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# The repackage_hidden(h) function is designed to detach the hidden states from their history in a
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# Recurrent Neural Network (RNN) or any of its variants like LSTM or GRU. This is necessary when
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# training RNNs to prevent the backpropagation through time (BPTT)
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# from going back to the very start of the sequence, which can lead to computational inefficiency
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# and the vanishing or exploding gradient problem.
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defrepackage_hidden(h):
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"""Wraps hidden states in new Tensors, to detach them from their history."""
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@@ -136,14 +142,47 @@ def repackage_hidden(h):
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# done along the batch dimension (i.e. dimension 1), since that was handled
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# by the batchify function. The chunks are along dimension 0, corresponding
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# to the seq_len dimension in the LSTM.
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# The get_batch function and BPTT (Backpropagation Through Time) work together to train RNNs on sequential data.
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# BPTT:
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# - BPTT is a technique for training RNNs where we unroll the network through time and apply backpropagation.
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# - It allows the model to learn from sequences of data by considering both current and past inputs in its predictions.
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# get_batch Function:
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# - This function prepares data for training by subdividing the source data into manageable chunks based on the bptt parameter.
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# - The bptt parameter represents the sequence length for each chunk, essentially defining how far back in time the model should learn dependencies.
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# - The example with a bptt-limit of 2 creates two variables, each containing a segment of the sequence to be processed by the RNN.
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# Relationship:
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# - The chunks created by get_batch are fed into the RNN model sequentially. Each chunk represents a timestep in the unrolled RNN for the BPTT process.
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# - During the forward pass, the RNN processes these chunks, maintaining hidden states that carry information from previous chunks (previous timesteps).
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# - In the backward pass, gradients are computed and propagated back through these unrolled timesteps, allowing the model to learn from errors at each timestep.
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# - The subdivision of data into chunks along dimension 0 (seq_len) and not along the batch dimension is crucial.
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# - It ensures that dependencies across timesteps (within each chunk) are preserved and learned, aligning with the sequential nature of RNNs and the essence of BPTT.
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# - By training on these chunks, the model learns to predict the next element in the sequence, considering the specified sequence length (bptt), which helps in capturing short-term dependencies within that range.
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# In summary, get_batch prepares data in a format that supports BPTT training by creating sequences of specified lengths. BPTT utilizes these sequences to train the RNN, allowing it to learn temporal dependencies within the data.
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defget_batch(source, i):
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seq_len=min(args.bptt, len(source) -1-i)
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data=source[i:i+seq_len]
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target=source[i+1:i+1+seq_len].view(-1)
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returndata, target
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-
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# model.eval(): Switches to evaluation mode, affecting dropout/batch normalization.
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# hidden = model.init_hidden(eval_batch_size): Initializes hidden state for non-Transformer models.
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# with torch.no_grad(): Disables gradient computation to save memory during evaluation.
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# for i in range(..., args.bptt): Iterates over data in chunks, stepping by bptt (backpropagation through time length).
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# data, targets = get_batch(data_source, i): Retrieves a batch and its corresponding targets.
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# if args.model == 'Transformer': Checks if the model is a Transformer to handle evaluation accordingly.
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# output = model(data): Gets the model's output for the current data batch.
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# output = output.view(-1, ntokens): Reshapes Transformer output to match expected dimensions.
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# output, hidden = model(data, hidden): For RNNs, gets output and updates hidden state.
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# hidden = repackage_hidden(hidden): Detaches hidden state from the graph to prevent memory buildup.
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# total_loss += len(data) * criterion(output, targets).item(): Adds scaled loss to total loss.
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# return total_loss / (len(data_source) - 1): Calculates and returns average loss per batch.
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defevaluate(data_source):
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# Turn on evaluation mode which disables dropout.
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model.eval()
@@ -164,6 +203,18 @@ def evaluate(data_source):
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returntotal_loss/ (len(data_source) -1)
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# model.train(): Switches to training mode, enabling dropout.
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# hidden = model.init_hidden(args.batch_size): Initializes hidden state for each batch in non-Transformer models.
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# for batch, i in enumerate(..., args.bptt): Iterates through the dataset in chunks defined by bptt.
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# model.zero_grad(): Clears old gradients; necessary before a new backward pass.
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# if args.model == 'Transformer': Adjusts processing for Transformer model.
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# output, hidden = model(data, hidden): Gets output and updates hidden state for RNNs.
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# loss = criterion(output, targets): Calculates loss between model output and actual targets.
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