                 position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 mapping=Mapping(),
                 use_auto_parallel=False,
                 quant_mode=QuantMode(0),
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0,
                 rms_norm_eps=1e-06,
                 use_fused_mlp=False,
                 attn_bias=False,
                 mlp_bias=False,
                 moe_config: MoeConfig = MoeConfig(),
                 use_prompt_tuning: bool = False,
                 enable_pos_shift=False,
                 dense_context_fmha=False,
                 max_lora_rank=None):
        super().__init__()
        self.mapping = mapping
        self.use_prompt_tuning = use_prompt_tuning

        EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
        if self.mapping.is_first_pp_rank():
            self.vocab_embedding = EmbeddingCls(
                num_embeddings=vocab_size,
                embedding_dim=hidden_size,
                dtype=dtype,
                tp_size=mapping.tp_size if use_parallel_embedding else 1,
                tp_group=mapping.tp_group if use_parallel_embedding else None,
                sharding_dim=embedding_sharding_dim,
                tp_rank=mapping.tp_rank,
            )

        self.layers = ModuleList([
            LLaMADecoderLayer(
                layer_id=i,
                hidden_size=hidden_size,
                num_attention_heads=num_heads,
                num_kv_heads=num_kv_heads,
                max_position_embeddings=max_position_embeddings,
                dtype=dtype,
                hidden_act=hidden_act,
                mlp_hidden_size=mlp_hidden_size,
                position_embedding_type=position_embedding_type,
                rotary_base=rotary_base,
                rotary_scaling=rotary_scaling,
                tp_group=mapping.tp_group,
                tp_size=mapping.tp_size,
                tp_rank=mapping.tp_rank,
                use_auto_parallel=use_auto_parallel,
                quant_mode=quant_mode,
                rms_norm_eps=rms_norm_eps,
                attn_bias=attn_bias,
                mlp_bias=mlp_bias,
                use_fused_mlp=use_fused_mlp,
                enable_pos_shift=enable_pos_shift,
                dense_context_fmha=dense_context_fmha,
                moe_config=moe_config,
                max_lora_rank=max_lora_rank,
            ) for i in self.mapping.pp_layers(num_layers)
        ])

        if self.mapping.is_last_pp_rank():
            self.ln_f = RmsNorm(normalized_shape=hidden_size,
                                eps=rms_norm_eps,
                                dtype=dtype)

    def forward(
            self,
            input_ids,
            position_ids=None,
            use_cache=False,
            attention_mask=None,
            medusa_position_offsets=None,  # For Medusa support
            medusa_packed_mask=None,  # For Medusa support
            kv_cache_params=None,
            attention_params=None,
            hidden_states=None,
            prompt_embedding_table: Optional[Tensor] = None,
            prompt_tasks: Optional[Tensor] = None,
            prompt_vocab_size: Optional[Tensor] = None,
            lora_params=None):

        kv_cache_params.fill_none_tensor_list(len(self.layers))

        if use_cache:
            presents = []

        ptuning_args = []
        if self.use_prompt_tuning:
            ptuning_args = [
                prompt_embedding_table, prompt_tasks, prompt_vocab_size
            ]
        if self.mapping.is_first_pp_rank():
            hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
        else:
            hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
        self.register_network_output(f"embd", hidden_states)

        for layer_idx, (
                layer, past, pointer, host_pointer,
                max_attention_window_size) in enumerate(
                    zip(self.layers, kv_cache_params.past_key_value,
                        kv_cache_params.kv_cache_block_pointers,
                        kv_cache_params.host_kv_cache_block_pointers,
                        kv_cache_params.host_max_attention_window_sizes)):
            lora_layer_params = None
            if lora_params is not None and lora_params.lora_ranks is not None:
                lora_layer_params = lora_params.get_layer_params(layer_idx)

            hidden_states = layer(
                hidden_states,
                use_cache=use_cache,
                attention_mask=attention_mask,
                medusa_packed_mask=medusa_packed_mask,  # For Medusa support
                medusa_position_offsets=medusa_position_offsets,
                kv_cache_params=KeyValueCacheParams(
                    past_key_value=[past],
                    host_past_key_value_lengths=kv_cache_params.
                    host_past_key_value_lengths,
                    host_max_attention_window_sizes=max_attention_window_size,
                    host_sink_token_length=kv_cache_params.
                    host_sink_token_length,
                    kv_cache_block_pointers=[pointer],
                    host_kv_cache_block_pointers=[host_pointer],
                    cache_indirection=kv_cache_params.cache_indirection),
                attention_params=attention_params,
                lora_layer_params=lora_layer_params)

            if use_cache:
                presents.append(hidden_states[1])
                hidden_states = hidden_states[0]

        if self.mapping.is_last_pp_rank():
            hidden_states = self.ln_f(hidden_states)
        else:
            hidden_states = send(hidden_states, self.mapping.next_pp_rank())

        if use_cache:
            return (hidden_states, tuple(presents))
        return hidden_states


class LLaMAForCausalLM(LLaMAModel, GenerationMixin, TopModelMixin):

    def __init__(self,
                 num_layers,
                 num_heads,
                 num_kv_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype,
                 logits_dtype="float32",
                 mlp_hidden_size=None,
                 position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 mapping=Mapping(),
                 use_auto_parallel=False,
                 quant_mode=QuantMode(0),
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0,
                 rms_norm_eps=1e-06,
                 use_fused_mlp=False,
                 attn_bias=False,
                 mlp_bias=False,
                 moe_config=MoeConfig(),
                 use_prompt_tuning: bool = False,
                 enable_pos_shift=False,
                 dense_context_fmha=False,
                 max_lora_rank=None):
        config = PretrainedConfig(
            architecture="LLaMAForCausalLM",
            dtype=dtype,
            logits_dtype=logits_dtype,
            vocab_size=vocab_size,
            max_position_embeddings=max_position_embeddings,
            hidden_size=hidden_size,
            num_hidden_layers=num_layers,
            num_attention_heads=num_heads,
            num_key_value_heads=num_kv_heads,
            hidden_act=hidden_act,
            intermediate_size=mlp_hidden_size,
            norm_epsilon=rms_norm_eps,
            position_embedding_type=str(position_embedding_type),
            world_size=mapping.world_size,
            tp_size=mapping.tp_size,
            pp_size=mapping.pp_size,
            quant_mode=quant_mode,
            quant_kwargs={},
            use_prompt_tuning=use_prompt_tuning,
            use_parallel_embedding=use_parallel_embedding,
            embedding_sharding_dim=embedding_sharding_dim)
        self.config = config
        # TODO: there is an issue of PretrainedConfig that it does not hold the info of "current rank"
        # it internally constructs a mapping object from the world_size/tp_size/pp_size
        # thus override the config.mapping here to the user provided one, which shall include current rank
        self.config.mapping = mapping

        if isinstance(dtype, str):
            self.dtype = str_dtype_to_trt(dtype)
        else:
            assert isinstance(dtype, trt.DataType)
            self.dtype = dtype

        if isinstance(logits_dtype, str):
            self.logits_dtype = str_dtype_to_trt(logits_dtype)
        else:
            assert isinstance(logits_dtype, trt.DataType)
            self.logits_dtype = logits_dtype

        self.num_layers = num_layers
        self.num_heads = num_heads
        if num_kv_heads is None or num_kv_heads <= 0:
            num_kv_heads = num_heads
        self.num_kv_heads = num_kv_heads
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.tp_size = mapping.tp_size

        self.kv_dtype = self.dtype
        if quant_mode.has_int8_kv_cache():
            self.kv_dtype = str_dtype_to_trt('int8')
        elif quant_mode.has_fp8_kv_cache():
            self.kv_dtype = str_dtype_to_trt('fp8')

        self.quant_mode = quant_mode
        self.use_parallel_embedding = use_parallel_embedding
        self.embedding_sharding_dim = embedding_sharding_dim
        self.moe_config = moe_config
        self.use_fused_mlp = use_fused_mlp

        super().__init__(num_layers, num_heads, num_kv_heads, hidden_size,
                         vocab_size, hidden_act, max_position_embeddings, dtype,
                         mlp_hidden_size, position_embedding_type, rotary_base,
                         rotary_scaling, mapping, use_auto_parallel, quant_mode,
                         use_parallel_embedding, embedding_sharding_dim,
                         rms_norm_eps, use_fused_mlp, attn_bias, mlp_bias,
                         moe_config, use_prompt_tuning, enable_pos_shift,
                         dense_context_fmha, max_lora_rank)

        vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
        if self.mapping.is_last_pp_rank():
            self.lm_head = ColumnLinear(hidden_size,
                                        vocab_size_padded,
                                        bias=False,
                                        dtype=dtype,
                                        tp_group=mapping.tp_group,
                                        tp_size=mapping.tp_size,
                                        gather_output=True)

    def forward(self,
                input_ids,
                position_ids=None,
                use_cache=False,
                last_token_ids=None,
                attention_mask=None,
                kv_cache_params=None,
                attention_params=None,
                hidden_states=None,
                prompt_embedding_table: Optional[Tensor] = None,
                prompt_tasks: Optional[Tensor] = None,
                prompt_vocab_size: Optional[Tensor] = None,
                lora_params=None):
        hidden_states = super().forward(
            input_ids,
            position_ids=position_ids,
            use_cache=use_cache,
            attention_mask=attention_mask,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
            hidden_states=hidden_states,
            prompt_embedding_table=prompt_embedding_table,
            prompt_tasks=prompt_tasks,
            prompt_vocab_size=prompt_vocab_size,
            lora_params=lora_params)

        if use_cache:
            hidden_states, presents = hidden_states

        if self.mapping.is_last_pp_rank():
            hidden_states = gather_last_token_logits(
                hidden_states, last_token_ids,
                default_net().plugin_config.remove_input_padding)

            # [batch_size, hidden_size] -> [batch_size, vocab_size]
            lm_logits = self.lm_head(hidden_states)
            lm_logits.mark_output('logits', self.logits_dtype)
        else:
            hidden_states.mark_output('hidden_states_output', self.dtype)

        if use_cache and default_net().plugin_config.paged_kv_cache == False:
            for i, present in zip(self.mapping.pp_layers(self.num_layers),
                                  presents):
                present.mark_output(f'present_key_value_{i}', self.kv_dtype)
            if self.mapping.is_last_pp_rank():
                return (lm_logits, presents)
            return (hidden_states, presents)
        else:
            if self.mapping.is_last_pp_rank():
                return lm_logits
            return hidden_states

    def prepare_inputs(
            self,
            max_batch_size,
            max_input_len,
            max_seq_len,
            use_cache,
            max_beam_width,
            max_num_tokens: int = None,
            prompt_embedding_table_size: int = 0,
            gather_context_logits: bool = False,
            gather_generation_logits: bool = False,
            lora_target_modules: List[str] = None,
            max_draft_len=0,  # For Medusa support
    ):
        '''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
            ranges of the dimensions of when using TRT dynamic shapes.

            @return: a list contains values which can be fed into the self.forward()
        '''

        # Prepare inputs
        head_size = self.hidden_size // self.num_heads
        remove_input_padding = default_net().plugin_config.remove_input_padding
        use_gpt_attention_plugin = default_net(
        ).plugin_config.gpt_attention_plugin
        use_gemm_plugin = default_net().plugin_config.gemm_plugin
        paged_kv_cache = default_net().plugin_config.paged_kv_cache
        tokens_per_block = default_net().plugin_config.tokens_per_block
        use_custom_all_reduce = default_net(
        ).plugin_config.use_custom_all_reduce
        use_lora_plugin = default_net().plugin_config.lora_plugin

        model_inputs = self.prepare_basic_inputs(
            max_batch_size=max_batch_size,
            max_beam_width=max_beam_width,
            max_input_len=max_input_len,
            max_seq_len=max_seq_len,
            num_kv_heads=self.num_kv_heads,
            head_size=head_size,
            num_layers=self.num_layers,
            kv_dtype=self.kv_dtype,
            remove_input_padding=remove_input_padding,
            use_gpt_attention_plugin=use_gpt_attention_plugin,
            use_gemm_plugin=use_gemm_plugin,
            use_custom_all_reduce=use_custom_all_reduce,
            paged_kv_cache=paged_kv_cache,
            tokens_per_block=tokens_per_block,
            dtype=self.dtype,
            num_heads=self.num_heads,
            mapping=self.mapping,
            max_num_tokens=max_num_tokens,
            prompt_embedding_table_size=prompt_embedding_table_size,
            gather_context_logits=gather_context_logits,
            gather_generation_logits=gather_generation_logits,
            use_lora_plugin=use_lora_plugin,
            lora_target_modules=lora_target_modules,
            max_draft_len=max_draft_len,  # For Medusa support
        )

        return (
            model_inputs['input_ids'],
            model_inputs['position_ids'],
            True,
            model_inputs['last_token_ids'],
            model_inputs['attention_mask'],
            KeyValueCacheParams(
                past_key_value=model_inputs['past_key_value'],
                host_past_key_value_lengths=model_inputs[
                    'host_past_key_value_lengths'],
                host_max_attention_window_sizes=model_inputs[
                    'host_max_attention_window_sizes'],
                host_sink_token_length=model_inputs['host_sink_token_length'],
                kv_cache_block_pointers=model_inputs[
                    'kv_cache_block_pointers_list'],
                host_kv_cache_block_pointers=model_inputs[
                    'host_kv_cache_block_pointers_list'],
                cache_indirection=model_inputs['cache_indirection'],
            ),
            AttentionParams(
                sequence_length=model_inputs['sequence_length'],
                context_lengths=model_inputs['context_lengths'],
                host_context_lengths=model_inputs['host_context_lengths'],
                max_context_length=max_input_len,
                host_request_types=model_inputs['host_request_types']),
            model_inputs['hidden_states_input'],
            model_inputs['prompt_embedding_table'],
            model_inputs['tasks'],
            model_inputs['prompt_vocab_size'],
            LoraParams(
                model_inputs['lora_ranks'],
                model_inputs['lora_weights_pointers'],
                host_context_lengths=model_inputs['host_context_lengths'],
                max_context_length=max_input_len,
                host_request_types=model_inputs['host_request_types']),
        )

    @classmethod
    def from_hugging_face(cls,
                          hf_model_dir,
                          dtype='float16',
                          mapping: Optional[Mapping] = None,
                          quant_mode: Optional[QuantMode] = None,
                          **kwargs):
        import transformers
        from transformers import LlamaConfig

        cfg = LlamaConfig.from_pretrained(hf_model_dir)

        num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
            else cfg.num_attention_heads
        if mapping is None:
            mapping = Mapping()
        if quant_mode is None:
            quant_mode = QuantMode(0)

        tllm_llama = LLaMAForCausalLM(
            num_layers=cfg.num_hidden_layers,
            num_heads=cfg.num_attention_heads,
            num_kv_heads=num_kv_heads,
            hidden_size=cfg.hidden_size,
            vocab_size=cfg.vocab_size,
            hidden_act=cfg.hidden_act,
            max_position_embeddings=cfg.max_position_embeddings,
            dtype=dtype,
            mlp_hidden_size=cfg.intermediate_size,
            position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
            mapping=mapping,
            rotary_base=getattr(cfg, 'rotary_base', 10000.0),
            rotary_scaling=getattr(cfg, 'rotary_scaling', None),
            rms_norm_eps=cfg.rms_norm_eps,
            # current load_from_hf_llama can read these, so need to set these here,
            # ideally these attributes shall be set after the from_hugging_face returned an object to user
            # since these attributes are not related to the hugging face model, they only affect the TRT-LLM module
            # the weights transformation or any model optimization shall be done outside from_hugging_face
            quant_mode=quant_mode,
            use_parallel_embedding=kwargs.get("use_parallel_embedding", False),
            embedding_sharding_dim=kwargs.get("embedding_sharding_dim", 0),
            use_fused_mlp=kwargs.get("use_fused_mlp", False),
            moe_config=kwargs.get("moe_config",
                                  MoeConfig()),  # load weights use this
            use_prompt_tuning=kwargs.get("use_prompt_tuning", False))

        if quant_mode.has_any_quant():
            tllm_llama._quantize(hf_model_dir, dtype, **kwargs)

        # For debug purpose, skip weights loading to be faster
        if kwargs.get("skip_loading_weights", False):
            return tllm_llama

        # TODO: support mixtral

        # weights already loaded in _quantize for int4 weight only
        if not quant_mode.is_int4_weight_only_per_group():
            hf_model = transformers.LlamaForCausalLM
            profiler.start("Loading weights from HF")
            hf_llama = hf_model.from_pretrained(
                hf_model_dir,
                device_map={
                    "model": "cpu",
                    "lm_head": "cpu",
                    "embed_tokens": "cpu",
                    "layers": "cpu",
                    "norm": "cpu",
                },  # Load to CPU memory
                torch_dtype='auto',
            )
            load_from_hf_llama(
                tllm_llama,
                hf_llama,
                mapping=mapping,
                dtype=dtype,
                # TODO: these shall be outside from_hugging_face too.
                use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
                lora_config=kwargs.get("lora_config", LoraConfig()),
            )
            profiler.stop("Loading weights from HF")
            del hf_llama
        return tllm_llama

    def _quantize(self, hf_model_dir, dtype, **kwargs):
        '''Given the quant_mode set in the Module object, read from given hf model
           call AMMO to generate quantization scales, and set the scales back the module parameters.
        '''
        # use self destructed temporary path if kwargs[quantization_cache_dir] is not specified
        # sometimes the quantization checkpoint path needs to be saved for debug purpose
        quantized_temp_dir = tempfile.TemporaryDirectory("llama-quantized")
        quantized_checkpoint_path = kwargs.get("quantization_cache_dir",
                                               quantized_temp_dir.name)
        quantize_lm_head = kwargs.get("quantize_lm_head", False)
        quant_mode = self.quant_mode
        ammo_qformat = None
        calib_size = None
        if quant_mode.has_fp8_qdq() or quant_mode.has_fp8_kv_cache():
            ammo_qformat = 'fp8'
            calib_size = 512
        # TODO: how to distinguish from quant_mode about int4_awq or int4_gptq?
        elif quant_mode.is_int4_weight_only_per_group():
            ammo_qformat = 'int4_awq'
            calib_size = 32
        assert ammo_qformat is not None

        # local import to avoid pytest issue when importing AMMO and transformers lib
        from .quantize import quantize_llama_and_export
        quantize_llama_and_export(hf_model_dir,
                                  quantized_checkpoint_path,
                                  ammo_qformat,
                                  dtype,
                                  calib_size=calib_size,
                                  quantize_lm_head=quantize_lm_head)

        ckpt = Path(quantized_checkpoint_path) / "llama_tp1_rank0.npz"
        assert ckpt.exists(), f"The expecting checkpoint path {ckpt} does not exist" \
                  "it's likely quantization failed, pls check error logs"
        if ammo_qformat == 'fp8':
            quant_scales = get_scaling_factors(ckpt,
                                               num_layers=self.num_layers,
                                               quant_mode=quant_mode)
            quantize_kwargs = {"quant_scales": quant_scales}
        else:
            assert ammo_qformat == 'int4_awq'
            exclude_modules = ['lm_head'] if not quantize_lm_head else []
            quantize_kwargs = {
                "group_size": 128,  # default from examples/llama/build.py
                "zero": False,
                "pre_quant_scale": True,
                "exclude_modules": exclude_modules,
            }
        # for fp8, the quantize_model only set scale factors to the model parameters
        quantize_model(self, quant_mode, **quantize_kwargs)

        if ammo_qformat == 'int4_awq':
            load_from_awq_llama(tensorrt_llm_llama=self,
                                quant_ckpt_path=str(ckpt),
                                mapping=self.mapping,
                                dtype=dtype,
                                quantize_lm_head=quantize_lm_head,
                                bin_model_dir=None)

    # llama specific setters, user shall has the chance to change the module attributes after
    # from_hugging_face factory method created the model when these attributes is not included in the huggingface checkpoint

    def rotary_base(self, val):
        for decoder in self.layers:
            decoder.attention.rotary_embedding_base = val
        return self

    def rotary_scaling(self, scaling_type, factor):
        # TODO: what if there are some other behaviors triggered by the these changes?
        # should implement these assignment as setters of the Attention Module
        assert scaling_type in ("linear", "dynamic"), f"Got {scaling_type}"
        assert factor > 1.0, f"Got {factor}"
        for decoder in self.layers:
            decoder.attention.rotary_embedding_scale_type = RotaryScalingType.linear if scaling_type == "linear" else RotaryScalingType.dynamic
            decoder.attention.rotary_embedding_scale = factor
        return self

    def default_plugin_config(self, **kwargs):
        plugin_config = super().default_plugin_config(**kwargs)
        if self.quant_mode.is_int4_weight_only_per_group():
            plugin_config.set_weight_only_groupwise_quant_matmul_plugin()
        return plugin_config