ColossalAI/colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py

"""
The ChatGLM2-6B License

1. Definitions

“Licensor” means the ChatGLM2-6B Model Team that distributes its Software.

“Software” means the ChatGLM2-6B model parameters made available under this license.

2. License Grant

Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

3. Restriction

You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes.

You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.

4. Disclaimer

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

5. Limitation of Liability

EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

6. Dispute Resolution

This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing.

Note that the license is subject to update to a more comprehensive version.  For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com.
"""
""" PyTorch ChatGLM model. """

import copy
import math
import sys
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from .configuration_chatglm import ChatGLMConfig

# 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


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, 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

    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.
        """
        # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
        theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))

        # Create position indexes `[0, 1, ..., seq_len - 1]`
        seq_idx = torch.arange(seq_len, dtype=dtype, 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 behavior 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.elementwise_affine = True
        self.normalized_shape = normalized_shape
        self.weight = torch.nn.Parameter(torch.ones(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:
            query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
            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.permute(2, 0, 1, 3)
            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(1),
                query_layer.size(2),
                query_layer.size(0),
                key_layer.size(0),
            )

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

            # preallocating 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.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).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 [sk, b * np, hn]
            value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -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.transpose(0, 1))
            # change view [b, np, sq, hn]
            context_layer = context_layer.view(*output_size)
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
            # [sq, b, np, hn] --> [sq, b, hp]
            new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
            context_layer = context_layer.view(*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 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)
        if use_cache:
            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,
                )
            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)
        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,
            # original_impl=config.original_rope, # config has no attribute 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)

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

    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.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,
        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 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):
                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,
            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 quantize(self, weight_bit_width: int):
        from .quantization import quantize

        quantize(self.encoder, weight_bit_width)
        return self


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.quantized = False

        if self.config.quantization_bit:
            self.quantize(self.config.quantization_bit, empty_init=True)

    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[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()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            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

    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
        prompt = tokenizer.build_prompt(query, history=history)
        inputs = tokenizer([prompt], return_tensors="pt")
        inputs = inputs.to(self.device)
        return inputs

    def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
        if history:
            prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query)
            input_ids = tokenizer.encode(prompt, add_special_tokens=False)
            input_ids = input_ids[1:]
            inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False)
        else:
            prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query)
            inputs = tokenizer([prompt], return_tensors="pt")
        inputs = inputs.to(self.device)
        return inputs

    @torch.no_grad()
    def chat(
        self,
        tokenizer,
        query: str,
        history: List[Tuple[str, str]] = None,
        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 = self.build_inputs(tokenizer, query, history=history)
        outputs = self.generate(**inputs, **gen_kwargs)
        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) :]
        response = tokenizer.decode(outputs)
        response = self.process_response(response)
        history = history + [(query, response)]
        return response, history

    @torch.no_grad()
    def stream_chat(
        self,
        tokenizer,
        query: str,
        history: List[Tuple[str, str]] = None,
        past_key_values=None,
        max_length: int = 8192,
        do_sample=True,
        top_p=0.8,
        temperature=0.8,
        logits_processor=None,
        return_past_key_values=False,
        **kwargs,
    ):
        if history is None:
            history = []
        if logits_processor is None:
            logits_processor = LogitsProcessorList()
        logits_processor.append(InvalidScoreLogitsProcessor())
        gen_kwargs = {
            "max_length": max_length,
            "do_sample": do_sample,
            "top_p": top_p,
            "temperature": temperature,
            "logits_processor": logits_processor,
            **kwargs,
        }
        if past_key_values is None and not return_past_key_values:
            inputs = self.build_inputs(tokenizer, query, history=history)
        else:
            inputs = self.build_stream_inputs(tokenizer, query, history=history)
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[0]
            if self.transformer.pre_seq_len is not None:
                past_length -= self.transformer.pre_seq_len
            inputs.position_ids += past_length
            attention_mask = inputs.attention_mask
            attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
            inputs["attention_mask"] = attention_mask
        for outputs in self.stream_generate(
            **inputs,
            past_key_values=past_key_values,
            return_past_key_values=return_past_key_values,
            **gen_kwargs,
        ):
            if return_past_key_values:
                outputs, past_key_values = outputs
            outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) :]
            response = tokenizer.decode(outputs)
            if response and response[-1] != "<EFBFBD>":
                response = self.process_response(response)
                new_history = history + [(query, response)]
                if return_past_key_values:
                    yield response, new_history, past_key_values
                else:
                    yield response, new_history

    @torch.no_grad()
    def stream_generate(
        self,
        input_ids,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        return_past_key_values=False,
        **kwargs,
    ):
        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]

        if generation_config is None:
            generation_config = self.generation_config
        generation_config = copy.deepcopy(generation_config)
        model_kwargs = generation_config.update(**kwargs)
        bos_token_id, eos_token_id = (
            generation_config.bos_token_id,
            generation_config.eos_token_id,
        )

        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]

        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        if has_default_max_length and generation_config.max_new_tokens is None:
            warnings.warn(
                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
                "This behavior is deprecated and will be removed from the config in v5 of Transformers -- we"
                " recommend using `max_new_tokens` to control the maximum length of the generation.",
                UserWarning,
            )
        elif generation_config.max_new_tokens is not None:
            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
            if not has_default_max_length:
                logger.warn(
                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                    "Please refer to the documentation for more information. "
                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
                    UserWarning,
                )

        if input_ids_seq_length >= generation_config.max_length:
            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
            logger.warning(
                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
                " increasing `max_new_tokens`."
            )

        # 2. Set generation parameters if not already defined
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_seq_length,
            encoder_input_ids=input_ids,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            logits_processor=logits_processor,
        )

        stopping_criteria = self._get_stopping_criteria(
            generation_config=generation_config, stopping_criteria=stopping_criteria
        )
        logits_warper = self._get_logits_warper(generation_config)

        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
        scores = None
        while True:
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
            # forward pass to get next token
            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=False,
                output_hidden_states=False,
            )

            next_token_logits = outputs.logits[:, -1, :]

            # pre-process distribution
            next_token_scores = logits_processor(input_ids, next_token_logits)
            next_token_scores = logits_warper(input_ids, next_token_scores)

            # sample
            probs = nn.functional.softmax(next_token_scores, dim=-1)
            if generation_config.do_sample:
                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
            else:
                next_tokens = torch.argmax(probs, dim=-1)

            # update generated ids, model inputs, and length for next step
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
            if return_past_key_values:
                yield input_ids, outputs.past_key_values
            else:
                yield input_ids
            # stop when each sentence is finished, or if we exceed the maximum length
            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
                break

    def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
        if bits == 0:
            return

        from .quantization import quantize

        if self.quantized:
            logger.info("Already quantized.")
            return self

        self.quantized = True

        self.config.quantization_bit = bits

        self.transformer.encoder = quantize(
            self.transformer.encoder,
            bits,
            empty_init=empty_init,
            device=device,
            **kwargs,
        )
        return self