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from functools import partial
from typing import Literal
from einops import rearrange
from torch import Tensor
from torch.nn import ConvTranspose2d, ConvTranspose3d
from flow_grpo.inflated_lib import (
MemoryState,
extend_head,
inflate_bias,
inflate_distribution_bias,
inflate_distribution_weight,
inflate_weight,
modify_state_dict,
)
from flow_grpo.conv_gradfix import GradFixConv2d, GradFixConv3d
VERBOSE = False
_inflation_mode_t = (Literal["none", "flatten", "partial_flatten", "pad", "tile"],)
_direction_t = Literal["", "out", "in"]
class InflatedCausalConv3d(GradFixConv3d):
"""
Note:
To align the behavior of pretrained 2D models,
if you compose a video clip from a single image by:
- duplicating: set shape_norm = True
- padding zeros: set shape_norm = False
to avoid gaps in the beginning of training process.
"""
def __init__(
self, *args, inflation_mode: _inflation_mode_t, shape_norm: bool = True, **kwargs
):
self.shape_norm = shape_norm
self.inflation_mode = inflation_mode
self.padding_bank = None
super().__init__(*args, **kwargs)
self.temporal_padding = self.padding[0]
self.padding = (0, *self.padding[1:]) # Remove temporal pad to keep causal.
def forward(self, input: Tensor, memory_state: MemoryState = MemoryState.DISABLED) -> Tensor:
bank_size = self.stride[0] - self.kernel_size[0]
padding_bank = (
input[:, :, bank_size:].detach()
if (bank_size != 0 and memory_state != MemoryState.DISABLED)
else None
)
if (self.padding_bank is not None) and (memory_state == MemoryState.ACTIVE):
input = extend_head(input, memory=self.padding_bank)
else:
input = extend_head(input, times=self.temporal_padding * 2)
if memory_state != MemoryState.DISABLED and not self.training:
self.padding_bank = padding_bank
return super().forward(input)
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
if self.inflation_mode == "none":
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
else:
# NOTE: need to switch off strict
super()._load_from_state_dict(
modify_state_dict(
self,
state_dict,
prefix,
verbose=VERBOSE,
inflate_weight_fn=partial(inflate_weight, position="tail"),
inflate_bias_fn=partial(inflate_bias, position="tail"),
),
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
class InflatedDistributionCausalConv3d(GradFixConv3d):
"""
Note:
Direction:
- out: this layer generates mean/std of some distribution;
- in: this layer takes tensors sampled from output of `out` layer as input.
"""
def __init__(
self,
*args,
direction: _direction_t,
inflation_mode: _inflation_mode_t,
shape_norm: bool = True,
**kwargs,
):
self.shape_norm = shape_norm
self.inflation_mode = inflation_mode
self.direction = direction
self.padding_bank = None
super().__init__(*args, **kwargs)
self.temporal_padding = self.padding[0]
self.padding = (0, *self.padding[1:]) # Remove temporal pad to keep causal.
def forward(self, input: Tensor, memory_state: MemoryState = MemoryState.DISABLED) -> Tensor:
bank_size = self.stride[0] - self.kernel_size[0]
padding_bank = (
input[:, :, bank_size:].detach()
if (bank_size != 0 and memory_state != MemoryState.DISABLED)
else None
)
if (self.padding_bank is not None) and (memory_state == MemoryState.ACTIVE):
input = extend_head(input, memory=self.padding_bank)
else:
input = extend_head(input, times=self.temporal_padding * 2)
if memory_state != MemoryState.DISABLED and not self.training:
self.padding_bank = padding_bank
return super().forward(input)
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
if self.inflation_mode == "none":
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
else:
super()._load_from_state_dict(
modify_state_dict(
self,
state_dict,
prefix,
verbose=VERBOSE,
inflate_weight_fn=partial(
inflate_distribution_weight, direction=self.direction, position="tail"
),
inflate_bias_fn=partial(
inflate_distribution_bias, direction=self.direction, position="tail"
),
),
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
class InflatedConvTranspose3d(ConvTranspose3d):
# Note: It's not a causal one.
def __init__(
self, *args, inflation_mode: _inflation_mode_t, shape_norm: bool = True, **kwargs
):
self.shape_norm = shape_norm
self.inflation_mode = inflation_mode
super().__init__(*args, **kwargs)
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
if self.inflation_mode == "none":
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
else:
# NOTE: need to switch off strict
super()._load_from_state_dict(
modify_state_dict(
self,
state_dict,
prefix,
verbose=VERBOSE,
inflate_weight_fn=partial(inflate_weight, position="center"),
inflate_bias_fn=partial(inflate_bias, position="center"),
),
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
class FlattenedConvTranspose3d(ConvTranspose2d):
def forward(self, input: Tensor, **kwargs) -> Tensor:
output = rearrange(input, "b c f h w -> (b f) c h w")
output = super().forward(output)
output = rearrange(output, "(b f) c h w -> b c f h w", f=input.size(2))
return output
class FlattenedConv3d(GradFixConv2d):
def forward(self, input: Tensor, **kwargs) -> Tensor:
output = rearrange(input, "b c f h w -> (b f) c h w")
output = super().forward(output)
output = rearrange(output, "(b f) c h w -> b c f h w", f=input.size(2))
return output
def init_causal_conv3d(
*args,
inflation_mode: _inflation_mode_t,
direction: _direction_t = "",
partial_switch: bool = False,
**kwargs,
):
"""
Initialize a Causal-3D convolution layer.
Parameters:
inflation_mode: Listed as below. It's compatible with all the 3D-VAE checkpoints we have.
- none: No inflation will be conducted.
The loading logic of state dict will fall back to default.
- flatten: It will produce a `fake` 3D layer,
which simply squeeze the axis of batch size and depth together,
and then conduct 2D convolution.
- partial_flatten:
- layers with `partial_switch` on: using `none` mode.
- layers with `partial_switch` off: using `flatten` mode.
- pad / tile: Refer to the definition of `InflatedCausalConv3d`.
direction:
- empty string: Ordinary causal convolution layer.
- out / in: Refer to the definition of `InflatedDistributionCausalConv3d`.
partial_switch: Only works when `inflation_mode` is `partial_flatten`.
"""
stride = kwargs.get("stride", args[3] if len(args) > 3 else None)
padding = kwargs.get("padding", args[4] if len(args) > 4 else None)
if "flatten" in inflation_mode:
if (
(
(not stride)
or isinstance(stride, int)
or (isinstance(stride, list or tuple) and len(stride) < 3)
) # if the config of stride can be used for 2D conv
and (
(not padding)
or isinstance(padding, int)
or (isinstance(padding, list or tuple) and len(padding) < 3)
) # if the config of padding can be used for 2D conv
and (("partial" not in inflation_mode) or (not partial_switch))
# if it's fully-flatten mode, or with `partial_switch` off
):
return FlattenedConv3d(*args, **kwargs)
else:
return InflatedCausalConv3d(*args, inflation_mode="none", **kwargs)
# Force-override
else:
if direction:
return InflatedDistributionCausalConv3d(
*args, direction=direction, inflation_mode=inflation_mode, **kwargs
)
else:
return InflatedCausalConv3d(*args, inflation_mode=inflation_mode, **kwargs)
def init_transposed_conv3d(
*args, inflation_mode: _inflation_mode_t, partial_switch: bool = False, **kwargs
):
stride = kwargs.get("stride", args[3] if len(args) > 3 else None)
padding = kwargs.get("padding", args[4] if len(args) > 4 else None)
if "flatten" in inflation_mode:
if (
(
(not stride)
or isinstance(stride, int)
or (isinstance(stride, list or tuple) and len(stride) < 3)
)
and (
(not padding)
or isinstance(padding, int)
or (isinstance(padding, list or tuple) and len(padding) < 3)
)
or (("partial" in inflation_mode) and not partial_switch)
):
return FlattenedConvTranspose3d(*args, **kwargs)
else:
return InflatedConvTranspose3d(
*args, inflation_mode="none", **kwargs
) # Force-override
else:
return InflatedConvTranspose3d(*args, inflation_mode=inflation_mode, **kwargs) |