1029 lines
32 KiB
Python
Executable File
1029 lines
32 KiB
Python
Executable File
import math
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import traceback
|
|
import numpy as np
|
|
import re
|
|
|
|
from typing import Literal, overload, Optional, Tuple
|
|
from functools import partial
|
|
from einops import rearrange
|
|
|
|
from torch import Tensor, einsum, nn
|
|
from torch.nn import Embedding
|
|
from torch.distributions import Categorical
|
|
from torch.nn.utils.rnn import pad_sequence
|
|
from torch.utils.checkpoint import checkpoint
|
|
from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
|
|
|
|
from ..utils import wrapper as ml
|
|
|
|
from ..samplers import reptition_penalize, length_penalize, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
|
|
|
|
try:
|
|
from .transformer import SinusoidalEmbedding, Block as TransformerBlock
|
|
except Exception as e:
|
|
print("Error importing `transformer` arch:", e)
|
|
pass
|
|
|
|
try:
|
|
#from .retnet import RetNetDecoder, RetNetConfig
|
|
from .retnet_ts import RetNetDecoder, RetNetConfig
|
|
except Exception as e:
|
|
print("Error importing `retnet` arch:", e)
|
|
pass
|
|
|
|
from .retnet_hf import RetNetDecoder as RetNetDecoder_HF, RetNetConfig as RetNetConfig_HF
|
|
"""
|
|
try:
|
|
except Exception as e:
|
|
print("Error importing `retnet-hf` arch:", e)
|
|
pass
|
|
"""
|
|
|
|
try:
|
|
from transformers import LlamaModel, LlamaConfig
|
|
except Exception as e:
|
|
print("Error importing `llama` arch:", e)
|
|
pass
|
|
|
|
try:
|
|
from transformers import MistralModel, MistralConfig
|
|
except Exception as e:
|
|
print("Error importing `mistral` arch:", e)
|
|
pass
|
|
|
|
try:
|
|
from bitnet.bit_transformer import Transformer as BitNetTransformerBlock, RMSNorm as BitNetRMSNorm
|
|
|
|
# override for wrapping checkpointing
|
|
def BitNetTransformerBlock_forward(self, x: Tensor, *args, **kwargs) -> Tensor:
|
|
skip = x
|
|
for attn, ffn in zip(self.layers, self.ffn_layers):
|
|
if x.requires_grad and self.activation_checkpointing:
|
|
x, _ = checkpoint(attn, x, x, x, is_causal=True, *args, **kwargs, use_reentrant=False)
|
|
else:
|
|
x, _ = attn(x, x, x, is_causal=True, *args, **kwargs)
|
|
x = x + skip
|
|
x = ffn(x) + x
|
|
return x
|
|
|
|
BitNetTransformerBlock.forward = BitNetTransformerBlock_forward
|
|
|
|
# override because bitnet's BitNetTransformer includes an embedding input / classifier output layers inside of it, which isn't favorable
|
|
class BitNetTransformer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
depth: int,
|
|
num_tokens: int,
|
|
heads=8,
|
|
ff_mult=4,
|
|
activation_checkpointing = True
|
|
):
|
|
super().__init__()
|
|
|
|
self.transformer = BitNetTransformerBlock( dim=dim, depth=depth, heads=heads, ff_mult=ff_mult )
|
|
self.norm = BitNetRMSNorm(dim)
|
|
self.transformer.activation_checkpointing = activation_checkpointing
|
|
|
|
def forward(self, x):
|
|
x = self.transformer(x)
|
|
return self.norm( x )
|
|
|
|
"""
|
|
from bitnet import BitNetTransformer
|
|
def NoEmbedding_BitNetTransformer_Forward(self, x):
|
|
x = self.transformer(x)
|
|
return self.to_logits[0](x)
|
|
|
|
BitNetTransformer.forward = NoEmbedding_BitNetTransformer_Forward
|
|
"""
|
|
|
|
except Exception as e:
|
|
print("Error importing `bitnet` arch:", e)
|
|
pass
|
|
|
|
try:
|
|
from transformers import MixtralModel, MixtralConfig
|
|
from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func, MixtralSparseMoeBlock
|
|
|
|
# This is required because batch sizes > 1 throws errors
|
|
def Fixed_MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
""" """
|
|
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
|
hidden_states = hidden_states.reshape(-1, hidden_dim) # was view()
|
|
# router_logits: (batch * sequence_length, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
|
|
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
|
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
# we cast back to the input dtype
|
|
routing_weights = routing_weights.to(hidden_states.dtype)
|
|
|
|
final_hidden_states = torch.zeros(
|
|
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
|
)
|
|
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
|
|
|
for expert_idx in range(self.num_experts):
|
|
expert_layer = self.experts[expert_idx]
|
|
idx, top_x = torch.where(expert_mask[expert_idx])
|
|
|
|
if top_x.shape[0] == 0:
|
|
continue
|
|
top_x_list = top_x.tolist()
|
|
idx_list = idx.tolist()
|
|
|
|
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
|
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
|
|
|
|
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
|
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
|
return final_hidden_states, router_logits
|
|
|
|
Original_MixtralSparseMoeBlock_forward = MixtralSparseMoeBlock.forward
|
|
MixtralSparseMoeBlock.forward = Fixed_MixtralSparseMoeBlock_forward
|
|
|
|
except Exception as e:
|
|
print("Error importing `mixtral` arch:", e)
|
|
|
|
|
|
AVAILABLE_ATTENTIONS = ["mem_efficient", "math"]
|
|
|
|
try:
|
|
from xformers.ops import LowerTriangularMask
|
|
from xformers.ops.fmha import memory_efficient_attention
|
|
|
|
AVAILABLE_ATTENTIONS.append("xformers")
|
|
except Exception as e:
|
|
print("Error while importing `xformers`", e)
|
|
|
|
try:
|
|
from transformers.utils import is_flash_attn_2_available
|
|
|
|
if is_flash_attn_2_available():
|
|
AVAILABLE_ATTENTIONS.append("flash")
|
|
except Exception as e:
|
|
raise e
|
|
|
|
try:
|
|
from transformers.cache_utils import Cache
|
|
from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
|
|
|
|
|
|
class Llama_Attention(LlamaAttention):
|
|
def __init__(self, *args, **kwargs):
|
|
if 'mode' in kwargs:
|
|
self.mode = kwargs['mode']
|
|
kwargs.pop("mode")
|
|
else:
|
|
self.mode = "math"
|
|
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
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)
|
|
|
|
cos, sin = self.rotary_emb(value_states, position_ids)
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
|
|
|
past_key_value = getattr(self, "past_key_value", past_key_value)
|
|
|
|
if past_key_value is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
query_states = query_states.transpose(1, 2)
|
|
key_states = key_states.transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
dropout_rate = self.attention_dropout if self.training else 0.0
|
|
|
|
if self.mode == "xformers":
|
|
if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
|
|
attn_output = memory_efficient_attention(query_states, key_states, value_states, attn_bias=None, p=dropout_rate)
|
|
else:
|
|
attn_output = memory_efficient_attention(query_states, key_states, value_states, attn_bias=LowerTriangularMask(), p=dropout_rate)
|
|
else:
|
|
#torch.nn.attention.sdpa_kernel
|
|
with torch.backends.cuda.sdp_kernel(enable_flash=self.mode == "flash", enable_math=self.mode == "math", enable_mem_efficient=self.mode == "mem_efficient"):
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=dropout_rate)
|
|
|
|
attn_weights = None
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
except Exception as e:
|
|
print("Error creating modified `LLamaAttention`:", e)
|
|
|
|
def _create_mask(l, device):
|
|
"""1 is valid region and 0 is invalid."""
|
|
seq = torch.arange(max(l), device=device).unsqueeze(0) # (1 t)
|
|
stop = torch.tensor(l, device=device).unsqueeze(1) # (b 1)
|
|
return (seq < stop).float() # (b t)
|
|
|
|
def _join(x: tuple[Tensor], sep: Tensor):
|
|
"""
|
|
Args:
|
|
x: (k t d)
|
|
sep: (d)
|
|
"""
|
|
ret = x[0]
|
|
for i in range(1, len(x)):
|
|
ret = torch.cat((ret, sep[None], x[i]), dim=0)
|
|
return ret
|
|
|
|
def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
|
|
"""
|
|
Args:
|
|
x_list: [(t d)]
|
|
Returns:
|
|
x: (? ? ?)
|
|
m: (? ? ?), same as x
|
|
"""
|
|
l = list(map(len, x_list))
|
|
x = rearrange(pad_sequence(x_list), pattern)
|
|
m = _create_mask(l, x_list[0].device)
|
|
m = m.t().unsqueeze(-1) # (t b 1)
|
|
m = rearrange(m, pattern)
|
|
m = m.to(x)
|
|
return x, m
|
|
|
|
# automagically parses a batch-list and returns it as a list
|
|
"""
|
|
class Embedding(nn.Embedding):
|
|
def forward(self, x_list: list[Tensor]) -> list[Tensor]:
|
|
if len(x_list) == 0:
|
|
return []
|
|
return super().forward(torch.cat(x_list)).split([*map(len, x_list)])
|
|
"""
|
|
|
|
# Deprecated implementation
|
|
class MultiEmbedding(nn.Module):
|
|
def __init__(self, max_n_levels, n_tokens, token_dim, monolithic=False):
|
|
super().__init__()
|
|
self.monolithic = monolithic
|
|
self.max_n_levels = max_n_levels
|
|
self.n_tokens = n_tokens
|
|
self.weight = nn.Parameter(torch.randn(max_n_levels, n_tokens, token_dim))
|
|
|
|
# to-do: select quant level from given quant_levels tensor if given (i.e. through the resp_emb)
|
|
# I imagine this is an oversight in the NAR.
|
|
def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None) -> list[Tensor]:
|
|
if len(x_list) == 0:
|
|
return []
|
|
|
|
# this "strategy" will reserve the weight[0] for te AR and weight[1:] for the NAR
|
|
# the NAR cannot share RVQ-bin level 0 with the AR for the resp_emb
|
|
if self.monolithic:
|
|
w = self.weight[:1] if quant_levels is None else self.weight[1:]
|
|
else:
|
|
w = self.weight
|
|
|
|
padded_x_list = []
|
|
|
|
for i, xi in enumerate(x_list):
|
|
xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens) # t l' k
|
|
wi = w.shape[0] - xi.shape[1]
|
|
xi = F.pad(xi, (0, 0, 0, wi)) # t l k
|
|
padded_x_list.append(xi.to(w))
|
|
|
|
x = torch.cat(padded_x_list) # n l k
|
|
x = einsum("l k d, n l k -> n d", w, x)
|
|
|
|
x_list = x.split([*map(len, x_list)])
|
|
|
|
return x_list
|
|
|
|
# Embedding that sums each RVQ-bin level within a given input acoustic prompt
|
|
class AudioEmbedding(nn.Module):
|
|
def __init__(
|
|
self,
|
|
l_tokens: int, # list of number of tokens (needed because AR resps includes stop token)
|
|
token_dim: int, # dimensionality of the embedding
|
|
levels: int | None = None, # number of RVQ-bins (I don't remember the specifics)
|
|
sums: bool = True # whether to sum all previous layers of embeddings to factor in other RVQ bin levels (I do not know which way is better)
|
|
):
|
|
super().__init__()
|
|
# array of embeddings
|
|
# proms are [0, prom_levels]
|
|
# resp are split to where [0] is for the AR, and [1:] are reserved for NAR
|
|
self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_tokens])
|
|
# weight influencer for the influence for each level (desu this should be really useless because the weights in the embedding themselves should factor this)
|
|
self.weight = nn.ParameterList([nn.Parameter( torch.Tensor([1]) ) for i in range(levels)]) if levels is not None else None
|
|
#
|
|
self.sums = sums
|
|
|
|
def forward(self, xi: Tensor, quant_levels: Tensor | None = None ) -> Tensor:
|
|
# prom
|
|
if quant_levels is None and xi.shape[-1] > 1:
|
|
if self.sums:
|
|
x = sum( [ self.embeddings[k]( xi[:, k] ) * (self.weight[k] if self.weight is not None else 1) for k in range(xi.shape[-1]) ] )
|
|
else:
|
|
k = 0 # only use the most significant RVQ bin level for the input prom
|
|
x = self.embeddings[k]( xi[:, k] ) * (self.weight[k] if self.weight is not None else 1)
|
|
# AR resp
|
|
elif quant_levels is None or quant_levels == 0:
|
|
x = self.embeddings[0]( xi[:, 0] )
|
|
# NAR resp
|
|
else:
|
|
if self.sums:
|
|
x = sum( [ self.embeddings[k+1]( xi[:, k] ) * (self.weight[k+1] if self.weight is not None else 1) for k in range(xi.shape[-1]) ] )
|
|
else:
|
|
k = xi.shape[-1] - 1 # only use the previous RVQ bin level for the current resp embedding
|
|
x = self.embeddings[k+1]( xi[:, k] ) * (self.weight[k+1] if self.weight is not None else 1)
|
|
|
|
return x
|
|
|
|
class Base(nn.Module):
|
|
@property
|
|
def causal(self) -> bool:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def arch_type(self) -> str:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def norm_type(self):
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_prom_levels(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_resp_levels(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_max_levels(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_langs(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_tasks(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def n_tones(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def recurrent_chunk_size(self) -> int:
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def rotary_embedding_base(self) -> float:
|
|
return 10000
|
|
|
|
@property
|
|
def interleave(self) -> bool:
|
|
return False
|
|
|
|
@property
|
|
def monolithic(self) -> bool:
|
|
return False
|
|
|
|
@property
|
|
def version(self) -> int:
|
|
return 1
|
|
|
|
@property
|
|
def stop_token(self):
|
|
if not self.causal:
|
|
raise ValueError("Not using stop token!")
|
|
return self.n_tokens
|
|
|
|
@property
|
|
def ignore_index(self):
|
|
return -100
|
|
|
|
def __init__(
|
|
self,
|
|
n_tokens: int = 1024,
|
|
d_model: int = 512,
|
|
n_heads: int = 8,
|
|
n_layers: int = 12,
|
|
p_dropout: float = 0.1,
|
|
|
|
n_experts: int = 1,
|
|
|
|
l_padding: int = 0,
|
|
|
|
training = True,
|
|
config = None,
|
|
):
|
|
super().__init__()
|
|
self.training = training
|
|
self.config = config
|
|
self.activation_checkpointing = self.config.activation_checkpointing if self.config is not None else True
|
|
|
|
self.n_tokens = n_tokens
|
|
self.d_model = d_model
|
|
self.n_heads = n_heads
|
|
self.n_layers = n_layers
|
|
self.n_experts = n_experts
|
|
|
|
self.l_padding = l_padding
|
|
|
|
# +1 to include the stop token
|
|
# to-do: undo this dogshit mistake; tasks tokens should be delegated to its own embedding
|
|
n_prom_tokens = n_tokens
|
|
n_resp_tokens = n_tokens + (1 if self.causal else 0) # AR requires a stop token to... know when to stop
|
|
|
|
self.text_emb = Embedding(n_tokens, d_model)
|
|
self.langs_emb = None
|
|
self.tones_emb = None
|
|
self.tasks_emb = None
|
|
|
|
if self.version == 1: # legacy
|
|
n_prom_tokens += (self.n_tasks - 1) # old models have the task tokens in the prom
|
|
self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
|
|
self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model, monolithic=self.monolithic)
|
|
else:
|
|
# [1024] * 8
|
|
self.proms_emb = AudioEmbedding(
|
|
[n_prom_tokens] * self.n_prom_levels, d_model,
|
|
levels=self.n_prom_levels if self.version > 3 else None,
|
|
sums=self.config.audio_embedding_sums if self.config is not None else True,
|
|
)
|
|
# [1025] + [1024] * 8
|
|
self.resps_emb = AudioEmbedding(
|
|
[n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1), d_model,
|
|
levels=self.n_resp_levels if self.version > 3 else None,
|
|
sums=self.config.audio_embedding_sums if self.config is not None else True
|
|
)
|
|
|
|
|
|
if self.version >= 3:
|
|
self.langs_emb = Embedding(self.n_langs, d_model) if self.n_langs > 0 else None
|
|
self.tasks_emb = Embedding(self.n_tasks, d_model) if self.n_tasks > 0 else None
|
|
|
|
if self.version >= 4:
|
|
self.tones_emb = Embedding(self.n_tones, d_model) if self.n_tones > 0 else None
|
|
|
|
self.sep = nn.Parameter(torch.randn(d_model))
|
|
|
|
# ick, there has to be a better way
|
|
hf_attention = self.config.attention if self.config is not None else None
|
|
|
|
if self.config.attention == "auto":
|
|
if "flash" in AVAILABLE_ATTENTIONS:
|
|
self.config.attention = "flash"
|
|
elif "xformers" in AVAILABLE_ATTENTIONS:
|
|
self.config.attention = "xformers"
|
|
else:
|
|
self.config.attention = "mem_efficient"
|
|
|
|
if self.config.attention in ["xformers", "mem_efficient", "math", "flash"]:
|
|
hf_attention = None
|
|
if self.config.attention not in AVAILABLE_ATTENTIONS:
|
|
raise ValueError(f"Requesting attention `{self.config.attention}` but is not available. Currently available: {AVAILABLE_ATTENTIONS}")
|
|
|
|
|
|
if self.arch_type == "transformer":
|
|
self.sin_emb = SinusoidalEmbedding(d_model)
|
|
self.blocks = nn.ModuleList([TransformerBlock(
|
|
d_model=d_model,
|
|
n_heads=n_heads,
|
|
p_dropout=p_dropout if training else 0.0,
|
|
causal=self.causal,
|
|
norm_type=self.norm_type,
|
|
n_levels=self.n_resp_levels,
|
|
) for _ in range(n_layers) ])
|
|
elif self.arch_type in ["mistral", "mixtral"]:
|
|
if n_experts <= 1:
|
|
self.model = MistralModel(MistralConfig(
|
|
vocab_size=n_resp_tokens,
|
|
hidden_size=d_model,
|
|
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
|
intermediate_size=d_model*4,
|
|
num_hidden_layers=n_layers,
|
|
num_attention_heads=n_heads,
|
|
attention_dropout=p_dropout if training else 0.0,
|
|
num_key_value_heads=n_heads,
|
|
hidden_act="gelu",
|
|
is_encoder_decoder=False,
|
|
is_decoder=True,
|
|
attn_implementation=hf_attention,
|
|
#gradient_checkpointing=self.activation_checkpointing,
|
|
))
|
|
else:
|
|
self.model = MixtralModel(MixtralConfig(
|
|
vocab_size =n_resp_tokens,
|
|
hidden_size=d_model,
|
|
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
|
intermediate_size=d_model*4,
|
|
num_hidden_layers=n_layers,
|
|
num_attention_heads=n_heads,
|
|
attention_dropout=p_dropout if training else 0.0,
|
|
num_key_value_heads=n_heads,
|
|
sliding_window=75 * 12, # 12 second context window
|
|
output_router_logits=training,
|
|
hidden_act="gelu",
|
|
is_encoder_decoder=False,
|
|
is_decoder=True,
|
|
num_local_experts=n_experts,
|
|
num_experts_per_tok=min(2, n_experts),
|
|
attn_implementation=hf_attention,
|
|
#gradient_checkpointing=self.activation_checkpointing,
|
|
))
|
|
|
|
if self.activation_checkpointing and not self.model.gradient_checkpointing:
|
|
self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=dict(
|
|
use_reentrant=False
|
|
))
|
|
|
|
#if training:
|
|
# self.model.training = True
|
|
elif self.arch_type == "llama":
|
|
if n_experts <= 1:
|
|
self.model = LlamaModel(LlamaConfig(
|
|
vocab_size=n_resp_tokens,
|
|
hidden_size=d_model,
|
|
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
|
intermediate_size=d_model*4,
|
|
num_hidden_layers=n_layers,
|
|
num_attention_heads=n_heads,
|
|
attention_dropout=p_dropout if training else 0.0,
|
|
num_key_value_heads=n_heads,
|
|
sliding_window=75 * 12, # 12 second context window
|
|
hidden_act="gelu",
|
|
is_encoder_decoder=False,
|
|
is_decoder=True,
|
|
attn_implementation=hf_attention,
|
|
#gradient_checkpointing=self.activation_checkpointing,
|
|
))
|
|
else:
|
|
self.model = MixtralModel(MixtralConfig(
|
|
vocab_size =n_resp_tokens,
|
|
hidden_size=d_model,
|
|
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
|
intermediate_size=d_model*4,
|
|
num_hidden_layers=n_layers,
|
|
num_attention_heads=n_heads,
|
|
attention_dropout=p_dropout if training else 0.0,
|
|
num_key_value_heads=n_heads,
|
|
sliding_window=75 * 12, # 12 second context window
|
|
output_router_logits=training,
|
|
hidden_act="gelu",
|
|
is_encoder_decoder=False,
|
|
is_decoder=True,
|
|
num_local_experts=n_experts,
|
|
num_experts_per_tok=min(2, n_experts),
|
|
attn_implementation=hf_attention,
|
|
#gradient_checkpointing=self.activation_checkpointing,
|
|
))
|
|
|
|
if self.activation_checkpointing and not self.model.gradient_checkpointing:
|
|
self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=dict(
|
|
use_reentrant=False
|
|
))
|
|
|
|
#if training:
|
|
# self.model.training = True
|
|
elif self.arch_type == "retnet":
|
|
kwargs = dict(
|
|
vocab_size=n_resp_tokens,
|
|
decoder_embed_dim=d_model,
|
|
decoder_value_embed_dim =d_model * 2,
|
|
decoder_retention_heads=n_heads,
|
|
decoder_ffn_embed_dim=d_model * 4,
|
|
decoder_layers=n_layers,
|
|
dropout=p_dropout if training else 0.0,
|
|
checkpoint_activations=self.activation_checkpointing,
|
|
activation_fn="gelu",
|
|
use_layernorm=self.version < 3,
|
|
use_biases=self.version < 3,
|
|
use_glu=self.version >= 3,
|
|
|
|
chunkwise_recurrent=self.causal and self.recurrent_chunk_size > 0,
|
|
recurrent_chunkwise_size=self.recurrent_chunk_size if self.causal else 0,
|
|
no_output_layer=True,
|
|
decoder_normalize_before=True,
|
|
|
|
rotary_embedding_base=self.rotary_embedding_base, # 10000
|
|
)
|
|
|
|
if n_experts > 1:
|
|
kwargs.update(dict(
|
|
use_xmoe=True,
|
|
moe_freq=1,
|
|
moe_expert_count=n_experts,
|
|
moe_gating_use_fp32=False,
|
|
))
|
|
|
|
self.model = RetNetDecoder(RetNetConfig(**kwargs))
|
|
elif self.arch_type == "retnet-hf":
|
|
kwargs = dict(
|
|
vocab_size=n_resp_tokens,
|
|
decoder_embed_dim=d_model,
|
|
decoder_value_embed_dim =d_model * 2,
|
|
decoder_retention_heads=n_heads,
|
|
decoder_ffn_embed_dim=d_model * 4,
|
|
decoder_layers=n_layers,
|
|
dropout=p_dropout if training else 0.0,
|
|
checkpoint_activations=self.activation_checkpointing,
|
|
activation_fn="gelu",
|
|
use_glu=False, # self.version >= 3,
|
|
|
|
recurrent_chunk_size=self.recurrent_chunk_size if self.causal else 0,
|
|
decoder_normalize_before=True,
|
|
|
|
deepnorm=False,
|
|
subln=True,
|
|
)
|
|
|
|
self.model = RetNetDecoder_HF(RetNetConfig_HF(**kwargs))
|
|
|
|
if self.activation_checkpointing and not self.model.gradient_checkpointing:
|
|
self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=dict(
|
|
use_reentrant=False
|
|
))
|
|
elif self.arch_type == "bitnet":
|
|
self.model = BitNetTransformer(
|
|
num_tokens=n_resp_tokens,
|
|
dim=d_model,
|
|
depth=n_layers,
|
|
heads=n_heads,
|
|
ff_mult=4,
|
|
activation_checkpointing=self.activation_checkpointing,
|
|
)
|
|
else:
|
|
raise RuntimeError(f'Unknown arch specified: {self.arch_type}')
|
|
|
|
if self.config.attention in ["xformers", "auto", "mem_efficient", "math", "flash"]:
|
|
self.model = ml.replace_attention( self.model, klass=Llama_Attention, target=LlamaAttention, mode=self.config.attention )
|
|
|
|
self.classifier = nn.Linear(d_model, n_resp_tokens)
|
|
|
|
self.accuracy_metric = MulticlassAccuracy(
|
|
n_resp_tokens,
|
|
top_k=10,
|
|
average="micro",
|
|
multidim_average="global",
|
|
ignore_index=self.ignore_index,
|
|
)
|
|
|
|
self.precision_metric = MulticlassPrecision(
|
|
n_resp_tokens,
|
|
top_k=10,
|
|
average="micro",
|
|
multidim_average="global",
|
|
ignore_index=self.ignore_index,
|
|
)
|
|
|
|
def _forward(
|
|
self,
|
|
inputs,
|
|
mask = None,
|
|
state = None,
|
|
):
|
|
x = inputs
|
|
m = mask.squeeze(-1).int()
|
|
aux_loss = None
|
|
|
|
"""
|
|
# Broken
|
|
if state is not None and (self.arch_type == "retnet" or self.arch_type == "retnet-hf"):
|
|
# prefill
|
|
if len(state) == 0:
|
|
prefill_size = x.shape[1]
|
|
# run the initial prompt to fill the KV cache
|
|
if self.arch_type == "retnet":
|
|
for n in range(prefill_size):
|
|
xi = x[:, n, :].unsqueeze(1)
|
|
self.model(xi, incremental_state=state, token_embeddings=xi, features_only=True)
|
|
elif self.arch_type == "retnet-hf":
|
|
state = None
|
|
for n in range(prefill_size):
|
|
xi = x[:, n, :].unsqueeze(1)
|
|
|
|
kwargs = dict(
|
|
attention_mask=m,
|
|
inputs_embeds=xi,
|
|
past_key_values=state,
|
|
use_cache=True,
|
|
forward_impl='recurrent',
|
|
# return_dict=True,
|
|
)
|
|
|
|
out = self.model(**kwargs)
|
|
state = out.past_key_values
|
|
|
|
# grab last token(s)
|
|
x = x[:, -1, :].unsqueeze(1)
|
|
"""
|
|
|
|
# HF transformer derived model
|
|
if self.arch_type in ["llama", "mistral", "mixtral"]:
|
|
kwargs = dict(
|
|
attention_mask=m,
|
|
inputs_embeds=x,
|
|
past_key_values=state,
|
|
use_cache=True,
|
|
# return_dict=True,
|
|
)
|
|
if self.n_experts > 1 and targ_list is not None:
|
|
kwargs["output_router_logits"] = True
|
|
|
|
t = self.model(**kwargs)
|
|
|
|
x = t[0]
|
|
|
|
if state is not None:
|
|
state = t[1]
|
|
|
|
if self.n_experts > 1 and targ_list is not None:
|
|
router_logits = t[-1]
|
|
aux_loss = self.model.config.router_aux_loss_coef * load_balancing_loss_func( router_logits, self.model.config.num_local_experts, self.model.config.num_experts_per_tok )
|
|
elif self.arch_type == "transformer":
|
|
# ensures we specify a quant_level for the transformer implementation's AdaLN
|
|
l = torch.zeros((batch_size,), dtype=torch.int32) if quant_levels is None else quant_levels
|
|
l = l.to(device)
|
|
# inject position information
|
|
x = self.sin_emb.add_pe(x)
|
|
# pass our inputs through the transformer
|
|
for block in self.blocks:
|
|
x = block(x, m, l)
|
|
elif self.arch_type == "retnet":
|
|
# pass our inputs through the RetNet
|
|
x, _ = self.model(x, incremental_state=state, token_embeddings=x, features_only=True)
|
|
if _ is not None and "l_aux" in _ and self.n_experts > 1:
|
|
aux_loss = torch.sum(torch.stack([ t for t in _["l_aux"] if t is not None])) * 0.001
|
|
elif self.arch_type == "retnet-hf":
|
|
first = state is None or len(state) == 0
|
|
|
|
kwargs = dict(
|
|
attention_mask=m,
|
|
inputs_embeds=x if first else x[:, -1, :].unsqueeze(1),
|
|
past_key_values=None if first else state,
|
|
use_cache=True,
|
|
forward_impl='parallel' if first else 'recurrent',
|
|
return_dict=True,
|
|
)
|
|
|
|
out = self.model(**kwargs)
|
|
x = out.last_hidden_state
|
|
if state is not None:
|
|
state = out.past_key_values
|
|
|
|
elif self.arch_type == "bitnet":
|
|
x = self.model(x)
|
|
|
|
# output projection layer with masking
|
|
x = self.classifier(x) * mask
|
|
|
|
return x, state, aux_loss
|
|
|
|
def inputs(
|
|
self,
|
|
text_list: list[Tensor],
|
|
proms_list: list[Tensor],
|
|
resps_list: list[Tensor],
|
|
targ_list: list[Tensor] | None = None,
|
|
|
|
lang_list: list[Tensor] | None = None,
|
|
tone_list: list[Tensor] | None = None,
|
|
):
|
|
device = text_list[0].device
|
|
batch_size = len(text_list)
|
|
|
|
inputs = [ [] for _ in range(batch_size) ]
|
|
for i in range(batch_size):
|
|
if text_list is not None:
|
|
inputs[i].append( ( "text", text_list[i] ) )
|
|
if proms_list is not None:
|
|
inputs[i].append( ( "prom", proms_list[i] ) )
|
|
if resps_list is not None:
|
|
inputs[i].append( ( "resp", resps_list[i] ) )
|
|
if targ_list is not None:
|
|
inputs[i].append( ( "targ", targ_list[i] ) )
|
|
|
|
return inputs
|
|
|
|
def inputs_to_embeddings(
|
|
self,
|
|
inputs: list,
|
|
quant_levels: Tensor | None = None
|
|
):
|
|
x_list = []
|
|
for b_i in range(len(inputs)):
|
|
batch = []
|
|
for i in range(len(inputs[b_i])):
|
|
name, input = inputs[b_i][i]
|
|
embedding = None
|
|
if name == "text":
|
|
embedding = self.text_emb( input )
|
|
elif name == "lang":
|
|
embedding = self.langs_emb( input )
|
|
elif name == "prom":
|
|
embedding = self.proms_emb( input )
|
|
elif name == "tone":
|
|
embedding = self.tones_emb( input )
|
|
elif name == "resp":
|
|
embedding = self.resps_emb( input, quant_levels[b_i] if quant_levels is not None else None )
|
|
else:
|
|
continue
|
|
|
|
batch.append(embedding)
|
|
|
|
x_list.append( _join( batch, self.sep ) )
|
|
|
|
return x_list
|
|
|
|
def training_targets(
|
|
self,
|
|
inputs: list,
|
|
):
|
|
x_list = []
|
|
for bi in range(len(inputs)):
|
|
batch = []
|
|
for i in range(len(inputs[bi])):
|
|
name, input = inputs[bi][i]
|
|
device = input.device
|
|
|
|
if name == "prom":
|
|
batch.append( torch.full_like(input[..., 0], self.ignore_index) )
|
|
elif name in ["text", "lang", "tone", "targ"]:
|
|
batch.append( input )
|
|
|
|
x_list.append( _join( batch, torch.tensor(self.ignore_index, device=device) ) )
|
|
|
|
return x_list
|
|
|
|
def forward(
|
|
self,
|
|
inputs: list,
|
|
|
|
quant_levels: Tensor | None = None,
|
|
state: dict | list | None = None,
|
|
):
|
|
|
|
x_list = self.inputs_to_embeddings( inputs, quant_levels )
|
|
x, m = list_to_tensor(x_list)
|
|
|
|
# yes, there's a better way.
|
|
training = False
|
|
for b_i in range(len(inputs)):
|
|
for i in range(len(inputs[b_i])):
|
|
name, input = inputs[b_i][i]
|
|
if name == "targ":
|
|
training = True
|
|
|
|
|
|
device = x.device
|
|
batch_size = len(x_list)
|
|
|
|
# pad our input and mask, but retain the original length by doing it after
|
|
if self.l_padding and x.shape[1] % self.l_padding != 0:
|
|
# pad input
|
|
shape = list(x.shape)
|
|
shape[1] = self.l_padding - shape[1] % self.l_padding
|
|
|
|
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
|
x = torch.cat([x, padding], dim=1)
|
|
|
|
# pad mask
|
|
shape[2] = 1
|
|
padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
|
|
m = torch.cat([m, padding], dim=1)
|
|
|
|
|
|
x, state, aux_loss = self._forward(
|
|
inputs=x,
|
|
mask=m,
|
|
state=state,
|
|
)
|
|
|
|
# Remove padding
|
|
logits = [ hi[:li] for hi, li in zip(x, map(len, x_list)) ]
|
|
|
|
# compute loss if the target is given
|
|
if training:
|
|
target_list = self.training_targets( inputs )
|
|
|
|
# modify only for the AR so it can properly behave like a transformer
|
|
for i in range(len(target_list)):
|
|
if quant_levels is not None and quant_levels[i] > 0:
|
|
continue
|
|
|
|
logits[i] = logits[i][..., :-1, :] # shift the target so that token n...
|
|
target_list[i] = target_list[i][..., 1:] # predicts token n + 1
|
|
|
|
target = torch.cat( target_list )
|
|
inputs = torch.cat( logits )
|
|
|
|
self.loss = dict(
|
|
# "nll" was in the original implementation and should actually just be called something else
|
|
nll = F.cross_entropy( inputs, target, ignore_index=self.ignore_index )
|
|
)
|
|
self.stats = dict(
|
|
acc = self.accuracy_metric( inputs, target ),
|
|
# precision = self.precision_metric( inputs, target ),
|
|
)
|
|
|
|
if aux_loss is not None:
|
|
self.loss["nll"] += aux_loss
|
|
|
|
return (logits, state) if state is not None else logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: list[Tensor],
|
|
resps_list: list[Tensor],
|
|
quant_levels: Tensor | None = None,
|
|
|
|
temperature: float = 1.0,
|
|
min_temperature: float = -1.0,
|
|
top_k: int = -100,
|
|
top_p: float = 1.0,
|
|
|
|
repetition_penalty: float = 1.0,
|
|
repetition_penalty_decay: float = 0.0,
|
|
|
|
length_penalty: float = 0.0,
|
|
|
|
beam_width: int = 0,
|
|
|
|
mirostat: list[dict] | None = None,
|
|
):
|
|
if min_temperature < 0:
|
|
min_temperature = temperature
|
|
# (NAR) return the entire generated response
|
|
if quant_levels is not None:
|
|
logits = [ logit[-l:] for logit, l in zip(logits, map(len, resps_list)) ]
|
|
# (AR chunkwise) return the last chunkwise piece
|
|
elif self.causal and self.recurrent_chunk_size > 0:
|
|
logits = [ logit[-l:] for logit, l in zip(logits, self.recurrent_chunk_size) ]
|
|
# (AR) return just the last code
|
|
else:
|
|
logits = [ logit[-1:] for logit in logits ]
|
|
|
|
devices = [ logit.device for logit in logits ]
|
|
logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
|
|
|
|
# perform repetition penalizing
|
|
logits = [ reptition_penalize(logit, previous=resps[:, -1], factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]
|
|
|
|
# (AR) perform length penalizing
|
|
if quant_levels is None and self.causal:
|
|
logits = [ length_penalize(logit, length=l + 1, factor=length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, resps_list) ) ]
|
|
|
|
# perform top_k/top_p filtering of our logits
|
|
if top_k > 0 or top_p < 1.0:
|
|
logits = [ top_k_top_p_filtering(logit, top_k=top_k, top_p=top_p) for logit in logits ]
|
|
|
|
# trigger dynamic temperature sampling if the minimum temperature is not the same as the sampling temperature
|
|
# epsilon float comparison because I don't trust Python
|
|
if abs(temperature - min_temperature) >= 0.001:
|
|
logits = [ dynamic_temperature(logit, temperature=temperature, min_temperature=min_temperature) for logit in logits ]
|
|
else:
|
|
logits = [ logit / temperature for logit in logits ]
|
|
|
|
# do mirostat sampling
|
|
# currently incompatible with beam searching with the way the two are implemented, perhaps a night of brain bashing can make the two work
|
|
if mirostat is not None:
|
|
# mirostat sampling
|
|
return [ mirostat_sample(logit, state=state) for logit, state in zip(logits, mirostat) ]
|
|
|
|
# do beam search (naive implementation)
|
|
# picks the top-k across all batches, and re-batches those resultant tokens
|
|
# returns the logit scores as well to be P-concatted with the previous scores
|
|
# to-do: not naively implement beam searching
|
|
if beam_width > 1:
|
|
candidates = top_k_logits_list( logits, beam_width )
|
|
res = [ torch.tensor(token, dtype=torch.int16).unsqueeze(dim=-1) for batch, token in candidates ]
|
|
scores = [ logits[batch].flatten()[token] for batch, token in candidates ]
|
|
return res, scores
|
|
|
|
# and sample
|
|
return [ Categorical(logits=logit).sample() for logit in logits ] |