Complete TinyInfiniTrain homework (EnzoDing-rgb) - #12
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Implement dispatcher Call/Register, Neg autograd, Matmul/Adam CPU+CUDA, Tensor Flatten/Backward, Shakespeare dataset and tokenizer; replace std::format with JoinDot for g++11 compatibility. Co-authored-by: Cursor <cursoragent@cursor.com>
…tics) The dataset previously used stride-1 overlapping windows (sample idx offset by one token), so every training batch saw tokens[idx : idx+seq_len]. Tiny Shakespeare data is produced by karpathy/llm.c's tinyshakespeare.py, which uses non-overlapping windows: sample idx covers tokens[idx*seq_len : (idx+1)*seq_len]. This made the GPT-2 training data differ from the reference run that generated gpt2_logits_reference.bin, so LogitsConsistency failed by a large margin (logits off by 10-270). With the correct windowing the test passes on CUDA within the 1e-3 tolerance. - num_samples = num_toks / sequence_length (was num_toks - sequence_length) - sequence_size_in_bytes_ = sequence_length * sizeof(int64_t) (was sizeof(int64_t)) Co-Authored-By: Claude <noreply@anthropic.com>
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Summary
std::formatwithJoinDotfor g++11 compatibility.Test plan
test_dispatcher/test_elementwise/test_matmul/test_matmul_cuda/test_adam/test_adam_cuda/test_tensortest_gpt2LogitsConsistencyMade with Cursor