Integrate diffusion policy
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Simon Alibert
286
lerobot/common/policies/diffusion/model/conditional_unet1d.py
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286
lerobot/common/policies/diffusion/model/conditional_unet1d.py
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import logging
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from typing import Union
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import einops
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import torch
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import torch.nn as nn
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from einops.layers.torch import Rearrange
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from lerobot.common.policies.diffusion.model.conv1d_components import Conv1dBlock, Downsample1d, Upsample1d
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from lerobot.common.policies.diffusion.model.positional_embedding import SinusoidalPosEmb
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logger = logging.getLogger(__name__)
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class ConditionalResidualBlock1D(nn.Module):
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def __init__(
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self, in_channels, out_channels, cond_dim, kernel_size=3, n_groups=8, cond_predict_scale=False
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):
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super().__init__()
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self.blocks = nn.ModuleList(
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[
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Conv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups),
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Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups),
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]
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)
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# FiLM modulation https://arxiv.org/abs/1709.07871
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# predicts per-channel scale and bias
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cond_channels = out_channels
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if cond_predict_scale:
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cond_channels = out_channels * 2
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self.cond_predict_scale = cond_predict_scale
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self.out_channels = out_channels
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self.cond_encoder = nn.Sequential(
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nn.Mish(),
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nn.Linear(cond_dim, cond_channels),
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Rearrange("batch t -> batch t 1"),
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)
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# make sure dimensions compatible
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self.residual_conv = (
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nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
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)
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def forward(self, x, cond):
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"""
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x : [ batch_size x in_channels x horizon ]
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cond : [ batch_size x cond_dim]
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returns:
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out : [ batch_size x out_channels x horizon ]
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"""
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out = self.blocks[0](x)
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embed = self.cond_encoder(cond)
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if self.cond_predict_scale:
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embed = embed.reshape(embed.shape[0], 2, self.out_channels, 1)
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scale = embed[:, 0, ...]
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bias = embed[:, 1, ...]
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out = scale * out + bias
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else:
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out = out + embed
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out = self.blocks[1](out)
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out = out + self.residual_conv(x)
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return out
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class ConditionalUnet1D(nn.Module):
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def __init__(
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self,
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input_dim,
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local_cond_dim=None,
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global_cond_dim=None,
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diffusion_step_embed_dim=256,
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down_dims=None,
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kernel_size=3,
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n_groups=8,
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cond_predict_scale=False,
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):
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super().__init__()
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if down_dims is None:
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down_dims = [256, 512, 1024]
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all_dims = [input_dim] + list(down_dims)
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start_dim = down_dims[0]
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dsed = diffusion_step_embed_dim
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diffusion_step_encoder = nn.Sequential(
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SinusoidalPosEmb(dsed),
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nn.Linear(dsed, dsed * 4),
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nn.Mish(),
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nn.Linear(dsed * 4, dsed),
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)
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cond_dim = dsed
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if global_cond_dim is not None:
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cond_dim += global_cond_dim
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in_out = list(zip(all_dims[:-1], all_dims[1:], strict=False))
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local_cond_encoder = None
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if local_cond_dim is not None:
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_, dim_out = in_out[0]
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dim_in = local_cond_dim
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local_cond_encoder = nn.ModuleList(
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[
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# down encoder
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ConditionalResidualBlock1D(
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dim_in,
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dim_out,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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# up encoder
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ConditionalResidualBlock1D(
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dim_in,
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dim_out,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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]
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)
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mid_dim = all_dims[-1]
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self.mid_modules = nn.ModuleList(
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[
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ConditionalResidualBlock1D(
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mid_dim,
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mid_dim,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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ConditionalResidualBlock1D(
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mid_dim,
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mid_dim,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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]
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)
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down_modules = nn.ModuleList([])
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for ind, (dim_in, dim_out) in enumerate(in_out):
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is_last = ind >= (len(in_out) - 1)
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down_modules.append(
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nn.ModuleList(
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[
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ConditionalResidualBlock1D(
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dim_in,
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dim_out,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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ConditionalResidualBlock1D(
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dim_out,
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dim_out,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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Downsample1d(dim_out) if not is_last else nn.Identity(),
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]
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)
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)
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up_modules = nn.ModuleList([])
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for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
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is_last = ind >= (len(in_out) - 1)
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up_modules.append(
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nn.ModuleList(
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[
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ConditionalResidualBlock1D(
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dim_out * 2,
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dim_in,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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ConditionalResidualBlock1D(
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dim_in,
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dim_in,
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cond_dim=cond_dim,
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kernel_size=kernel_size,
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n_groups=n_groups,
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cond_predict_scale=cond_predict_scale,
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),
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Upsample1d(dim_in) if not is_last else nn.Identity(),
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]
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)
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)
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final_conv = nn.Sequential(
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Conv1dBlock(start_dim, start_dim, kernel_size=kernel_size),
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nn.Conv1d(start_dim, input_dim, 1),
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)
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self.diffusion_step_encoder = diffusion_step_encoder
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self.local_cond_encoder = local_cond_encoder
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self.up_modules = up_modules
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self.down_modules = down_modules
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self.final_conv = final_conv
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logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
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def forward(
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self,
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sample: torch.Tensor,
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timestep: Union[torch.Tensor, float, int],
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local_cond=None,
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global_cond=None,
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**kwargs,
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):
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"""
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x: (B,T,input_dim)
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timestep: (B,) or int, diffusion step
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local_cond: (B,T,local_cond_dim)
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global_cond: (B,global_cond_dim)
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output: (B,T,input_dim)
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"""
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sample = einops.rearrange(sample, "b h t -> b t h")
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# 1. time
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timesteps = timestep
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if not torch.is_tensor(timesteps):
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# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
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timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
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elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
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timesteps = timesteps[None].to(sample.device)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timesteps = timesteps.expand(sample.shape[0])
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global_feature = self.diffusion_step_encoder(timesteps)
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if global_cond is not None:
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global_feature = torch.cat([global_feature, global_cond], axis=-1)
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# encode local features
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h_local = []
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if local_cond is not None:
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local_cond = einops.rearrange(local_cond, "b h t -> b t h")
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resnet, resnet2 = self.local_cond_encoder
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x = resnet(local_cond, global_feature)
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h_local.append(x)
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x = resnet2(local_cond, global_feature)
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h_local.append(x)
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x = sample
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h = []
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for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules):
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x = resnet(x, global_feature)
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if idx == 0 and len(h_local) > 0:
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x = x + h_local[0]
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x = resnet2(x, global_feature)
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h.append(x)
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x = downsample(x)
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for mid_module in self.mid_modules:
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x = mid_module(x, global_feature)
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for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules):
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x = torch.cat((x, h.pop()), dim=1)
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x = resnet(x, global_feature)
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# The correct condition should be:
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# if idx == (len(self.up_modules)-1) and len(h_local) > 0:
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# However this change will break compatibility with published checkpoints.
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# Therefore it is left as a comment.
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if idx == len(self.up_modules) and len(h_local) > 0:
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x = x + h_local[1]
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x = resnet2(x, global_feature)
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x = upsample(x)
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x = self.final_conv(x)
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x = einops.rearrange(x, "b t h -> b h t")
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return x
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