added missing valid-data line - #9
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Data processing patch
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* Update config.yml * Update config.yml --------- Co-authored-by: Lakshmisha Prabhu <lprabhu@habana.ai>
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Signed-off-by: ashors1 <ashors@nvidia.com>
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…xiv:2503.05139 (NVIDIA#9) * Add NormHead as descrived in arxiv:2503.05139 * Add Stochastic Routing Warmup introduced in arxiv:2503.05139
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* Remove redundant logits calculations in gpt_model * Merge the linear-cross-entropy-fusion flag and the cross-entropy-fusion flag
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* Remove redundant logits calculations in gpt_model * Merge the linear-cross-entropy-fusion flag and the cross-entropy-fusion flag
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handle fp8_tensor _data is None situation
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Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Yueming Yuan <yym022502@gmail.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Add THD (packed sequence) support for Qwen3.5-VL decoder
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: Yan Xu <yxu1@nvidia.com>
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: Yan Xu <yxu1@nvidia.com>
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Fix logging issues from failed rollouts
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Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: Yan Xu <yxu1@nvidia.com>
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Aug 17, 2026
Resolves all eleven comments on the PR thread: * Rename CheckpointManager → CheckpointWithoutOutputManager and update the docstring; the class strictly manages CheckpointWithoutOutput instances, so the new name avoids the broader "checkpoint" overloading. Updates all importers and tests. (#1) * Document why subtracting the per-row max in SinkhornKnopp.forward is benign — Sinkhorn's first row-normalization cancels any per-row scalar, so the shifted and unshifted exp produce the same fixed point and gradient. (NVIDIA#2) * Use NotImplementedError for the mhc + fine_grained_activation_offloading block — it's a known unimplemented interaction, not a config error. (NVIDIA#3) * Drop the new __call__ override and backward_dw_cudagraph from base TransformerLayer; the mHC kwarg extraction now lives on HyperConnectionTransformerLayer.__call__, with _mhc_recompute_manager initialized in __init__ so forward() reads it directly without a getattr fallback. cuda_graphs.py reads is_decode_only() directly, so dropping the dynamic_inference_decode_only injection is safe. (NVIDIA#4, NVIDIA#5, NVIDIA#10) * Rename the FineGrainedActivationOffloadingInterface alias off_interface → offload_interface in transformer_layer.py for clarity. (NVIDIA#6) * Extract a _run_mlp helper on TransformerLayer that owns the MLP-call branching (recompute / chunked-prefill / fp8-fp4 / plain-mlp); both base and HC _forward_mlp call it, eliminating the previous ~80-line duplication. The MoE-cudagraph early-return remains in base _forward_mlp after the helper call (HC is guarded against MoE). (NVIDIA#8) * Raise NotImplementedError at HyperConnectionTransformerLayer.__init__ when is_moe_layer is True and point users at HyperConnectionHybridLayer; drop the dead MoE branch in _get_submodules_under_cudagraphs. (NVIDIA#9) * No code change for the MoE composition / extensibility comment (NVIDIA#7) — see the PR thread reply for the rationale. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Signed-off-by: Yan Xu <yxu1@nvidia.com>
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