modeling_chatglm.py

import math
import sys
import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
from torch.nn.utils import skip_init
from typing import Optional, Tuple, Union, List, Dict, Any

import pdb
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging, is_torch_npu_available
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import (
    LogitsProcessorList,
    StoppingCriteriaList,
    GenerationConfig,
    ModelOutput,
)

from .visual import EVA2CLIPModel
from .configuration_chatglm import ChatGLMConfig

try:
    from transformers.utils import (
        is_flash_attn_greater_or_equal_2_10,
        is_flash_attn_2_available,
    )

    if is_flash_attn_2_available():
        from flash_attn import flash_attn_func, flash_attn_varlen_func
        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
except:
    pass

if sys.platform != "darwin" and not is_torch_npu_available():
    torch._C._jit_set_profiling_mode(False)
    torch._C._jit_set_profiling_executor(False)
    torch._C._jit_override_can_fuse_on_cpu(True)
    torch._C._jit_override_can_fuse_on_gpu(True)

logger = logging.get_logger(__name__)

LANGUAGE_TOKEN_TYPE = 0
VISION_TOKEN_TYPE = 1

_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
_CONFIG_FOR_DOC = "ChatGLMConfig"


def default_init(cls, *args, **kwargs):
    return cls(*args, **kwargs)


class InvalidScoreLogitsProcessor(LogitsProcessor):
    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor
    ) -> torch.FloatTensor:
        if torch.isnan(scores).any() or torch.isinf(scores).any():
            scores.zero_()
            scores[..., 198] = 5e4
        return scores


class PrefixEncoder(torch.nn.Module):
    """
    The torch.nn model to encode the prefix
    Input shape: (batch-size, prefix-length)
    Output shape: (batch-size, prefix-length, 2*layers*hidden)
    """

    def __init__(self, config: ChatGLMConfig):
        super().__init__()
        self.prefix_projection = config.prefix_projection
        if self.prefix_projection:
            # Use a two-layer MLP to encode the prefix
            kv_size = (
                config.num_layers
                * config.kv_channels
                * config.multi_query_group_num
                * 2
            )
            self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
            self.trans = torch.nn.Sequential(
                torch.nn.Linear(kv_size, config.hidden_size),
                torch.nn.Tanh(),
                torch.nn.Linear(config.hidden_size, kv_size),
            )
        else:
            self.embedding = torch.nn.Embedding(
                config.pre_seq_len,
                config.num_layers
                * config.kv_channels
                * config.multi_query_group_num
                * 2,
            )

    def forward(self, prefix: torch.Tensor):
        if self.prefix_projection:
            prefix_tokens = self.embedding(prefix)
            past_key_values = self.trans(prefix_tokens)
        else:
            past_key_values = self.embedding(prefix)
        return past_key_values


def split_tensor_along_last_dim(
    tensor: torch.Tensor,
    num_partitions: int,
    contiguous_split_chunks: bool = False,
) -> List[torch.Tensor]:
    """Split a tensor along its last dimension.

    Arguments:
        tensor: input tensor.
        num_partitions: number of partitions to split the tensor
        contiguous_split_chunks: If True, make each chunk contiguous
                                 in memory.

    Returns:
        A list of Tensors
    """
    # Get the size and dimension.
    last_dim = tensor.dim() - 1
    last_dim_size = tensor.size()[last_dim] // num_partitions
    # Split.
    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
    # Note: torch.split does not create contiguous tensors by default.
    if contiguous_split_chunks:
        return tuple(chunk.contiguous() for chunk in tensor_list)

    return tensor_list


class RotaryEmbedding(nn.Module):
    def __init__(self, dim, rope_ratio=1, original_impl=False, device=None, dtype=None):
        super().__init__()
        inv_freq = 1.0 / (
            10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)
        )
        self.register_buffer("inv_freq", inv_freq)
        self.dim = dim
        self.original_impl = original_impl
        self.rope_ratio = rope_ratio

    def impl(self, seq_length: int, dim: int, device: torch.device, dtype: torch.dtype):
        base = 10000 * self.rope_ratio
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
        )
        seq = torch.arange(seq_length, device=inv_freq.device, dtype=torch.float32)
        freqs = torch.outer(seq, inv_freq)
        # first part even vector components, second part odd vector components,
        #  2 * dim in dimension size
        # emb = torch.cat((freqs, freqs), dim=-1)
        # emb = torch.stack((freqs, freqs), dim=-1).to(dtype)
        emb = torch.stack((freqs.cos(), freqs.sin()), dim=-1).to(dtype=dtype)
        return emb

    def forward_impl(
        self,
        seq_len: int,
        n_elem: int,
        dtype: torch.dtype,
        device: torch.device,
        base: int = 10000,
    ):
        """Enhanced Transformer with Rotary Position Embedding.

        Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
        transformers/rope/__init__.py. MIT License:
        https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
        """
        # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
        base = base * self.rope_ratio
        theta = 1.0 / (
            base
            ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)
        )

        # Create position indexes `[0, 1, ..., seq_len - 1]`
        seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)

        # Calculate the product of position index and $\theta_i$
        idx_theta = torch.outer(seq_idx, theta).float()

        cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)

        # this is to mimic the behaviour of complex32, else we will get different results
        if dtype in (torch.float16, torch.bfloat16, torch.int8):
            cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
        return cache

    def forward(self, max_seq_len, offset=0):
        if self.original_impl:
            return self.forward_impl(
                max_seq_len,
                self.dim,
                dtype=self.inv_freq.dtype,
                device=self.inv_freq.device,
            )
        else:
            return self.impl(
                max_seq_len,
                self.dim,
                dtype=self.inv_freq.dtype,
                device=self.inv_freq.device,
            )


@torch.jit.script
def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
    # x: [b, np, sq, hn]
    sq =  x.size(1)
    rot_dim = rope_cache.shape[-2] * 2
    x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
    # truncate to support variable sizes
    rope_cache = rope_cache[:, :sq]
    xshaped = x.chunk(2, -1)
    cos, sin = rope_cache[...,0].unsqueeze(2), rope_cache[...,1].unsqueeze(2)
    x_out2 = torch.concat(
        [
            xshaped[0] * cos - xshaped[1] * sin,
            xshaped[1] * cos + xshaped[0] * sin,
        ],
        -1,
    )
    return torch.cat((x_out2, x_pass), dim=-1)


class RMSNorm(torch.nn.Module):
    def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
        super().__init__()
        self.weight = torch.nn.Parameter(
            torch.empty(normalized_shape, device=device, dtype=dtype)
        )
        self.eps = eps

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

        return (self.weight * hidden_states).to(input_dtype)


class CoreAttention(torch.nn.Module):
    def __init__(self, config: ChatGLMConfig, layer_number):
        super(CoreAttention, self).__init__()

        self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
        if self.apply_query_key_layer_scaling:
            self.attention_softmax_in_fp32 = True
        self.layer_number = max(1, layer_number)

        projection_size = config.kv_channels * config.num_attention_heads

        # Per attention head and per partition values.
        self.hidden_size_per_partition = projection_size
        self.hidden_size_per_attention_head = (
            projection_size // config.num_attention_heads
        )
        self.num_attention_heads_per_partition = config.num_attention_heads

        coeff = None
        self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
        if self.apply_query_key_layer_scaling:
            coeff = self.layer_number
            self.norm_factor *= coeff
        self.coeff = coeff

        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)

    def forward(self, query_layer, key_layer, value_layer, attention_mask):
        pytorch_major_version = int(torch.__version__.split(".")[0])
        if pytorch_major_version >= 2:
            if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
                context_layer = torch.nn.functional.scaled_dot_product_attention(
                    query_layer, key_layer, value_layer, is_causal=True
                )
            else:
                if attention_mask is not None:
                    attention_mask = ~attention_mask
                context_layer = torch.nn.functional.scaled_dot_product_attention(
                    query_layer, key_layer, value_layer, attention_mask
                )
            context_layer = context_layer.transpose(1, 2).contiguous()
            new_context_layer_shape = context_layer.size()[:-2] + (
                self.hidden_size_per_partition,
            )
            context_layer = context_layer.reshape(*new_context_layer_shape)
        else:
            # Raw attention scores

            # [b, np, sq, sk]
            output_size = (
                query_layer.size(0),
                query_layer.size(1),
                query_layer.size(2),
                key_layer.size(2),
            )

            # [b, np, sq, hn] -> [b * np, sq, hn]
            query_layer = query_layer.view(
                output_size[0] * output_size[1], output_size[2], -1
            )
            # [b, np, sk, hn] -> [b * np, sk, hn]
            key_layer = key_layer.view(
                output_size[0] * output_size[1], output_size[3], -1
            )

            # preallocting input tensor: [b * np, sq, sk]
            matmul_input_buffer = torch.empty(
                output_size[0] * output_size[1],
                output_size[2],
                output_size[3],
                dtype=query_layer.dtype,
                device=query_layer.device,
            )

            # Raw attention scores. [b * np, sq, sk]
            matmul_result = torch.baddbmm(
                matmul_input_buffer,
                query_layer,  # [b * np, sq, hn]
                key_layer.transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
                alpha=(1.0 / self.norm_factor),
            )

            # change view to [b, np, sq, sk]
            attention_scores = matmul_result.view(*output_size)

            # ===========================
            # Attention probs and dropout
            # ===========================

            # attention scores and attention mask [b, np, sq, sk]
            if self.attention_softmax_in_fp32:
                attention_scores = attention_scores.float()
            if self.coeff is not None:
                attention_scores = attention_scores * self.coeff
            if (
                attention_mask is None
                and attention_scores.shape[2] == attention_scores.shape[3]
            ):
                attention_mask = torch.ones(
                    output_size[0],
                    1,
                    output_size[2],
                    output_size[3],
                    device=attention_scores.device,
                    dtype=torch.bool,
                )
                attention_mask.tril_()
                attention_mask = ~attention_mask
            if attention_mask is not None:
                attention_scores = attention_scores.masked_fill(
                    attention_mask, float("-inf")
                )
            attention_probs = F.softmax(attention_scores, dim=-1)
            attention_probs = attention_probs.type_as(value_layer)

            # This is actually dropping out entire tokens to attend to, which might
            # seem a bit unusual, but is taken from the original Transformer paper.
            attention_probs = self.attention_dropout(attention_probs)
            # =========================
            # Context layer. [sq, b, hp]
            # =========================

            # value_layer -> context layer.
            # [sk, b, np, hn] --> [b, np, sq, hn]

            # context layer shape: [b, np, sq, hn]
            output_size = (
                value_layer.size(1),
                value_layer.size(2),
                query_layer.size(0),
                value_layer.size(3),
            )
            # change view [b * np, sk, hn]
            value_layer = value_layer.view(
                output_size[0] * output_size[1], value_layer.size(2), -1
            )
            # change view [b * np, sq, sk]
            attention_probs = attention_probs.view(
                output_size[0] * output_size[1], output_size[2], -1
            )
            # matmul: [b * np, sq, hn]
            context_layer = torch.bmm(attention_probs, value_layer)
            # change view [b, np, sq, hn]
            context_layer = context_layer.view(*output_size)
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.transpose(1, 2).contiguous()
            # [b, sq, np, hn] --> [b, sq, hp]
            new_context_layer_shape = context_layer.size()[:-2] + (
                self.hidden_size_per_partition,
            )
            context_layer = context_layer.reshape(*new_context_layer_shape)

        return context_layer


class SdpaAttention(CoreAttention):
    def forward(self, query_layer, key_layer, value_layer, attention_mask):
        if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
            context_layer = torch.nn.functional.scaled_dot_product_attention(
                query_layer,
                key_layer,
                value_layer,
                is_causal=True,
                dropout_p=self.config.attention_dropout if self.training else 0.0,
            )
        else:
            if attention_mask is not None:
                attention_mask = ~attention_mask
            context_layer = torch.nn.functional.scaled_dot_product_attention(
                query_layer,
                key_layer,
                value_layer,
                attention_mask,
                dropout_p=self.config.attention_dropout if self.training else 0.0,
            )
        context_layer = context_layer.transpose(1, 2).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (
            self.hidden_size_per_partition,
        )
        context_layer = context_layer.reshape(*new_context_layer_shape)
        return context_layer


def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2
class FlashAttention2(CoreAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(self, query_states, key_states, value_states, attention_mask):
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        batch_size, query_length = query_states.shape[:2]
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1
        dropout = self.config.attention_dropout if self.training else 0.0
        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            (
                query_states,
                key_states,
                value_states,
                indices_q,
                cu_seq_lens,
                max_seq_lens,
            ) = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=None,
                causal=causal,
            )

            attn_output = pad_input(
                attn_output_unpad, indices_q, batch_size, query_length
            )
        else:
            attn_output = flash_attn_func(
                query_states,
                key_states,
                value_states,
                dropout,
                softmax_scale=None,
                causal=causal,
            )
        attn_output = attn_output.reshape(
            batch_size, query_length, self.hidden_size_per_partition
        ).contiguous()
        return attn_output

    def _upad_input(
        self, query_layer, key_layer, value_layer, attention_mask, query_length
    ):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
            indices_k,
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
            indices_k,
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(
                    batch_size * kv_seq_len,
                    self.num_attention_heads_per_partition,
                    head_dim,
                ),
                indices_k,
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
                query_layer, attention_mask
            )

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


CORE_ATTENTION_CLASSES = {
    "eager": CoreAttention,
    "sdpa": SdpaAttention,
    "flash_attention_2": FlashAttention2,
}


class SelfAttention(torch.nn.Module):
    """Parallel self-attention layer abstract class.

    Self-attention layer takes input with size [s, b, h]
    and returns output of the same size.
    """

    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
        super(SelfAttention, self).__init__()
        self.layer_number = max(1, layer_number)

        self.projection_size = config.kv_channels * config.num_attention_heads

        # Per attention head and per partition values.
        self.hidden_size_per_attention_head = (
            self.projection_size // config.num_attention_heads
        )
        self.num_attention_heads_per_partition = config.num_attention_heads

        self.multi_query_attention = config.multi_query_attention
        self.qkv_hidden_size = 3 * self.projection_size
        self.original_rope = config.original_rope
        if self.multi_query_attention:
            self.num_multi_query_groups_per_partition = config.multi_query_group_num
            self.qkv_hidden_size = (
                self.projection_size
                + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
            )
        self.query_key_value = nn.Linear(
            config.hidden_size,
            self.qkv_hidden_size,
            bias=config.add_bias_linear or config.add_qkv_bias,
            device=device,
            **_config_to_kwargs(config),
        )

        self.core_attention = CoreAttention(config, self.layer_number)

        # Output.
        self.dense = nn.Linear(
            self.projection_size,
            config.hidden_size,
            bias=config.add_bias_linear,
            device=device,
            **_config_to_kwargs(config),
        )

    def _allocate_memory(
        self, inference_max_sequence_len, batch_size, device=None, dtype=None
    ):
        if self.multi_query_attention:
            num_attention_heads = self.num_multi_query_groups_per_partition
        else:
            num_attention_heads = self.num_attention_heads_per_partition
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
            num_attention_heads,
            self.hidden_size_per_attention_head,
            dtype=dtype,
            device=device,
        )

    def forward(
        self,
        hidden_states,
        attention_mask,
        rotary_pos_emb,
        kv_cache=None,
        use_cache=True,
    ):
        # hidden_states: [b, sq, h]

        # =================================================
        # Pre-allocate memory for key-values for inference.
        # =================================================
        # =====================
        # Query, Key, and Value
        # =====================

        # Attention heads [b, sq, h] --> [b, sq, (np * 3 * hn)]
        mixed_x_layer = self.query_key_value(hidden_states)

        if self.multi_query_attention:
            (query_layer, key_layer, value_layer) = mixed_x_layer.split(
                [
                    self.num_attention_heads_per_partition
                    * self.hidden_size_per_attention_head,
                    self.num_multi_query_groups_per_partition
                    * self.hidden_size_per_attention_head,
                    self.num_multi_query_groups_per_partition
                    * self.hidden_size_per_attention_head,
                ],
                dim=-1,
            )
            query_layer = query_layer.view(
                query_layer.size()[:-1]
                + (
                    self.num_attention_heads_per_partition,
                    self.hidden_size_per_attention_head,
                )
            )
            key_layer = key_layer.view(
                key_layer.size()[:-1]
                + (
                    self.num_multi_query_groups_per_partition,
                    self.hidden_size_per_attention_head,
                )
            )
            value_layer = value_layer.view(
                value_layer.size()[:-1]
                + (
                    self.num_multi_query_groups_per_partition,
                    self.hidden_size_per_attention_head,
                )
            )
        else:
            new_tensor_shape = mixed_x_layer.size()[:-1] + (
                self.num_attention_heads_per_partition,
                3 * self.hidden_size_per_attention_head,
            )
            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

            # [b, sq, np, 3 * hn] --> 3 [b, sq, np, hn]
            (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(
                mixed_x_layer, 3
            )



        # apply relative positional encoding (rotary embedding)
        if rotary_pos_emb is not None:
            query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
            key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)

    
        # [b, sq, np, hn] -> [b, np, sq, hn]
        query_layer, key_layer, value_layer = [
            k.transpose(1, 2) for k in [query_layer, key_layer, value_layer]
        ]

        # adjust key and value for inference
        if kv_cache is not None:
            cache_k, cache_v = kv_cache
            key_layer = torch.cat((cache_k, key_layer), dim=2)
            value_layer = torch.cat((cache_v, value_layer), dim=2)
            
        if use_cache:
            kv_cache = (key_layer, value_layer)
        else:
            kv_cache = None

        if self.multi_query_attention:
            key_layer = key_layer.repeat_interleave(self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, dim=1)
            value_layer = value_layer.repeat_interleave(self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, dim=1)

        # ==================================
        # core attention computation
        # ==================================

        context_layer = self.core_attention(
            query_layer, key_layer, value_layer, attention_mask
        )

        # =================
        # Output. [sq, b, h]
        # =================

        output = self.dense(context_layer)

        return output, kv_cache


def _config_to_kwargs(args):
    common_kwargs = {
        "dtype": args.torch_dtype,
    }
    return common_kwargs


class MLP(torch.nn.Module):
    """MLP.

    MLP will take the input with h hidden state, project it to 4*h
    hidden dimension, perform nonlinear transformation, and project the
    state back into h hidden dimension.
    """

    def __init__(self, config: ChatGLMConfig, device=None):
        super(MLP, self).__init__()

        self.add_bias = config.add_bias_linear

        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
        self.dense_h_to_4h = nn.Linear(
            config.hidden_size,
            config.ffn_hidden_size * 2,
            bias=self.add_bias,
            device=device,
            **_config_to_kwargs(config),
        )

        def swiglu(x):
            x = torch.chunk(x, 2, dim=-1)
            return F.silu(x[0]) * x[1]

        self.activation_func = swiglu

        # Project back to h.
        self.dense_4h_to_h = nn.Linear(
            config.ffn_hidden_size,
            config.hidden_size,
            bias=self.add_bias,
            device=device,
            **_config_to_kwargs(config),
        )

    def forward(self, hidden_states):
        # [s, b, 4hp]
        intermediate_parallel = self.dense_h_to_4h(hidden_states)
        intermediate_parallel = self.activation_func(intermediate_parallel)
        # [s, b, h]
        output = self.dense_4h_to_h(intermediate_parallel)
        return output


class GLMBlock(torch.nn.Module):
    """A single transformer layer.

    Transformer layer takes input with size [s, b, h] and returns an
    output of the same size.
    """

    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
        super(GLMBlock, self).__init__()
        self.layer_number = layer_number

        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm
        )

        self.fp32_residual_connection = config.fp32_residual_connection

        LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
        # Layernorm on the input data.
        self.input_layernorm = LayerNormFunc(
            config.hidden_size,
            eps=config.layernorm_epsilon,
            device=device,
            dtype=config.torch_dtype,
        )

        # Self attention.
        self.self_attention = SelfAttention(config, layer_number, device=device)
        self.hidden_dropout = config.hidden_dropout

        # Layernorm on the attention output
        self.post_attention_layernorm = LayerNormFunc(
            config.hidden_size,
            eps=config.layernorm_epsilon,
            device=device,
            dtype=config.torch_dtype,
        )

        # MLP
        self.mlp = MLP(config, device=device)

    def forward(
        self,
        hidden_states,
        attention_mask,
        rotary_pos_emb,
        kv_cache=None,
        use_cache=True,
    ):
        # hidden_states: [s, b, h]

        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)
        # Self attention.
        attention_output, kv_cache = self.self_attention(
            layernorm_output,
            attention_mask,
            rotary_pos_emb,
            kv_cache=kv_cache,
            use_cache=use_cache,
        )

        # Residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        layernorm_input = torch.nn.functional.dropout(
            attention_output, p=self.hidden_dropout, training=self.training
        )
        layernorm_input = residual + layernorm_input

        # Layer norm post the self attention.
        layernorm_output = self.post_attention_layernorm(layernorm_input)

        # MLP.
        mlp_output = self.mlp(layernorm_output)

        # Second residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = layernorm_input

        output = torch.nn.functional.dropout(
            mlp_output, p=self.hidden_dropout, training=self.training
        )
        output = residual + output

        return output, kv_cache


class GLMTransformer(torch.nn.Module):
    """Transformer class."""

    def __init__(self, config: ChatGLMConfig, device=None):
        super(GLMTransformer, self).__init__()

        self.fp32_residual_connection = config.fp32_residual_connection
        self.post_layer_norm = config.post_layer_norm

        # Number of layers.
        self.num_layers = config.num_layers

        # Transformer layers.
        def build_layer(layer_number):
            return GLMBlock(config, layer_number, device=device)

        self.layers = torch.nn.ModuleList(
            [build_layer(i + 1) for i in range(self.num_layers)]
        )

        if self.post_layer_norm:
            LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
            # Final layer norm before output.
            self.final_layernorm = LayerNormFunc(
                config.hidden_size,
                eps=config.layernorm_epsilon,
                device=device,
                dtype=config.torch_dtype,
            )

        self.gradient_checkpointing = False

    def _get_layer(self, layer_number):
        return self.layers[layer_number]

    def forward(
        self,
        hidden_states,
        attention_mask,
        rotary_pos_emb,
        kv_caches=None,
        use_cache: Optional[bool] = True,
        output_hidden_states: Optional[bool] = False,
    ):
        if not kv_caches:
            kv_caches = [None for _ in range(self.num_layers)]
        presents = () if use_cache else None
        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

        all_self_attentions = None
        all_hidden_states = () if output_hidden_states else None
        for index in range(self.num_layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer = self._get_layer(index)
            if self.gradient_checkpointing and self.training:
                layer_ret = torch.utils.checkpoint.checkpoint(
                    layer,
                    hidden_states,
                    attention_mask,
                    rotary_pos_emb,
                    kv_caches[index],
                    use_cache,
                    use_reentrant=False,
                )
            else:
                layer_ret = layer(
                    hidden_states,
                    attention_mask,
                    rotary_pos_emb,
                    kv_cache=kv_caches[index],
                    use_cache=use_cache,
                )
            hidden_states, kv_cache = layer_ret
            if use_cache:
                presents = presents + (kv_cache,)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        # Final layer norm.
        if self.post_layer_norm:
            hidden_states = self.final_layernorm(hidden_states)

        return hidden_states, presents, all_hidden_states, all_self_attentions


class ChatGLMPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and
    a simple interface for downloading and loading pretrained models.
    """

    is_parallelizable = False
    supports_gradient_checkpointing = True
    config_class = ChatGLMConfig
    base_model_prefix = "transformer"
    _no_split_modules = ["GLMBlock"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        return

    def get_masks(self, input_embeds, past_key_values, padding_mask=None):
        batch_size, seq_length, embed_size = input_embeds.shape
        full_attention_mask = torch.ones(
            batch_size, seq_length, seq_length, device=input_embeds.device
        )
        full_attention_mask.tril_()
        past_length = 0
        if past_key_values:
            past_length = past_key_values[0][0].shape[2]
        if past_length:
            full_attention_mask = torch.cat(
                (
                    torch.ones(
                        batch_size, seq_length, past_length, device=input_embeds.device
                    ),
                    full_attention_mask,
                ),
                dim=-1,
            )
        if padding_mask is not None:
            full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
        if not past_length and padding_mask is not None:
            full_attention_mask -= padding_mask.unsqueeze(-1) - 1
        full_attention_mask = (full_attention_mask < 0.5).bool()
        full_attention_mask.unsqueeze_(1)
        return full_attention_mask

    def get_position_ids(self, input_ids, device):
        batch_size, seq_length = input_ids.shape
        position_ids = (
            torch.arange(seq_length, dtype=torch.long, device=device)
            .unsqueeze(0)
            .repeat(batch_size, 1)
        )
        return position_ids

    def get_multimodal_position_ids(self, input_ids, device):
        batch_size, seq_length = input_ids.shape
        position_ids = (
            torch.arange(seq_length, dtype=torch.long, device=device)
            .unsqueeze(0)
            .repeat(batch_size, 1)
        )


class Embedding(torch.nn.Module):
    """Language model embeddings."""

    def __init__(self, config: ChatGLMConfig, device=None):
        super(Embedding, self).__init__()

        self.hidden_size = config.hidden_size
        # Word embeddings (parallel).
        self.word_embeddings = nn.Embedding(
            config.padded_vocab_size,
            self.hidden_size,
            dtype=config.torch_dtype,
            device=device,
        )
        self.fp32_residual_connection = config.fp32_residual_connection

    def forward(self, input_ids):
        # Embeddings.
        words_embeddings = self.word_embeddings(input_ids)
        embeddings = words_embeddings
        # If the input flag for fp32 residual connection is set, convert for float.
        if self.fp32_residual_connection:
            embeddings = embeddings.float()
        return embeddings


def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
    if images_list is None or len(images_list) == 0:
        return True
    for image_list in images_list:
        if image_list is not None:
            return False
    return True


class ChatGLMModel(ChatGLMPreTrainedModel):
    def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
        super().__init__(config)
        if empty_init:
            init_method = skip_init
        else:
            init_method = default_init
        init_kwargs = {}
        if device is not None:
            init_kwargs["device"] = device
        self.embedding = init_method(Embedding, config, **init_kwargs)
        self.num_layers = config.num_layers
        self.multi_query_group_num = config.multi_query_group_num
        self.kv_channels = config.kv_channels

        # Rotary positional embeddings
        self.seq_length = config.seq_length
        rotary_dim = (
            config.hidden_size // config.num_attention_heads
            if config.kv_channels is None
            else config.kv_channels
        )

        self.rotary_pos_emb = RotaryEmbedding(
            rotary_dim // 2,
            rope_ratio=config.rope_ratio,
            original_impl=config.original_rope,
            device=device,
            dtype=config.torch_dtype,
        )
        self.encoder = init_method(GLMTransformer, config, **init_kwargs)
        self.output_layer = init_method(
            nn.Linear,
            config.hidden_size,
            config.padded_vocab_size,
            bias=False,
            dtype=config.torch_dtype,
            **init_kwargs,
        )
        self.pre_seq_len = config.pre_seq_len
        self.prefix_projection = config.prefix_projection
        if self.pre_seq_len is not None:
            for param in self.parameters():
                param.requires_grad = False
            self.prefix_tokens = torch.arange(self.pre_seq_len).long()
            self.prefix_encoder = PrefixEncoder(config)
            self.dropout = torch.nn.Dropout(0.1)

        self.vision = EVA2CLIPModel(config)
        self.position_ids_skipped = False

    def get_input_embeddings(self):
        return self.embedding.word_embeddings

    def set_input_embeddings(self, value):
        self.embedding.word_embeddings = value

    def get_prompt(self, batch_size, device, dtype=torch.half):
        prefix_tokens = (
            self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
        )
        past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
        past_key_values = past_key_values.view(
            batch_size,
            self.pre_seq_len,
            self.pre_seq_len,
            self.num_layers * 2,
            self.multi_query_group_num,
            self.kv_channels,
        )
        # seq_len, b, nh, hidden_size
        past_key_values = self.dropout(past_key_values)
        past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
        return past_key_values

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        images: torch.Tensor = None,
        position_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.BoolTensor] = None,
        full_attention_mask: Optional[torch.BoolTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """take care of image_encode, position_ids and (attention_mask = None is fine)"""
        # generate mode with past_key_values. the image features are already mapped
        if past_key_values is None:
            self.position_ids_skipped = False
            # not allow for inputs_embeds, because we want to process image feature
            assert (
                input_ids is not None and inputs_embeds is None
            ), f"{input_ids} {inputs_embeds}"
            if not is_empty(images):  # multi-modality
                image_size: int = self.config.vision_config["image_size"]
                patch_size: int = self.config.vision_config["patch_size"]
                num_patches = (image_size // patch_size // 2) ** 2
                assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"

                inputs_embeds = self.embedding(input_ids)

                images = images.to(dtype=inputs_embeds.dtype)
                images_features = self.vision(images)

                if position_ids is None:
                    position_ids = self.get_position_ids(
                        input_ids, device=inputs_embeds.device
                    )
                new_input_embeds, new_position_ids = [], []

                for i in range(len(input_ids)):
                    input_id = input_ids[i].tolist()
                    boi_token_pos, eoi_token_pos = (
                        input_id.index(self.config.boi_token_id),
                        input_id.index(self.config.eoi_token_id),
                    )
                    assert eoi_token_pos - boi_token_pos == 2
                    new_input_embeds.append(
                        torch.cat(
                            (
                                inputs_embeds[i, :boi_token_pos],
                                images_features[i].to(inputs_embeds.device),
                                inputs_embeds[i, eoi_token_pos + 1 :],
                            )
                        )
                    )
                    new_position_ids.append(
                        torch.arange(
                            0,
                            len(input_id) + num_patches - 1,
                            dtype=position_ids.dtype,
                            device=inputs_embeds.device,
                        )
                    )
                inputs_embeds = torch.stack(new_input_embeds, dim=0)
                position_ids = torch.stack(new_position_ids, dim=0)
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        batch_size, seq_length = input_ids.shape

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

        if self.pre_seq_len is not None:
            if past_key_values is None:
                past_key_values = self.get_prompt(
                    batch_size=batch_size,
                    device=input_ids.device,
                    dtype=inputs_embeds.dtype,
                )
            if attention_mask is not None:
                attention_mask = torch.cat(
                    [
                        attention_mask.new_ones((batch_size, self.pre_seq_len)),
                        attention_mask,
                    ],
                    dim=-1,
                )

        if full_attention_mask is None:
            if (attention_mask is not None and not attention_mask.all()) or (
                past_key_values and seq_length != 1
            ):
                if self.training:
                    # https://github.com/THUDM/GLM-4/issues/264
                    new_input_ids, new_attention_mask = [], []
                    for i in range(len(input_ids)):
                        input_id = input_ids[i].tolist()
                        boi_token_pos, eoi_token_pos = (
                            input_id.index(self.config.boi_token_id),
                            input_id.index(self.config.eoi_token_id),
                        )
                        assert eoi_token_pos - boi_token_pos == 2

                        new_attention_mask.append(
                            torch.cat(
                                (
                                    attention_mask[i, : boi_token_pos + 1],
                                    torch.ones(num_patches).to(attention_mask.device),
                                    attention_mask[i, eoi_token_pos:],
                                )
                            )
                        )

                        new_input_ids.append(
                            torch.cat(
                                (
                                    input_ids[i, : boi_token_pos + 1],
                                    input_ids[i, -1].repeat(num_patches),
                                    input_ids[i, eoi_token_pos:],
                                )
                            )
                        )

                    attention_mask = torch.stack(new_attention_mask, dim=0)
                    input_ids = torch.stack(new_input_ids, dim=0)
                    inputs_embeds = self.embedding(input_ids)

                full_attention_mask = self.get_masks(
                    inputs_embeds, past_key_values, padding_mask=attention_mask
                )

        # Rotary positional embeddings
        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
        if position_ids[0].size()[0] == 1 and not self.position_ids_skipped:
            self.position_ids_skipped = True
            position_ids[:, 0] = position_ids[:, 0] + 1600 - 1

        if position_ids is not None:
            rotary_pos_emb = rotary_pos_emb[position_ids]
        else:
            rotary_pos_emb = rotary_pos_emb[None, :seq_length]

        hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
            inputs_embeds,
            full_attention_mask,
            rotary_pos_emb=rotary_pos_emb,
            kv_caches=past_key_values,
            use_cache=use_cache,
            output_hidden_states=output_hidden_states,
        )

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    presents,
                    all_hidden_states,
                    all_self_attentions,
                ]
                if v is not None
            )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


def _history_to_prompt(history, query):
    prompt = ""
    flag = False
    for i, (old_query, response) in enumerate(history):
        prompt += (
            ("<|user|>" if flag else "")
            + old_query
            + "<|assistant|>"
            + response
            + "<|endoftext|>"
        )
        flag = True
    prompt += "{}{}<|assistant|>".format("<|user|>" if flag else "", query)
    return prompt


class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
        super().__init__(config)

        self.max_sequence_length = config.max_length
        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
        self.config = config

    def _update_model_kwargs_for_generation(
        self,
        outputs: ModelOutput,
        model_kwargs: Dict[str, Any],
        is_encoder_decoder: bool = False,
    ) -> Dict[str, Any]:

        # update past_key_values
        cache_name, cache = self._extract_past_from_model_output(outputs)
        model_kwargs[cache_name] = cache

        # update attention mask
        if "attention_mask" in model_kwargs:
            attention_mask = model_kwargs["attention_mask"]
            model_kwargs["attention_mask"] = torch.cat(
                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))],
                dim=-1,
            )

        # update position ids
        if "position_ids" in model_kwargs:
            position_ids = model_kwargs["position_ids"]
            new_position_id = position_ids[..., -1:].clone()
            new_position_id += 1
            model_kwargs["position_ids"] = torch.cat(
                [position_ids, new_position_id], dim=-1
            )

        model_kwargs["is_first_forward"] = False
        return model_kwargs

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        images: Optional[torch.Tensor] = None,
        past_key_values: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        is_first_forward: bool = True,
        **kwargs,
    ) -> dict:
        # only last token for input_ids if past is not None
        if position_ids is None:
            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
        if attention_mask is not None:
            image_size: int = self.config.vision_config["image_size"]
            patch_size: int = self.config.vision_config["patch_size"]
            num_patches = (image_size // patch_size // 2) ** 2
            new_attention_masks = []

            # if not image, use this default id
            eoi_token_pos = 6
            boi_token_pos = 4

            for i in range(len(input_ids)):
                input_id = input_ids[i].tolist()
                if not is_empty(images):
                    boi_token_pos, eoi_token_pos = (
                        input_id.index(self.config.boi_token_id),
                        input_id.index(self.config.eoi_token_id),
                    )
                assert eoi_token_pos - boi_token_pos == 2
                new_attention_masks.append(
                    torch.cat(
                        (
                            attention_mask[i, : boi_token_pos + 1],
                            attention_mask.new_ones(num_patches),
                            attention_mask[i, eoi_token_pos:],
                        )
                    )
                )
            attention_mask = torch.stack(new_attention_masks, dim=0)
        if not is_first_forward:
            if past_key_values is not None:
                position_ids = position_ids[..., -1:]
                input_ids = input_ids[:, -1:]
        return {
            "input_ids": input_ids,
            "images": images,
            "past_key_values": past_key_values,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "return_last_logit": True,
            "use_cache": use_cache,
        }

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        images: List[List[torch.Tensor]] = None,
        position_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        return_last_logit: Optional[bool] = False,
    ):
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids=input_ids,
            images=images,
            position_ids=position_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        if return_last_logit:
            hidden_states = hidden_states[:, -1:]
        lm_logits = self.transformer.output_layer(hidden_states)

        loss = None
        if labels is not None:
            new_labels = []
            for i in range(len(input_ids)):
                input_id = input_ids[i].tolist()
                boi_token_pos, eoi_token_pos = (
                    input_id.index(self.config.boi_token_id),
                    input_id.index(self.config.eoi_token_id),
                )
                assert eoi_token_pos - boi_token_pos == 2

                new_labels.append(
                    torch.cat(
                        (
                            labels[i, : boi_token_pos + 1],
                            torch.tensor([-100])
                            .to(labels.device)
                            .to(labels.dtype)
                            .repeat(1600),
                            labels[i, eoi_token_pos:],
                        )
                    )
                )

            labels = torch.stack(new_labels, dim=0)
            lm_logits = lm_logits.to(torch.float32)
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
            )

            lm_logits = lm_logits.to(hidden_states.dtype)
            loss = loss.to(hidden_states.dtype)

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

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

    @staticmethod
    def _reorder_cache(
        past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        return tuple(
            (
                layer_past[0].index_select(0, beam_idx.to(layer_past[0].device)),
                layer_past[1].index_select(0, beam_idx.to(layer_past[1].device)),
            )
            for layer_past in past
        )


class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)

        self.classifier_head = nn.Linear(
            config.hidden_size, config.num_labels, bias=True, dtype=torch.half
        )
        if config.classifier_dropout is not None:
            self.dropout = nn.Dropout(config.classifier_dropout)
        else:
            self.dropout = None
        self.config = config

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        full_attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        transformer_outputs = self.transformer(
            input_ids=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            full_attention_mask=full_attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        pooled_hidden_states = hidden_states[-1]
        if self.dropout is not None:
            pooled_hidden_states = self.dropout(pooled_hidden_states)
        logits = self.classifier_head(pooled_hidden_states)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (
                    labels.dtype == torch.long or labels.dtype == torch.int
                ):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze().float(), labels.squeeze())
                else:
                    loss = loss_fct(logits.float(), labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(
                    logits.view(-1, self.num_labels).float(), labels.view(-1)
                )
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))

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

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )