modeling_chatglm.py

""" PyTorch ChatGLM model. """

import math
import copy
import warnings
import re
import sys

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

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

from .configuration_chatglm import ChatGLMConfig
from einops import rearrange
try:
    from flash_attn.flash_attn_interface import flash_attn_unpadded_func
except ImportError:
    try:
        # FlashAttention-2
        from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
    except ImportError:
        flash_attn_unpadded_func = None

# flags required to enable jit fusion kernels

if sys.platform != 'darwin':
    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__)

_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B"
_CONFIG_FOR_DOC = "ChatGLM6BConfig"

CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "THUDM/chatglm2-6b",
    # See all ChatGLM models at https://huggingface.co/models?filter=chatglm
]

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[..., 5] = 5e4
        return scores

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 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):
        return self.forward_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: [sq, b, np, hn]
    sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
    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.reshape(sq, -1, np, rot_dim // 2, 2)
    rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
    x_out2 = torch.stack(
        [
            xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
            xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
        ],
        -1,
    )
    x_out2 = x_out2.flatten(3)
    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

        self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
        self.attention_dropout = config.attention_dropout

    def forward(self, query_layer, key_layer, value_layer, attention_mask):
        seqlen_q, batch_size = query_layer.shape[0], query_layer.shape[1]
        seqlen_k = key_layer.shape[0]
        query_layer, key_layer, value_layer = [rearrange(x, 's b ... -> (b s) ...') for x in [query_layer, key_layer, value_layer]]
        # DO flash_attn_varlen_func
        if attention_mask is None or attention_mask.ndim != 1:
            cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
                                    device=query_layer.device)
        else:
            assert seqlen_q == seqlen_k
            cu_seqlens_q = attention_mask
        if self.training:
            assert seqlen_k == seqlen_q
            is_causal = True
            cu_seqlens_k = cu_seqlens_q
        else:
            is_causal = seqlen_q == seqlen_k
            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
                        device=query_layer.device) if not is_causal else cu_seqlens_q
            self.attention_dropout = 0
        context_layer = flash_attn_unpadded_func(
            query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
            self.attention_dropout,
            softmax_scale=1.0 / self.norm_factor, causal=is_causal
        )
        context_layer = rearrange(context_layer, '(b s) ... -> s b ...', b=batch_size)
        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 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
        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: [sq, b, h]

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

        # Attention heads [sq, b, h] --> [sq, b, (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)

            # [sq, b, np, 3 * hn] --> 3 [sq, b, 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)

        # adjust key and value for inference
        if use_cache:
            if kv_cache is not None:
                cache_k, cache_v = kv_cache
                key_layer = torch.cat((cache_k, key_layer), dim=0)
                value_layer = torch.cat((cache_v, value_layer), dim=0)
            kv_cache = (key_layer, value_layer)
        else:
            kv_cache = None
        
            
        if self.multi_query_attention:
            key_layer = key_layer.unsqueeze(-2)
            key_layer = key_layer.expand(
                -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
            )
            key_layer = key_layer.contiguous().view(
                key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
            )
            value_layer = value_layer.unsqueeze(-2)
            value_layer = value_layer.expand(
                -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
            )
            value_layer = value_layer.contiguous().view(
                value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
            )

        # ==================================
        # 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"]

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

    def get_masks(self, input_ids, past_key_values, padding_mask=None):
        batch_size, seq_length = input_ids.shape
        full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
        full_attention_mask.tril_()
        past_length = 0
        if past_key_values:
            past_length = past_key_values[0][0].shape[0]
        if past_length:
            full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
                                                        device=input_ids.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 _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, GLMTransformer):
            module.gradient_checkpointing = value


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
        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
        embeddings = embeddings.transpose(0, 1).contiguous()
        # If the input flag for fp32 residual connection is set, convert for float.
        if self.fp32_residual_connection:
            embeddings = embeddings.float()
        return embeddings


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)

        # 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)

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

    def forward(
            self,
            input_ids,
            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,
    ):
        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 full_attention_mask is None:
        #     if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
        #         full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)

        # Rotary positional embeddings
        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
        if position_ids is not None:
            rotary_pos_emb = rotary_pos_emb[position_ids]
        else:
            rotary_pos_emb = rotary_pos_emb[None, :seq_length]
        rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()

        # Run encoder.
        hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
            inputs_embeds, 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,
        )


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
        self.pack_loss = False

    def _update_model_kwargs_for_generation(
            self,
            outputs: ModelOutput,
            model_kwargs: Dict[str, Any],
            is_encoder_decoder: bool = False,
            standardize_cache_format: bool = False,
    ) -> Dict[str, Any]:
        # update past_key_values
        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
            outputs, standardize_cache_format=standardize_cache_format
        )

        # 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,
            past_key_values: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.Tensor] = 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 not is_first_forward:
            position_ids = position_ids[..., -1:]
            input_ids = input_ids[:, -1:]
        return {
            "input_ids": input_ids,
            "past_key_values": past_key_values,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "return_last_logit": True
        }

    def forward(
            self,
            input_ids: Optional[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[Tuple[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,
            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)
        lm_logits = lm_logits.transpose(0, 1).contiguous()

        loss = None
        if labels is not None:
            lm_logits = lm_logits.to(torch.float32)
            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :].contiguous()
            if isinstance(labels, tuple) or isinstance(labels, list):
                labels, weights = labels
            shift_labels = labels[..., 1:].contiguous()
            if self.pack_loss:
                loss_fct = CrossEntropyLoss(ignore_index=-100)#, reduction='none')
                loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
                loss *= weights
            # if self.pack_loss:
            #     shift_weights = weights[..., 1:].contiguous()
            #     loss_fct = CrossEntropyLoss(ignore_index=-100, reduction='none')
            #     loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
            #     loss = (loss * shift_weights).sum()
            else:
                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(1, beam_idx.to(layer_past[0].device)),
                layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
            )
            for layer_past in past
        )

    def process_response(self, response):
        response = response.strip()
        response = response.replace("[[训练时间]]", "2023年")
        return response

    @torch.inference_mode()
    def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
             max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
             **kwargs):
        if history is None:
            history = []
        if logits_processor is None:
            logits_processor = LogitsProcessorList()
        logits_processor.append(InvalidScoreLogitsProcessor())
        gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
        inputs = tokenizer.build_chat_input(query, history=history, role=role)
        inputs = inputs.to(self.device)
        eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
                        tokenizer.get_command("<|observation|>")]
        outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
        response = tokenizer.decode(outputs)
        history.append({"role": role, "content": query})
        response = self.process_response(response)
        return response, history