LLamaRecipes/research/long-context-llama/H2O/utils_llama.py

import math
from typing import Optional, Tuple

import pdb
import types
import torch
from torch import nn
import torch.utils.checkpoint
import torch.nn.functional as F

from transformers.models.llama.configuration_llama import LlamaConfig
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    rotate_half,
    apply_rotary_pos_emb,
    repeat_kv,
    LlamaRotaryEmbedding,
    apply_rotary_pos_emb,
    LlamaForCausalLM,
)

__all__ = ["H2OLlamaForCausalLM"]

def _make_causal_mask(
    bsz: int, tgt_len: int, past_key_values_length: int, dtype: torch.dtype, device: torch.device):
    """
    Make causal mask used for bi-directional self-attention.
    """
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

def apply_rotary_pos_emb_single(x, cos, sin, position_ids):
    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    x_embed = (x * cos) + (rotate_half(x) * sin)
    return x_embed

class H2OKVCache_LayerWise:
    def __init__(
        self,
        hh_size=4,
        recent_size=512,
        k_seq_dim=2,
        v_seq_dim=2,
    ):
        self.hh_size = hh_size
        self.recent_size = recent_size
        self.cache_size = hh_size + recent_size
        self.k_seq_dim = k_seq_dim
        self.v_seq_dim = v_seq_dim
        self.k_slice = DIM_TO_SLICE[k_seq_dim]
        self.v_slice = DIM_TO_SLICE[v_seq_dim]
        self.hh_score = None

    def __call__(self, past_key_values, attn_score_cache):

        self._update_hh_score(attn_score_cache)

        if past_key_values is None:
            return None
        seq_len = past_key_values[0].size(self.k_seq_dim)
        if seq_len <= self.cache_size:
            return past_key_values

        # hh-selection
        bsz, num_heads, _, head_dim = past_key_values[0].shape

        select_hh_scores = self.hh_score[:, :seq_len - self.recent_size]
        _, keep_topk = torch.topk(select_hh_scores, self.hh_size, dim=-1)
        keep_topk = keep_topk.sort().values

        # keep_recent = torch.arange(seq_len - self.recent_size, seq_len).expand(keep_topk.shape[0], 1).to(keep_topk.device)
        keep_recent = torch.arange(seq_len - self.recent_size, seq_len, device=keep_topk.device).repeat(keep_topk.shape[0], 1)
        keep_idx = torch.cat([keep_topk, keep_recent], dim=-1)

        mask = torch.zeros(self.hh_score.shape, dtype=torch.bool).to(past_key_values[0].device)
        mask = mask.scatter(-1, keep_idx, 1)

        k_hh_recent = past_key_values[0].squeeze()[mask].view(bsz, num_heads, -1, head_dim)
        v_hh_recent = past_key_values[1].squeeze()[mask].view(bsz, num_heads, -1, head_dim)

        self.hh_score= self.hh_score[mask].view(num_heads, self.cache_size)

        return (k_hh_recent, v_hh_recent)

    def evict_for_space(self, past_key_values, num_coming):
        if past_key_values is None:
            return None
        seq_len = past_key_values[0][0].size(self.k_seq_dim)
        if seq_len + num_coming <= self.cache_size:
            return past_key_values

        # hh-selection
        bsz, num_heads, _, head_dim = past_key_values[0].shape

        select_hh_scores = self.hh_score[:, :seq_len - self.recent_size + num_coming]
        _, keep_topk = torch.topk(select_hh_scores, self.hh_size, dim=-1)
        keep_topk = keep_topk.sort().values

        # keep_recent = torch.arange(seq_len - self.recent_size, seq_len).expand(keep_topk.shape[0], 1).to(keep_topk.device)
        keep_recent = torch.arange(seq_len - self.recent_size + num_coming, seq_len, device=keep_topk.device).repeat(keep_topk.shape[0], 1)
        keep_idx = torch.cat([keep_topk, keep_recent], dim=-1)

        mask = torch.zeros(self.hh_score.shape, dtype=torch.bool).to(past_key_values[0].device)
        mask = mask.scatter(-1, keep_idx, 1)

        k_hh_recent = past_key_values[0].squeeze()[mask].view(bsz, num_heads, -1, head_dim)
        v_hh_recent = past_key_values[1].squeeze()[mask].view(bsz, num_heads, -1, head_dim)

        self.hh_score= self.hh_score[mask].view(num_heads, self.cache_size)

        return (k_hh_recent, v_hh_recent)

    def _update_hh_score(self, attn_score_cache):

        num_new_tokens = attn_score_cache.shape[2]

        if self.hh_score is None:
            self.hh_score = attn_score_cache.sum(0).sum(1)
        else:
            attn_score_cache = attn_score_cache.sum(0).sum(1)
            attn_score_cache[:, :-num_new_tokens] += self.hh_score
            self.hh_score = attn_score_cache

    def _clean_scores(self):
        self.hh_score = None

class H2OLlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: LlamaConfig):
        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.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self._init_rope()

        self.kv_cache = H2OKVCache_LayerWise(
            hh_size=config.hh_size,
            recent_size=config.recent_size,
            k_seq_dim=2,
            v_seq_dim=2,
        )

    def _init_rope(self):
        if self.config.rope_scaling is None:
            self.rotary_emb = LlamaRotaryEmbedding(
                self.head_dim,
                max_position_embeddings=self.max_position_embeddings,
                base=self.rope_theta,
            )
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "linear":
                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            elif scaling_type == "dynamic":
                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
                    self.head_dim,
                    max_position_embeddings=self.max_position_embeddings,
                    scaling_factor=scaling_factor,
                    base=self.rope_theta,
                )
            else:
                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

    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 _clean_cache(self):
        self.kv_cache._clean_scores()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = 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()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (
                self.num_key_value_heads * self.head_dim
            ) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
            )
            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

            query_states = [
                F.linear(hidden_states, query_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [
                F.linear(hidden_states, key_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [
                F.linear(hidden_states, value_slices[i])
                for i in range(self.config.pretraining_tp)
            ]
            value_states = torch.cat(value_states, dim=-1)

        else:
            query_states = self.q_proj(hidden_states)
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(hidden_states)

        query_states = query_states.view(
            bsz, q_len, self.num_heads, self.head_dim
        ).transpose(1, 2)
        key_states = key_states.view(
            bsz, q_len, self.num_key_value_heads, self.head_dim
        ).transpose(1, 2)
        value_states = value_states.view(
            bsz, q_len, self.num_key_value_heads, self.head_dim
        ).transpose(1, 2)

        # remake causal mask
        attention_mask = _make_causal_mask(
            bsz=bsz,
            tgt_len=q_len,
            past_key_values_length=past_key_value[0].shape[-2] if past_key_value is not None else 0,
            dtype=query_states.dtype,
            device=query_states.device,
        )

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

        if not position_ids.nelement() > 1:
            position_ids[0][0] = kv_seq_len - 1

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        ### Shift Pos: query pos is min(cache_size, idx)
        # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        query_states = apply_rotary_pos_emb_single(query_states, cos, sin, position_ids)
        ###

        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

        ### Shift Pos: key pos is the pos in cache (Rolling KV Cache and using relative pos emb)
        key_position_ids = torch.arange(kv_seq_len, device=position_ids.device).unsqueeze(0)
        key_states = apply_rotary_pos_emb_single(key_states, cos, sin, key_position_ids)
        ###

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(
            self.head_dim
        )

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
            query_states.dtype
        )

        past_key_value = self.kv_cache(past_key_value, attn_weights.detach().clone())

        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

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

        if self.config.pretraining_tp > 1:
            attn_output = attn_output.split(
                self.hidden_size // self.config.pretraining_tp, dim=2
            )
            o_proj_slices = self.o_proj.weight.split(
                self.hidden_size // self.config.pretraining_tp, dim=1
            )
            attn_output = sum(
                [
                    F.linear(attn_output[i], o_proj_slices[i])
                    for i in range(self.config.pretraining_tp)
                ]
            )
        else:
            attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

class H2OLlamaForCausalLM(LlamaForCausalLM):
    def __init__(self, config):
        super().__init__(config)
        num_layers = len(self.model.layers)
        for layer_idx in range(num_layers):
            self.model.layers[layer_idx].self_attn = H2OLlamaAttention(config)