fix: skip prev_logprobs computation when force_on_policy_ratio is true#2177
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fix: skip prev_logprobs computation when force_on_policy_ratio is true#2177yfw wants to merge 1 commit into
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When force_on_policy_ratio=True, the importance sampling ratio is forced to 1.0, so prev_logprobs are unnecessary. Skip the expensive prepare_for_lp_inference() and get_logprobs() calls in both sync and async GRPO paths. In the loss function, use curr_logprobs.detach() as prev_logprobs instead of loading placeholder zeros from data. Also guards against incompatible use of seq_logprob_error_threshold with force_on_policy_ratio (the threshold requires real prev_logprobs). Part of #1906 Co-Authored-By: Jiaqi Zeng <jiaqiz@nvidia.com> Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Signed-off-by: Yi-Fu Wu <yifu.wu@gmail.com>
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…ratio Adds two parametrized unit tests in tests/unit/algorithms/test_grpo.py that cover both grpo_train and async_grpo_train: - test_grpo_train_skips_reference_policy_logprobs_when_configured: guards issue NVIDIA-NeMo#1968 / PRs NVIDIA-NeMo#2174, NVIDIA-NeMo#2178 by asserting that policy.get_reference_policy_logprobs is never called when grpo.skip_reference_policy_logprobs_calculation=True. - test_grpo_train_skips_prev_logprobs_when_force_on_policy_ratio: guards PR NVIDIA-NeMo#2177 by asserting that policy.get_logprobs is never called when loss_fn.force_on_policy_ratio=True. Both tests reuse the existing mock_grpo_components fixture and the mock_async_grpo_infrastructure helper so they require no GPU / Ray cluster and run in CI in milliseconds (modulo cold-start import cost). Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: Linglin Jing <linglinj@nvidia.com>
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The two regression tests added in this PR drive `grpo_train` / `async_grpo_train` through code paths that call `torch.zeros_like(prev_logprobs)` (PRs NVIDIA-NeMo#2174 / NVIDIA-NeMo#2178) and `torch.zeros_like(generation_logprobs)` (PR NVIDIA-NeMo#2177). Under the bare `mock_grpo_components` fixture those inputs are `MagicMock` objects, so CI failed with `TypeError: zeros_like(): argument 'input' (position 1) must be Tensor, not MagicMock` at `nemo_rl/algorithms/grpo.py:1801`. Add a `_patched_logprob_phase` context manager that swaps in real tensors for `policy.get_logprobs`, `policy.get_reference_policy_logprobs`, and `batched_message_log_to_flat_message`, and use it in both the sync and async branches of the two new tests. Signed-off-by: Linglin Jing <linglinj@nvidia.com> Co-authored-by: Cursor <cursoragent@cursor.com>
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closes since included in #2443 |
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When force_on_policy_ratio=True, the importance sampling ratio is forced to 1.0, so prev_logprobs are unnecessary. Skip the expensive prepare_for_lp_inference() and get_logprobs() calls in both sync and async GRPO paths. In the loss function, use curr_logprobs.detach() as prev_logprobs instead of loading placeholder zeros from data.
Also guards against incompatible use of seq_logprob_error_threshold with force_on_policy_ratio (the threshold requires real prev_logprobs).
Part of #1906
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