custom_diffusers/src/diffusers/pipelines/kolors/text_encoder.py

# Copyright 2024 ChatGLM3-6B Model Team, Kwai-Kolors Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from typing import List, Optional, Tuple

import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import LayerNorm
from torch.nn.utils import skip_init
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPast

from ...utils import logging


logger = logging.get_logger(__name__)


class ChatGLMConfig(PretrainedConfig):
    model_type = "chatglm"

    def __init__(
        self,
        num_layers=28,
        padded_vocab_size=65024,
        hidden_size=4096,
        ffn_hidden_size=13696,
        kv_channels=128,
        num_attention_heads=32,
        seq_length=2048,
        hidden_dropout=0.0,
        classifier_dropout=None,
        attention_dropout=0.0,
        layernorm_epsilon=1e-5,
        rmsnorm=True,
        apply_residual_connection_post_layernorm=False,
        post_layer_norm=True,
        add_bias_linear=False,
        add_qkv_bias=False,
        bias_dropout_fusion=True,
        multi_query_attention=False,
        multi_query_group_num=1,
        apply_query_key_layer_scaling=True,
        attention_softmax_in_fp32=True,
        fp32_residual_connection=False,
        quantization_bit=0,
        pre_seq_len=None,
        prefix_projection=False,
        **kwargs,
    ):
        self.num_layers = num_layers
        self.vocab_size = padded_vocab_size
        self.padded_vocab_size = padded_vocab_size
        self.hidden_size = hidden_size
        self.ffn_hidden_size = ffn_hidden_size
        self.kv_channels = kv_channels
        self.num_attention_heads = num_attention_heads
        self.seq_length = seq_length
        self.hidden_dropout = hidden_dropout
        self.classifier_dropout = classifier_dropout
        self.attention_dropout = attention_dropout
        self.layernorm_epsilon = layernorm_epsilon
        self.rmsnorm = rmsnorm
        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
        self.post_layer_norm = post_layer_norm
        self.add_bias_linear = add_bias_linear
        self.add_qkv_bias = add_qkv_bias
        self.bias_dropout_fusion = bias_dropout_fusion
        self.multi_query_attention = multi_query_attention
        self.multi_query_group_num = multi_query_group_num
        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
        self.fp32_residual_connection = fp32_residual_connection
        self.quantization_bit = quantization_bit
        self.pre_seq_len = pre_seq_len
        self.prefix_projection = prefix_projection
        super().__init__(**kwargs)


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)


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


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)

            # 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.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


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


@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 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


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 torch.is_grad_enabled() and self.gradient_checkpointing:
            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 torch.is_grad_enabled() and self.gradient_checkpointing:
                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


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


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 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=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)


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


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, 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,
        )