153 lines
3.9 KiB
Python
153 lines
3.9 KiB
Python
from typing import Callable
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import torch
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import torch.nn as nn
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class ModulateDiT(nn.Module):
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"""Modulation layer for DiT."""
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def __init__(
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self,
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hidden_size: int,
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factor: int,
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act_layer: Callable,
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dtype=None,
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device=None,
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):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.act = act_layer()
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self.linear = nn.Linear(hidden_size, factor * hidden_size, bias=True, **factory_kwargs)
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# Zero-initialize the modulation
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nn.init.zeros_(self.linear.weight)
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nn.init.zeros_(self.linear.bias)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.linear(self.act(x))
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def modulate(x, shift=None, scale=None):
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"""modulate by shift and scale
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Args:
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x (torch.Tensor): input tensor.
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shift (torch.Tensor, optional): shift tensor. Defaults to None.
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scale (torch.Tensor, optional): scale tensor. Defaults to None.
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Returns:
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torch.Tensor: the output tensor after modulate.
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"""
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if scale is None and shift is None:
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return x
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elif shift is None:
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return x * (1 + scale.unsqueeze(1))
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elif scale is None:
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return x + shift.unsqueeze(1)
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else:
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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def apply_gate(x, gate=None, tanh=False):
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"""AI is creating summary for apply_gate
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Args:
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x (torch.Tensor): input tensor.
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gate (torch.Tensor, optional): gate tensor. Defaults to None.
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tanh (bool, optional): whether to use tanh function. Defaults to False.
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Returns:
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torch.Tensor: the output tensor after apply gate.
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"""
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if gate is None:
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return x
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if tanh:
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return x * gate.unsqueeze(1).tanh()
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else:
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return x * gate.unsqueeze(1)
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def ckpt_wrapper(module):
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def ckpt_forward(*inputs):
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outputs = module(*inputs)
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return outputs
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return ckpt_forward
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class RMSNorm(nn.Module):
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def __init__(
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self,
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dim: int,
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elementwise_affine=True,
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eps: float = 1e-6,
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device=None,
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dtype=None,
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):
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"""
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Initialize the RMSNorm normalization layer.
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Args:
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dim (int): The dimension of the input tensor.
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eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
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Attributes:
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eps (float): A small value added to the denominator for numerical stability.
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weight (nn.Parameter): Learnable scaling parameter.
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"""
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.eps = eps
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if elementwise_affine:
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self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
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def _norm(self, x):
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"""
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Apply the RMSNorm normalization to the input tensor.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The normalized tensor.
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"""
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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"""
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Forward pass through the RMSNorm layer.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The output tensor after applying RMSNorm.
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"""
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output = self._norm(x.float()).type_as(x)
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if hasattr(self, "weight"):
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output = output * self.weight
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return output
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def get_norm_layer(norm_layer):
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"""
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Get the normalization layer.
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Args:
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norm_layer (str): The type of normalization layer.
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Returns:
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norm_layer (nn.Module): The normalization layer.
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"""
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if norm_layer == "layer":
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return nn.LayerNorm
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elif norm_layer == "rms":
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return RMSNorm
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else:
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raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
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