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Add some debug output for profiling, the bottleneck is in DecodeLayer but also comes generally from computing always all nodes.

master
Stanislaw Adaszewski 4 lat temu
rodzic
commit
a5b8701a0d
4 zmienionych plików z 33 dodań i 0 usunięć
  1. +3
    -0
      src/icosagon/convlayer.py
  2. +5
    -0
      src/icosagon/declayer.py
  3. +15
    -0
      src/icosagon/trainloop.py
  4. +10
    -0
      tests/icosagon/test_trainloop.py

+ 3
- 0
src/icosagon/convlayer.py Wyświetl plik

@@ -7,6 +7,7 @@ from typing import List, \
Callable
from collections import defaultdict
from dataclasses import dataclass
import time
class Convolutions(torch.nn.Module):
@@ -104,6 +105,7 @@ class DecagonLayer(torch.nn.Module):
self.build_family(fam)
def __call__(self, prev_layer_repr):
t = time.time()
next_layer_repr = [ [] for _ in range(len(self.data.node_types)) ]
n = len(self.data.node_types)
@@ -120,4 +122,5 @@ class DecagonLayer(torch.nn.Module):
next_layer_repr[node_type_row] = sum(next_layer_repr[node_type_row])
next_layer_repr[node_type_row] = self.layer_activation(next_layer_repr[node_type_row])
print('DecagonLayer.forward() took', time.time() - t)
return next_layer_repr

+ 5
- 0
src/icosagon/declayer.py Wyświetl plik

@@ -16,6 +16,7 @@ from typing import Type, \
Tuple
from .decode import DEDICOMDecoder
from dataclasses import dataclass
import time
@dataclass
@@ -75,6 +76,7 @@ class DecodeLayer(torch.nn.Module):
self.decoders.append(dec)
def _get_tvt(self, r, edge_list_attr_names, row, column, k, last_layer_repr, dec):
start_time = time.time()
pred = []
for p in edge_list_attr_names:
tvt = []
@@ -86,9 +88,11 @@ class DecodeLayer(torch.nn.Module):
tvt.append(dec(inputs_row, inputs_column, k))
tvt = TrainValTest(*tvt)
pred.append(tvt)
print('DecodeLayer._get_tvt() took:', time.time() - start_time)
return pred
def forward(self, last_layer_repr: List[torch.Tensor]) -> List[List[torch.Tensor]]:
t = time.time()
res = []
for i, fam in enumerate(self.data.relation_families):
fam_pred = []
@@ -103,4 +107,5 @@ class DecodeLayer(torch.nn.Module):
fam_pred = RelationFamilyPredictions(fam_pred)
res.append(fam_pred)
res = Predictions(res)
print('DecodeLayer.forward() took', time.time() - t)
return res

+ 15
- 0
src/icosagon/trainloop.py Wyświetl plik

@@ -5,6 +5,7 @@ from .batch import PredictionsBatch, \
gather_batch_indices
from typing import Callable
from types import FunctionType
import time
class TrainLoop(object):
@@ -54,9 +55,15 @@ class TrainLoop(object):
loss_sum = 0
for i, indices in enumerate(batch):
print('%.2f%% (%d/%d)' % (i * batch.batch_size * 100 / batch.total_edge_count, i * batch.batch_size, batch.total_edge_count))
t = time.time()
self.opt.zero_grad()
print('zero_grad() took:', time.time() - t)
t = time.time()
pred = self.model(None)
print('model() took:', time.time() - t)
t = time.time()
pred = flatten_predictions(pred)
print('flatten_predictions() took:', time.time() - t)
# batch = PredictionsBatch(pred, batch_size=self.batch_size, shuffle=True)
# seed = torch.rand(1).item()
# rng_state = torch.get_rng_state()
@@ -66,10 +73,18 @@ class TrainLoop(object):
#for k in range(i):
#_ = next(it)
#(input, target) = next(it)
t = time.time()
(input, target) = gather_batch_indices(pred, indices)
print('gather_batch_indices() took:', time.time() - t)
t = time.time()
loss = self.loss(input, target)
print('loss() took:', time.time() - t)
t = time.time()
loss.backward()
print('backward() took:', time.time() - t)
t = time.time()
self.opt.step()
print('step() took:', time.time() - t)
loss_sum += loss.detach().cpu().item()
return loss_sum


+ 10
- 0
tests/icosagon/test_trainloop.py Wyświetl plik

@@ -6,6 +6,7 @@ from icosagon.trainloop import TrainLoop
import torch
import pytest
import pdb
import time
def test_train_loop_01():
@@ -69,3 +70,12 @@ def test_train_loop_03():
loop = TrainLoop(m)
loop.run_epoch()
def test_timing_01():
adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32).to_sparse()
rep = torch.eye(2000).requires_grad_(True)
t = time.time()
for _ in range(1300):
_ = torch.sparse.mm(adj_mat, rep)
print('Elapsed:', time.time() - t)

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