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match pytoch imeplentation for loss calculation
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@ -381,9 +381,7 @@ def grpo_loss(
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# Average over tokens
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sequence_sums = per_token_loss.sum(axis=1)
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sequence_lengths = length_mask.sum(axis=1)
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loss = (sequence_sums / sequence_lengths).mean()
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loss = (per_token_loss * length_mask).sum() / length_mask.sum() # Matches the pytorch implementaiton
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# Calculate mean KL divergence for metrics
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mean_kl = ((kl_div * length_mask).sum(axis=1) / length_mask.sum(axis=1)).mean()
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@ -454,7 +452,7 @@ def grpo_loss(
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mx.metal.clear_cache()
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return loss, sequence_lengths.sum(), metrics
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return loss, length_mask.sum(axis=1).sum(), metrics
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def iterate_grpo_batches(dataset, batch_size, max_seq_length, train=False):
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