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from __future__ import annotations
import logging
import warnings
import numpy as np
import torch
import torch.nn as nn
from matplotlib import pyplot as plt
from onescience.modules.head.uma_head import (
DatasetSpecificMoEWrapper,
DatasetSpecificSingleHeadWrapper,
)
from onescience.utils.uma.common.registry import registry
from model.uma_escn_md import eSCNMDBackbone
from onescience.modules.func_utils.uma_mole_utils import (
MOLEInterface,
convert_model_to_MOLE_model,
model_search_and_replace,
recursive_replace_so2_MOLE,
replace_MOLE_with_linear,
)
# This will catch the warning despite its C++ origin
# torch.Tensor.index_reduce is in beta
warnings.filterwarnings(
"ignore",
message="index_reduce\\(\\) is in beta",
category=UserWarning,
)
@registry.register_model("escnmd_moe_backbone")
class eSCNMDMoeBackbone(eSCNMDBackbone, MOLEInterface):
def __init__(
self,
num_experts: int = 8,
moe_dropout: float = 0.0,
use_global_embedding: bool = False, # obsolete
use_composition_embedding: bool = False,
moe_expert_coefficient_norm: str = "softmax",
act=torch.nn.SiLU,
layers_moe=None,
moe_layer_type: str = "pytorch",
moe_single: bool = False,
moe_type: str = "so2",
model_version: float = 1.0,
**kwargs,
):
super().__init__(**kwargs)
self.parent_kwargs = kwargs
self.num_experts = num_experts
self.model_version = model_version
if num_experts > 0:
convert_model_to_MOLE_model(
model=self,
num_experts=num_experts,
mole_dropout=moe_dropout,
mole_expert_coefficient_norm=moe_expert_coefficient_norm,
act=act,
layers_mole=layers_moe,
use_composition_embedding=use_composition_embedding,
mole_layer_type=moe_layer_type,
mole_single=moe_single,
mole_type=moe_type,
)
def merge_MOLE_model(self, data):
if self.num_experts == 0:
return self
data["atomic_numbers"] = data["atomic_numbers"].long()
csd_mixed_emb = self.csd_embedding(
charge=data["charge"],
spin=data["spin"],
dataset=data["dataset"],
)
self.set_MOLE_coefficients(
atomic_numbers_full=data["atomic_numbers"],
batch_full=data["batch"],
csd_mixed_emb=csd_mixed_emb,
)
if self.mole_type != "so2":
raise ValueError("Only mole_type=so2 supported for merging")
model_search_and_replace(
self, recursive_replace_so2_MOLE, replace_MOLE_with_linear
)
# drop moe parameters from merged model
self.routing_mlp = None
self.composition_embedding = None
self.num_experts = 0
# create a new non moe model and load weights into there
new_model = eSCNMDBackbone(**self.parent_kwargs)
new_model.load_state_dict(self.state_dict())
new_model.eval()
return new_model
def set_MOLE_coefficients(self, atomic_numbers_full, batch_full, csd_mixed_emb):
if self.num_experts == 0:
return
with torch.autocast(device_type=atomic_numbers_full.device.type, enabled=False):
embeddings = []
if self.use_composition_embedding:
composition_by_atom = self.composition_embedding(atomic_numbers_full)
composition = composition_by_atom.new_zeros(
csd_mixed_emb.shape[0],
self.sphere_channels,
).index_reduce_(
0,
batch_full,
composition_by_atom,
reduce="mean",
include_self=np.isclose(self.model_version, 1.0).item(),
)
embeddings.append(composition.unsqueeze(0))
embeddings.append(csd_mixed_emb[None])
expert_mixing_coefficients_before_norm = self.routing_mlp(
torch.vstack(embeddings)
.transpose(0, 1)
.reshape(csd_mixed_emb.shape[0], -1)
)
self.global_mole_tensors.expert_mixing_coefficients = (
self.mole_expert_coefficient_norm(
self.mole_dropout(expert_mixing_coefficients_before_norm)
)
)
def set_MOLE_sizes(self, nsystems, batch_full, edge_index):
if self.num_experts == 0:
return
with torch.autocast(device_type=batch_full.device.type, enabled=False):
# Generate edge mix_size routing each edge in this instance (GP or not)
# using its local edge and batch routing
# Local edge_index is 2xN where [1,:] is the target node, the target node does not
# have the gp offset applied, which means we need to lookup in the full batch_full
# _, mix_size = torch.unique(data.batch_full[edge_index[1]], return_counts=True)
mole_sizes = torch.zeros(
nsystems, # data.natoms.shape[0],
dtype=torch.int,
device=batch_full[edge_index[1]].device,
).scatter_(0, batch_full[edge_index[1]], 1, reduce="add")
self.global_mole_tensors.mole_sizes = mole_sizes.cpu()
def log_MOLE_stats(self):
if not self.training or self.num_experts == 0:
return
if not hasattr(self, "fig"):
self.fig, self.axs = plt.subplots(2, 1)
with torch.no_grad():
if self.counter % 500 == 0:
logging.info(
f"{self.counter }: Expert variance: "
+ ",".join(
[
f"{x:.2e}"
for x in self.global_mole_tensors.expert_mixing_coefficients.var(
axis=0
).tolist()
]
)
)
logging.info(
f"{self.counter }: Expert mean: "
+ ",".join(
[
f"{x:.2e}"
for x in self.global_mole_tensors.expert_mixing_coefficients.mean(
axis=0
).tolist()
]
)
)
self.fig.tight_layout()
self.plot_ready = True
self.counter += 1
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