Add worker and policy hooks for cross-tokenizer distillation flow#2
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Add worker and policy hooks for cross-tokenizer distillation flow#2avenkateshha wants to merge 1 commit into
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Extend LMPolicy and DTensorPolicyWorkerV2 with teacher-forward and cross-tokenizer state update paths while preserving the current worker architecture and IPC-based distillation flow. Made-with: Cursor
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PR NVIDIA-NeMo#2508 review (@RayenTian): - #2: Fold data["sample_mask"] into the gold-loss path's valid-chunk mask (chunk_mask & sample_mask.bool().unsqueeze(-1)) so samples with loss_multiplier=0 stop contributing to KL-on-common, L1-on-uncommon, top-1 accuracy, and the returned valid-count. Mirrors the P-KL path. - #3: Size both projection-matrix axes from the configured tokenizer vocabs (student + teacher), not max(observed_idx) + 1. CrossTokenizerDistillationLossConfig declares student_vocab_size and teacher_vocab_size; xtoken_distillation.setup() injects both at runtime from len(student_tokenizer) / len(teacher_tokenizer). get_sparse_projection_matrix now takes both as keyword-only args and clamps V_s / V_t up against the projection's observed maxima as a defensive fallback. Same-magnitude-int positional swap is guarded by the keyword-only signature. Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>
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PR NVIDIA-NeMo#2508 review (@RayenTian): - #2: Fold data["sample_mask"] into the gold-loss path's valid-chunk mask (chunk_mask & sample_mask.bool().unsqueeze(-1)) so samples with loss_multiplier=0 stop contributing to KL-on-common, L1-on-uncommon, top-1 accuracy, and the returned valid-count. Mirrors the P-KL path. - #3: Size both projection-matrix axes from the configured tokenizer vocabs (student + teacher), not max(observed_idx) + 1. CrossTokenizerDistillationLossConfig declares student_vocab_size and teacher_vocab_size; xtoken_distillation.setup() injects both at runtime from len(student_tokenizer) / len(teacher_tokenizer). get_sparse_projection_matrix now takes both as keyword-only args and clamps V_s / V_t up against the projection's observed maxima as a defensive fallback. Same-magnitude-int positional swap is guarded by the keyword-only signature. Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>
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PR NVIDIA-NeMo#2508 review (@RayenTian): - #2: Fold data["sample_mask"] into the gold-loss path's valid-chunk mask (chunk_mask & sample_mask.bool().unsqueeze(-1)) so samples with loss_multiplier=0 stop contributing to KL-on-common, L1-on-uncommon, top-1 accuracy, and the returned valid-count. Mirrors the P-KL path. - #3: Size both projection-matrix axes from the configured tokenizer vocabs (student + teacher), not max(observed_idx) + 1. CrossTokenizerDistillationLossConfig declares student_vocab_size and teacher_vocab_size; xtoken_distillation.setup() injects both at runtime from len(student_tokenizer) / len(teacher_tokenizer). get_sparse_projection_matrix now takes both as keyword-only args and clamps V_s / V_t up against the projection's observed maxima as a defensive fallback. Same-magnitude-int positional swap is guarded by the keyword-only signature. Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>
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PR NVIDIA-NeMo#2508 review (@RayenTian): - #2: Fold data["sample_mask"] into the gold-loss path's valid-chunk mask (chunk_mask & sample_mask.bool().unsqueeze(-1)) so samples with loss_multiplier=0 stop contributing to KL-on-common, L1-on-uncommon, top-1 accuracy, and the returned valid-count. Mirrors the P-KL path. - #3: Size both projection-matrix axes from the configured tokenizer vocabs (student + teacher), not max(observed_idx) + 1. CrossTokenizerDistillationLossConfig declares student_vocab_size and teacher_vocab_size; xtoken_distillation.setup() injects both at runtime from len(student_tokenizer) / len(teacher_tokenizer). get_sparse_projection_matrix now takes both as keyword-only args and clamps V_s / V_t up against the projection's observed maxima as a defensive fallback. Same-magnitude-int positional swap is guarded by the keyword-only signature. Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>
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Extend LMPolicy and DTensorPolicyWorkerV2 with teacher-forward and cross-tokenizer state update paths while preserving the current worker architecture and IPC-based distillation flow.
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