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| # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a | |
| # copy of this software and associated documentation files (the "Software"), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the | |
| # Software is furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in | |
| # all copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL | |
| # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING | |
| # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER | |
| # DEALINGS IN THE SOFTWARE. | |
| # | |
| # SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES | |
| # SPDX-License-Identifier: MIT | |
| import dgl | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from dgl import DGLGraph | |
| from dgl.ops import edge_softmax | |
| from torch import Tensor | |
| from typing import Dict, Optional, Union | |
| from se3_transformer.model.fiber import Fiber | |
| from se3_transformer.model.layers.convolution import ConvSE3, ConvSE3FuseLevel | |
| from se3_transformer.model.layers.linear import LinearSE3 | |
| from se3_transformer.runtime.utils import degree_to_dim, aggregate_residual, unfuse_features | |
| from torch.cuda.nvtx import range as nvtx_range | |
| class AttentionSE3(nn.Module): | |
| """ Multi-headed sparse graph self-attention (SE(3)-equivariant) """ | |
| def __init__( | |
| self, | |
| num_heads: int, | |
| key_fiber: Fiber, | |
| value_fiber: Fiber | |
| ): | |
| """ | |
| :param num_heads: Number of attention heads | |
| :param key_fiber: Fiber for the keys (and also for the queries) | |
| :param value_fiber: Fiber for the values | |
| """ | |
| super().__init__() | |
| self.num_heads = num_heads | |
| self.key_fiber = key_fiber | |
| self.value_fiber = value_fiber | |
| def forward( | |
| self, | |
| value: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused) | |
| key: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused) | |
| query: Dict[str, Tensor], # node features | |
| graph: DGLGraph | |
| ): | |
| with nvtx_range('AttentionSE3'): | |
| with nvtx_range('reshape keys and queries'): | |
| if isinstance(key, Tensor): | |
| # case where features of all types are fused | |
| key = key.reshape(key.shape[0], self.num_heads, -1) | |
| # need to reshape queries that way to keep the same layout as keys | |
| out = torch.cat([query[str(d)] for d in self.key_fiber.degrees], dim=-1) | |
| query = out.reshape(list(query.values())[0].shape[0], self.num_heads, -1) | |
| else: | |
| # features are not fused, need to fuse and reshape them | |
| key = self.key_fiber.to_attention_heads(key, self.num_heads) | |
| query = self.key_fiber.to_attention_heads(query, self.num_heads) | |
| with nvtx_range('attention dot product + softmax'): | |
| # Compute attention weights (softmax of inner product between key and query) | |
| edge_weights = dgl.ops.e_dot_v(graph, key, query).squeeze(-1) | |
| edge_weights /= np.sqrt(self.key_fiber.num_features) | |
| edge_weights = edge_softmax(graph, edge_weights) | |
| edge_weights = edge_weights[..., None, None] | |
| with nvtx_range('weighted sum'): | |
| if isinstance(value, Tensor): | |
| # features of all types are fused | |
| v = value.view(value.shape[0], self.num_heads, -1, value.shape[-1]) | |
| weights = edge_weights * v | |
| feat_out = dgl.ops.copy_e_sum(graph, weights) | |
| feat_out = feat_out.view(feat_out.shape[0], -1, feat_out.shape[-1]) # merge heads | |
| out = unfuse_features(feat_out, self.value_fiber.degrees) | |
| else: | |
| out = {} | |
| for degree, channels in self.value_fiber: | |
| v = value[str(degree)].view(-1, self.num_heads, channels // self.num_heads, | |
| degree_to_dim(degree)) | |
| weights = edge_weights * v | |
| res = dgl.ops.copy_e_sum(graph, weights) | |
| out[str(degree)] = res.view(-1, channels, degree_to_dim(degree)) # merge heads | |
| return out | |
| class AttentionBlockSE3(nn.Module): | |
| """ Multi-headed sparse graph self-attention block with skip connection, linear projection (SE(3)-equivariant) """ | |
| def __init__( | |
| self, | |
| fiber_in: Fiber, | |
| fiber_out: Fiber, | |
| fiber_edge: Optional[Fiber] = None, | |
| num_heads: int = 4, | |
| channels_div: int = 2, | |
| use_layer_norm: bool = False, | |
| max_degree: bool = 4, | |
| fuse_level: ConvSE3FuseLevel = ConvSE3FuseLevel.FULL, | |
| **kwargs | |
| ): | |
| """ | |
| :param fiber_in: Fiber describing the input features | |
| :param fiber_out: Fiber describing the output features | |
| :param fiber_edge: Fiber describing the edge features (node distances excluded) | |
| :param num_heads: Number of attention heads | |
| :param channels_div: Divide the channels by this integer for computing values | |
| :param use_layer_norm: Apply layer normalization between MLP layers | |
| :param max_degree: Maximum degree used in the bases computation | |
| :param fuse_level: Maximum fuse level to use in TFN convolutions | |
| """ | |
| super().__init__() | |
| if fiber_edge is None: | |
| fiber_edge = Fiber({}) | |
| self.fiber_in = fiber_in | |
| # value_fiber has same structure as fiber_out but #channels divided by 'channels_div' | |
| value_fiber = Fiber([(degree, channels // channels_div) for degree, channels in fiber_out]) | |
| # key_query_fiber has the same structure as fiber_out, but only degrees which are in in_fiber | |
| # (queries are merely projected, hence degrees have to match input) | |
| key_query_fiber = Fiber([(fe.degree, fe.channels) for fe in value_fiber if fe.degree in fiber_in.degrees]) | |
| self.to_key_value = ConvSE3(fiber_in, value_fiber + key_query_fiber, pool=False, fiber_edge=fiber_edge, | |
| use_layer_norm=use_layer_norm, max_degree=max_degree, fuse_level=fuse_level, | |
| allow_fused_output=True) | |
| self.to_query = LinearSE3(fiber_in, key_query_fiber) | |
| self.attention = AttentionSE3(num_heads, key_query_fiber, value_fiber) | |
| self.project = LinearSE3(value_fiber + fiber_in, fiber_out) | |
| def forward( | |
| self, | |
| node_features: Dict[str, Tensor], | |
| edge_features: Dict[str, Tensor], | |
| graph: DGLGraph, | |
| basis: Dict[str, Tensor] | |
| ): | |
| with nvtx_range('AttentionBlockSE3'): | |
| with nvtx_range('keys / values'): | |
| fused_key_value = self.to_key_value(node_features, edge_features, graph, basis) | |
| key, value = self._get_key_value_from_fused(fused_key_value) | |
| with nvtx_range('queries'): | |
| query = self.to_query(node_features) | |
| z = self.attention(value, key, query, graph) | |
| z_concat = aggregate_residual(node_features, z, 'cat') | |
| return self.project(z_concat) | |
| def _get_key_value_from_fused(self, fused_key_value): | |
| # Extract keys and queries features from fused features | |
| if isinstance(fused_key_value, Tensor): | |
| # Previous layer was a fully fused convolution | |
| value, key = torch.chunk(fused_key_value, chunks=2, dim=-2) | |
| else: | |
| key, value = {}, {} | |
| for degree, feat in fused_key_value.items(): | |
| if int(degree) in self.fiber_in.degrees: | |
| value[degree], key[degree] = torch.chunk(feat, chunks=2, dim=-2) | |
| else: | |
| value[degree] = feat | |
| return key, value | |