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smthem
2025-06-18 08:54:53 +08:00
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PartPacker/vae/modules/__init__.py
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PartPacker/vae/modules/attention.py
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"""
-----------------------------------------------------------------------------
Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
-----------------------------------------------------------------------------
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import ( # , unpad_input # noqa
index_first_axis,
pad_input,
)
FLASH_ATTN_AVAILABLE = True
except Exception as e:
print("[WARN] flash_attn not available, using torch/naive implementation")
FLASH_ATTN_AVAILABLE = False
# Adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/bert_padding.py#L98
# flashattn 2.7.0 changes the API, we are overriding it here
def unpad_input(hidden_states, attention_mask):
"""
Arguments:
hidden_states: (batch, seqlen, ...)
attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
Return:
hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
max_seqlen_in_batch: int
"""
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
# TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
# bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
# times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
# index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
# so we write custom forward and backward to make it a bit faster.
return (
index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices),
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def attention(q, k, v, mask_q=None, mask_kv=None, dropout=0, causal=False, window_size=(-1, -1), backend="torch"):
# q: (B, N, H, D)
# k: (B, M, H, D)
# v: (B, M, H, D)
# mask_q: (B, N)
# mask_kv: (B, M)
# return: (B, N, H, D)
B, N, H, D = q.shape
M = k.shape[1]
if causal:
assert N == 1 or N == M, "Causal mask only supports self-attention"
# unmasked case (usually inference)
# will ignore window_size except flash-attn impl. Only provide the effective window!
if mask_q is None and mask_kv is None:
if backend == "flash-attn" and FLASH_ATTN_AVAILABLE:
return flash_attn_func(q, k, v, dropout, causal=causal, window_size=window_size) # [B, N, H, D]
elif backend == "torch": # torch implementation
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=dropout, is_causal=causal)
out = out.permute(0, 2, 1, 3).contiguous()
return out
else: # naive implementation
q = q.transpose(1, 2).reshape(B * H, N, D)
k = k.transpose(1, 2).reshape(B * H, M, D)
v = v.transpose(1, 2).reshape(B * H, M, D)
w = torch.bmm(q, k.transpose(1, 2)) / (D**0.5) # [B*H, N, M]
if causal and N > 1:
causal_mask = torch.full((N, M), float("-inf"), device=w.device, dtype=w.dtype)
causal_mask = torch.triu(causal_mask, diagonal=1)
w = w + causal_mask.unsqueeze(0)
w = F.softmax(w, dim=-1)
if dropout > 0:
w = F.dropout(w, p=dropout)
out = torch.bmm(w, v) # [B*H, N, D]
out = out.reshape(B, H, N, D).transpose(1, 2).contiguous() # [B, N, H, D]
return out
# at least one of q or kv is masked (training)
# only support flash-attn for now...
if mask_q is None:
mask_q = torch.ones(B, N, dtype=torch.bool, device=q.device)
elif mask_kv is None:
mask_kv = torch.ones(B, M, dtype=torch.bool, device=q.device)
if FLASH_ATTN_AVAILABLE:
# unpad (gather) input
# mask_q: [B, N], first row has N1 1s, second row has N2 1s, ...
# indices: [Ns,], Ns = N1 + N2 + ...
# cu_seqlens_q: [B+1,], (0, N1, N1+N2, ...), cu=cumulative
# max_len_q: scalar, max(N1, N2, ...)
q, indices_q, cu_seqlens_q, max_len_q = unpad_input(q, mask_q)
k, indices_kv, cu_seqlens_kv, max_len_kv = unpad_input(k, mask_kv)
v = index_first_axis(v.reshape(-1, H, D), indices_kv) # same indice as k
# call varlen_func
out = flash_attn_varlen_func(
q,
k,
v,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_kv,
max_seqlen_q=max_len_q,
max_seqlen_k=max_len_kv,
dropout_p=dropout,
causal=causal,
window_size=window_size,
)
# pad (put back) output
out = pad_input(out, indices_q, B, N)
return out
else:
raise NotImplementedError("masked attention requires flash_attn!")
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
rnorm = torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps)
return (x * rnorm).to(dtype=self.weight.dtype) * self.weight
class SelfAttention(nn.Module):
def __init__(
self,
hidden_dim,
num_heads,
input_dim=None,
output_dim=None,
dropout=0,
causal=False,
qknorm=False,
qknorm_type="LayerNorm",
):
super().__init__()
self.hidden_dim = hidden_dim
self.input_dim = input_dim if input_dim is not None else hidden_dim
self.output_dim = output_dim if output_dim is not None else hidden_dim
self.num_heads = num_heads
assert hidden_dim % num_heads == 0, "hidden_dim must be divisible by num_heads"
self.head_dim = hidden_dim // num_heads
self.causal = causal
self.dropout = dropout
self.qknorm = qknorm
self.qkv_proj = nn.Linear(self.input_dim, 3 * self.hidden_dim)
self.out_proj = nn.Linear(self.hidden_dim, self.output_dim)
if self.qknorm:
if qknorm_type == "RMSNorm":
self.q_norm = RMSNorm(self.hidden_dim, eps=1e-6)
self.k_norm = RMSNorm(self.hidden_dim, eps=1e-6)
else:
self.q_norm = nn.LayerNorm(self.hidden_dim, eps=1e-6, elementwise_affine=False)
self.k_norm = nn.LayerNorm(self.hidden_dim, eps=1e-6, elementwise_affine=False)
def forward(self, x, mask=None):
# x: [B, N, C]
# mask: [B, N]
B, N, C = x.shape
qkv = self.qkv_proj(x) # [B, N, C] -> [B, N, 3 * D]
qkv = qkv.reshape(B, N, 3, -1).permute(2, 0, 1, 3) # [3, B, N, D]
q, k, v = qkv.chunk(3, dim=0) # [3, B, N, D] -> 3 * [1, B, N, D]
q = q.squeeze(0)
k = k.squeeze(0)
v = v.squeeze(0)
if self.qknorm:
q = self.q_norm(q)
k = self.k_norm(k)
q = q.reshape(B, N, self.num_heads, self.head_dim)
k = k.reshape(B, N, self.num_heads, self.head_dim)
v = v.reshape(B, N, self.num_heads, self.head_dim)
x = attention(q, k, v, mask_q=mask, mask_kv=mask, dropout=self.dropout, causal=self.causal) # [B, N, H, D]
x = self.out_proj(x.reshape(B, N, -1))
return x
class CrossAttention(nn.Module):
def __init__(
self,
hidden_dim,
num_heads,
input_dim=None,
context_dim=None,
output_dim=None,
dropout=0,
qknorm=False,
qknorm_type="LayerNorm",
):
super().__init__()
self.hidden_dim = hidden_dim
self.input_dim = input_dim if input_dim is not None else hidden_dim
self.context_dim = context_dim if context_dim is not None else hidden_dim
self.output_dim = output_dim if output_dim is not None else hidden_dim
self.num_heads = num_heads
assert hidden_dim % num_heads == 0, "hidden_dim must be divisible by num_heads"
self.head_dim = hidden_dim // num_heads
self.dropout = dropout
self.qknorm = qknorm
self.q_proj = nn.Linear(self.input_dim, self.hidden_dim)
self.k_proj = nn.Linear(self.context_dim, self.hidden_dim)
self.v_proj = nn.Linear(self.context_dim, self.hidden_dim)
self.out_proj = nn.Linear(self.hidden_dim, self.output_dim)
if self.qknorm:
if qknorm_type == "RMSNorm":
self.q_norm = RMSNorm(self.hidden_dim, eps=1e-6)
self.k_norm = RMSNorm(self.hidden_dim, eps=1e-6)
else:
self.q_norm = nn.LayerNorm(self.hidden_dim, eps=1e-6, elementwise_affine=False)
self.k_norm = nn.LayerNorm(self.hidden_dim, eps=1e-6, elementwise_affine=False)
def forward(self, x, context, mask_q=None, mask_kv=None):
# x: [B, N, C]
# context: [B, M, C']
# mask_q: [B, N]
# mask_kv: [B, M]
B, N, C = x.shape
M = context.shape[1]
q = self.q_proj(x)
k = self.k_proj(context)
v = self.v_proj(context)
if self.qknorm:
q = self.q_norm(q)
k = self.k_norm(k)
q = q.reshape(B, N, self.num_heads, self.head_dim)
k = k.reshape(B, M, self.num_heads, self.head_dim)
v = v.reshape(B, M, self.num_heads, self.head_dim)
x = attention(q, k, v, mask_q=mask_q, mask_kv=mask_kv, dropout=self.dropout, causal=False) # [B, N, H, D]
x = self.out_proj(x.reshape(B, N, -1))
return x
PartPacker/vae/modules/transformer.py
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"""
-----------------------------------------------------------------------------
Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
-----------------------------------------------------------------------------
"""
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from .attention import CrossAttention, SelfAttention
class FeedForward(nn.Module):
def __init__(self, dim, mult=4):
super().__init__()
self.net = nn.Sequential(nn.Linear(dim, dim * mult), nn.GELU(), nn.Linear(dim * mult, dim))
def forward(self, x):
return self.net(x)
class AttentionBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
dim_context=None,
qknorm=False,
gradient_checkpointing=True,
qknorm_type="LayerNorm",
):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.dim_context = dim_context
self.gradient_checkpointing = gradient_checkpointing
self.norm_attn = nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
if dim_context is not None:
self.norm_context = nn.LayerNorm(dim_context, eps=1e-6, elementwise_affine=False)
self.attn = CrossAttention(dim, num_heads, context_dim=dim_context, qknorm=qknorm, qknorm_type=qknorm_type)
else:
self.attn = SelfAttention(dim, num_heads, qknorm=qknorm, qknorm_type=qknorm_type)
self.norm_ff = nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
self.ff = FeedForward(dim)
def forward(self, x, c=None, mask=None, mask_c=None):
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, x, c, mask, mask_c, use_reentrant=False)
else:
return self._forward(x, c, mask, mask_c)
def _forward(self, x, c=None, mask=None, mask_c=None):
# x: [B, N, C], hidden states
# c: [B, M, C'], condition (assume normed and projected to C)
# mask: [B, N], mask for x
# mask_c: [B, M], mask for c
# return: [B, N, C], updated hidden states
if c is not None:
x = x + self.attn(self.norm_attn(x), self.norm_context(c), mask_q=mask, mask_kv=mask_c)
else:
x = x + self.attn(self.norm_attn(x), mask=mask)
x = x + self.ff(self.norm_ff(x))
return x
# special attention block for the last cross-attn query layer
# 1. simple feed-forward (mult=1, no post ln)
# 2. no residual connection
# 3. no context ln
class FlashQueryLayer(nn.Module):
def __init__(
self,
dim,
num_heads,
dim_context,
qknorm=False,
gradient_checkpointing=True,
qknorm_type="LayerNorm",
):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.dim_context = dim_context
self.gradient_checkpointing = gradient_checkpointing
self.norm_attn = nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
self.attn = CrossAttention(dim, num_heads, context_dim=dim_context, qknorm=qknorm, qknorm_type=qknorm_type)
self.ff = FeedForward(dim, mult=1)
def forward(self, x, c=None, mask=None, mask_c=None):
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, x, c, mask, mask_c, use_reentrant=False)
else:
return self._forward(x, c, mask, mask_c)
def _forward(self, x, c, mask=None, mask_c=None):
# x: [B, N, C], hidden states
# c: [B, M, C'], condition (assume normed and projected to C)
# mask: [B, N], mask for x
# mask_c: [B, M], mask for c
# return: [B, N, C], updated hidden states
x = self.attn(self.norm_attn(x), c, mask_q=mask, mask_kv=mask_c)
x = self.ff(x)
return x