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Fix Torch crash caused by pinning on CPU

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This commit is contained in:
Luke Stanley 2025-10-21 19:43:38 +00:00
parent 2e938530ce
commit defd1246aa
2 changed files with 4 additions and 2 deletions
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3
nanochat/dataloader.py
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@ -38,7 +38,8 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
batch_index += 1 batch_index += 1
# Move tokens from the deque into the scratch buffer # Move tokens from the deque into the scratch buffer
tokens = [token_buffer.popleft() for _ in range(needed_tokens)] tokens = [token_buffer.popleft() for _ in range(needed_tokens)]
scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=True) # CUDA supports memory pinning for faster transfers between CPU and GPU:
scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=(device == "cuda"))
# Create the inputs/targets as 1D tensors # Create the inputs/targets as 1D tensors
inputs_cpu = scratch[:-1].to(dtype=torch.int32) inputs_cpu = scratch[:-1].to(dtype=torch.int32)
targets_cpu = scratch[1:] targets_cpu = scratch[1:]

3
scripts/mid_train.py
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@ -119,7 +119,8 @@ def mid_data_generator(split):
assert dataset_size > 0 assert dataset_size > 0
needed_tokens = device_batch_size * max_seq_len + 1 # to form one training batch of inputs,targets needed_tokens = device_batch_size * max_seq_len + 1 # to form one training batch of inputs,targets
token_buffer = deque() token_buffer = deque()
scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=True) # CUDA supports memory pinning for faster transfers between CPU and GPU:
scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=(device_type == "cuda"))
cursor = ddp_rank # increments by ddp_world_size each time, so each rank processes unique documents cursor = ddp_rank # increments by ddp_world_size each time, so each rank processes unique documents
it = 0 # iteration counter it = 0 # iteration counter
while True: while True: