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03022ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | from contextlib import nullcontext
import torch
import torch.nn as nn
from typing import Union
from funcineforge.utils.hinter import hint_once
import numpy as np
dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
class LLMDecoder(nn.Module):
def __init__(self, **kwargs):
super(LLMDecoder, self).__init__()
self.eos_token = kwargs["eos"]
if isinstance(self.eos_token, int):
self.eos_token = [self.eos_token]
self.token_embeder = kwargs["token_embeder"]
self.ras_conf = kwargs.get("ras_conf", {})
self.token_offset = kwargs.get("token_offset", 0)
def nucleus_sampling(self, weighted_scores, top_p=0.8, top_k=25, beam_size=1):
prob, indices = [], []
cum_prob = 0.0
sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
for i in range(len(sorted_idx)):
# sampling both top-p and numbers.
if cum_prob < top_p and len(prob) < top_k:
cum_prob += sorted_value[i]
prob.append(sorted_value[i])
indices.append(sorted_idx[i])
else:
break
prob = torch.tensor(prob).to(weighted_scores)
indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
sampling_ids = prob.multinomial(beam_size, replacement=True)
top_ids = indices[sampling_ids]
return top_ids
def random_sampling(self, weighted_scores, beam_size=1):
top_ids = weighted_scores.softmax(dim=0).multinomial(beam_size, replacement=True)
return top_ids
# Repetition Aware Sampling in VALL-E 2
def ras_sampling(
self, weighted_scores, decoded_tokens, *,
top_p=0.8, top_k=25, win_size=10, tau_r=0.1
):
if self.ras_conf is not None:
top_p = self.ras_conf.get("top_p", top_p)
top_k = self.ras_conf.get("top_k", top_k)
win_size = self.ras_conf.get("win_size", win_size)
tau_r = self.ras_conf.get("tau_r", tau_r)
hint_once(f"using Repetition Aware Sampling: top_p: {top_p}, top_k: {top_k},win_size: {win_size}, tau_r: {tau_r}", "ras_sampling")
top_ids = self.nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(top_ids) == top_ids).sum().item()
if rep_num >= win_size * tau_r:
top_ids = self.random_sampling(weighted_scores)
return top_ids
def sampling_ids(
self,
weighted_scores: torch.Tensor,
sampling: Union[bool, int, float] = True,
decoded_tokens: list = None,
):
if isinstance(sampling, bool):
if sampling:
top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
else:
top_ids = weighted_scores.topk(1)[1]
elif isinstance(sampling, int):
prob, indices = weighted_scores.softmax(dim=0).topk(sampling)
sampling_ids = prob.multinomial(1, replacement=True)
top_ids = indices[sampling_ids]
elif isinstance(sampling, float):
prob, indices = [], []
cum_prob = 0.0
sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
for i in range(len(sorted_idx)):
# sampling both top-p and numbers.
if cum_prob < sampling and len(prob) < 25:
cum_prob += sorted_value[i]
prob.append(sorted_value[i])
indices.append(sorted_idx[i])
else:
break
prob = torch.tensor(prob).to(weighted_scores)
indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
sampling_ids = prob.multinomial(1, replacement=True)
top_ids = indices[sampling_ids]
elif isinstance(sampling, str) and sampling.lower() == "ras":
top_ids = self.ras_sampling(weighted_scores, decoded_tokens=decoded_tokens)
else:
raise NotImplementedError(f"Not implemented for {type(sampling)} sampling")
return top_ids
def __call__(self, input_embeddings, llm, states, quantize=False, **kwargs):
max_length = kwargs.get("max_length", 60 * 25)
min_length = kwargs.get("min_length", 2 * 25)
sampling = kwargs.get("sampling", True)
device = kwargs.get("device", "cuda")
llm_dtype = kwargs.get("llm_dtype", "fp32")
use_llm_cache = kwargs.get("use_llm_cache", True)
include_eos = kwargs.get("include_eos", False)
custom_eos_token = kwargs.get("custom_eos_token", self.eos_token)
avoid_token = kwargs.get("avoid_token", None)
llm_cache = states.get("llm_cache", None)
out_tokens, hit_eos = [], False
for i in range(max_length):
with torch.cuda.amp.autocast(
enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
) if quantize is False else nullcontext():
# default attention_mask is causal, no longer need manually construct
# input_masks = torch.ones((1, input_embeddings.shape[1]), device=input_embeddings.device).to(torch.bool)
if (kwargs.get("use_qlora",False) or kwargs.get("infer_use_lora",False)) and (not kwargs.get("infer_lora_merged",False)):
outputs = llm.base_model.model(
inputs_embeds=input_embeddings.to(torch.bfloat16) if quantize is True else input_embeddings,
# attention_mask=input_masks,
output_hidden_states=True,
return_dict=True,
use_cache=use_llm_cache,
past_key_values=llm_cache,
)
else:
outputs = llm(
inputs_embeds=input_embeddings.to(torch.bfloat16) if quantize is True else input_embeddings,
# attention_mask=input_masks,
output_hidden_states=True,
return_dict=True,
use_cache=use_llm_cache,
past_key_values=llm_cache,
)
lm_hidden_states = outputs.hidden_states[-1]
h = llm.lm_head(lm_hidden_states[:, -1])
# logp = h.log_softmax(dim=-1).squeeze(0)
logp = h.squeeze(0)
if use_llm_cache:
llm_cache = outputs.past_key_values
pred = torch.log_softmax(logp, dim=-1)
if min_length is not None and i < min_length:
for x in custom_eos_token:
if pred.dtype == torch.bfloat16:
pred[x] = float(np.finfo(np.float16).min)
else:
pred[x] = float(np.finfo(np.float32).min)
if avoid_token is not None and len(avoid_token) > 0:
for x in avoid_token:
if pred.dtype == torch.bfloat16:
pred[x] = float(np.finfo(np.float16).min)
else:
pred[x] = float(np.finfo(np.float32).min)
top_id = self.sampling_ids(pred, sampling, out_tokens)[0].item()
if top_id in custom_eos_token:
if include_eos:
out_tokens.append(top_id)
hit_eos = True
break
out_tokens.append(top_id)
if use_llm_cache:
input_embeddings = self.token_embeder(torch.tensor([[top_id]], dtype=torch.int64, device=device) + self.token_offset)
else:
input_embeddings = torch.cat([
input_embeddings,
self.token_embeder(torch.tensor([[top_id]], dtype=torch.int64, device=device) + self.token_offset)
], dim=1)
out_tokens = torch.tensor([out_tokens], dtype=torch.int64, device=device)
states = {"llm_cache": llm_cache}
return out_tokens, hit_eos, states
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