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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
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