modeling_transformer.py

# -*- coding: utf-8 -*-

from __future__ import annotations

import math
import warnings
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn import functional as F
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import (BaseModelOutputWithPast,
                                           CausalLMOutputWithPast)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

# from fla.layers.attn import Attention
from fla.modules import FusedCrossEntropyLoss, RMSNorm
from fla.modules.activations import swiglu_linear

from fla.modules import RotaryEmbedding
try:
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import (index_first_axis, pad_input,
                                         unpad_input)
except ImportError:
    warnings.warn("Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`")
    flash_attn_func = None
from einops import rearrange

from forgetting_transformer.model.transformer.configuration_transformer import TransformerConfig

from functools import partial

logger = logging.get_logger(__name__)

class Attention(nn.Module):

    def __init__(
        self,
        hidden_size: int = 2048,
        num_heads: int = 32,
        num_kv_heads: Optional[int] = None,
        window_size: Optional[int] = None,
        max_position_embeddings: Optional[int] = None,
        rope_base: float = 500000.0,
        use_rope: bool = True,
        layer_idx: int = None,
    ):
        super().__init__()

        self.num_heads = num_heads
        if num_kv_heads is None:
            self.num_kv_heads = self.num_heads
        else:
            self.num_kv_heads = num_kv_heads
        self.num_kv_groups = num_heads // self.num_kv_heads
        self.hidden_size = hidden_size
        self.head_dim = self.hidden_size // self.num_heads
        self.kv_dim = self.num_kv_heads * self.head_dim
        self.kv_dim = self.num_kv_heads * self.head_dim
        self.window_size = window_size
        self.max_position_embeddings = max_position_embeddings
        self.layer_idx = layer_idx

        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

        if use_rope:
            self.rotary = RotaryEmbedding(self.head_dim, base=rope_base)
        else:
            self.rotary = None


        self.apply(self._initialize_weights)

    def _initialize_weights(self, module: nn.Module):
        pass

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        batch_size, q_len, _ = hidden_states.size()
        q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', h=self.num_heads)
        k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', h=self.num_kv_heads)
        v = rearrange(self.v_proj(hidden_states), 'b t (h d) -> b h t d', h=self.num_kv_heads)

        seqlen_offset, max_seqlen = 0, q.shape[1]
        if past_key_values is not None:
            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
            max_seqlen = q.shape[1] + seqlen_offset

            if attention_mask is not None:
                # to deliminate the offsets of padding tokens
                seqlen_offset = (seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1])
                max_seqlen = q.shape[1] + max(seqlen_offset)

        if self.max_position_embeddings is not None:
            max_seqlen = max(max_seqlen, self.max_position_embeddings)
        if self.rotary is not None:
            q, k = self.rotary(q, k, seqlen_offset, max_seqlen)

        k = rearrange(k, 'b t h d -> b h t d')
        if past_key_values is not None:
            k, v = past_key_values.update(k, v, self.layer_idx)
        k, v = rearrange(k, 'b h t d -> b t h d'), rearrange(v, 'b h t d -> b t h d')
        if self.num_kv_groups > 1:
            k = rearrange(k.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')
            v = rearrange(v.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')

        if flash_attn_func is None:
            raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first")

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            q, k, v, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(q, k, v, attention_mask, q_len)
            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_q, max_seqlen_k = max_seq_lens
            o = flash_attn_varlen_func(
                q, k, v,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_q,
                max_seqlen_k=max_seqlen_k,
                causal=True,
                window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0)
            )
            o = pad_input(o, indices_q, batch_size, q_len)
        else:
            o = flash_attn_func(
                q, k, v,
                causal=True,
                window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0)
            )
        o = o.reshape(batch_size, q_len, self.hidden_size)
        o = self.o_proj(o)

        if not output_attentions:
            attentions = None

        return o, attentions, past_key_values

    def _upad_input(self, q, k, v, attention_mask, q_len):
        seqlens = attention_mask.sum(-1, dtype=torch.int32)
        indices_k = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
        max_seqlen_k = seqlens.max().item()
        cu_seqlens_k = F.pad(torch.cumsum(seqlens, dim=0, dtype=torch.int32), (1, 0))
        batch_size, seq_len, num_key_value_heads, head_dim = k.shape

        k = index_first_axis(k.reshape(batch_size * seq_len, num_key_value_heads, head_dim), indices_k)
        v = index_first_axis(v.reshape(batch_size * seq_len, num_key_value_heads, head_dim), indices_k)
        if q_len == seq_len:
            q = index_first_axis(q.reshape(batch_size * seq_len, self.num_heads, head_dim), indices_k)
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_q = max_seqlen_k
            indices_q = indices_k
        elif q_len == 1:
            max_seqlen_q = 1
            # There is a memcpy here, that is very bad.
            cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=q.device)
            indices_q = cu_seqlens_q[:-1]
            q = q.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -q_len:]
            q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask)

        return q, k, v, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)


class TransformerMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        hidden_ratio: Optional[int] = None,
        intermediate_size: Optional[int] = None,
        hidden_act: str = 'swish'
    ) -> TransformerMLP:
        super().__init__()

        self.hidden_size = hidden_size
        # the final number of params is `hidden_ratio * hidden_size^2`
        # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
        if hidden_ratio is None:
            hidden_ratio = 4
        if intermediate_size is None:
            intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
            intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
        self.hidden_ratio = hidden_ratio
        self.intermediate_size = intermediate_size

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        y = self.gate_proj(x)
        gate, y = y.chunk(2, -1)
        # TODO: maybe wrap swiglu_linear in custom_fwd/custom_bwd
        return swiglu_linear(
            gate, y,
            self.down_proj.weight.to(y.dtype),
            self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
        )


class TransformerBlock(nn.Module):
    def __init__(self, config: TransformerConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
        self.attn = Attention(
            hidden_size=config.hidden_size,
            num_heads=config.num_heads,
            num_kv_heads=config.num_kv_heads,
            window_size=config.window_size,
            max_position_embeddings=config.max_position_embeddings,
            rope_base=config.rope_base,
            use_rope=config.use_rope,
            layer_idx=layer_idx
        )
        self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
        self.mlp = TransformerMLP(
            hidden_size=config.hidden_size,
            hidden_ratio=config.hidden_ratio,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act
        )

    def forward_attn(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ):
        # reisual handled outside
        # residual = hidden_states
        hidden_states = self.attn_norm(hidden_states)
        hidden_states, attentions, past_key_values = self.attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions
        )
        return hidden_states, attentions, past_key_values

    def forward_mlp(
        self,
        hidden_states: torch.Tensor,
        residual: torch.Tensor,
    ):
        hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        gradient_checkpointing: bool = False
        # **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:

        residual = hidden_states


        if gradient_checkpointing:
            forward_attn = partial(torch.utils.checkpoint.checkpoint, self.forward_attn, use_reentrant=False)
            forward_mlp = partial(torch.utils.checkpoint.checkpoint, self.forward_mlp, use_reentrant=False)
        else:
            forward_attn = self.forward_attn
            forward_mlp = self.forward_mlp

        hidden_states, attentions, past_key_values = forward_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions
        )

        hidden_states = forward_mlp(
            hidden_states,
            residual,
        )

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attentions,)

        if use_cache:
            outputs += (past_key_values,)

        return outputs



class TransformerPreTrainedModel(PreTrainedModel):

    config_class = TransformerConfig
    supports_gradient_checkpointing = True
    _no_split_modules = ['TransformerBlock']

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(
        self,
        module: nn.Module,
    ):
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


class TransformerModel(TransformerPreTrainedModel):

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([TransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)

        self.gradient_checkpointing = False

        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings

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

    def forward(
        self,
        input_ids: Optional[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] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if output_attentions:
            warnings.warn(
                "`TransformerModel` does not support output attention weights now, so `output_attentions` is set to `False`."
            )
            output_attentions = False
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        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 if not self.training else False)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)

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

        # embed positions
        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

        all_hidden_states = () if output_hidden_states else None
        all_attns = () if output_attentions else None
        next_decoder_cache = None

        for layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                gradient_checkpointing=self.gradient_checkpointing and self.training
            )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_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 = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_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_attns
        )


class TransformerForCausalLM(TransformerPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = TransformerModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

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

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

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor = None,
        past_key_values: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs
    ):
        # only last token for `inputs_ids` if the `past_key_values` is passed along.
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]
        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {'inputs_embeds': inputs_embeds}
        else:
            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
            # recompiles graphs as the stride of the inputs is a guard.
            # Ref: https://github.com/huggingface/transformers/pull/29114
            # TODO: use `next_tokens` directly instead.
            model_inputs = {'input_ids': input_ids.contiguous()}

        model_inputs.update({
            'past_key_values': past_key_values,
            'use_cache': kwargs.get('use_cache'),
            'attention_mask': attention_mask,
        })
        return model_inputs

    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,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.model(
            input_ids=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 = outputs[0]

        loss = None
        if labels is not None:
            if self.config.fuse_cross_entropy:
                loss_fct = FusedCrossEntropyLoss(inplace_backward=True, reduction='none')
            else:
                loss_fct = nn.CrossEntropyLoss(reduction='none')
            logits = self.lm_head(hidden_states)
            # Enable model parallelism
            labels = labels.to(logits.device)
            # labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
            loss = loss.view(*labels.size())
            del logits
            logits = None
        else:
            logits = self.lm_head(hidden_states)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )