modeling_baichuan.py

# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.

from .configuration_baichuan import BaichuanConfig
# from .generation_utils import build_chat_input, TextIterStreamer

import math
from threading import Thread
from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig
from transformers.activations import ACT2FN
from transformers.generation.utils import GenerationConfig
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.utils import logging, ContextManagers

import os
from contextlib import contextmanager
from accelerate import init_empty_weights

logger = logging.get_logger(__name__)

try:
    from xformers import ops as xops
except ImportError:
    xops = None
    logger.warning(
        "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
    )


def _get_interleave(n):
    def _get_interleave_power_of_2(n):
        start = 2 ** (-(2 ** -(math.log2(n) - 3)))
        ratio = start
        return [start * ratio**i for i in range(n)]

    if math.log2(n).is_integer():
        return _get_interleave_power_of_2(n)
    else:
        closest_power_of_2 = 2 ** math.floor(math.log2(n))
        return (
            _get_interleave_power_of_2(closest_power_of_2)
            + _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
        )


def _fill_with_neg_inf(t):
    """FP16-compatible function that fills a tensor with -inf."""
    return t.float().fill_(float("-inf")).type_as(t)


def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
    _future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
    _future_mask = _future_mask.unsqueeze(0) + alibi
    new_future_mask = _future_mask.to(tensor)
    return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]


def _gen_alibi_mask(tensor, n_head, max_pos):
    slopes = torch.Tensor(_get_interleave(n_head))
    position_point = torch.arange(max_pos) - max_pos + 1
    position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
    diag = torch.diag(position_point[0])
    position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
    alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
    alibi = alibi.view(n_head, 1, max_pos)
    alibi_mask = torch.triu(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
    alibi_mask = alibi_mask.unsqueeze(0) + alibi
    return alibi_mask


class RMSNorm(torch.nn.Module):
    def __init__(self, hidden_size, epsilon=1e-6):
        super().__init__()
        self.weight = torch.nn.Parameter(torch.empty(hidden_size))
        self.epsilon = epsilon

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

        # convert into half-precision
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


class MLP(torch.nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
    ):
        super().__init__()
        self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
        self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class BaichuanAttention(torch.nn.Module):
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.model_max_length

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
            )
        self.W_pack = torch.nn.Linear(
            self.hidden_size, 3 * self.hidden_size, bias=False
        )
        self.o_proj = torch.nn.Linear(
            self.num_heads * self.head_dim, self.hidden_size, bias=False
        )

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return (
            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
            .transpose(1, 2)
            .contiguous()
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        proj = self.W_pack(hidden_states)
        proj = (
            proj.unflatten(-1, (3, self.hidden_size))
            .unsqueeze(0)
            .transpose(0, -2)
            .squeeze(-2)
        )
        query_states = (
            proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )
        key_states = (
            proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )
        value_states = (
            proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        )

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None
        if xops is not None and self.training:
            attn_weights = None
            # query_states = query_states.transpose(1, 2)
            # key_states = key_states.transpose(1, 2)
            # value_states = value_states.transpose(1, 2)
            # attn_output = xops.memory_efficient_attention(
            #     query_states, key_states, value_states, attn_bias=attention_mask
            # )
            with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
            attn_output = attn_output.transpose(1, 2)
        else:
            attn_weights = torch.matmul(
                query_states, key_states.transpose(2, 3)
            ) / math.sqrt(self.head_dim)

            if attention_mask is not None:
                if q_len == 1:  # inference with cache
                    if len(attention_mask.size()) == 4:
                        attention_mask = attention_mask[:, :, -1:, :]
                    else:
                        attention_mask = attention_mask[:, -1:, :]
                attn_weights = attn_weights + attention_mask
                attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )

            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
            attn_output = torch.matmul(attn_weights, value_states)

            attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class BaichuanLayer(torch.nn.Module):
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = BaichuanAttention(config=config)
        self.mlp = MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
        )
        self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, epsilon=config.rms_norm_eps
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class BaichuanPreTrainedModel(PreTrainedModel):
    config_class = BaichuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BaichuanLayer"]
    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, torch.nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, torch.nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, BaichuanModel):
            module.gradient_checkpointing = value


class BaichuanModel(BaichuanPreTrainedModel):
    def __init__(self, config: BaichuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.n_head = config.num_attention_heads
        self.embed_tokens = torch.nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = torch.nn.ModuleList(
            [BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
        )
        self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)

        self.gradient_checkpointing = config.gradient_checkpointing
        self.post_init()
        self.max_cache_pos = config.model_max_length
        self.first_run = True
        self.alibi_mask = None

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def get_alibi_mask(self, tensor, seq_length_with_past):
        if self.training:
            slopes = torch.Tensor(_get_interleave(self.n_head))
            position_point = (
                torch.arange(seq_length_with_past) - seq_length_with_past + 1
            )
            position_point = (
                position_point.unsqueeze(0)
                .unsqueeze(0)
                .expand(self.n_head, seq_length_with_past, -1)
            )
            diag = torch.diag(position_point[0])
            position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
                -1, -2
            )
            alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
            mask = _buffered_future_mask(
                tensor, seq_length_with_past, alibi, self.n_head
            )
        else:
            if self.first_run:
                self.first_run = False
                self.register_buffer(
                    "future_mask",
                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
                        tensor
                    ),
                    persistent=False,
                )
            if seq_length_with_past > self.max_cache_pos:
                self.max_cache_pos = seq_length_with_past
                self.register_buffer(
                    "future_mask",
                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
                        tensor
                    ),
                    persistent=False,
                )
            mask = self.future_mask[
                : self.n_head, :seq_length_with_past, :seq_length_with_past
            ]
        return mask

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot provide both input_ids and inputs_embeds simultaneously"
            )
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You need to provide input_ids or inputs_embeds")

        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
        )

        seq_length_with_past = seq_length

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

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

        if self.training:
            if (
                self.alibi_mask is None
                or self.alibi_mask.shape[-1] != seq_length_with_past
            ):
                self.alibi_mask = self.get_alibi_mask(
                    inputs_embeds, seq_length_with_past
                )
            alibi_mask = self.alibi_mask
        else:
            alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)

        if attention_mask is not None:
            if len(attention_mask.shape) == 2:
                expanded_mask = attention_mask.to(alibi_mask.dtype)
                expanded_mask = torch.tril(
                    torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
                ) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
            else:
                expanded_mask = attention_mask
            bsz = inputs_embeds.size(0)
            src_len, tgt_len = alibi_mask.size()[-2:]
            expanded_mask = (
                expanded_mask.unsqueeze(1)
                .expand(bsz, 1, src_len, tgt_len)
                .to(alibi_mask.dtype)
            )
            inverted_mask = 1.0 - expanded_mask
            inverted_mask = inverted_mask.masked_fill(
                inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
            )
            attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
        else:
            attention_mask = alibi_mask

        hidden_states = inputs_embeds

        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

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = (
                past_key_values[idx] if past_key_values is not None else None
            )

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
                if v is not None
            )
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class NormHead(nn.Module):
    def __init__(self, hidden_size, vocab_size, bias=False):
        super().__init__()
        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        self.first_flag = True

    def forward(self, hidden_states):
        if self.training:
            norm_weight = nn.functional.normalize(self.weight)
            self.first_flag = True
        elif self.first_flag:
            self.first_flag = False
            self.weight = nn.Parameter(nn.functional.normalize(self.weight))
            norm_weight = self.weight
        else:
            norm_weight = self.weight
        return nn.functional.linear(hidden_states, norm_weight)

_init_weights = True
@contextmanager
def no_init_weights(_enable=True):
    global _init_weights
    old_init_weights = _init_weights
    if _enable:
        _init_weights = False
    try:
        yield
    finally:
        _init_weights = old_init_weights


class BaichuanCharRM(BaichuanPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.model = BaichuanModel(config)
        self.score = nn.Linear(config.hidden_size, 1, bias=True)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        hidden_states = hidden_states[:, -1, :]
        logits = F.sigmoid(self.score(hidden_states).squeeze())
    
        loss = None
        if labels is not None:
            labels = labels.type_as(logits)
            loss_fct = nn.MSELoss()
            loss = loss_fct(logits.view(-1), labels.view(-1)/4)

        return loss, logits