chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from timm.models.vision_transformer import Mlp
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from torch.distributed.nn import functional as dist_nn
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from diffusion.distributed.context_parallel.config import cp_enabled, get_cp_group
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from diffusion.distributed.context_parallel.halo_exchange import cp_halo_exchange
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from diffusion.model.act import build_act, get_act_name
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from diffusion.model.norms import build_norm, get_norm_name
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from diffusion.model.registry import FFN_BLOCKS
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from diffusion.model.utils import get_same_padding, val2tuple
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class ConvLayer(nn.Module):
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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kernel_size=3,
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stride=1,
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dilation=1,
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groups=1,
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padding: int or None = None,
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use_bias=False,
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dropout=0.0,
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conv_type="2d",
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norm="bn2d",
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act="relu",
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):
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super().__init__()
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if padding is None:
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padding = get_same_padding(kernel_size)
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padding *= dilation
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.kernel_size = kernel_size
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self.stride = stride
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self.dilation = dilation
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self.groups = groups
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self.padding = padding
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self.use_bias = use_bias
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self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
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if conv_type == "2d":
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self.conv = nn.Conv2d(
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in_dim,
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out_dim,
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kernel_size=(kernel_size, kernel_size),
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stride=(stride, stride),
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padding=padding,
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dilation=(dilation, dilation),
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groups=groups,
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bias=use_bias,
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)
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elif conv_type == "3d":
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self.conv = nn.Conv3d(
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in_dim,
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out_dim,
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kernel_size=(kernel_size, kernel_size, kernel_size),
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stride=(stride, stride, stride),
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padding=padding,
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dilation=(dilation, dilation, dilation),
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groups=groups,
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bias=use_bias,
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)
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else:
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self.conv = None
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self.norm = build_norm(norm, num_features=out_dim)
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self.act = build_act(act)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.dropout is not None:
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x = self.dropout(x)
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x = self.conv(x)
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if self.norm:
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x = self.norm(x)
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if self.act:
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x = self.act(x)
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return x
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# Safe element-count threshold for a single conv call: PyTorch's 2D conv kernels
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# (both cuDNN and the ATEN fallback) use 32-bit indexing internally, so very
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# large ``(BT, C, H, W)`` inputs (e.g. minute-scale video at default CFG) can
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# overflow. Empirically a single call up to ~1 B elements is safe; above that
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# we chunk along the leading dim. Set so short videos stay on the original
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# fused path (no chunking, no overhead) and long videos transparently split.
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_INT32_SAFE_CONV_ELEMENTS = 1 << 30 # 1,073,741,824
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class GLUMBConv(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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out_feature=None,
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kernel_size=3,
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stride=1,
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padding: int or None = None,
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use_bias=False,
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norm=(None, None, None),
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act=("silu", "silu", None),
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dilation=1,
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):
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out_feature = out_feature or in_features
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super().__init__()
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use_bias = val2tuple(use_bias, 3)
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norm = val2tuple(norm, 3)
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act = val2tuple(act, 3)
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self.glu_act = build_act(act[1], inplace=False)
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self.inverted_conv = ConvLayer(
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in_features,
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hidden_features * 2,
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1,
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use_bias=use_bias[0],
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norm=norm[0],
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act=act[0],
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)
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self.depth_conv = ConvLayer(
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hidden_features * 2,
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hidden_features * 2,
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kernel_size,
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stride=stride,
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groups=hidden_features * 2,
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padding=padding,
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use_bias=use_bias[1],
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norm=norm[1],
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act=None,
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dilation=dilation,
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)
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self.point_conv = ConvLayer(
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hidden_features,
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out_feature,
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1,
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use_bias=use_bias[2],
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norm=norm[2],
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act=act[2],
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)
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def _apply_spatial(self, x: torch.Tensor) -> torch.Tensor:
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"""Fused spatial pipeline: inverted_conv -> depth_conv -> GLU -> point_conv."""
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x = self.inverted_conv(x)
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x = self.depth_conv(x)
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a, g = torch.chunk(x, 2, dim=1)
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g = self.glu_act(g)
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return self.point_conv(a * g)
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def _apply_spatial_autochunked(self, x: torch.Tensor) -> torch.Tensor:
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"""Run :meth:`_apply_spatial`, chunking dim 0 to keep each call under
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PyTorch's 32-bit conv indexing limit. No-op for short inputs."""
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BT, _, H, W = x.shape
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# Conservative estimate of the largest intermediate (after inverted_conv).
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elements_per_bt = self.inverted_conv.conv.out_channels * H * W
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max_bt = max(1, _INT32_SAFE_CONV_ELEMENTS // elements_per_bt)
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if BT <= max_bt:
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return self._apply_spatial(x)
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return torch.cat([self._apply_spatial(x[s : s + max_bt]) for s in range(0, BT, max_bt)], dim=0)
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def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
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B, N, C = x.shape
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if HW is None:
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H = W = int(N**0.5)
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elif len(HW) == 2:
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H, W = HW
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x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
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elif len(HW) == 3:
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T, H, W = HW
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x = x.reshape(B * T, H, W, C).permute(0, 3, 1, 2)
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x = self._apply_spatial_autochunked(x)
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if len(HW) == 3:
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x = x.reshape(B * T, C, H * W).permute(0, 2, 1)
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x = x.reshape(B, N, C)
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else:
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x = x.reshape(B, C, N).permute(0, 2, 1)
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return x
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class GLUMBConvTemp(GLUMBConv):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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out_feature=None,
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kernel_size=3,
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stride=1,
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padding: int or None = None,
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use_bias=False,
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norm=(None, None, None),
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act=("silu", "silu", None),
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t_kernel_size=3,
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):
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super().__init__(
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in_features=in_features,
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hidden_features=hidden_features,
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out_feature=out_feature,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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use_bias=use_bias,
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norm=norm,
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act=act,
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)
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out_feature = out_feature or in_features
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t_padding = t_kernel_size // 2
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self.t_conv = nn.Conv2d(
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out_feature,
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out_feature,
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kernel_size=(t_kernel_size, 1),
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stride=1,
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padding=(t_padding, 0),
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bias=False,
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)
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nn.init.zeros_(self.t_conv.weight)
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def forward(self, x: torch.Tensor, HW=None, **kwargs) -> torch.Tensor:
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B, N, C = x.shape
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assert len(HW) == 3, "HW must be a tuple of (T, H, W)"
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T, H, W = HW
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x = x.reshape(B * T, H, W, C).permute(0, 3, 1, 2)
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x = self._apply_spatial_autochunked(x)
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# Temporal aggregation
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x_reshaped = x.view(B, T, C, H * W).permute(0, 2, 1, 3)
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frame_mask = kwargs.get("frame_valid_mask", None)
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if frame_mask is not None:
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frame_mask = frame_mask.reshape(B, T).to(x_reshaped)
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x_reshaped = x_reshaped * frame_mask[:, None, :, None]
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cp_active = cp_enabled() and get_cp_group() is not None
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if cp_active and self.t_conv.padding[0] > 0:
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halo = int(self.t_conv.padding[0])
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x_halo = cp_halo_exchange(x_reshaped, left_size=halo, right_size=halo, dim=2, group=get_cp_group())
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t_out = self.t_conv(x_halo)[:, :, halo : halo + T, :]
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else:
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t_out = self.t_conv(x_reshaped)
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x_out = x_reshaped + t_out
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if frame_mask is not None:
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x_out = x_out * frame_mask[:, None, :, None]
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x_out = x_out.permute(0, 2, 3, 1).reshape(B, N, C)
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return x_out
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class ChunkGLUMBConvTemp(GLUMBConvTemp):
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def forward(self, x: torch.Tensor, HW=None, chunk_index=None, **kwargs) -> torch.Tensor:
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if chunk_index is None:
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chunk_index = [0]
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B, N, C = x.shape
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assert len(HW) == 3, "HW must be a tuple of (T, H, W)"
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T, H, W = HW
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x = x.reshape(B * T, H, W, C).permute(0, 3, 1, 2)
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x = self._apply_spatial_autochunked(x)
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x_reshaped = x.view(B, T, C, H * W).permute(0, 2, 1, 3) # B, C, T, H*W
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frame_mask = kwargs.get("frame_valid_mask")
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if frame_mask is not None:
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frame_mask = frame_mask.reshape(B, T).to(x_reshaped)
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x_reshaped = x_reshaped * frame_mask[:, None, :, None]
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x_local = x_reshaped
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cp_group = get_cp_group() if cp_enabled() else None
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if cp_group is not None and kwargs.get("chunk_index_global") is not None:
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cp_rank = dist.get_rank(cp_group)
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x_reshaped = torch.cat(dist_nn.all_gather(x_reshaped.contiguous(), group=cp_group), dim=2)
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chunk_index = kwargs["chunk_index_global"]
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T_work = x_reshaped.shape[2]
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else:
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cp_rank = 0
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T_work = T
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padding_size = self.t_conv.kernel_size[0] // 2
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chunk_boundaries = sorted({0, *(int(idx) for idx in chunk_index if 0 < int(idx) < T_work), T_work})
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chunk_sizes = [end - start for start, end in zip(chunk_boundaries, chunk_boundaries[1:])]
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x_reshaped_list = x_reshaped.split(chunk_sizes, dim=-2)
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padded_x_reshaped_list = []
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padded_x_reshaped_list.append(
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torch.cat([x_reshaped_list[0], x_reshaped.new_zeros(B, C, padding_size, H * W)], dim=-2)
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)
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for i in range(1, len(x_reshaped_list)):
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prev_chunk = x_reshaped_list[i - 1][:, :, -padding_size:, :]
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cur_chunk = x_reshaped_list[i]
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padded_x_reshaped_list.append(
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torch.cat(
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[
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prev_chunk,
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cur_chunk,
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x_reshaped.new_zeros(B, C, padding_size, H * W),
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],
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dim=-2,
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)
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)
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x_reshaped_t_conv = torch.cat(padded_x_reshaped_list, dim=-2)
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t_conv_out = self.t_conv(x_reshaped_t_conv)
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padded_chunk_sizes = [chunk_sizes[0] + padding_size] + [
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padding_size + chunk_size + padding_size for chunk_size in chunk_sizes[1:]
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]
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t_conv_out_list = t_conv_out.split(padded_chunk_sizes, dim=-2)
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unpadded_chunks = []
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for i, chunk in enumerate(t_conv_out_list):
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if i == 0:
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unpadded_chunk = chunk[:, :, : chunk_sizes[i], :]
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else:
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start_idx = padding_size
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end_idx = start_idx + chunk_sizes[i]
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unpadded_chunk = chunk[:, :, start_idx:end_idx, :]
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unpadded_chunks.append(unpadded_chunk)
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t_conv_out_final = torch.cat(unpadded_chunks, dim=-2)
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assert (
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t_conv_out_final.shape[-2] == T_work
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), f"Expected temporal dimension {T_work}, got {t_conv_out_final.shape[-2]}"
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if cp_group is not None and kwargs.get("chunk_index_global") is not None:
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t_conv_out_final = t_conv_out_final[:, :, cp_rank * T : (cp_rank + 1) * T]
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x_out = x_local + t_conv_out_final
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if frame_mask is not None:
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x_out = x_out * frame_mask[:, None, :, None]
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x_out = x_out.permute(0, 2, 3, 1).reshape(B, N, C)
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return x_out
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@FFN_BLOCKS.register_module()
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class CachedGLUMBConvTemp(GLUMBConvTemp):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def forward(self, x: torch.Tensor, HW=None, save_kv_cache=False, kv_cache=None, **kwargs) -> torch.Tensor:
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B, N, C = x.shape
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assert len(HW) == 3, "HW must be a tuple of (T, H, W)"
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T, H, W = HW
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x = x.reshape(B * T, H, W, C).permute(0, 3, 1, 2)
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x = self._apply_spatial_autochunked(x)
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# Temporal aggregation
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x_reshaped = x.view(B, T, C, H * W).permute(0, 2, 1, 3) # B,C,T,HW
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padding_size = self.t_conv.kernel_size[0] // 2
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x_t_conv_in = x_reshaped
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padded_size = 0
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# Tconv state lives in the last slot of the per-block KV cache list.
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# The streaming sampler's 10-slot layout uses indices 0-3 / 4-6 for
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# main + camera attention state and the final slot (-1) for the
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# temporal short conv left context written here.
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if kv_cache is not None:
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if kv_cache[-1] is not None:
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# Slice to the actual conv padding window in case the cache
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# spans multiple past chunks.
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x_t_conv_in = torch.cat([kv_cache[-1][:, :, -padding_size:], x_reshaped], dim=2) # B,C,P+T,HW
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padded_size = x_t_conv_in.shape[2] - x_reshaped.shape[2]
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if save_kv_cache: # Save current chunk's cache for next chunk
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kv_cache[-1] = x_reshaped[:, :, -padding_size:, :].detach().clone()
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t_conv_out = self.t_conv(x_t_conv_in)[:, :, padded_size:]
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x_out = x_reshaped + t_conv_out
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x_out = x_out.permute(0, 2, 3, 1).reshape(B, N, C)
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if kv_cache is not None:
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return x_out, kv_cache
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return x_out
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class MBConvPreGLU(nn.Module):
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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||||
kernel_size=3,
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stride=1,
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mid_dim=None,
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expand=6,
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padding: int or None = None,
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use_bias=False,
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norm=(None, None, "ln2d"),
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||||
act=("silu", "silu", None),
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):
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super().__init__()
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||||
use_bias = val2tuple(use_bias, 3)
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||||
norm = val2tuple(norm, 3)
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||||
act = val2tuple(act, 3)
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mid_dim = mid_dim or round(in_dim * expand)
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self.inverted_conv = ConvLayer(
|
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in_dim,
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mid_dim * 2,
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||||
1,
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||||
use_bias=use_bias[0],
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||||
norm=norm[0],
|
||||
act=None,
|
||||
)
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||||
self.glu_act = build_act(act[0], inplace=False)
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||||
self.depth_conv = ConvLayer(
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||||
mid_dim,
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||||
mid_dim,
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||||
kernel_size,
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||||
stride=stride,
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||||
groups=mid_dim,
|
||||
padding=padding,
|
||||
use_bias=use_bias[1],
|
||||
norm=norm[1],
|
||||
act=act[1],
|
||||
)
|
||||
self.point_conv = ConvLayer(
|
||||
mid_dim,
|
||||
out_dim,
|
||||
1,
|
||||
use_bias=use_bias[2],
|
||||
norm=norm[2],
|
||||
act=act[2],
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
|
||||
B, N, C = x.shape
|
||||
if HW is None:
|
||||
H = W = int(N**0.5)
|
||||
else:
|
||||
H, W = HW
|
||||
|
||||
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
|
||||
|
||||
x = self.inverted_conv(x)
|
||||
x, gate = torch.chunk(x, 2, dim=1)
|
||||
gate = self.glu_act(gate)
|
||||
x = x * gate
|
||||
|
||||
x = self.depth_conv(x)
|
||||
x = self.point_conv(x)
|
||||
|
||||
x = x.reshape(B, C, N).permute(0, 2, 1)
|
||||
return x
|
||||
|
||||
@property
|
||||
def module_str(self) -> str:
|
||||
_str = f"{self.depth_conv.kernel_size}{type(self).__name__}("
|
||||
_str += f"in={self.inverted_conv.in_dim},mid={self.depth_conv.in_dim},out={self.point_conv.out_dim},s={self.depth_conv.stride}"
|
||||
_str += (
|
||||
f",norm={get_norm_name(self.inverted_conv.norm)}"
|
||||
f"+{get_norm_name(self.depth_conv.norm)}"
|
||||
f"+{get_norm_name(self.point_conv.norm)}"
|
||||
)
|
||||
_str += (
|
||||
f",act={get_act_name(self.inverted_conv.act)}"
|
||||
f"+{get_act_name(self.depth_conv.act)}"
|
||||
f"+{get_act_name(self.point_conv.act)}"
|
||||
)
|
||||
_str += f",glu_act={get_act_name(self.glu_act)})"
|
||||
return _str
|
||||
|
||||
|
||||
class DWMlp(Mlp):
|
||||
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_features,
|
||||
hidden_features=None,
|
||||
out_features=None,
|
||||
act_layer=nn.GELU,
|
||||
bias=True,
|
||||
drop=0.0,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
padding=None,
|
||||
):
|
||||
super().__init__(
|
||||
in_features=in_features,
|
||||
hidden_features=hidden_features,
|
||||
out_features=out_features,
|
||||
act_layer=act_layer,
|
||||
bias=bias,
|
||||
drop=drop,
|
||||
)
|
||||
hidden_features = hidden_features or in_features
|
||||
self.hidden_features = hidden_features
|
||||
if padding is None:
|
||||
padding = get_same_padding(kernel_size)
|
||||
padding *= dilation
|
||||
|
||||
self.conv = nn.Conv2d(
|
||||
hidden_features,
|
||||
hidden_features,
|
||||
kernel_size=(kernel_size, kernel_size),
|
||||
stride=(stride, stride),
|
||||
padding=padding,
|
||||
dilation=(dilation, dilation),
|
||||
groups=hidden_features,
|
||||
bias=bias,
|
||||
)
|
||||
|
||||
def forward(self, x, HW=None):
|
||||
B, N, C = x.shape
|
||||
if HW is None:
|
||||
H = W = int(N**0.5)
|
||||
else:
|
||||
H, W = HW
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop1(x)
|
||||
x = x.reshape(B, H, W, self.hidden_features).permute(0, 3, 1, 2)
|
||||
x = self.conv(x)
|
||||
x = x.reshape(B, self.hidden_features, N).permute(0, 2, 1)
|
||||
x = self.fc2(x)
|
||||
x = self.drop2(x)
|
||||
return x
|
||||
|
||||
|
||||
class Mlp(Mlp):
|
||||
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0):
|
||||
super().__init__(
|
||||
in_features=in_features,
|
||||
hidden_features=hidden_features,
|
||||
out_features=out_features,
|
||||
act_layer=act_layer,
|
||||
bias=bias,
|
||||
drop=drop,
|
||||
)
|
||||
|
||||
def forward(self, x, HW=None):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop1(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop2(x)
|
||||
return x
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = GLUMBConv(
|
||||
1152,
|
||||
1152 * 4,
|
||||
1152,
|
||||
use_bias=(True, True, False),
|
||||
norm=(None, None, None),
|
||||
act=("silu", "silu", None),
|
||||
).cuda()
|
||||
input = torch.randn(4, 256, 1152).cuda()
|
||||
output = model(input)
|
||||
Reference in New Issue
Block a user