modeling_baichuan.py

# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.

from .configuration_baichuan import BaichuanConfig
from .generation_utils import build_chat_input, TextIterStreamer

import math
from threading import Thread
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig
from transformers.activations import ACT2FN
from transformers.generation.utils import GenerationConfig
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.utils import logging, ContextManagers

import os
from contextlib import contextmanager
from accelerate import init_empty_weights

logger = logging.get_logger(__name__)

try:
    from xformers import ops as xops
except ImportError:
    xops = None
    logger.warning(
        "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
    )


def _get_interleave(n):
    def _get_interleave_power_of_2(n):
        start = 2 ** (-(2 ** -(math.log2(n) - 3)))
        ratio = start
        return [start * ratio**i for i in range(n)]

    if math.log2(n).is_integer():
        return _get_interleave_power_of_2(n)
    else:
        closest_power_of_2 = 2 ** math.floor(math.log2(n))
        return (
            _get_interleave_power_of_2(closest_power_of_2)
            + _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
        )


def _fill_with_neg_inf(t):
    """FP16-compatible function that fills a tensor with -inf."""
    return t.float().fill_(float("-inf")).type_as(t)


def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
    _future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
    _future_mask = _future_mask.unsqueeze(0) + alibi
    new_future_mask = _future_mask.to(tensor)
    return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]


def _gen_alibi_mask(tensor, n_head, max_pos):
    slopes = torch.Tensor(_get_interleave(n_head))
    position_point = torch.arange(max_pos) - max_pos + 1
    position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
    diag = torch.diag(position_point[0])
    position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
    alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
    alibi = alibi.view(n_head, 1, max_pos)
    alibi_mask = torch.triu(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
    alibi_mask = alibi_mask.unsqueeze(0) + alibi
    return alibi_mask


class RMSNorm(torch.nn.Module):
    def __init__(self, hidden_size, epsilon=1e-6):
        super().__init__()
        self.weight = torch.nn.Parameter(torch.empty(hidden_size))
        self.epsilon = epsilon

    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)

        # convert into half-precision
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


class MLP(torch.nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
    ):
        super().__init__()
        self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
        self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class BaichuanAttention(torch.nn.Module):
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.model_max_length

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
            )
        self.W_pack = torch.nn.Linear(
            self.hidden_size, 3 * self.hidden_size, bias=False
        )
        self.o_proj = torch.nn.Linear(
            self.num_heads * self.head_dim, self.hidden_size, bias=False
        )

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return (
            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
            .transpose(1, 2)
            .contiguous()
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        proj = self.W_pack(hidden_states)
        proj = (
            proj.unflatten(-1, (3, self.hidden_size))
            .unsqueeze(0)
            .transpose(0, -2)
            .squeeze(-2)
        )
        query_states = (
            proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )
        key_states = (
            proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )
        value_states = (
            proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None
        if xops is not None and self.training:
            attn_weights = None
            # query_states = query_states.transpose(1, 2)
            # key_states = key_states.transpose(1, 2)
            # value_states = value_states.transpose(1, 2)
            # attn_output = xops.memory_efficient_attention(
            #     query_states, key_states, value_states, attn_bias=attention_mask
            # )
            with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
            attn_output = attn_output.transpose(1, 2)
        else:
            attn_weights = torch.matmul(
                query_states, key_states.transpose(2, 3)
            ) / math.sqrt(self.head_dim)

            if attention_mask is not None:
                if q_len == 1:  # inference with cache
                    if len(attention_mask.size()) == 4:
                        attention_mask = attention_mask[:, :, -1:, :]
                    else:
                        attention_mask = attention_mask[:, -1:, :]
                attn_weights = attn_weights + attention_mask
                attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )

            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
            attn_output = torch.matmul(attn_weights, value_states)

            attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class BaichuanLayer(torch.nn.Module):
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = BaichuanAttention(config=config)
        self.mlp = MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
        )
        self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, epsilon=config.rms_norm_eps
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class BaichuanPreTrainedModel(PreTrainedModel):
    config_class = BaichuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BaichuanLayer"]
    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, torch.nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, torch.nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, BaichuanModel):
            module.gradient_checkpointing = value


class BaichuanModel(BaichuanPreTrainedModel):
    def __init__(self, config: BaichuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.n_head = config.num_attention_heads
        self.embed_tokens = torch.nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = torch.nn.ModuleList(
            [BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
        )
        self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)

        self.gradient_checkpointing = config.gradient_checkpointing
        self.post_init()
        self.max_cache_pos = config.model_max_length
        self.first_run = True
        self.alibi_mask = None

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def get_alibi_mask(self, tensor, seq_length_with_past):
        if self.training:
            slopes = torch.Tensor(_get_interleave(self.n_head))
            position_point = (
                torch.arange(seq_length_with_past) - seq_length_with_past + 1
            )
            position_point = (
                position_point.unsqueeze(0)
                .unsqueeze(0)
                .expand(self.n_head, seq_length_with_past, -1)
            )
            diag = torch.diag(position_point[0])
            position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
                -1, -2
            )
            alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
            mask = _buffered_future_mask(
                tensor, seq_length_with_past, alibi, self.n_head
            )
        else:
            if self.first_run:
                self.first_run = False
                self.register_buffer(
                    "future_mask",
                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
                        tensor
                    ),
                    persistent=False,
                )
            if seq_length_with_past > self.max_cache_pos:
                self.max_cache_pos = seq_length_with_past
                self.register_buffer(
                    "future_mask",
                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
                        tensor
                    ),
                    persistent=False,
                )
            mask = self.future_mask[
                : self.n_head, :seq_length_with_past, :seq_length_with_past
            ]
        return mask

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot provide both input_ids and inputs_embeds simultaneously"
            )
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You need to provide input_ids or inputs_embeds")

        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        seq_length_with_past = seq_length

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if self.training:
            if (
                self.alibi_mask is None
                or self.alibi_mask.shape[-1] != seq_length_with_past
            ):
                self.alibi_mask = self.get_alibi_mask(
                    inputs_embeds, seq_length_with_past
                )
            alibi_mask = self.alibi_mask
        else:
            alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)

        if attention_mask is not None:
            if len(attention_mask.shape) == 2:
                expanded_mask = attention_mask.to(alibi_mask.dtype)
                expanded_mask = torch.tril(
                    torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
                ) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
            else:
                expanded_mask = attention_mask
            bsz = inputs_embeds.size(0)
            src_len, tgt_len = alibi_mask.size()[-2:]
            expanded_mask = (
                expanded_mask.unsqueeze(1)
                .expand(bsz, 1, src_len, tgt_len)
                .to(alibi_mask.dtype)
            )
            inverted_mask = 1.0 - expanded_mask
            inverted_mask = inverted_mask.masked_fill(
                inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
            )
            attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
        else:
            attention_mask = alibi_mask

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = (
                past_key_values[idx] if past_key_values is not None else None
            )

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
                if v is not None
            )
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class NormHead(nn.Module):
    def __init__(self, hidden_size, vocab_size, bias=False):
        super().__init__()
        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        self.first_flag = True

    def forward(self, hidden_states):
        if self.training:
            norm_weight = nn.functional.normalize(self.weight)
            self.first_flag = True
        elif self.first_flag:
            self.first_flag = False
            self.weight.data = nn.functional.normalize(self.weight)
            norm_weight = self.weight
        else:
            norm_weight = self.weight
        return nn.functional.linear(hidden_states, norm_weight)

_init_weights = True
@contextmanager
def no_init_weights(_enable=True):
    global _init_weights
    old_init_weights = _init_weights
    if _enable:
        _init_weights = False
    try:
        yield
    finally:
        _init_weights = old_init_weights


class BaichuanForCausalLM(BaichuanPreTrainedModel):
    def __init__(self, config, *model_args, **model_kwargs):
        super().__init__(config, *model_args, **model_kwargs)
        self.model = BaichuanModel(config)
        self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
        #if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
        if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
            try:
                from .quantizer import quantize_offline, init_model_weight_int4
            except ImportError:
                raise ImportError(f"Needs quantize_offline to run quantize.")
            quantize_offline(self, 4)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model
    
    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
        *model_args,
        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        ignore_mismatched_sizes: bool = False,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        use_safetensors: bool = None,
        **kwargs,
    ):
    
        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=False,
                proxies=None,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder="",
                _from_auto=False,
                _from_pipeline=None,
                **kwargs,
            )
        else:
            model_kwargs = kwargs
        
        if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
            try:
                from .quantizer import init_model_weight_int4
                from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
                from accelerate.utils import CustomDtype
                from accelerate.utils import get_balanced_memory
            except ImportError:
                raise ImportError(f"Needs import model weight init func to run quantize.") 
            # Instantiate model.
            init_contexts = [no_init_weights(_enable=True)]
            init_contexts.append(init_empty_weights())
            with ContextManagers(init_contexts):
                model = cls(config)
            
            model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
            state_dict = torch.load(model_file, map_location="cpu") 
            model.is_quantized = True
                        
            device_map = kwargs.pop("device_map", None)
            torch_dtype = kwargs.pop("torch_dtype", None)
            if device_map is not None:
                kwargs = {"no_split_module_classes": model._no_split_modules}
                target_dtype = CustomDtype.INT4
                max_memory = get_balanced_memory(
                    model,
                    dtype=target_dtype,
                    low_zero=(device_map == "balanced_low_0"),
                    max_memory=None,
                    **kwargs,
                )
                kwargs["max_memory"] = max_memory
                device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
            model = init_model_weight_int4(config, model, state_dict)
            
            # Set model in evaluation mode to deactivate DropOut modules by default
            model.eval()
            # If it is a model with generation capabilities, attempt to load the generation config
            if model.can_generate():
                try:
                    model.generation_config = GenerationConfig.from_pretrained(
                        pretrained_model_name_or_path,
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=False,
                        proxies=None,
                        local_files_only=local_files_only,
                        token=token,
                        revision=revision,
                        subfolder="",
                        _from_auto=False,
                        _from_pipeline=None,
                        **kwargs,
                    )
                except (OSError, TypeError):
                    logger.info(
                        "Generation config file not found, using a generation config created from the model config."
                    )
                    pass
            
            if device_map is not None:
                dispatch_model(model, device_map=device_map)
            
            return model

        return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args, 
                config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, 
                force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, 
                use_safetensors=use_safetensors, **kwargs)

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            softmax_normalizer = shift_logits.max(-1).values ** 2
            z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels) + z_loss

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def quantize(self, bits: int):
        try:
            from .quantizer import quantize_online
        except ImportError:
            raise ImportError(f"Needs QLinear to run quantize.")
        return quantize_online(self, bits)
        
    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        return tuple(
            tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
            for layer_past in past_key_values
        )

    def _build_chat_input(
        self, tokenizer, messages: List[dict], max_new_tokens: int = 0
    ):
        max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
        max_input_tokens = self.config.model_max_length - max_new_tokens
        max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
        total_input, round_input = [], []
        for i, message in enumerate(messages[::-1]):
            content_tokens = tokenizer.encode(message["content"])
            if message["role"] == "user":
                round_input = (
                    [self.generation_config.user_token_id]
                    + content_tokens
                    + round_input
                )
                if (
                    total_input
                    and len(total_input) + len(round_input) > max_input_tokens
                ):
                    break
                else:
                    total_input = round_input + total_input
                    if len(total_input) >= max_input_tokens:
                        break
                    else:
                        round_input = []
            elif message["role"] == "assistant":
                round_input = (
                    [self.generation_config.assistant_token_id]
                    + content_tokens
                    + [self.generation_config.eos_token_id]
                    + round_input
                )
            else:
                raise ValueError(f"message role not supported yet: {message['role']}")
        total_input = total_input[-max_input_tokens:]  # truncate left
        total_input.append(self.generation_config.assistant_token_id)
        total_input = torch.LongTensor([total_input]).to(self.device)
        return total_input

    def chat(self, tokenizer, messages: List[dict], stream=False,
             generation_config: Optional[GenerationConfig]=None):
        generation_config = generation_config or self.generation_config
        input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
        if stream:
            streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
            Thread(target=self.generate, kwargs=dict(
                inputs=input_ids, streamer=streamer,
                generation_config=generation_config,
            )).start()
            return streamer
        else:
            outputs = self.generate(input_ids, generation_config=generation_config)
            response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
            return response