From cb1a33430c6c3c40453c030f00dc2e79b2ddd13d Mon Sep 17 00:00:00 2001
From: Alexandru Gherghescu <gherghescu_alex1@yahoo.ro>
Date: Mon, 3 Jun 2024 22:33:06 +0300
Subject: [PATCH] Change quotes style
Make double quotes for doc-strings and f-strings which contain single
quotes inside. Single quotes everywhere else.
---
inference.py | 24 ++++++++++++------------
optimus/models/optimus.py | 10 +++++-----
training.py | 12 ++++++------
3 files changed, 23 insertions(+), 23 deletions(-)
diff --git a/inference.py b/inference.py
index 0f69c01..2c0bd02 100644
--- a/inference.py
+++ b/inference.py
@@ -88,7 +88,7 @@ def main(model_path: str = 'model.pth',
warnings.simplefilter('ignore')
torch.set_default_tensor_type(torch.cuda.HalfTensor)
elif device == 'cpu':
- assert 0 == 1, "Cannot run 16-bit inference on CPU!"
+ assert 0 == 1, 'Cannot run 16-bit inference on CPU!'
else:
if device == 'cuda':
warnings.simplefilter('ignore')
@@ -97,7 +97,7 @@ def main(model_path: str = 'model.pth',
warnings.simplefilter('ignore')
torch.set_default_tensor_type(torch.FloatTensor)
- print("Loading model from disk...")
+ print('Loading model from disk...')
# load state from file
assert os.path.exists(model_path)
@@ -113,9 +113,9 @@ def main(model_path: str = 'model.pth',
p_drop = float(state['p_drop'])
weight_tying = bool(state['weight_tying'])
- assert vocab_sz == len(tok), ("The tokenizer passed for inference is "
- "different from the tokenizer used for training! This will result in "
- "erroneous generation!")
+ assert vocab_sz == len(tok), ('The tokenizer passed for inference is '
+ 'different from the tokenizer used for training! This will result in '
+ 'erroneous generation!')
# create model, load weights
config = OptimusConfig(vocab_size=vocab_sz,
@@ -128,19 +128,19 @@ def main(model_path: str = 'model.pth',
model.load_state_dict(state, strict=True)
model.eval()
- print(f"Loaded model on device {device}!")
+ print(f'Loaded model on device {device}!')
_total_params = sum(p.numel() for p in model.parameters())
- print(f"Number of model parameters: {_total_params}")
+ print(f'Number of model parameters: {_total_params}')
# inference loop
- print("Starting inference...")
+ print('Starting inference...')
if prompt is not None:
input_sentence = prompt
else:
- print("Waiting for user input... (prompt to complete)")
- input_sentence = input("User: ")
+ print('Waiting for user input... (prompt to complete)')
+ input_sentence = input('User: ')
# tokenize input
inp = torch.tensor(tok.encode(input_sentence, bos=True, eos=False),
@@ -183,9 +183,9 @@ def main(model_path: str = 'model.pth',
seq_len = inp.shape[-1]
print(f"Model output: {' '.join(tok.decode(inp.tolist()))}")
- print(f"Tokens / second: {toks_generated / (time.time() - start_time):.2f}")
+ print(f'Tokens / second: {toks_generated / (time.time() - start_time):.2f}')
- print("Finished inference!")
+ print('Finished inference!')
if __name__=='__main__':
diff --git a/optimus/models/optimus.py b/optimus/models/optimus.py
index fc11a93..d478e3f 100644
--- a/optimus/models/optimus.py
+++ b/optimus/models/optimus.py
@@ -67,12 +67,12 @@ class OptimusConfig():
intermediate_size=4352,
num_hidden_layers=48,
num_attention_heads=40,
- hidden_act="silu",
+ hidden_act='silu',
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
tie_word_embeddings=False,
- attn_implementation="sdpa",
+ attn_implementation='sdpa',
attention_bias=False,
attention_dropout=0.0,
gradient_checkpointing=True,
@@ -152,7 +152,7 @@ class OptimusFeedForward(nn.Module):
if config.hidden_act == 'silu' or config.hidden_act == 'swish':
self.act_fn = F.silu
else:
- raise KeyError(f"Currently only silu and swish are supported as activation functions, but got {config.hidden_act}")
+ raise KeyError(f'Currently only silu and swish are supported as activation functions, but got {config.hidden_act}')
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
@@ -266,8 +266,8 @@ class OptimusSdpaAttention(OptimusAttention):
OPTIMUS_ATTENTION_CLASSES = {
- "eager": OptimusAttention,
- "sdpa": OptimusSdpaAttention,
+ 'eager': OptimusAttention,
+ 'sdpa': OptimusSdpaAttention,
}
diff --git a/training.py b/training.py
index 897a285..dc56920 100644
--- a/training.py
+++ b/training.py
@@ -68,8 +68,8 @@ def main(batch_size: int = 8,
train_ds = WikiText103Dataset(split='train')
test_ds = WikiText103Dataset(split='test')
- print(f"Number of examples in training set: {len(train_ds)}")
- print(f"Number of examples in testing set: {len(test_ds)}")
+ print(f'Number of examples in training set: {len(train_ds)}')
+ print(f'Number of examples in testing set: {len(test_ds)}')
# create dataloader object and move to device
dl = OptimusDataLoader(train_ds, test_ds, tok,
@@ -88,7 +88,7 @@ def main(batch_size: int = 8,
model = model.to('cuda')
_total_params = sum(p.numel() for p in model.parameters())
- print(f"Number of model parameters: {_total_params}")
+ print(f'Number of model parameters: {_total_params}')
# define loss metric
criterion = nn.CrossEntropyLoss()
@@ -99,7 +99,7 @@ def main(batch_size: int = 8,
# [1]: https://github.com/pytorch/pytorch/issues/26218
optimizer = torch.optim.Adam(model.parameters(), betas=(0.9, 0.999), eps=1e-7)
- print("Starting training...")
+ print('Starting training...')
# create trainer and start fitting
trainer = Trainer(dl=dl,
@@ -114,8 +114,8 @@ def main(batch_size: int = 8,
progress_bar=True)
trainer.fit(epochs)
- print(f"Finished training! Best model weights saved at '{checkpoints_path}'")
+ print(f'Finished training! Best model weights saved at '{checkpoints_path}'')
-if __name__=="__main__":
+if __name__=='__main__':
fire.Fire(main)
--
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