-
Notifications
You must be signed in to change notification settings - Fork 74
/
model.py
271 lines (231 loc) · 9.86 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
import torch.nn.functional as F
from ..modules.alphafold import *
from .modules import SymmInputEmbedder, PseudoResidueEmbedder, SymmStructureModule
from .assemble import expand_symmetry
class UFSymmetry(AlphaFold): # inherit the main model. alterations implemented here.
def __init__(self, config):
assert not config.globals.alphafold_original_mode
super(UFSymmetry, self).__init__(config)
# replace input embedder with symm input embedder
self.input_embedder = SymmInputEmbedder(**config.model["input_embedder"], use_chain_relative=True)
self.pseudo_residue_embedder = PseudoResidueEmbedder(**config.model["pseudo_residue_embedder"])
self.structure_module = SymmStructureModule(
**config.model["structure_module"],
)
def __make_input_float__(self):
super().__make_input_float__()
self.pseudo_residue_embedder = self.pseudo_residue_embedder.float()
def iteration_evoformer(self, feats, m_1_prev, z_prev, x_prev):
batch_dims = feats["target_feat"].shape[:-2]
n = feats["target_feat"].shape[-2] + 1 # pr
seq_mask = feats["seq_mask"]
msa_mask = feats["msa_mask"]
pair_mask = seq_mask[..., None] * seq_mask[..., None, :]
pr_feat = feats["pseudo_residue_feat"]
pr_feat = self.pseudo_residue_embedder(pr_feat)
m, z, pr_m = self.input_embedder(
feats["target_feat"],
feats["msa_feat"],
pr_feat
)
if m_1_prev is None:
m_1_prev = m.new_zeros(
(*batch_dims, n, self.config.input_embedder.d_msa),
requires_grad=False,
)
if z_prev is None:
z_prev = z.new_zeros(
(*batch_dims, n, n, self.config.input_embedder.d_pair),
requires_grad=False,
)
if x_prev is None:
x_prev = z.new_zeros(
(*batch_dims, n, residue_constants.atom_type_num, 3),
requires_grad=False,
)
x_prev_ = pseudo_beta_fn(feats["aatype"], x_prev[..., 1:, :, :], None)
x_prev = torch.cat([x_prev[..., 0:1, 0, :], x_prev_], dim=-2)
z += self.recycling_embedder.recyle_pos(x_prev)
m_1_prev_emb, z_prev_emb = self.recycling_embedder(
m_1_prev,
z_prev,
)
m[..., 0, :, :] += m_1_prev_emb
z += z_prev_emb
relpos = self.input_embedder.relpos_emb(
feats["residue_index"].long(),
feats.get("sym_id", None),
feats.get("asym_id", None),
feats.get("entity_id", None),
feats.get("num_sym", None),
)
z[..., 1:, 1:, :] += relpos
m = m.type(self.dtype)
z = z.type(self.dtype)
tri_start_attn_mask, tri_end_attn_mask = gen_tri_attn_mask(pair_mask, self.inf)
# m_in = m[..., 1:, :]
z_in = z[..., 1:, 1:, :]
if self.config.template.enabled:
template_mask = feats["template_mask"]
if torch.any(template_mask):
z_in = residual(
z_in,
self.embed_templates_pair(
feats,
z_in,
pair_mask,
tri_start_attn_mask,
tri_end_attn_mask,
templ_dim=-4,
),
self.training,
)
if self.config.extra_msa.enabled:
a = self.extra_msa_embedder(build_extra_msa_feat(feats))
extra_msa_row_mask = gen_msa_attn_mask(
feats["extra_msa_mask"],
inf=self.inf,
gen_col_mask=False,
)
z_in = self.extra_msa_stack(
a,
z_in,
msa_mask=feats["extra_msa_mask"],
chunk_size=self.globals.chunk_size,
block_size=self.globals.block_size,
pair_mask=pair_mask,
msa_row_attn_mask=extra_msa_row_mask,
msa_col_attn_mask=None,
tri_start_attn_mask=tri_start_attn_mask,
tri_end_attn_mask=tri_end_attn_mask,
)
if self.config.template.embed_angles:
template_1d_feat, template_1d_mask = self.embed_templates_angle(feats)
expand_shape = list(pr_m.shape)
expand_shape[-3] = template_1d_feat.shape[-3]
template_1d_feat = torch.cat([pr_m.expand(expand_shape), template_1d_feat], dim=-2)
msa_mask = torch.cat([feats["msa_mask"], template_1d_mask], dim=-2)
# compose tensors back
m = torch.cat([m, template_1d_feat], dim=-3)
z_tmp = torch.cat([z[..., 1:, 0:1, :], z_in], dim=-2)
z = torch.cat([z[..., 0:1, :, :], z_tmp], dim=-3)
# pad pr mask
pad_fn = lambda msk: F.pad(msk, (1, 0), "constant", 1.)
seq_mask = pad_fn(seq_mask)
msa_mask = pad_fn(msa_mask)
pair_mask = seq_mask[..., None] * seq_mask[..., None, :]
msa_row_mask, msa_col_mask = gen_msa_attn_mask(
msa_mask,
inf=self.inf,
)
tri_start_attn_mask, tri_end_attn_mask = gen_tri_attn_mask(pair_mask, self.inf)
m, z, s = self.evoformer(
m,
z,
msa_mask=msa_mask,
pair_mask=pair_mask,
msa_row_attn_mask=msa_row_mask,
msa_col_attn_mask=msa_col_mask,
tri_start_attn_mask=tri_start_attn_mask,
tri_end_attn_mask=tri_end_attn_mask,
chunk_size=self.globals.chunk_size,
block_size=self.globals.block_size,
)
return m, z, s, msa_mask, pair_mask, m_1_prev_emb, z_prev_emb
def iteration_evoformer_structure_module(
self, batch, m_1_prev, z_prev, x_prev, cycle_no, num_recycling, num_ensembles=1
):
z, s = 0, 0
n_seq = batch["msa_feat"].shape[-3]
assert num_ensembles >= 1
for ensemble_no in range(num_ensembles):
idx = cycle_no * num_ensembles + ensemble_no
fetch_cur_batch = lambda t: t[min(t.shape[0] - 1, idx), ...]
feats = tensor_tree_map(fetch_cur_batch, batch)
m, z0, s0, msa_mask, pair_mask, m_1_prev_emb, z_prev_emb = self.iteration_evoformer(
feats, m_1_prev, z_prev, x_prev
)
z += z0
s += s0
del z0, s0
if num_ensembles > 1:
z /= float(num_ensembles)
s /= float(num_ensembles)
outputs = {}
outputs["msa"] = m[..., :n_seq, 1:, :]
outputs["pair"] = z[..., 1:, 1:, :]
outputs["single"] = s[..., 1:, :]
# norm loss
if (not getattr(self, "inference", False)) and num_recycling == (cycle_no + 1):
delta_msa = m
delta_msa[..., 0, :, :] = delta_msa[..., 0, :, :] - m_1_prev_emb.detach()
delta_pair = z - z_prev_emb.detach()
outputs["delta_msa"] = delta_msa
outputs["delta_pair"] = delta_pair
outputs["msa_norm_mask"] = msa_mask
outputs["pair_norm_mask"] = pair_mask
outputs["sm"] = self.structure_module(
s,
z,
feats["aatype"],
mask=feats["seq_mask"],
)
outputs["final_atom_positions"] = atom14_to_atom37(
outputs["sm"]["positions"], feats
)
outputs["final_atom_mask"] = feats["atom37_atom_exists"]
outputs["pred_frame_tensor"] = outputs["sm"]["frames"][-1]
global_center = outputs['sm']['global_center_position'][..., None, :]
global_center = global_center.repeat(*((1,) * len(global_center.shape[:-2])), 37, 1)
x_prev = torch.cat([global_center, outputs["final_atom_positions"]], dim=-3)
# use float32 for numerical stability
if (not getattr(self, "inference", False)):
m_1_prev = m[..., 0, :, :].float()
z_prev = z.float()
x_prev = x_prev.float()
else:
m_1_prev = m[..., 0, :, :]
z_prev = z
x_prev = x_prev
return outputs, m_1_prev, z_prev, x_prev
def forward(self, batch, expand=True):
m_1_prev = batch.get("m_1_prev", None)
z_prev = batch.get("z_prev", None)
x_prev = batch.get("x_prev", None)
is_grad_enabled = torch.is_grad_enabled()
num_iters = int(batch["num_recycling_iters"]) + 1
num_ensembles = int(batch["msa_mask"].shape[0]) // num_iters
if self.training:
# don't use ensemble during training
assert num_ensembles == 1
# convert dtypes in batch
batch = self.__convert_input_dtype__(batch)
for cycle_no in range(num_iters):
is_final_iter = cycle_no == (num_iters - 1)
with torch.set_grad_enabled(is_grad_enabled and is_final_iter):
(
outputs,
m_1_prev,
z_prev,
x_prev,
) = self.iteration_evoformer_structure_module(
batch,
m_1_prev,
z_prev,
x_prev,
cycle_no=cycle_no,
num_recycling=num_iters,
num_ensembles=num_ensembles,
)
if not is_final_iter:
del outputs
if expand:
symmetry_feat, symmetry_output = expand_symmetry(outputs["sm"], batch)
outputs["expand_batch"] = symmetry_feat
outputs["expand_sm"] = symmetry_output
outputs["expand_final_atom_positions"] = symmetry_output["expand_final_atom_positions"]
outputs["expand_final_atom_mask"] = symmetry_output["expand_final_atom_mask"]
if "asym_id" in batch:
outputs["asym_id"] = batch["asym_id"][0, ...]
outputs.update(self.aux_heads(outputs))
return outputs