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Zero
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch import nn
from transformers import AutoModelForCausalLM
from third_party.Michelangelo.encode import load_model
from skeleton_models.shape_opt import ShapeOPTConfig
def undiscretize(t, low, high, num_discrete):
assert (t >= 0).all() and (t <= num_discrete-1).all()
assert high > low
t = t.float()
t /= num_discrete
t = t * (high - low) + low
assert (t < high).all() and (t >= low).all()
return t
class SkeletonGPT(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.point_encoder = load_model()
self.cond_length = 257
self.cond_dim = 768
self.n_discrete_size = args.n_discrete_size
self.bone_per_token = 6 # (2 joints per bone)
self.max_length = int(args.n_max_bones * self.bone_per_token + 2 + self.cond_length)
self.pad_id = -1
self.coor_continuous_range = (-0.5, 0.5)
vocab_size = self.n_discrete_size + 3 # 3 for bos, eos, pad
self.config = ShapeOPTConfig.from_pretrained(
args.llm,
n_positions=self.max_length,
max_position_embeddings=self.max_length,
vocab_size = vocab_size,
_attn_implementation="flash_attention_2"
)
self.bos_token_id = 0
self.eos_token_id = 1
self.pad_token_id = 2
self.config.bos_token_id = self.bos_token_id
self.config.eos_token_id = self.eos_token_id
self.config.pad_token_id = self.pad_token_id
self.config._attn_implementation ="flash_attention_2"
self.config.n_discrete_size = self.n_discrete_size
self.config.bone_per_token = self.bone_per_token
self.config.cond_length = self.cond_length
self.config.word_embed_proj_dim = self.config.hidden_size # 1024
self.transformer = AutoModelForCausalLM.from_config(
config=self.config, attn_implementation="flash_attention_2")
self.cond_head_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
self.cond_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
self.eval()
def detokenize(self, input_ids):
# input_ids: torch.Tensor of shape (batch_size, seq_length)
batch_size = input_ids.size(0)
continuous_coors_list = []
num_bones_list = []
for i in range(batch_size):
cur_ids = input_ids[i] # Shape: (seq_length,)
# Remove padding tokens
cur_ids = cur_ids[cur_ids != self.pad_id] # Shape: (effective_seq_length,)
# Check if length is a multiple of 6 (2 joints * 3 coordinates)
if cur_ids.numel() % 6 != 0:
return None
# raise ValueError(f"Invalid length of input_ids in sample {i}. It should be a multiple of 6.")
num_bones = cur_ids.numel() // 6
num_bones_list.append(num_bones)
# Reshape into (num_bones, 6)
bone_coords = cur_ids.view(num_bones, 6) # Shape: (num_bones, 6)
# Undiscretize the coordinates
# Initialize tensor to hold bone coordinates
bones_coors = torch.zeros((num_bones, 2, 3), dtype=torch.float16, device=cur_ids.device)
for j in range(num_bones):
bone_coord = bone_coords[j] # Shape: (6,)
# Split into two joints
joint1_ids = bone_coord[:3]
joint2_ids = bone_coord[3:]
# Undiscretize joint coordinates
joint1_coords = undiscretize(joint1_ids, self.coor_continuous_range[0], self.coor_continuous_range[1], self.n_discrete_size)
joint2_coords = undiscretize(joint2_ids, self.coor_continuous_range[0], self.coor_continuous_range[1], self.n_discrete_size)
# Assign to bones_coors
bones_coors[j, 0, :] = joint1_coords
bones_coors[j, 1, :] = joint2_coords
continuous_coors_list.append(bones_coors)
max_num_bones = max(num_bones_list)
# Initialize the continuous_coors tensor with NaNs
continuous_coors = torch.full(
(batch_size, max_num_bones, 2, 3),
float('nan'),
dtype=torch.float16,
device=input_ids.device
)
# Place the bones_coors into continuous_coors
for i in range(batch_size):
num_bones = num_bones_list[i]
continuous_coors[i, :num_bones, :, :] = continuous_coors_list[i]
return continuous_coors # Shape: (batch_size, max_num_bones, 2, 3)
# def forward(self, data_dict: dict, is_eval: bool = False) -> dict:
# return self.generate(data_dict)
def process_point_feature(self, point_feature):
encode_feature = torch.zeros(self.args.batchsize_per_gpu, self.cond_length, self.config.word_embed_proj_dim,
device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
encode_feature[:, 0] = self.cond_head_proj(point_feature[:, 0])
shape_latents = self.point_encoder.to_shape_latents(point_feature[:, 1:])
encode_feature[:, 1:] = self.cond_proj(shape_latents)
return encode_feature
@torch.no_grad()
def generate(self, data_dict) -> dict:
point_feature = self.point_encoder.encode_latents(data_dict["pc_normal"])
processed_point_feature = self.process_point_feature(point_feature=point_feature)
generate_length = self.max_length - self.cond_length
net_device = next(self.parameters()).device
outputs = torch.ones(self.args.batchsize_per_gpu, generate_length).long().to(net_device) * self.eos_token_id
# batch x ntokens
if self.args.num_beams is not None and "pc_normal" in data_dict:
results = self.transformer.generate(
inputs_embeds=processed_point_feature,
max_new_tokens=generate_length, # all faces plus two
num_beams=self.args.num_beams,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
else:
results = self.transformer.generate(
inputs_embeds = processed_point_feature,
max_new_tokens = generate_length, # all faces plus two
do_sample=True,
top_k=50,
top_p=0.95,
bos_token_id = self.bos_token_id,
eos_token_id = self.eos_token_id,
pad_token_id = self.pad_token_id,
)
assert results.shape[1] <= generate_length # B x ID bos is not included since it's predicted
outputs[:, :results.shape[1]] = results
# batch x ntokens ====> batch x ntokens x D
outputs = outputs[:, 1: -1] # eos and bos removed
outputs[outputs == self.bos_token_id] = self.pad_id
outputs[outputs == self.eos_token_id] = self.pad_id
outputs[outputs == self.pad_token_id] = self.pad_id
outputs[outputs != self.pad_id] -= 3
gen_joints = self.detokenize(outputs)
return gen_joints |