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rf2aa/SE3Transformer/se3_transformer/model/layers/attention.py
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186
rf2aa/SE3Transformer/se3_transformer/model/layers/attention.py
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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#
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
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# SPDX-License-Identifier: MIT
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import dgl
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import numpy as np
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import torch
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import torch.nn as nn
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from dgl import DGLGraph
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from dgl.ops import edge_softmax
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from torch import Tensor
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from typing import Dict, Optional, Union
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from rf2aa.SE3Transformer.se3_transformer.model.fiber import Fiber
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from rf2aa.SE3Transformer.se3_transformer.model.layers.convolution import ConvSE3, ConvSE3FuseLevel
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from rf2aa.SE3Transformer.se3_transformer.model.layers.linear import LinearSE3
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from rf2aa.SE3Transformer.se3_transformer.runtime.utils import degree_to_dim, aggregate_residual, unfuse_features
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from torch.cuda.nvtx import range as nvtx_range
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class AttentionSE3(nn.Module):
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""" Multi-headed sparse graph self-attention (SE(3)-equivariant) """
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def __init__(
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self,
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num_heads: int,
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key_fiber: Fiber,
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value_fiber: Fiber
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):
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"""
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:param num_heads: Number of attention heads
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:param key_fiber: Fiber for the keys (and also for the queries)
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:param value_fiber: Fiber for the values
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"""
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super().__init__()
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self.num_heads = num_heads
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self.key_fiber = key_fiber
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self.value_fiber = value_fiber
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def forward(
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self,
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value: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused)
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key: Union[Tensor, Dict[str, Tensor]], # edge features (may be fused)
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query: Dict[str, Tensor], # node features
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graph: DGLGraph
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):
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with nvtx_range('AttentionSE3'):
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with nvtx_range('reshape keys and queries'):
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if isinstance(key, Tensor):
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# case where features of all types are fused
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key = key.reshape(key.shape[0], self.num_heads, -1)
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# need to reshape queries that way to keep the same layout as keys
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out = torch.cat([query[str(d)] for d in self.key_fiber.degrees], dim=-1)
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query = out.reshape(list(query.values())[0].shape[0], self.num_heads, -1)
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else:
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# features are not fused, need to fuse and reshape them
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key = self.key_fiber.to_attention_heads(key, self.num_heads)
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query = self.key_fiber.to_attention_heads(query, self.num_heads)
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with nvtx_range('attention dot product + softmax'):
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# Compute attention weights (softmax of inner product between key and query)
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with torch.cuda.amp.autocast(False):
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edge_weights = dgl.ops.e_dot_v(graph, key, query).squeeze(-1)
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edge_weights /= np.sqrt(self.key_fiber.num_features)
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edge_weights = edge_softmax(graph, edge_weights)
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edge_weights = edge_weights[..., None, None]
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with nvtx_range('weighted sum'):
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if isinstance(value, Tensor):
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# features of all types are fused
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v = value.view(value.shape[0], self.num_heads, -1, value.shape[-1])
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weights = edge_weights * v
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feat_out = dgl.ops.copy_e_sum(graph, weights)
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feat_out = feat_out.view(feat_out.shape[0], -1, feat_out.shape[-1]) # merge heads
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out = unfuse_features(feat_out, self.value_fiber.degrees)
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else:
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out = {}
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for degree, channels in self.value_fiber:
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v = value[str(degree)].view(-1, self.num_heads, channels // self.num_heads,
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degree_to_dim(degree))
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weights = edge_weights * v
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res = dgl.ops.copy_e_sum(graph, weights)
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out[str(degree)] = res.view(-1, channels, degree_to_dim(degree)) # merge heads
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return out
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class AttentionBlockSE3(nn.Module):
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""" Multi-headed sparse graph self-attention block with skip connection, linear projection (SE(3)-equivariant) """
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def __init__(
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self,
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fiber_in: Fiber,
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fiber_out: Fiber,
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fiber_edge: Optional[Fiber] = None,
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num_heads: int = 4,
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channels_div: Optional[Dict[str,int]] = None,
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use_layer_norm: bool = False,
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max_degree: bool = 4,
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fuse_level: ConvSE3FuseLevel = ConvSE3FuseLevel.FULL,
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**kwargs
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):
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"""
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:param fiber_in: Fiber describing the input features
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:param fiber_out: Fiber describing the output features
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:param fiber_edge: Fiber describing the edge features (node distances excluded)
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:param num_heads: Number of attention heads
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:param channels_div: Divide the channels by this integer for computing values
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:param use_layer_norm: Apply layer normalization between MLP layers
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:param max_degree: Maximum degree used in the bases computation
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:param fuse_level: Maximum fuse level to use in TFN convolutions
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"""
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super().__init__()
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if fiber_edge is None:
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fiber_edge = Fiber({})
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self.fiber_in = fiber_in
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# value_fiber has same structure as fiber_out but #channels divided by 'channels_div'
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if channels_div is not None:
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value_fiber = Fiber([(degree, channels // channels_div[str(degree)]) for degree, channels in fiber_out])
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else:
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value_fiber = Fiber([(degree, channels) for degree, channels in fiber_out])
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# key_query_fiber has the same structure as fiber_out, but only degrees which are in in_fiber
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# (queries are merely projected, hence degrees have to match input)
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key_query_fiber = Fiber([(fe.degree, fe.channels) for fe in value_fiber if fe.degree in fiber_in.degrees])
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self.to_key_value = ConvSE3(fiber_in, value_fiber + key_query_fiber, pool=False, fiber_edge=fiber_edge,
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use_layer_norm=use_layer_norm, max_degree=max_degree, fuse_level=fuse_level,
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allow_fused_output=True)
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self.to_query = LinearSE3(fiber_in, key_query_fiber)
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self.attention = AttentionSE3(num_heads, key_query_fiber, value_fiber)
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self.project = LinearSE3(value_fiber + fiber_in, fiber_out)
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def forward(
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self,
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node_features: Dict[str, Tensor],
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edge_features: Dict[str, Tensor],
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graph: DGLGraph,
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basis: Dict[str, Tensor]
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):
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with nvtx_range('AttentionBlockSE3'):
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with nvtx_range('keys / values'):
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fused_key_value = self.to_key_value(node_features, edge_features, graph, basis)
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key, value = self._get_key_value_from_fused(fused_key_value)
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with nvtx_range('queries'):
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with torch.cuda.amp.autocast(False):
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query = self.to_query(node_features)
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z = self.attention(value, key, query, graph)
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z_concat = aggregate_residual(node_features, z, 'cat')
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return self.project(z_concat)
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def _get_key_value_from_fused(self, fused_key_value):
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# Extract keys and queries features from fused features
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if isinstance(fused_key_value, Tensor):
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# Previous layer was a fully fused convolution
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value, key = torch.chunk(fused_key_value, chunks=2, dim=-2)
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else:
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key, value = {}, {}
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for degree, feat in fused_key_value.items():
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if int(degree) in self.fiber_in.degrees:
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value[degree], key[degree] = torch.chunk(feat, chunks=2, dim=-2)
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else:
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value[degree] = feat
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return key, value
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