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from policy_models.module.transformers.transformer_blocks import *
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
logger = logging.getLogger(__name__)
def return_model_parameters_in_millions(model):
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
num_params_in_millions = round(num_params / 1_000_000, 2)
return num_params_in_millions
class DiffusionTransformer(nn.Module):
"""the full GPT score model, with a context size of block_size"""
def __init__(
self,
obs_dim: int,
goal_dim: int,
device: str,
n_obs_token: int,
goal_conditioned: bool,
action_dim: int,
proprio_dim: int,
embed_dim: int,
embed_pdrob: float,
attn_pdrop: float,
resid_pdrop: float,
mlp_pdrop: float,
n_dec_layers: int,
n_enc_layers: int,
n_heads: int,
goal_seq_len: int,
obs_seq_len: int,
action_seq_len: int,
goal_drop: float = 0.1,
bias=False,
use_mlp_goal: bool = False,
use_rot_embed: bool = False,
rotary_xpos: bool = False,
linear_output: bool = True,
use_noise_encoder: bool = False,
use_ada_conditioning: bool = True,
):
super().__init__()
self.device = device
self.goal_conditioned = goal_conditioned
self.obs_dim = obs_dim
self.embed_dim = embed_dim
self.n_obs_token = n_obs_token
self.use_ada_conditioning = use_ada_conditioning
if self.goal_conditioned:
block_size = goal_seq_len + action_seq_len + obs_seq_len * self.n_obs_token + 2
else:
block_size = action_seq_len + obs_seq_len * self.n_obs_token + 2
self.action_seq_len = action_seq_len
if self.goal_conditioned:
seq_size = goal_seq_len + obs_seq_len * self.n_obs_token + action_seq_len
else:
seq_size = obs_seq_len * self.n_obs_token + action_seq_len
print(f"obs dim: {obs_dim}, goal_dim: {goal_dim}, action_dim: {action_dim}, proprio_dim: {proprio_dim}")
self.tok_emb = nn.Linear(obs_dim, embed_dim)
if use_mlp_goal:
self.goal_emb = nn.Sequential(
nn.Linear(goal_dim, embed_dim * 2),
nn.GELU(),
nn.Linear(embed_dim * 2, embed_dim)
)
else:
self.goal_emb = nn.Linear(goal_dim, embed_dim)
if use_mlp_goal:
self.lang_emb = nn.Sequential(
nn.Linear(goal_dim, embed_dim * 2),
nn.GELU(),
nn.Linear(embed_dim * 2, embed_dim)
)
else:
self.lang_emb = nn.Linear(goal_dim, embed_dim)
if not self.goal_conditioned:
for param in self.lang_emb.parameters():
param.requires_grad = False
for param in self.goal_emb.parameters():
param.requires_grad = False
self.pos_emb = nn.Parameter(torch.zeros(1, seq_size, embed_dim))
print('seq_size:',seq_size)
self.drop = nn.Dropout(embed_pdrob)
self.proprio_drop = nn.Dropout(0.5)
self.cond_mask_prob = goal_drop
self.use_rot_embed = use_rot_embed
self.action_dim = action_dim
self.obs_dim = obs_dim
self.embed_dim = embed_dim
self.latent_encoder_emb = None
self.encoder = TransformerEncoder(
embed_dim=embed_dim,
n_heads=n_heads,
attn_pdrop=attn_pdrop,
resid_pdrop=resid_pdrop,
n_layers=n_enc_layers,
block_size=block_size,
bias=bias,
use_rot_embed=use_rot_embed,
rotary_xpos=rotary_xpos,
mlp_pdrop=mlp_pdrop,
)
self.decoder = TransformerFiLMDecoder(
embed_dim=embed_dim,
n_heads=n_heads,
attn_pdrop=attn_pdrop,
resid_pdrop=resid_pdrop,
n_layers=n_dec_layers,
film_cond_dim=embed_dim,
block_size=block_size,
bias=bias,
use_rot_embed=use_rot_embed,
rotary_xpos=rotary_xpos,
mlp_pdrop=mlp_pdrop,
use_cross_attention=True,
use_noise_encoder=use_noise_encoder,
)
self.latent_encoder_emb = None
self.proprio_emb = nn.Sequential(
nn.Linear(proprio_dim, embed_dim * 2),
nn.Mish(),
nn.Linear(embed_dim * 2, embed_dim),
).to(self.device)
self.block_size = block_size
self.goal_seq_len = goal_seq_len
self.obs_seq_len = obs_seq_len
self.sigma_emb = nn.Sequential(
SinusoidalPosEmb(embed_dim),
nn.Linear(embed_dim, embed_dim * 2),
nn.Mish(),
nn.Linear(embed_dim * 2, embed_dim),
).to(self.device)
self.action_emb = nn.Linear(action_dim, embed_dim)
if linear_output:
self.action_pred = nn.Linear(embed_dim, self.action_dim)
else:
self.action_pred = nn.Sequential(
nn.Linear(embed_dim, 100),
nn.GELU(),
nn.Linear(100, self.action_dim)
)
self.apply(self._init_weights)
logger.info(f'Number of encoder parameters: {return_model_parameters_in_millions(self.encoder)}')
logger.info(f'Number of decoder parameters: {return_model_parameters_in_millions(self.decoder)}')
logger.info(
"number of parameters: %e", sum(p.numel() for p in self.parameters())
)
def get_block_size(self):
return self.block_size
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
torch.nn.init.zeros_(module.bias)
torch.nn.init.ones_(module.weight)
elif isinstance(module, DiffusionTransformer):
torch.nn.init.normal_(module.pos_emb, mean=0.0, std=0.02)
def forward(self, states, actions, goals, sigma, uncond: Optional[bool] = False):
context = self.forward_enc_only(states, actions, goals, sigma, uncond)
pred_actions = self.forward_dec_only(context, actions, sigma)
return pred_actions
def forward_enc_only(self, states, actions=None, goals=None, sigma=None, uncond: Optional[bool] = False):
emb_t = self.process_sigma_embeddings(sigma) if not self.use_ada_conditioning else None
goals = self.preprocess_goals(goals, states['state_images'].size(1), uncond)
state_embed, proprio_embed = self.process_state_embeddings(states)
goal_embed = self.process_goal_embeddings(goals)
input_seq = self.concatenate_inputs(emb_t, goal_embed, state_embed, proprio_embed, uncond)
context = self.encoder(input_seq)
self.latent_encoder_emb = context
return context
def forward_dec_only(self, context, actions, sigma):
emb_t = self.process_sigma_embeddings(sigma)
action_embed = self.action_emb(actions)
action_x = self.drop(action_embed)
x = self.decoder(action_x, emb_t, context)
pred_actions = self.action_pred(x)
return pred_actions
def process_sigma_embeddings(self, sigma):
sigmas = sigma.log() / 4
sigmas = einops.rearrange(sigmas, 'b -> b 1')
emb_t = self.sigma_emb(sigmas)
if len(emb_t.shape) == 2:
emb_t = einops.rearrange(emb_t, 'b d -> b 1 d')
return emb_t
def preprocess_goals(self, goals, states_length, uncond=False):
if len(goals.shape) == 2:
goals = einops.rearrange(goals, 'b d -> b 1 d')
if goals.shape[1] == states_length and self.goal_seq_len == 1:
goals = goals[:, 0, :]
goals = einops.rearrange(goals, 'b d -> b 1 d')
if goals.shape[-1] == 2 * self.obs_dim:
goals = goals[:, :, :self.obs_dim]
if self.training:
goals = self.mask_cond(goals)
if uncond:
goals = torch.zeros_like(goals).to(self.device)
return goals
def process_state_embeddings(self, states):
states_global = self.tok_emb(states['state_images'])
if 'state_obs' in states:
proprio_embed = self.proprio_emb(states['state_obs'])
else:
proprio_embed = None
return states_global, proprio_embed
def process_goal_embeddings(self, goals):
goal_embed = self.lang_emb(goals)
return goal_embed
def apply_position_embeddings(self, goal_embed, state_embed, action_embed, proprio_embed, t):
pos_len = t + self.goal_seq_len + self.action_seq_len - 1
position_embeddings = self.pos_emb[:, :pos_len, :]
goal_x = self.drop(goal_embed + position_embeddings[:, :self.goal_seq_len, :])
state_x = self.drop(state_embed + position_embeddings[:, self.goal_seq_len:(self.goal_seq_len + t), :])
action_x = self.drop(action_embed + position_embeddings[:, (self.goal_seq_len + t - 1):, :])
proprio_x = self.drop(proprio_embed + position_embeddings[:, self.goal_seq_len:(self.goal_seq_len + t), :]) if proprio_embed is not None else None
return goal_x, state_x, action_x, proprio_x
def concatenate_inputs(self, emb_t, goal_x, state_x, proprio_x, uncond=False):
input_seq_components = [state_x]
if self.goal_conditioned:
input_seq_components.insert(0, goal_x)
if proprio_x is not None:
input_seq_components.append(proprio_x)
#else:
# if not self.goal_conditioned:
# input_seq_components.append(self.drop(goal_x))
input_seq = torch.cat(input_seq_components, dim=1)
return input_seq
def mask_cond(self, cond, force_mask=False):
bs, t, d = cond.shape
if force_mask:
return torch.zeros_like(cond)
elif self.training and self.cond_mask_prob > 0.:
mask = torch.bernoulli(torch.ones((bs, t, d), device=cond.device) * self.cond_mask_prob)
return cond * (1. - mask)
else:
return cond
def get_params(self):
return self.parameters() |