| 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: |
| |
| 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 |
|
|
