Hi guys,
I assume you all know the paper "DEEP LEARNING FOR SYMBOLIC MATHEMATICS"
by facebook AI researchers, almost one year ago, posted on
https://arxiv.org/abs/1912.01412
And the code was posted 8 months ago:
https://github.com/facebookresearch/SymbolicMathematics
Have you played with it?
Finally I have some time recently and played with it for a while,
and I believe I found some flaws. I will post my findings with
more details later. And it's a really interesting experience.
If you have some spare time and want to have fun, I strongly
advise you to play with it and try to break it :-)
Tips: to run the jupyter notebook example, apply the following
patch to run it on CPU instead of CUDA:
- Best,
- Qian
=====================
diff --git a/beam_integration.ipynb b/beam_integration.ipynb
index f9ef329..00754e3 100644
--- a/beam_integration.ipynb
+++ b/beam_integration.ipynb
@@ -64,6 +64,6 @@
"\n",
" # model parameters\n",
- " 'cpu': False,\n",
+ " 'cpu': True,\n",
" 'emb_dim': 1024,\n",
" 'n_enc_layers': 6,\n",
" 'n_dec_layers': 6,\n",
diff --git a/src/model/__init__.py b/src/model/__init__.py
index 2b0a044..73ec446 100644
--- a/src/model/__init__.py
+++ b/src/model/__init__.py
@@ -38,7 +38,7 @@
# reload pretrained modules
if params.reload_model != '':
logger.info(f"Reloading modules from {params.reload_model} ...")
- reloaded = torch.load(params.reload_model)
+ reloaded = torch.load(params.reload_model,
map_location=torch.device('cpu'))
for k, v in modules.items():
assert k in reloaded
if all([k2.startswith('module.') for k2 in
reloaded[k].keys()]):
diff --git a/src/utils.py b/src/utils.py
index bd90608..ef87582 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -25,7 +25,7 @@
FALSY_STRINGS = {'off', 'false', '0'}
TRUTHY_STRINGS = {'on', 'true', '1'}
-CUDA = True
+CUDA = False
class AttrDict(dict):
--
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