modeling_geb.py

"""PyTorch GEB model."""

import math
import copy
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Dict, Any, List
import importlib.util
from torch.nn.utils import skip_init
import torch.nn.functional as F
import torch
import torch.utils.checkpoint
from torch import einsum, nn
from torch.cuda.amp import autocast
from torch.nn import BCEWithLogitsLoss, LayerNorm, CrossEntropyLoss, MSELoss
from copy import deepcopy
from deepspeed.accelerator import get_accelerator
try:
    from einops import rearrange
except ImportError:
    rearrange = None
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
from .configuration_geblm import GEBConfig
try:
    # FlashAttention-2
    from flash_attn.flash_attn_interface import flash_attn_varlen_func
except ImportError:
    flash_attn_varlen_func = None
FlashAttentionBuilder = get_accelerator().get_op_builder("FlashAttentionBuilder")
flash_attn_builder = None


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "geb"
_CONFIG_FOR_DOC = "GEBConfig"

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

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 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: GEBConfig):
        super().__init__()
        self.prefix_projection = config.prefix_projection
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        if self.prefix_projection:
            # Use a two-layer MLP to encode the prefix
            kv_size = config.num_layers * config.kv_channels * self.num_key_value_groups * 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 * self.num_key_value_groups * 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 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
#         )


# @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 RotaryEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)
        if importlib.util.find_spec('einops') is None:
            raise RuntimeError("einops is required for Rotary Embedding")

    def forward(self, max_seq_len, offset=0):
        seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
        # Calculate the product of seq and inv_freq
        freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)
        # first part even vector components, second part odd vector components,
        #  2 * dim in dimension size
        emb = torch.cat((freqs, freqs), dim=-1)
        # emb [seq_length, .., dim]
        from einops import rearrange
        # print('rearrange:', rearrange(emb, 'n d -> n 1 1 d').size())
        return rearrange(emb, 'n d -> n 1 1 d')


def _rotate_half(x):
    """
    change sign so the last dimension becomes [-odd, +even]
    """
    from einops import rearrange
    x = rearrange(x, '... (j d) -> ... j d', j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(t, freqs):
    """
    input tensor t is of shape [seq_length, ..., dim]
    rotary positional embeding tensor freqs is of shape [seq_length, ..., dim]
    check https://kexue.fm/archives/8265 for detailed formulas
    """
    # print('t:', t.size())
    # print('freqs:', freqs.size())
    rot_dim = freqs.shape[-1]
    # print('rot_dim:', rot_dim)
    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]

    # first part is cosine component
    # second part is sine component, need to change signs with _rotate_half method
    # print(t.shape, t_pass.shape, freqs.shape)
    t = (t * freqs.cos().to(t.dtype)) + (_rotate_half(t) * freqs.sin().to(t.dtype))
    
    return torch.cat((t, t_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 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: GEBConfig, device=None):
        super(MLP, self).__init__()

        self.add_bias = config.add_bias_linear #false

        # 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, # 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 CoreAttention(torch.nn.Module):

    def __init__(self, config: GEBConfig, layer_number):
        super(CoreAttention, self).__init__()
        # self.fp16 = config.fp16
        # self.bf16 = config.bf16

        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)
        self.num_layers = config.num_layers

        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
        # Dropout. Note that for a single iteration, this layer will generate
        # different outputs on different number of parallel partitions but
        # on average it should not be partition dependent.
        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)

    def forward(self, query_layer, key_layer,
                value_layer, attention_mask):

        # ===================================
        # Raw attention scores. [b, np, s, s]
        # ===================================

        # [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]，Tensor to store matrix multiplication of query and key
        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.contiguous().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 FlashSelfAttention(torch.nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """
    def __init__(self, config: GEBConfig, causal=False, softmax_scale=None, attention_dropout=0.0,
                 device=None, dtype=None):
        super().__init__()
        assert flash_attn_varlen_func is not None or flash_attn_builder is not None, \
            ('Please install FlashAttention first, e.g., with pip install flash-attn or implement your own flash attention')
        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
        self.config = config
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

        # Use FlashAttention-2 when args.use_flash_attn_v2 is True
        self.flash_attn_func = flash_attn_varlen_func if config.use_flash_attn else print('false to Use FlashAttention-2')

    def forward(self, q, k, v):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
        """
        # print(i.dtype() for i in (q,k,v) )
        # assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
        # assert all((get_accelerator().on_accelerator(i) for i in (q, k, v)))
        # if get_accelerator().device_name() == 'cuda':
        #     assert all((i.is_cuda for i in (q,k,v)))
        # else:
        #     assert all((i.is_xpu for i in (q,k,v)))

        batch_size, seqlen_q = q.shape[0], q.shape[1]
        seqlen_k = k.shape[1]

        if get_accelerator().device_name() == 'cuda':
            # goes for cuda device
            q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
            cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
                                        device=q.device)
        else:
            # goes for other device
            q, k, v = [rearrange(x, 'b s h d -> b h s d').contiguous() for x in [q, k, v]]

        if self.training:
            # during training q,k,v always have same seqlen
            assert seqlen_k == seqlen_q

            is_causal = self.causal
            cu_seqlens_k = cu_seqlens_q if get_accelerator().device_name() == 'cuda' else None
            dropout_p = self.dropout_p
        else:
            # turn off FA causal mask after first inference autoregressive iteration
            # only on first autoregressive step q,k,v have same seqlen
            is_causal = seqlen_q == seqlen_k
            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
                        device=q.device) if get_accelerator().device_name() == 'cuda' else None
            dropout_p = 0

        output = self.flash_attn_func(
            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
            dropout_p,
            softmax_scale=self.softmax_scale, causal=is_causal
        ) if get_accelerator().device_name() == 'cuda' else flash_attn_builder.flash_attn_func(
            q, k, v, self.dropout_p, self.softmax_scale, is_causal
        )

        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size) if get_accelerator().device_name() == 'cuda' else rearrange(
            output, 'b h s d -> b s h d').contiguous()
        return output    
    
class GEBAttention(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: GEBConfig, layer_number, device=None):
        super().__init__()
        self.config = config
        self.layer_number = max(1, layer_number)
        self.projection_size = config.kv_channels * config.num_attention_heads
        self.use_flash_attn = config.use_flash_attn
        # Per attention head and per partition values.
        self.hidden_size_per_partition = self.projection_size
        self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
        self.num_attention_heads_per_partition = config.num_attention_heads
        self.num_key_value_heads_per_partition = config.num_key_value_heads
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.kv_projection_size = config.kv_channels * config.num_key_value_heads
        assert self.hidden_size_per_attention_head == self.kv_projection_size // config.num_key_value_heads
        # self.max_position_embeddings = config.model_max_length
        if self.use_flash_attn:
            global flash_attn_builder
            try:
                flash_attn_builder = FlashAttentionBuilder().load()
            except TypeError:
                flash_attn_builder = None
            assert flash_attn_varlen_func != None, "Cannot import FlashAttention v2 "
            if rearrange is None:
                raise ImportError('einops is not installed, please install with pip install einops')
        
        self.query = nn.Linear(config.hidden_size, self.projection_size,
                                         bias=config.add_bias_linear,
                                         device=device, **_config_to_kwargs(config)
                                         )  

        self.key_value = nn.Linear(config.hidden_size, 2 * self.kv_projection_size,
                                         bias=config.add_bias_linear,
                                         device=device, **_config_to_kwargs(config)
                                         )
        
        if config.use_flash_attn:
            self.core_attention_flash = FlashSelfAttention(config, causal=True, attention_dropout=config.attention_dropout)
        else:
            self.core_attention = CoreAttention(config, self.layer_number)
            
        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):
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
            self.num_key_value_groups,
            self.hidden_size_per_attention_head,
            dtype=dtype,
            device=device)
            
    def repeat_kv(self, hidden_states, n_rep):
        slen, batch, num_key_value_heads_per_partition, head_dim = hidden_states.shape
        if n_rep == 1:
            return hidden_states
        hidden_states = hidden_states[:, :, :, None, :].expand(
            slen, batch, num_key_value_heads_per_partition, n_rep, head_dim)
        return hidden_states.reshape(slen, batch,
                                     num_key_value_heads_per_partition * n_rep,
                                     head_dim)

    def forward(self, hidden_states, attention_mask,
                rotary_pos_emb=None, kv_cache=None, use_cache=True):
        # Attention head [sq, b, h]--> [sq, b, hp]
        query_layer = self.query(hidden_states)
        # [sq, b, hp] --> [sq, b, np, hn]
        new_tensor_shape = query_layer.size()[:-1] + \
            (self.num_attention_heads_per_partition,
                self.hidden_size_per_attention_head)
        query_layer = query_layer.view(*new_tensor_shape)

        # Attention heads [sq, b, h] --> [sq, b, (np * 2 * hn)]
        mixed_kv_layer = self.key_value(hidden_states)
        # [sq, b, (np * 2 * hn)] --> [sq, b, np, 2 * hn]
        new_tensor_shape = mixed_kv_layer.size()[:-1] + \
            (self.num_key_value_heads_per_partition,
                2 * self.hidden_size_per_attention_head)
        mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)
        # [sq, b, np, 2 * hn] --> 2 [sq, b, np, hn]
        (key_layer,
            value_layer) = split_tensor_along_last_dim(
                mixed_kv_layer, 2)
            
        

        # Repeat kv
        key_layer = self.repeat_kv(key_layer, self.num_key_value_groups)
        value_layer = self.repeat_kv(value_layer,
                                        self.num_key_value_groups)
        
        # 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)
        
        # duplicate the pos_emb for self attention
    
        if rotary_pos_emb is not None:
            if isinstance(rotary_pos_emb, tuple):
                rotary_pos_emb = rotary_pos_emb
            else:
                rotary_pos_emb = ((rotary_pos_emb,) * 2)
            q_pos_emb, k_pos_emb = rotary_pos_emb
            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
            key_layer = apply_rotary_pos_emb(key_layer, k_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.use_flash_attn:
            query_layer, key_layer, value_layer = [rearrange(x, 's b ... -> b s ...').contiguous()
                    for x in (query_layer, key_layer, value_layer)]
            context_layer = self.core_attention_flash(query_layer, key_layer, value_layer)
            context_layer = rearrange(context_layer, 'b s h d -> s b (h d)').contiguous()
        else:
            context_layer = self.core_attention(
                query_layer, key_layer, value_layer, attention_mask)
        
        output= self.dense(context_layer)# output, bias = self.dense(context_layer)
       
        return output, kv_cache
    
class GEBBlock(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: GEBConfig, layer_number, device=None):
        super(GEBBlock, self).__init__()
        self.layer_number = layer_number
        self.apply_residual_connection_post_layernorm \
            = config.apply_residual_connection_post_layernorm

        # self.bf16 = config.bf16
        self.fp32_residual_connection = config.fp32_residual_connection
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon, device=device,
                                             dtype=config.torch_dtype)
        self.self_attention = GEBAttention(config, layer_number, device=device)
        self.hidden_dropout = config.hidden_dropout
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon, device=device,
                                             dtype=config.torch_dtype)
        self.mlp = MLP(config, device=device)
        
    def forward(self, hidden_states, attention_mask=None,
                rotary_pos_emb=None,
                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=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=0.0,
                                            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
        out = torch.nn.functional.dropout(mlp_output,
                                            p=0.0,
                                            training=self.training)
        output = residual + out
        return output, kv_cache
    
class GEBTransformer(torch.nn.Module):
    """Transformer class."""

    def __init__(self, config: GEBConfig, device=None):
        super(GEBTransformer, self).__init__()

        self.fp32_residual_connection = config.fp32_residual_connection
        self.post_layer_norm = config.post_layer_norm
        self.num_layers = config.num_layers
        def build_layer(layer_number):
            return GEBBlock(
                config,
                layer_number,
                device=device)
        # Build the layers
        self.layers = []
        for i in range(self.num_layers):
            layer_num = i + 1
            self.layers.append(build_layer(layer_num))
        self.layers = torch.nn.ModuleList(self.layers)
        if self.post_layer_norm:
            self.final_layernorm = RMSNorm(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_hidden = torch.utils.checkpoint.checkpoint(
                    layer,
                    hidden_states,
                    attention_mask,
                    rotary_pos_emb,
                    kv_caches[index],
                    use_cache
                )
            else:
                layer_hidden = layer(
                    hidden_states,
                    attention_mask,
                    rotary_pos_emb,
                    kv_cache=kv_caches[index],
                    use_cache=use_cache
                )
            hidden_states, kv_cache = layer_hidden
            if use_cache:
                presents = presents + (kv_cache,)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)
        if self.post_layer_norm:
            hidden_states = self.final_layernorm(hidden_states)
        return hidden_states, presents, all_hidden_states, all_self_attentions
    

class GEBPreTrainedModel(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 = GEBConfig
    base_model_prefix = "transformer"
    _no_split_modules = ["GEBBlock"]

    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, GEBTransformer):
            module.gradient_checkpointing = value

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

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

        self.hidden_size = config.hidden_size
        # Word embeddings.
        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 GEBModel(GEBPreTrainedModel):
    def __init__(self, config: GEBConfig, 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.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        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= True, device=device,
        #                                       dtype=config.torch_dtype)
        
        self.rotary_pos_emb = RotaryEmbedding(rotary_dim)


        self.encoder = init_method(GEBTransformer, 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.num_key_value_groups,
            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()
            # Rotary positional embeddings
        # print(position_ids[0].tolist())
        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
        rotary_pos_emb = rotary_pos_emb[position_ids[0].tolist()]
        # rotary_pos_emb = self.rotary_pos_emb(position_ids.shape[-1])
    

        # # Rotary positional embeddings emb [seq_length, .., dim] no not need transpose
        # rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
        # rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
        # print('rotary_pos_emb:', rotary_pos_emb.size())
        # if position_ids is not None:
        #     rotary_pos_emb = rotary_pos_emb[position_ids]
        #     print('rotary_pos_emb:', rotary_pos_emb.size())
        # 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,
        )

class GEBForCausalLM(GEBPreTrainedModel):
    def __init__(self, config: GEBConfig, empty_init=True, device=None):
        super().__init__(config)

        self.max_sequence_length = config.max_length
        self.transformer = GEBModel(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,
            use_cache: Optional[bool] = None,
            is_first_forward: bool = True,
            **kwargs
    ) -> dict:
        # only last token for input_ids if past is not None
        if position_ids is None:
            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
        if not is_first_forward:
            if past_key_values is not None:
                position_ids = position_ids[..., -1:]
                input_ids = input_ids[:, -1:]
        return {
            "input_ids": input_ids,
            "past_key_values": past_key_values,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "return_last_logit": True,
            "use_cache": use_cache
        }  

    def forward(
            self,
            input_ids: Optional[torch.Tensor] = None,
            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 build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
        prompt = tokenizer.build_prompt(query, history=history)
        tokens = [tokenizer.get_command("<bos>")] + tokenizer.encode(prompt)
        inputs = tokenizer.batch_encode_plus([tokens], return_tensors="pt", is_split_into_words=True)
        inputs = inputs.to(self.device)
        return inputs
  
    # 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")
    #     # print(inputs)
    #     inputs = inputs.to(self.device)
    #     return inputs
    
    @torch.inference_mode()
    def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 512, num_beams=1,
             do_sample=True, top_p=0.5, temperature=0.3, logits_processor=None, repetition_penalty = 1.15, **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, "repetition_penalty":repetition_penalty, **kwargs}
        prompt = tokenizer.build_prompt(query, history=[])
        system = "You are a helpful assistant.\n"
        system_ids = [
            tokenizer.get_command("<bos>") 
            ] + tokenizer.encode(text=system) + [
            tokenizer.get_command("<eos>")]
            
        prompt_ids = [
            tokenizer.get_command("<bos>")
            ] + tokenizer.encode(
                text=prompt,
                add_special_tokens=False
            ) + [
            tokenizer.get_command("<eos>")] + [
            tokenizer.get_command("<bos>")]
        tokens = system_ids + prompt_ids
        inputs = tokenizer.batch_encode_plus([tokens], return_tensors="pt", is_split_into_words=True)
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        inputs = inputs.to(device)
        outputs = self.generate(**inputs, **gen_kwargs)
        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
        response = tokenizer.decode(outputs)
        return response, history