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