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from typing import *
import torch
import torch.nn as nn
from ...modules.utils import convert_module_to_f16, convert_module_to_f32
from ...modules import sparse as sp
from ...modules.transformer import AbsolutePositionEmbedder
from ...modules.sparse.transformer import SparseTransformerBlock
def block_attn_config(self):
"""
Return the attention configuration of the model.
"""
for i in range(self.num_blocks):
if self.attn_mode == "shift_window":
yield "serialized", self.window_size, 0, (16 * (i % 2),) * 3, sp.SerializeMode.Z_ORDER
elif self.attn_mode == "shift_sequence":
yield "serialized", self.window_size, self.window_size // 2 * (i % 2), (0, 0, 0), sp.SerializeMode.Z_ORDER
elif self.attn_mode == "shift_order":
yield "serialized", self.window_size, 0, (0, 0, 0), sp.SerializeModes[i % 4]
elif self.attn_mode == "full":
yield "full", None, None, None, None
elif self.attn_mode == "swin":
yield "windowed", self.window_size, None, self.window_size // 2 * (i % 2), None
class SparseTransformerBase(nn.Module):
"""
Sparse Transformer without output layers.
Serve as the base class for encoder and decoder.
"""
def __init__(
self,
in_channels: int,
model_channels: int,
num_blocks: int,
num_heads: Optional[int] = None,
num_head_channels: Optional[int] = 64,
mlp_ratio: float = 4.0,
attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
window_size: Optional[int] = None,
pe_mode: Literal["ape", "rope"] = "ape",
use_fp16: bool = False,
use_checkpoint: bool = False,
qk_rms_norm: bool = False,
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.num_blocks = num_blocks
self.window_size = window_size
self.num_heads = num_heads or model_channels // num_head_channels
self.mlp_ratio = mlp_ratio
self.attn_mode = attn_mode
self.pe_mode = pe_mode
self.use_fp16 = use_fp16
self.use_checkpoint = use_checkpoint
self.qk_rms_norm = qk_rms_norm
self.dtype = torch.float16 if use_fp16 else torch.float32
if pe_mode == "ape":
self.pos_embedder = AbsolutePositionEmbedder(model_channels)
self.input_layer = sp.SparseLinear(in_channels, model_channels)
self.blocks = nn.ModuleList([
SparseTransformerBlock(
model_channels,
num_heads=self.num_heads,
mlp_ratio=self.mlp_ratio,
attn_mode=attn_mode,
window_size=window_size,
shift_sequence=shift_sequence,
shift_window=shift_window,
serialize_mode=serialize_mode,
use_checkpoint=self.use_checkpoint,
use_rope=(pe_mode == "rope"),
qk_rms_norm=self.qk_rms_norm,
)
for attn_mode, window_size, shift_sequence, shift_window, serialize_mode in block_attn_config(self)
])
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def convert_to_fp16(self) -> None:
"""
Convert the torso of the model to float16.
"""
self.blocks.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.blocks.apply(convert_module_to_f32)
def initialize_weights(self) -> None:
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
def forward(self, x: sp.SparseTensor) -> sp.SparseTensor:
h = self.input_layer(x)
if self.pe_mode == "ape":
h = h + self.pos_embedder(x.coords[:, 1:])
h = h.type(self.dtype)
for block in self.blocks:
h = block(h)
return h
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