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Generation and inference utilities with constrained decoding
"""
import torch
from transformers import LogitsProcessor, LogitsProcessorList
from typing import Dict
from config import (
SYSTEM_MSG, GEN_MAX_NEW_TOKENS, GEN_TEMPERATURE,
GEN_TOP_P, GEN_TOP_K, GEN_NO_REPEAT_NGRAM_SIZE,
GEN_REPETITION_PENALTY, GEN_END_LOGIT_SLOPE
)
class LengthAwareMotionLogitsProcessor(LogitsProcessor):
"""
Constrained decoding processor that:
1. Enforces motion token vocabulary
2. Controls sequence length (min/soft_target/max)
3. Biases toward ending at soft_target length
"""
def __init__(self, prompt_len, mot_begin_id, mot_end_id, motion_ids,
hard_min, soft_target, hard_max, end_logit_slope=0.25):
super().__init__()
self.prompt_len = int(prompt_len)
self.mot_begin_id = int(mot_begin_id)
self.mot_end_id = int(mot_end_id)
self.motion_ids = torch.tensor(sorted(set(int(x) for x in motion_ids)))
self.motion_plus_end = torch.tensor(
sorted(set(list(self.motion_ids.tolist()) + [self.mot_end_id]))
)
self.hard_min = int(hard_min)
self.soft_target = int(soft_target)
self.hard_max = int(hard_max)
self.end_logit_slope = float(end_logit_slope)
def __call__(self, input_ids, scores):
device = scores.device
bs = scores.size(0)
mask = torch.full_like(scores, float("-inf"))
for b in range(bs):
gen = input_ids[b, self.prompt_len:]
# No tokens generated yet - must start with MOT_BEGIN
if gen.numel() == 0:
allowed = torch.tensor([self.mot_begin_id], device=device)
mask[b].index_fill_(0, allowed, 0.0)
continue
# Find MOT_BEGIN position
begin_pos = (gen == self.mot_begin_id).nonzero(as_tuple=True)[0]
if begin_pos.numel() == 0:
allowed = torch.tensor([self.mot_begin_id], device=device)
mask[b].index_fill_(0, allowed, 0.0)
continue
# Already generated MOT_END - force EOS
if (gen == self.mot_end_id).any():
allowed = torch.tensor([self.mot_end_id], device=device)
mask[b].index_fill_(0, allowed, 0.0)
continue
# Count motion tokens after MOT_BEGIN
after_begin = gen[begin_pos[0].item() + 1:]
cur_len = after_begin.numel()
# Before minimum length - only allow motion tokens
if cur_len < self.hard_min:
allowed = self.motion_ids.to(device)
mask[b].index_fill_(0, allowed, 0.0)
# After maximum length - force end
elif cur_len >= self.hard_max:
allowed = torch.tensor([self.mot_end_id], device=device)
mask[b].index_fill_(0, allowed, 0.0)
# Between min and max - allow motion tokens or end
else:
allowed = self.motion_plus_end.to(device)
mask[b].index_fill_(0, allowed, 0.0)
# Bias toward ending at soft_target
distance = max(0, cur_len - self.soft_target)
bias = self.end_logit_slope * float(distance)
scores[b, self.mot_end_id] = scores[b, self.mot_end_id] + bias
return scores + mask
def get_len_controls(prompt_text: str, length_stats_by_text: Dict, global_median_len: int):
"""
Get length controls (min/soft_target/max) for a given prompt
"""
s = length_stats_by_text.get(prompt_text)
if s is None:
med = global_median_len
else:
med = s["median"]
hard_min = max(1, int(0.6 * med))
soft_tgt = med
hard_max = max(hard_min + 4, int(1.4 * med))
return hard_min, soft_tgt, hard_max
def generate_t2m(
model,
tokenizer,
prompt_text: str,
mot_begin_id: int,
mot_end_id: int,
motion_token_ids: list,
length_stats_by_text: Dict,
global_median_len: int,
prompt_vocab: Dict = None,
pid: str = None,
has_pid: bool = False,
max_new_tokens: int = None,
per_prompt_vocab: bool = True
):
"""
Generate motion sequence from text prompt with constrained decoding
"""
model.eval()
device = next(model.parameters()).device
if max_new_tokens is None:
max_new_tokens = GEN_MAX_NEW_TOKENS
# Build prompt
pid_tok = ""
if has_pid and pid is not None:
pid_tok = f"<PID_{pid}>"
user_text = f"<T2M>{pid_tok}\n\n" + prompt_text
prompt = (
"<|im_start|>system\n" + SYSTEM_MSG + "<|im_end|>\n"
+ "<|im_start|>user\n" + user_text + "\n<|im_end|>\n"
+ "<|im_start|>assistant\n"
)
# Tokenize
inputs = tokenizer(prompt, return_tensors="pt").to(device)
prompt_len = inputs["input_ids"].size(1)
# Get length controls
hard_min, soft_tgt, hard_max = get_len_controls(
prompt_text, length_stats_by_text, global_median_len
)
# Get allowed motion tokens
if per_prompt_vocab and prompt_vocab:
allowed_motion_ids = prompt_vocab.get(prompt_text, motion_token_ids)
else:
allowed_motion_ids = motion_token_ids
# Setup constrained decoding
processors = LogitsProcessorList([
LengthAwareMotionLogitsProcessor(
prompt_len=prompt_len,
mot_begin_id=mot_begin_id,
mot_end_id=mot_end_id,
motion_ids=allowed_motion_ids,
hard_min=hard_min,
soft_target=soft_tgt,
hard_max=hard_max,
end_logit_slope=GEN_END_LOGIT_SLOPE,
)
])
# Generate
with torch.no_grad():
out = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs.get("attention_mask"),
max_new_tokens=min(max_new_tokens, hard_max + 4),
do_sample=True,
temperature=GEN_TEMPERATURE,
top_p=GEN_TOP_P,
top_k=GEN_TOP_K,
no_repeat_ngram_size=GEN_NO_REPEAT_NGRAM_SIZE,
repetition_penalty=GEN_REPETITION_PENALTY,
logits_processor=processors,
eos_token_id=mot_end_id,
pad_token_id=tokenizer.eos_token_id,
)
# Decode
decoded = tokenizer.decode(out[0], skip_special_tokens=False)
reply = decoded.split("<|im_start|>assistant\n")[-1].split("<|im_end|>")[0]
return reply |