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from dataclasses import dataclass
from typing import Optional,Tuple,List
from collections import namedtuple
import math
import time
import json
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
from torch.utils.checkpoint import checkpoint
try:
from .configuration_dcpythia import DCPythiaConfig
except:
from configuration_dcpythia import DCPythiaConfig
from transformers.modeling_utils import PreTrainedModel
class KVKWCache(nn.Module):
def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, window_size=2048, dtype=torch.float16, use_kw_cache=True):
super().__init__()
self.head_dim = head_dim
self.kw_dim = 2 * n_heads
self.n_heads = n_heads
self.window_size = window_size
self.use_kw_cache = use_kw_cache
if window_size is None:
self.seq_length = max_seq_length
else:
self.seq_length = min(window_size, max_seq_length)
cache_shape = (max_batch_size, n_heads, self.seq_length, head_dim)
kw_cache_shape = (max_batch_size, self.seq_length, 2, n_heads, n_heads)
self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
if self.use_kw_cache:
self.register_buffer('kw_cache', torch.zeros(kw_cache_shape, dtype=dtype))
def update(self, input_pos, k_val, v_val, kw_val=None): # kw_val B,N,S,2,N B2NSD
# input_pos: [S], k_val: [B, H, S, D]
assert input_pos.shape[-1] == k_val.shape[2]
B,N,S,D = v_val.shape
k_out = self.k_cache
v_out = self.v_cache
if self.use_kw_cache:
kw_out = self.kw_cache
else:
kw_out = None
if self.window_size is None:
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
if self.use_kw_cache and kw_val is not None:
kw_out[:,input_pos] = kw_val
elif S == 1:
input_pos = input_pos % self.seq_length
v_out[:, :, input_pos] = v_val
k_out[:, :, input_pos] = k_val
if self.use_kw_cache and kw_val is not None:
kw_out[:,input_pos] = kw_val
else: # prefill
start = max(0, input_pos[-1]-self.seq_length+1)
input_pos = input_pos[start:] % self.seq_length
v_out[:, :, input_pos] = v_val[:,:,start:]
k_out[:, :, input_pos] = k_val[:,:,start:]
if self.use_kw_cache and kw_val is not None:
kw_out[:, input_pos] = kw_val[:,start:]
return k_out, v_out, kw_out
class DCPythia(PreTrainedModel):
config_class=DCPythiaConfig
def __init__(self, config: DCPythiaConfig) -> None:
super().__init__(config)
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
self.layers = nn.ModuleList(DCPythiaBlock(config, lidx) for lidx in range(config.n_layer))
self.norm = nn.LayerNorm(config.dim, eps=config.norm_eps)
self.output = nn.Linear(config.dim, config.vocab_size, bias=False) # no bias in pythia
self.use_gradient_checkpointing = config.use_gradient_checkpointing
self.is_training = config.is_training
self.freqs_cis: Optional[Tensor] = None
self.rotary_ndims = int(config.head_dim * config.rotary_pct)
self.mask_cache: Optional[Tensor] = None
self.window_size = config.window_size
self.max_batch_size = -1
self.max_seq_length = -1
def setup_caches(self, max_batch_size, max_seq_length, set_kv_cache=True):
if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
return
head_dim = self.config.dim // self.config.n_head
max_seq_length = find_multiple(max_seq_length, 8)
self.max_seq_length = max_seq_length
self.max_batch_size = max_batch_size
if not self.is_training:
for b in self.layers:
if set_kv_cache:
use_kw_cache = False if b.attention.query_wise else True
b.attention.kv_cache = KVKWCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim, window_size=b.attention.window_size, use_kw_cache=use_kw_cache)
b.attention.dyn_w_proj.merge_weights()
if not b.attention.use_sw:
dtype = b.attention.wo.weight.dtype
device = b.attention.wo.weight.device
b.attention.dyn_w_proj.sw = b.attention.dyn_w_proj.sw.to(device=device, dtype=dtype)
b.attention.dyn_w_proj.pre_proj.w = b.attention.dyn_w_proj.pre_proj.w.to(device=device, dtype=dtype)
b.attention.dyn_w_proj.post_proj.w = b.attention.dyn_w_proj.post_proj.w.to(device=device, dtype=dtype)
self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.rotary_ndims, self.config.rope_base).to(self.tok_embeddings.weight.device)
if self.is_training:
self.causal_mask = torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool, device=self.tok_embeddings.weight.device))
elif self.window_size is None:
self.causal_mask = torch.tril(torch.ones(max_seq_length, max_seq_length, dtype=torch.bool, device=self.tok_embeddings.weight.device))
else:
self.causal_mask = torch.stack([make_window_mask(max_seq_length, self.config.window_size), torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))]) # LG
def generate(self, input_ids, num_tokens_to_generate=10, compiled_decode_one_token=None):
batch_size, seq_length = input_ids.shape
input_pos = torch.arange(seq_length, device=self.device)
generated_ids = torch.zeros(batch_size, seq_length + num_tokens_to_generate + 1, dtype=torch.int, device=self.device)
generated_ids[:, :seq_length] = input_ids.to(self.device).to(torch.int)
logits = self.forward(input_ids, input_pos=input_pos,return_tensor=True)
_next_token = torch.argmax(logits[:, -1], dim=-1)[:, None]
next_token = torch.zeros(self.max_batch_size, 1, device=self.device, dtype=torch.int)
next_token[:batch_size] = _next_token
generated_ids[:, seq_length] = next_token[:batch_size, 0]
input_pos = torch.tensor([seq_length], device=self.device)
for _ in range(1, num_tokens_to_generate):
if compiled_decode_one_token is not None:
next_token = compiled_decode_one_token(self, next_token.clone(), input_pos)
else:
next_token = self.decode_one_token(next_token.clone(), input_pos)
generated_ids[:, input_pos+1] = next_token.int()[:batch_size]
input_pos += 1
return generated_ids
def decode_one_token(self, cur_token, input_pos):
logits = self.forward(
cur_token,
input_pos=input_pos,
return_tensor=True,
)
new_token = torch.argmax(logits[:, -1], dim=-1)[:,None]
return new_token
def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None, return_tensor=False) -> Tensor:
assert self.freqs_cis is not None, "Caches must be initialized first"
if input_pos is None:
input_pos = torch.arange(idx.shape[-1], device=idx.device, dtype=torch.int)
if self.window_size is None or self.is_training:
mask = self.causal_mask[None, None, input_pos]
else:
mask = self.causal_mask[None, None,:,input_pos]
freqs_cis = self.freqs_cis[input_pos][:idx.shape[-1]]
x = self.tok_embeddings(idx)
for i, layer in enumerate(self.layers):
if self.is_training or self.window_size is None :
layer_mask = mask
elif self.window_size is not None:
layer_mask = mask[:,:,1] if layer.attention.window_size is None else mask[:,:,0]
if self.use_gradient_checkpointing:
x = checkpoint(layer, x, input_pos, freqs_cis, layer_mask)
else:
x = layer(x, input_pos, freqs_cis, layer_mask)
x = self.norm(x)
logits = self.output(x)
if return_tensor:
return logits
else:
CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
return CausalLMOutput(logits=logits)
class DCPythiaBlock(nn.Module):
def __init__(self, config: DCPythiaConfig, lidx) -> None:
super().__init__()
self.lidx = lidx
self.attention = DCMHAttention(config, lidx)
self.feed_forward = FeedForward(config)
self.ffn_norm = nn.LayerNorm(config.dim, eps=config.norm_eps)
self.attention_norm = nn.LayerNorm(config.dim, eps=config.norm_eps)
self.use_parallel_residual = config.use_parallel_residual
def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos, fast_infer=True)
if self.use_parallel_residual:
out = h + self.feed_forward(self.ffn_norm(x))
else:
out = h + self.feed_forward(self.ffn_norm(h))
return out
class DynamicWeightProjection(nn.Module):
def __init__(self, num_heads=32, num_groups=1, residual=True, query_input_dim=4096, dynamic_squeeze_ratio=16, dynamic_w_hidden_dim=128,dtype=torch.float16,use_sw=False):
super().__init__()
self.num_heads = num_heads
self.num_groups = num_groups
self.query_input_dim = query_input_dim
self.dynamic_squeeze_ratio = dynamic_squeeze_ratio
self.dynamic_w_hidden_dim = dynamic_w_hidden_dim
self.dw_hidden_activation = nn.GELU()
self.num_heads_per_group = self.num_heads // self.num_groups
self.dw_activation = nn.Tanh()
self.dw1_norm = RMSnormNoscale(dim=-1)
self.use_sw = use_sw
self.pre_proj = CrossHeadProjection('pre', num_heads=self.num_heads, use_sw=use_sw)
self.post_proj = CrossHeadProjection('post', num_heads=self.num_heads, use_sw=use_sw)
dynamic_hidden_dim = self.num_heads_per_group // self.dynamic_squeeze_ratio
self.dynamic_hidden_dim = dynamic_hidden_dim
self.dw1 = nn.parameter.Parameter(torch.zeros(self.query_input_dim, self.num_groups, 4, self.dynamic_w_hidden_dim, dtype=dtype)) #(4096, 1, 4, 128)
G, K, M = self.num_groups, self.dynamic_w_hidden_dim, self.num_heads_per_group
I = dynamic_hidden_dim * 2
self.qkw = nn.parameter.Parameter(torch.zeros([G, 4, K, I, M], dtype=dtype)) # (1, 4, 128, 4, 32)
self.dd = nn.parameter.Parameter(torch.zeros(self.query_input_dim, self.num_groups, self.num_heads_per_group * 4, dtype=dtype)) # (4096, 1, 128)
self.merge_weights()
def merge_weights(self):
self.dw_m = nn.parameter.Parameter(torch.cat([self.dw1.reshape(self.query_input_dim, -1), self.dd.squeeze(1)], dim=-1)).to(self.dw1.device) # E,(4*K + K) K=2*N*I
self.qkw_m = nn.parameter.Parameter(self.qkw.permute(0,1,2,3,4).reshape(4,self.dynamic_w_hidden_dim,-1)).to(self.dw1.device) #(4,K,I*M)
if self.use_sw:
self.sw = nn.parameter.Parameter(torch.stack([self.pre_proj.w, self.post_proj.w]).squeeze(1) + torch.eye(self.num_heads) ).to(self.dw1.device) # (2,N,N) sw + identity matrix
else:
self.sw = (torch.eye(self.num_heads).expand(2,self.num_heads,self.num_heads)).to(self.dw1.device) # identity matrix (2,N,N)
def forward(self,query_vec,KW:Optional[torch.Tensor]=None, gen_cache:Optional[bool]=True):
dw_hidden = torch.einsum('BTD,DGCK->BTGCK', query_vec, self.dw1) # C=4 [pre,post]*[query,key]
dw_hidden = self.dw_hidden_activation(dw_hidden) #BTGCK
w1, w2 = torch.split(torch.einsum('BTGCK,GCKIM->BTGCIM', dw_hidden, self.qkw), self.qkw.shape[-2]//2, dim=-2) #BTGC(2I)M -> [BTGCIM] * 2
w1 = self.dw1_norm(w1) # BTGCIM
pre_qw1, pre_kw1, post_qw1, post_kw1 = unbind(w1, 4, dim=3) # BTG4IM->[BTGIM]*4
pre_qw2, pre_kw2, post_qw2, post_kw2 = unbind(w2, 4, dim=3)
dd = torch.einsum('BTD,DGM->BTGM', query_vec, self.dd) # BTG(4M)
dd = self.dw_activation(dd)
pre_qdd, pre_kdd, post_qdd, post_kdd = torch.split(dd, dd.shape[-1] // 4, dim=-1) # BTG(4N)->[BTGN]*4
pre_dw_args = (pre_qw1, pre_qw2, pre_kw1, pre_kw2, pre_qdd, pre_kdd)
post_dw_args = (post_qw1, post_qw2, post_kw1, post_kw2, post_qdd, post_kdd)
if gen_cache: # generate KW cache
pre_kw = torch.einsum('BSGIM, BSGIN->BSMN', pre_kw1, pre_kw2) + torch.diag_embed(pre_kdd.squeeze(2)) # merge kw and kdd
post_kw = torch.einsum('BSGIM, BSGIN->BSMN', post_kw1, post_kw2) + torch.diag_embed(post_kdd.squeeze(2))
KW = torch.stack((pre_kw, post_kw), dim=-3) # BSMN,BSMN->BS2MN
return pre_dw_args, post_dw_args, KW
class RMSnormNoscale(nn.Module):
def __init__(self, epsilon=1e-6, dim=-1):
super().__init__()
self.dim = dim
self.epsilon = epsilon
def forward(self, inputs):
var = inputs.pow(2).mean(dim=self.dim, keepdim=True)
normed_inputs = inputs * torch.rsqrt(var + self.epsilon)
return normed_inputs
class RMSnorm(nn.Module):
def __init__(self, hid_dim=128, epsilon=1e-6, dim=-1):
super().__init__()
self.dim = dim
self.hid_dim = hid_dim
self.epsilon = epsilon
self.scale = nn.parameter.Parameter(data=torch.ones(self.hid_dim))
def forward(self, inputs):
var = inputs.pow(2).mean(dim=self.dim, keepdim=True)
normed_inputs = inputs * torch.rsqrt(var + self.epsilon)
normed_inputs = normed_inputs * self.scale
return normed_inputs
class CrossHeadProjection(nn.Module):
def __init__(self, mode, num_heads=16, num_groups=1, dtype=torch.float16, use_sw=False):
super().__init__()
self.mode = mode
self.use_sw = use_sw
self.num_heads = num_heads
self.num_groups = num_groups
self.num_heads_per_group = self.num_heads // self.num_groups
if self.use_sw:
self.w = nn.parameter.Parameter(data=torch.zeros(self.num_groups, self.num_heads_per_group, self.num_heads_per_group, dtype=dtype))
else:
self.register_buffer('w', torch.eye(self.num_heads_per_group, dtype=dtype).expand(self.num_groups, self.num_heads_per_group, self.num_heads_per_group))
def forward(self, inputs,
dws:Optional[Tuple[Tensor,Tensor, Tensor,Tensor, Tensor,Tensor]]=None,
query_vec=None, key_vec=None,
proj_w:Optional[Tensor]=None,
fast_infer=True):
if proj_w is not None:
ret = torch.einsum('BNTS,BSNM->BMTS', inputs, proj_w)
else:
assert dws is not None
qw1, qw2, kw1, kw2, qdd, kdd = dws
inputs = inputs.unsqueeze(1) #BNTS->BGNTS
# apply sw
ret = torch.einsum('BGMTS,GMN->BGNTS', inputs, self.w) if self.use_sw else inputs
if fast_infer:
inputs_label = 'BGMTS'
hidden_sym = 'I'; hidden_label = inputs_label.replace('M', 'I') # BGITS
# apply qw and kw
for sym, (w1, w2) in zip(['T', 'S'], [(qw1, qw2), (kw1, kw2)]):
dw_label = f'B{sym}G{hidden_sym}M' # w1: BTGIM, dw_label:BTGIM
dynamic_hidden_dim = w1.shape[dw_label.index(hidden_sym)]
eqn1 = f'{inputs_label},{dw_label}->{hidden_label}' # 'BGMTS,BTGMI->BGITS'
eqn2 = f'{hidden_label},{dw_label}->{inputs_label}' # 'BGITS,BTGMI->BGMTS'
for i in range(dynamic_hidden_dim):
hidden = torch.einsum(eqn1.replace(hidden_sym, ''), inputs, w1[..., i, :]) # BGMTS,BTG(I)M->BGTS
out = torch.einsum(eqn2.replace(hidden_sym, ''), hidden, w2[..., i, :]) # 'BG(I)TS,BTG(I)M->BGMTS'
ret = ret + out
# apply qdd and kdd
for sym, dd in zip(['T', 'S'], [qdd, kdd]):
dd_label = f'B{sym}GM'
dout = torch.einsum(f'{inputs_label},{dd_label}->{inputs_label}', inputs, dd) # BGMTS,B(T/S)GM->BGMTS
ret = ret + dout
else:
# apply qw and kw (BTGIN)
x_inter = torch.einsum('BGNTS, BTGIN->BGTSI', inputs, qw1)
qw_out = torch.einsum('BGTSI, BTGIN->BGNTS', x_inter, qw2)
ret = ret + qw_out
x_inter = torch.einsum('BGNTS, BSGIN->BGTSI', inputs, kw1)
kw_out = torch.einsum('BGTSI, BSGIN->BGNTS', x_inter, kw2)
ret = ret + kw_out
# apply qdd(BTGN) and kdd(BSGN)
ret = ret + torch.einsum('BGNTS, BTGN->BGNTS', inputs, qdd)
ret = ret + torch.einsum('BGNTS, BSGN->BGNTS', inputs, kdd)
ret = ret.squeeze(1) # BGNTS->BNTS
return ret
class DCMHAttention(nn.Module):
def __init__(self, config: DCPythiaConfig, lidx, use_sw=False):
super().__init__()
assert config.dim % config.n_head == 0
total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
# key, query, value projections for all heads, but in a batch
self.lidx = lidx
self.wqkv = nn.Linear(config.dim, total_head_dim, bias=config.use_linear_bias)
self.wo = nn.Linear(config.dim, config.dim, bias=config.use_linear_bias)
self.kv_cache = None
self.n_head = config.n_head
self.head_dim = config.head_dim
self.n_local_heads = config.n_local_heads
self.is_training = config.is_training
self.dim = config.dim
self.use_dcmha = config.use_dcmha
self.scale_factor = 1 / math.sqrt(self.head_dim)
self.q_chunk_size = config.q_chunk_size
self.use_sw = use_sw
self.dyn_w_proj = DynamicWeightProjection(num_heads=self.n_head, query_input_dim=config.dim, dynamic_squeeze_ratio=self.n_head//2, dynamic_w_hidden_dim=self.n_head*4, use_sw=use_sw)
self.use_qk_norm = config.use_qk_norm
if self.use_qk_norm:
self.q_norm = RMSnorm(hid_dim=self.head_dim)
self.k_norm = RMSnorm(hid_dim=self.head_dim)
self.window_types = {
"LG":[256, None],
"LGLL":[256, None, 256, 256],
"LGL6":[256, None, 256, 256, 256, 256, 256, 256],
}
self.query_wise = config.query_wise
if config.window_type is None: # LG
self.window_size = None if self.lidx % 2 == 1 else config.window_size
else:
window_l = self.window_types[config.window_type]
self.window_size = window_l[self.lidx % len(window_l)]
self.rotary_ndims = int(self.head_dim * config.rotary_pct)
if not self.is_training:
self._register_load_state_dict_pre_hook(self.load_hook)
def load_hook(self, state_dict, prefix, *args):
if prefix + "wq.weight" in state_dict:
wq = state_dict.pop(prefix + "wq.weight")
wk = state_dict.pop(prefix + "wk.weight")
wv = state_dict.pop(prefix + "wv.weight")
state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
if prefix + "wq.bias" in state_dict:
wq_b = state_dict.pop(prefix + "wq.bias")
wk_b = state_dict.pop(prefix + "wk.bias")
wv_b = state_dict.pop(prefix + "wv.bias")
state_dict[prefix + "wqkv.bias"] = torch.cat([wq_b, wk_b, wv_b])
def _generate_fast(self, x, input_pos, q, k, v, k_mask):
B,T,D = x.shape
N,I = self.n_head, self.dyn_w_proj.dynamic_hidden_dim # 32, 2
dw_hidden, dd = (x @ self.dyn_w_proj.dw_m).split([2*2*N*(2*I), 2*2*N*1], -1) # BTD, D(4K+4N) -> BT(4K+4N) -> BT(4K), BT(4N)
dw_hidden = dw_hidden.view((B,T,4,-1,1)) # BT(4K) -> BT4K1
dw = (self.dyn_w_proj.dw_hidden_activation(dw_hidden) * self.dyn_w_proj.qkw_m).sum(-2) # gelu, BT4K1, 4K(IM)->BT4K(IM)->BT4(IM)
w1, w2 = dw.view((B,T,2,2,-1,N)).split(I,-2) # BT4(IM)->BT{pre/post}{q/k}IM->[BT22IM] * 2
w1 = self.dyn_w_proj.dw1_norm(w1) # BT22IN
qkdd = self.dyn_w_proj.dw_activation(dd.view((B,T,2,2,N))) # BT2{2}N1->BT2{2}N tanh
qkw = torch.einsum('BTKJIN,BTKJIM->BTKJNM', w1, w2) + torch.diag_embed(qkdd) # j=k=2, BT2{2}NM q/k, pre/post
if self.query_wise: # TODO: do not generate kw and kdd
qw, _ = qkw.unbind(3) # BS2NM
kw_new = None
qw = qw + self.dyn_w_proj.sw
else:
qw, kw_new = qkw.unbind(3) # BS{pre/post}{q/k}NM -> BS{pre/post}NM * 2
kw_new = kw_new + self.dyn_w_proj.sw # BS2NM + 2NM-> BS2NM
if self.kv_cache is not None:
k, v, kw_out = self.kv_cache.update(input_pos, k, v, kw_val=kw_new) #BNT2M
logits = q @ k.transpose(-2, -1) * self.scale_factor
if self.query_wise:
w = qw # B12NM
else:
w = qw + kw_out # B12NM,BS2NM -> BS2NM
wl, w = w.permute(0,2,3,4,1).unbind(1) # BS2NM->B2NMS->[BNMS]*2
logits = (logits * wl).sum(1).unsqueeze(2) # BN1S, BNMS -> BNMS-> BMS-> BM1S
min_value = torch.finfo(torch.float16).min
logits = torch.where(k_mask, logits, min_value)
probs = logits.softmax(-1)
probs = (probs * w).sum(1).unsqueeze(2)
y = probs @ v
return y
def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None, fast_infer=True) -> Tensor:
bsz, seqlen, _ = x.shape
kv_size = self.n_local_heads * self.head_dim
q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
q = q.view(bsz, seqlen, self.n_head, self.head_dim) # BSND
k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
if self.use_qk_norm:
q, k = self.q_norm(q), self.k_norm(k)
if self.rotary_ndims == self.head_dim:
q = apply_rotary_emb(q, freqs_cis) #BTND
k = apply_rotary_emb(k, freqs_cis)
else:
q_rot = q[..., : self.rotary_ndims]
q_pass = q[..., self.rotary_ndims :]
k_rot = k[..., : self.rotary_ndims]
k_pass = k[..., self.rotary_ndims :]
q_rot = apply_rotary_emb(q_rot, freqs_cis, mode='half') #BTND
k_rot = apply_rotary_emb(k_rot, freqs_cis, mode='half')
q = torch.cat((q_rot, q_pass), dim=-1)
k = torch.cat((k_rot, k_pass), dim=-1)
q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v)) # BNSD
if self.is_training:
N, D, I = self.n_head, self.head_dim, self.dyn_w_proj.dynamic_hidden_dim; # 6.7B
B,T,E = x.shape
if self.use_dcmha:
project_logits = True
project_probs = True
if project_probs:
dw_hidden, dd = (x @ self.dyn_w_proj.dw_m).split([2*2*N*(2*I), 2*2*N*1], -1)
dw_hidden = self.dyn_w_proj.dw_hidden_activation(dw_hidden)
dw_hidden = dw_hidden.view(dw_hidden.shape[:2]+(4,-1)) #B T (4 K) -> B T 4 K # reshape
dw = torch.einsum('B T C K, C K D -> B T C D', dw_hidden, self.dyn_w_proj.qkw_m) # BT4K,4K(MI)->BT4(MI)
shape = (B,T,2*2,-1,N)# if project_logits else (B,T,2,N,-1) # BT(pre/post)(q/k)IN
w1, w2 = dw.view(shape).split(I,-2)
w1 = self.dyn_w_proj.dw1_norm(w1) # BT22IN
if self.use_sw:
pre_sw, post_sw = self.dyn_w_proj.sw.unbind(0)
else:
pre_sw, post_sw = None, None
pre_qw1, pre_kw1, post_qw1, post_kw1 = w1.unbind(2) # BT(2{*2})IN->[BTIN]*4
pre_qw2, pre_kw2, post_qw2, post_kw2 = w2.unbind(2)
qkdd = F.tanh(dd).squeeze(-1).view(shape[:-2] + (N,)) # BT(2{*2})N1->BT(2{*2})N
pre_qdd, pre_kdd, post_qdd, post_kdd = qkdd.unbind(2) # BT(2{*2})N->[BTN]*4
y = torch.zeros(B, N, T, D).to(q.device, dtype=torch.float16)
for i in range(T // self.q_chunk_size):
start, stop = i * self.q_chunk_size, (i + 1) * self.q_chunk_size
kv_start = max(0, stop - self.q_chunk_size -self.window_size)
_q = q[:, :, start : stop, :]
_k, _v = k[:, :, kv_start : stop, :], v[:, :, kv_start : stop, :]
_atten_mask = mask[:, :, start : stop, kv_start : stop]
_pre_proj_dw_args = slice_dw(pre_sw, pre_qw1, pre_qw2, pre_kw1, pre_kw2, pre_qdd, pre_kdd, start, stop, kv_start) \
if project_logits else None
_post_proj_dw_args = slice_dw(post_sw, post_qw1, post_qw2, post_kw1, post_kw2, post_qdd, post_kdd, start,stop,kv_start) \
if project_probs else None
_o = _atten_context(_q, _k, _v, _atten_mask, _pre_proj_dw_args, _post_proj_dw_args)
y[:,:,start:stop] = _o
else:
y = torch.zeros(B, N, T, D).to(q.device, dtype=torch.float16)
for i in range(T // self.q_chunk_size):
start, stop = i * self.q_chunk_size, (i + 1) * self.q_chunk_size
kv_start = max(0, stop - self.q_chunk_size -self.window_size)
_q = q[:, :, start : stop, :]
_k, _v = k[:, :, kv_start : stop, :], v[:, :, kv_start : stop, :]
_atten_mask = mask[:, :, start : stop, kv_start : stop]
_pre_proj_dw_args, _post_proj_dw_args = None, None
_o = _atten_context(_q, _k, _v, _atten_mask, _pre_proj_dw_args, _post_proj_dw_args)
y[:,:,start:stop] = _o
else: # inference
if seqlen == 1: # one-token generation
k_mask = mask if self.window_size is None else mask[:,:,:,:self.kv_cache.seq_length]
if fast_infer:
y = self._generate_fast(x, input_pos, q, k, v, k_mask)
else:
assert not self.query_wise
# generate dw from hidden_state
pre_proj_dw_args, post_proj_dw_args, kw_new = self.dyn_w_proj(x, gen_cache=True)
# update kvkw cache
kw_new = kw_new + self.dyn_w_proj.sw # absorb residual or sw into kw cache
if self.kv_cache is not None:
k, v, kw_out = self.kv_cache.update(input_pos, k, v, kw_val=kw_new) # BNSD, BNSD, BS2NN
logits = q @ k.transpose(-2, -1) * self.scale_factor
# merge pre_w and apply it
pre_qw1, pre_qw2, pre_kw1, pre_kw2, pre_qdd, pre_kdd = pre_proj_dw_args
pre_qw = torch.einsum('BTGIN, BTGIM->BTNM',pre_qw1, pre_qw2) + torch.diag_embed(pre_qdd.squeeze(2))
pre_w = pre_qw + kw_out[:,:,0] # B1NM, BSNM -> BSNM
logits = self.dyn_w_proj.pre_proj(logits, proj_w=pre_w.squeeze(1))
logits = torch.where(k_mask, logits, torch.finfo(torch.float16).min)
probs = logits.softmax(-1)
# merge post_w and apply it
post_qw1, post_qw2, post_kw1, post_kw2, post_qdd, post_kdd = post_proj_dw_args
post_qw = torch.einsum('BTGIN, BTGIM->BTNM', post_qw1, post_qw2) + torch.diag_embed(post_qdd.squeeze(2))
post_w = post_qw + kw_out[:,:,1]
probs = self.dyn_w_proj.post_proj(probs, proj_w=post_w.squeeze(1))
y = probs @ v
else: # prefill
k_mask = mask[:,:,:,:k.shape[-2]]
pre_proj_dw_args, post_proj_dw_args,kw_new = self.dyn_w_proj(x, gen_cache=True)
kw_new = kw_new + self.dyn_w_proj.sw # absorb residual or sw into kw cache
if self.kv_cache is not None:
self.kv_cache.update(input_pos, k, v, kw_val=kw_new) # BNSD, BNSD, BS2NN
logits = q @ k.transpose(-2, -1) * self.scale_factor
logits = self.dyn_w_proj.pre_proj(logits, dws=pre_proj_dw_args, query_vec=x, key_vec=x, fast_infer=True) # XD BN1S
logits = torch.where(k_mask, logits, torch.finfo(torch.float16).min)
probs = logits.softmax(-1)
probs = self.dyn_w_proj.post_proj(probs, dws=post_proj_dw_args, query_vec=x, key_vec=x, fast_infer=True) # BN1S
y = probs @ v
y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
y = self.wo(y)
return y
class FeedForward(nn.Module):
def __init__(self, config: DCPythiaConfig) -> None:
super().__init__()
self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=config.use_linear_bias)
self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=config.use_linear_bias)
def forward(self, x: Tensor) -> Tensor:
return self.w2(F.gelu(self.w1(x)))
def _atten_context(query, key, value, atten_mask, pre_proj_dw_args, post_proj_dw_args):
logits = query @ key.transpose(-2, -1)
if pre_proj_dw_args is not None: logits = _cross_head_proj(logits, *pre_proj_dw_args)
logits = torch.where(atten_mask, logits, torch.finfo(torch.float16).min)
probs = logits.softmax(-1)
if post_proj_dw_args is not None: probs = _cross_head_proj(probs, *post_proj_dw_args)
o = probs @ value # BNTS,BNSD->BNTD
return o
def _cross_head_proj(inputs, sw, qw1, qw2, kw1, kw2, qdd, kdd, loop_over_dynamic_hd=False):
out = inputs + torch.einsum('BNTS,NM->BMTS', inputs, sw) if sw is not None else inputs
for i in range(2): # qw1.shape[-2]):
qhidden = (inputs * qw1[..., i, :].transpose(-2, -1).unsqueeze(-1)).sum(1) # BNTS,(BTN->BNT->BNT1)->BNTS->BTS
qout = qhidden.unsqueeze(1) * qw2[..., i, :].transpose(-2, -1).unsqueeze(-1) # (BTS->B1TS),(BTN->BNT->BNT1)->BNTS
out = out + qout
khidden = (inputs * kw1[..., i, :].transpose(-2, -1).unsqueeze(-2)).sum(1) # BNTS,(BSN->BNS->BN1S)->BNTS->BTS
kout = khidden.unsqueeze(1) * kw2[..., i, :].transpose(-2, -1).unsqueeze(-2) # (BTS->B1TS),(BSN->BNS->BNS1)->BNTS
out = out + kout
qdout = inputs * qdd.transpose(-2, -1).unsqueeze(-1); out = out + qdout # BNTS,(BTN->BNT->BNT1)->BNTS
kdout = inputs * kdd.transpose(-2, -1).unsqueeze(-2); out = out + kdout # BNTS,(BSN->BNS->BN1S)->BNTS
return out
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
def make_window_mask(t, window_size):
col_idx = torch.tile(torch.arange(t).unsqueeze(0), [t, 1])
row_idx = torch.tile(torch.arange(t).unsqueeze(1), [1, t])
bias_mask = (col_idx + window_size >= row_idx).tril().view(t, t)
return bias_mask
def slice_dw(sw, qw1, qw2, kw1, kw2, qdd, kdd, start, stop, kv_start):
return (sw,
qw1[:, start : stop] if qw1 is not None else None,
qw2[:, start : stop] if qw2 is not None else None,
kw1[:, kv_start : stop] if kw1 is not None else None,
kw2[:, kv_start : stop] if kw2 is not None else None,
qdd[:, start : stop] if qdd is not None else None,
kdd[:, kv_start : stop] if kdd is not None else None)
def precompute_freqs_cis(
seq_len: int, n_elem: int, base: int = 10000
) -> Tensor:
freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
t = torch.arange(seq_len, device=freqs.device)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
return cache.to(dtype=torch.float16)
def unbind(ary, n, dim=0):
return [torch.squeeze(a, dim=dim) for a in torch.split(ary, ary.shape[dim] // n, dim=dim)]
def apply_rotary_emb(x: Tensor, freqs_cis: Tensor, mode='half') -> Tensor:
if mode == 'half':
xshaped = x.float().reshape(*x.shape[:-1], 2,-1).transpose(-1,-2)
elif mode == 'alternative':
xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
freqs_cis = freqs_cis.view(-1, xshaped.size(1), 1, xshaped.size(3), 2)
x_out2 = torch.stack(
[
xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
],
-1,
)
x_out2 = x_out2.flatten(3)
return x_out2.type_as(x)
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