[DO NOT MERGE] trainer ft#21
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Add _pre_decoder_hooks list and register_pre_decoder_hook() method. Hooks are called between _preprocess and decoder, allowing external code to transform decoder_input without Megatron knowing specifics.
Already initialized in __init__, no need for getattr fallback.
Remove _pre_decoder_hooks list and register_pre_decoder_hook() method. Add witness_ids parameter to forward() and build_schedule_plan(). Witness logic is inline: hasattr(self, 'head_witness') check + add to decoder_input or decoder.input_tensor depending on PP stage.
- build_schedule_plan: accept witness_ids, pass to schedule plan - TransformerModelChunkSchedulePlan: store witness_ids in chunk_state - PreProcessNode.forward_impl: apply witness after _preprocess (same logic as GPTModel.forward)
- GPTModel.forward: add tail_witness after decoder, before _postprocess - build_schedule_plan: revert witness_ids param (not supported) - model_chunk_schedule_plan: revert witness_ids in chunk_state - fine_grained_callables: revert witness logic in Pre/PostProcessNode
_DataWitness.forward returns [b, s, 1] but Megatron's decoder_input is in [s, b, h] format after the embedding layer. Without transposing, broadcasting [s, b, h] + [b, s, 1] creates a [s, s, h] tensor, causing OOM (648 GiB for s=18432).
When sequence parallel is active, decoder_input and hidden_states are scattered along the sequence dimension ([s/tp, b, h]). The witness output must also be scattered to match, otherwise shapes mismatch (e.g. [18432, 1, 1] vs [9216, 1, h] with TP=2).
…ptimizer The distributed optimizer replaces optimizer param_groups with shard main params (fp32 copies). get_main_grads_for_grad_norm checks _is_witness_param on these main params, but the flag was only set on the original model params. Copy the flag when building main param groups for both float16→fp32 and fp32 paths.
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