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import torch
from torch import nn, FloatTensor, LongTensor
import numpy as np
from torch.nn.functional import pad
from typing import Dict, List, Union
from transformers import AutoModelForCausalLM, AutoConfig, LogitsProcessor, LogitsProcessorList
from .spec import ModelSpec, ModelInput
from .parse_encoder import MAP_MESH_ENCODER, get_mesh_encoder
from ..tokenizer.spec import TokenizerSpec, DetokenizeOutput
from copy import deepcopy
class VocabSwitchingLogitsProcessor(LogitsProcessor):
def __init__(self, tokenizer: TokenizerSpec, start_tokens: LongTensor):
self.tokenizer = tokenizer
self.start_tokens = start_tokens
assert start_tokens.ndim == 1
def __call__(self, input_ids: LongTensor, scores: FloatTensor) -> FloatTensor:
# input_ids shape: (batch_size, seq_len)
for batch_idx, sequence in enumerate(input_ids):
mask = torch.full_like(scores[batch_idx], float('-inf'))
sequence = torch.cat([self.start_tokens, sequence])
tokens = self.tokenizer.next_posible_token(ids=sequence.detach().cpu().numpy())
mask[tokens] = 0
scores[batch_idx] = scores[batch_idx] + mask
return scores
class UniRigAR(ModelSpec):
def process_fn(self, batch: List[ModelInput]) -> List[Dict]:
if batch[0].joints is None: # predict
return [{} for _ in range(len(batch))]
max_length = 0
for b in batch:
max_length = max(max_length, b.tokens.shape[0])
res = [{
'input_ids': np.pad(b.tokens, ((0, max_length-b.tokens.shape[0])), 'constant', constant_values=b.pad),
'attention_mask': np.pad(torch.ones(b.tokens.shape[0]), ((0, max_length - b.tokens.shape[0])), 'constant', constant_values=0.),
} for b in batch]
return res
def __init__(self, llm, mesh_encoder, **kwargs):
super().__init__()
self.tokenizer: TokenizerSpec = kwargs.get('tokenizer')
self.vocab_size = self.tokenizer.vocab_size
_d = llm.copy()
_d['vocab_size'] = self.tokenizer.vocab_size
llm_config = AutoConfig.from_pretrained(**_d)
# Force float32 precision for the model
llm_config.torch_dtype = torch.float32
# Force enable pre_norm
llm_config.pre_norm = True
self.transformer = AutoModelForCausalLM.from_config(config=llm_config)
self.hidden_size = llm.hidden_size
self.mesh_encoder = get_mesh_encoder(**mesh_encoder)
if (
isinstance(self.mesh_encoder, MAP_MESH_ENCODER.michelangelo) or
isinstance(self.mesh_encoder, MAP_MESH_ENCODER.michelangelo_encoder)
):
self.output_proj = nn.Linear(self.mesh_encoder.width, self.hidden_size)
else:
raise NotImplementedError()
def encode_mesh_cond(self, vertices: FloatTensor, normals: FloatTensor) -> FloatTensor:
assert not torch.isnan(vertices).any()
assert not torch.isnan(normals).any()
if (
isinstance(self.mesh_encoder, MAP_MESH_ENCODER.michelangelo) or
isinstance(self.mesh_encoder, MAP_MESH_ENCODER.michelangelo_encoder)
):
if (len(vertices.shape) == 3):
shape_embed, latents, token_num, pre_pc = self.mesh_encoder.encode_latents(pc=vertices, feats=normals)
else:
shape_embed, latents, token_num, pre_pc = self.mesh_encoder.encode_latents(pc=vertices.unsqueeze(0), feats=normals.unsqueeze(0))
latents = self.output_proj(latents)
return latents
else:
raise NotImplementedError()
def training_step(self, batch: Dict) -> Dict[str, FloatTensor]:
cond = self.encode_mesh_cond(vertices=batch['vertices'], normals=batch['normals']).to(dtype=self.transformer.dtype)
B = cond.shape[0]
input_ids: LongTensor = batch['input_ids']
inputs_embeds = self.transformer.get_input_embeddings()(input_ids).to(dtype=self.transformer.dtype)
inputs_embeds = torch.concat([cond, inputs_embeds], dim=1)
attention_mask = batch['attention_mask']
# add attention to condition
attention_mask = pad(attention_mask, (cond.shape[1], 0, 0, 0), value=1.)
output = self.transformer(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
)
# (B, L, vocab_size)
logit = output.logits[:, cond.shape[1]:].reshape(B, -1, self.vocab_size)
# compute loss with shift one-token right
device = logit.device # (B, n, num_discrete)
logit = logit[:, :-1] # (B, n)
num_discrete = self.tokenizer.num_discrete
s = torch.nn.functional.softmax(logit, dim=-1)
label = input_ids[:, 1:].clone() # (B, n)
mask = label < num_discrete
dis = torch.arange(num_discrete, device=device).view(1, 1, -1) # (B, n, num_discrete)
dis = (dis - label.unsqueeze(2).repeat(1, 1, num_discrete)).type(torch.float32) / num_discrete
dis_loss = (s[:, :, :num_discrete] * torch.abs(dis))[mask].sum() / 50 # ignore padding loss
label[attention_mask[:, cond.shape[1] + 1:]==0] = -100
assert not torch.isnan(logit).any(), logit
ce_loss = nn.functional.cross_entropy(logit.permute(0, 2, 1), label)
return {
'ce_loss': ce_loss,
'dis_loss': dis_loss,
}
def forward(self, data: Dict):
return self.training_step(data=data)
@torch.no_grad()
def generate(
self,
vertices: FloatTensor,
normals: FloatTensor,
cls: Union[str, None]=None,
**kwargs,
) -> DetokenizeOutput:
'''
Do not support batch!
'''
cond = self.encode_mesh_cond(vertices=vertices, normals=normals).to(dtype=self.transformer.dtype)
start_tokens = [self.tokenizer.bos]
if cls is not None:
start_tokens.append(self.tokenizer.cls_name_to_token(cls=cls))
start_tokens = torch.tensor(start_tokens).to(cond.device)
start_embed = self.transformer.get_input_embeddings()(
start_tokens.unsqueeze(0)
).to(dtype=self.transformer.dtype)
cond = torch.cat([cond, start_embed], dim=1)
processor = VocabSwitchingLogitsProcessor(
tokenizer=self.tokenizer,
start_tokens=start_tokens,
)
results = self.transformer.generate(
inputs_embeds=cond,
bos_token_id=self.tokenizer.bos,
eos_token_id=self.tokenizer.eos,
pad_token_id=self.tokenizer.pad,
logits_processor=LogitsProcessorList([processor]),
**kwargs,
)
output_ids = results[0, :]
for token in reversed(start_tokens):
output_ids = pad(output_ids, (1, 0), value=token)
output_ids = output_ids.detach().cpu().numpy()
res = self.tokenizer.detokenize(ids=output_ids)
return res
def predict_step(self, batch: Dict, no_cls: bool=False):
vertices: FloatTensor = batch['vertices']
normals : FloatTensor = batch['normals']
paths : List[str] = batch['path']
cls = batch['cls']
generate_kwargs = deepcopy(batch['generate_kwargs'])
no_cls = generate_kwargs.get('no_cls', False)
use_dir_cls = generate_kwargs.get('use_dir_cls', False)
assign_cls = generate_kwargs.get('assign_cls', None)
generate_kwargs.pop('no_cls', None)
generate_kwargs.pop('use_dir_cls', None)
generate_kwargs.pop('assign_cls', None)
if vertices.dim() == 2:
vertices = vertices.unsqueeze(0)
normals = normals.unsqueeze(0)
outputs = []
for i in range(vertices.shape[0]):
if no_cls:
_cls = None
elif assign_cls is not None:
_cls = assign_cls
elif use_dir_cls:
_cls = paths[i].removeprefix('./').split('/')[0]
else:
_cls = cls[i]
res = self.generate(vertices=vertices[i], normals=normals[i], cls=_cls, **generate_kwargs)
outputs.append(res)
return outputs |