aider
aider copied to clipboard
Published
20 hours ago •
paul-gauthier
paul-gauthier
Failed to apply edit to a python script when it writes one code markdown block maybe with two diffs inside of it?
What happened:
The script is slow because it's doing a lot of work. It's reading a large CSV file, transforming the data, creating a DataLoader, initializing a neural network and an optimizer, and then training the network over 100 epochs. Each of
these steps can take a significant amount of time, especially if the dataset is large.
The script uses the tqdm library to display progress bars, but these only update once per epoch during training, and once per row during data transformation. If the dataset is large and the transformations are complex, it can take a
long time to see any progress.
To make the script more responsive, we can add more frequent progress updates. For example, we can update the progress bar after each mini-batch during training, and after each row during data transformation.
Here are the changes I propose:
train.py
<<<<<<< ORIGINAL
for i in tqdm(range(len(self.data['exp'])), desc="Transforming data", leave=True):
self.data.at[i, 'exp'] = self.le.fit_transform([self.data.at[i, 'exp']])[0]
self.data['exp'] = self.ohe.fit_transform(self.data['exp'].values.reshape(-1, 1)).toarray()
=======
for i in range(len(self.data['exp'])):
self.data.at[i, 'exp'] = self.le.fit_transform([self.data.at[i, 'exp']])[0]
if i % 1000 == 0: # update progress every 1000 rows
print(f"Transformed {i} rows")
self.data['exp'] = self.ohe.fit_transform(self.data['exp'].values.reshape(-1, 1)).toarray()
>>>>>>> UPDATED
train.py
<<<<<<< ORIGINAL
for epoch in tqdm(range(100), desc="Training", leave=True): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(tqdm(train_loader, desc="Batch", leave=False), 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = nn.CrossEntropyLoss()(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
=======
for epoch in range(100): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = nn.CrossEntropyLoss()(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 200 == 199: # print every 200 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 200))
running_loss = 0.0
print(f"Finished epoch {epoch}")
>>>>>>> UPDATED
These changes will make the script print progress updates more frequently, which should make it feel more responsive. However, they won't make the script run any faster. If you want to speed up the script, you'll need to optimize the
data transformations or the training process.
Failed to apply edit to train.py
Applied edit to train.py
Maybe it is not a problem of two diffs, because I asked GPT to repeat it again but it is not applied.
#### Can you repeat the first diff because it hasnt applied to the script
Sure, here is the first diff again:
```python
train.py
<<<<<<< ORIGINAL
for i in tqdm(range(len(self.data['exp'])), desc="Transforming data", leave=True):
self.data.at[i, 'exp'] = self.le.fit_transform([self.data.at[i, 'exp']])[0]
self.data['exp'] = self.ohe.fit_transform(self.data['exp'].values.reshape(-1, 1)).toarray()
=======
for i in range(len(self.data['exp'])):
self.data.at[i, 'exp'] = self.le.fit_transform([self.data.at[i, 'exp']])[0]
if i % 1000 == 0: # update progress every 1000 rows
print(f"Transformed {i} rows")
self.data['exp'] = self.ohe.fit_transform(self.data['exp'].values.reshape(-1, 1)).toarray()
>>>>>>> UPDATED
This change removes the tqdm progress bar from the data transformation loop and adds a print statement that updates every 1000 rows. This should make the script print progress updates more frequently during data transformation.
Failed to apply edit to train.py
Oh, I tried to find the original part in the script but it doesn't exists. maybe aider does not update the context when GPT need it.
Ya, there shouldn't be any problem with multiple edits in the same reply. That's supported and happens all the time.
If you can share the train.py I might be able to offer some ideas about what happened. GPT's responses look perfectly sensible to me.
Any chance you were editing train.py at the same time GPT was trying to make changes to it?
Nope, the version of the train.py is too new to backup it for me. But I didn't edit when GPT-4 is applying edit. Sorry that I can't give the script.
Ok. I really recommend using git with aider so you have a record of all the changes. And the ability to undo.
I'll close this ticket for now. Please feel free to reopen it if you see this happen again with a known file state.
I think I'm getting this problem: multiple edits put together incorrectly into on diff which then cannot be applied.
src/utils.mjs
<<<<<<< ORIGINAL
function saveSummary(libraryName, summaryData) {
const outputPath = path.join(process.cwd(), 'summaries', `${libraryName}.json`);
fs.writeFileSync(outputPath, JSON.stringify(summaryData, null, 2));
console.log(`Saved summary for ${libraryName} to ${outputPath}`);
}
function saveIndex(libraryName, indexData) {
const outputPath = path.join(process.cwd(), 'indexes', `${libraryName}.json`);
fs.writeFileSync(outputPath, JSON.stringify(indexData, null, 2));
console.log(`Saved index for ${libraryName} to ${outputPath}`);
}
=======
function saveSummary(libraryName, summaryData) {
const dirPath = path.join(process.cwd(), 'summaries');
fs.mkdirSync(dirPath, { recursive: true });
const outputPath = path.join(dirPath, `${libraryName}.json`);
fs.writeFileSync(outputPath, JSON.stringify(summaryData, null, 2));
console.log(`Saved summary for ${libraryName} to ${outputPath}`);
}
function saveIndex(libraryName, indexData) {
const dirPath = path.join(process.cwd(), 'indexes');
fs.mkdirSync(dirPath, { recursive: true });
const outputPath = path.join(dirPath, `${libraryName}.json`);
fs.writeFileSync(outputPath, JSON.stringify(indexData, null, 2));
console.log(`Saved index for ${libraryName} to ${outputPath}`);
}
>>>>>>> UPDATED
This change will make the saveSummary and saveIndex functions create the directories if they do not exist before trying to write
the files.
Failed to apply edit to src/utils.mjs
My functions all have JSDoc headers, so this diff cannot be applied:
/**
* Saves the summarized documentation to a specified directory.
*
* @param {string} libraryName - Name of the library.
* @param {Object} summaryData - Summarized documentation data.
*/
function saveSummary(libraryName, summaryData) {
const outputPath = path.join(process.cwd(), 'summaries', `${libraryName}.json`); // Adjust directory structure as needed
fs.writeFileSync(outputPath, JSON.stringify(summaryData, null, 2));
console.log(`Saved summary for ${libraryName} to ${outputPath}`);
}
/**
* Saves the detailed documentation index to a specified directory.
*
* @param {string} libraryName - Name of the library.
* @param {Object} indexData - Detailed documentation data.
*/
function saveIndex(libraryName, indexData) {
const outputPath = path.join(process.cwd(), 'indexes', `${libraryName}.json`); // Adjust directory structure as needed
fs.writeFileSync(outputPath, JSON.stringify(indexData, null, 2));
console.log(`Saved index for ${libraryName} to ${outputPath}`);
}
