Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@modelopt/torch/puzzletron/bypass_distillation/bypass_checkpoint_utils.py`:
- Around line 45-58: The fallback currently only sorts checkpoint directories by
iteration (get_iter_num) so when multiple checkpoints exist for the same iter we
may pick an older step; update the selection to parse both iteration and step
and sort by (iter, step) descending. Modify get_iter_num (or replace with
get_iter_and_step) to extract iter via r"iter-(\d+)" and step via r"step-(\d+)"
(defaulting to 0 when not present), return a tuple of two ints, and use that
tuple as the sort key for checkpoint_dirs before iterating to find the first
directory with "saving_completed" and returning str(latest_dir).

In `@modelopt/torch/puzzletron/bypass_distillation/training_loop.py`:
- Around line 673-677: Replace the hardcoded trust_remote_code=True in the
AutoTokenizer.from_pretrained call with the same caller-configurable
trust_remote_code flag you already read from the descriptor earlier (the
variable used for model config loading at lines ~597/631); specifically update
the tokenizer = AutoTokenizer.from_pretrained(...) invocation that uses
cfg.teacher_dir so it passes the descriptor-derived trust_remote_code value
instead of True, ensuring the flag remains configurable and defaults to False.

In `@modelopt/torch/puzzletron/nas/plugins/puzzletron_nas_plugin.py`:
- Around line 146-149: The pre-checks that treat presence of files like
(teacher_dir / "config.json"), any rank_*.pth, files under
pruned_ckpts_output_dir, or library outputs as sufficient to skip stages are
unsafe; change these guards to rely on durable completion markers (e.g., a .done
or .complete file) created at the successful end of
conversion/scoring/pruning/library build instead of existence-only checks, so
functions like the conversion branch around teacher_dir/config.json, the rank_*
checkpoint checks, and the pruned_ckpts_output_dir/library checks only skip when
their corresponding completion marker exists; ensure launch_score_activations()
remains the stricter gate for pruning-activation scoring but remove or weaken
the naive existence checks noted at the conversion lines (the block using
teacher_dir/config.json) and the other mentioned blocks (191-193, 286-289) to
check for the specific "<stage>.complete" marker before skipping.

In `@modelopt/torch/puzzletron/sewing_kit/utils.py`:
- Around line 452-454: The normalization denominator is computed as
F.mse_loss(target, torch.zeros_like(target) + epsilon, ...) which shifts the
target by epsilon and biases the scale; instead compute the denominator as
F.mse_loss(target, torch.zeros_like(target), reduction=reduction) + epsilon (or
clamp_min the denominator to epsilon) so you add epsilon to the final scalar
denominator instead of to the zero tensor; update the occurrences around the
loss assignment (loss, input, target, epsilon, F.mse_loss) and the similar block
at lines 479-482 accordingly.

In `@modelopt/torch/puzzletron/tools/checkpoint_utils_hf.py`:
- Around line 380-396: The auto_map parsing in checkpoint_utils_hf.py
incorrectly assumes each model_config.auto_map value is a dotted string; update
the logic that builds module_files (and any usage of class_ref) to first
normalize each value by: if it's a list/tuple take the first element, if it
contains a repo qualifier split off the "repo_id--" prefix, then take the module
part before the first '.' and append ".py" (so "tokenization_my.py"); apply this
normalization where module_files is created and when iterating filenames so
lists/tuples and repo-qualified references are handled and the correct source
filenames are copied.

In `@modelopt/torch/puzzletron/utils/data/dataloaders.py`:
- Around line 89-90: The DataLoader factory allows num_workers>0 while
ConstantLengthDataset.__iter__ does not shard via get_worker_info(), causing
duplicate samples; update the dataloaders (the function returning DataLoader in
modelopt/torch/puzzletron/utils/data/dataloaders.py) to either reject or
override num_workers>0 (e.g., force num_workers=0 or raise an error) until
ConstantLengthDataset.__iter__ is updated to use
torch.utils.data.get_worker_info() for worker sharding, and apply the same guard
to the other DataLoader creation call sites noted around the 114-118 region;
reference ConstantLengthDataset.__iter__ when adding the guard so future
implementers know to remove it after adding proper worker-aware iteration.
- Around line 98-99: The call to train_data.shuffle(seed=shuffle_seed,
keep_in_memory=True) fails for streaming (Iterable) datasets because
IterableDataset.shuffle() doesn't accept keep_in_memory; update the code that
checks shuffle_seed to detect streaming datasets (e.g., via whatever marker
load_streaming_fn sets or by checking hasattr(train_data, "__iter__") vs
__len__/isinstance of IterableDataset) and branch: for non-streaming datasets
call train_data.shuffle(seed=shuffle_seed, keep_in_memory=True) as before, and
for streaming/iterable datasets call train_data.shuffle(seed=shuffle_seed)
without keep_in_memory; ensure you modify the block that references shuffle_seed
and train_data.shuffle so runtime errors are avoided when load_streaming_fn()
returns a streaming dataset.

In `@tests/gpu/torch/puzzletron/test_bypass.py`:
- Line 213: The timeout passed to dist.setup uses timedelta(10) which means 10
days; change it to an explicit unit like timedelta(seconds=10) (or
timedelta(minutes=10) if intended) to avoid 10-day test hangs — locate the call
to dist.setup (symbol: dist.setup) in tests/gpu/torch/puzzletron/test_bypass.py
and the other listed files and replace timedelta(10) with timedelta(seconds=10)
(or the correct unit) in each occurrence.

---

Outside diff comments:
In `@modelopt/torch/puzzletron/pruning/pruning_utils.py`:
- Around line 40-47: The enum MlpInitMode now includes MoEChannelPruning but
_init_mlp_module still treats that case as unsupported; update the
_init_mlp_module implementation to handle MlpInitMode.MoEChannelPruning (the
same call-site that child_init.py forwards into) by adding a branch for
MlpInitMode.MoEChannelPruning that performs the correct initialization when
expert widths change (e.g., adapt the weight/activation shapes by
slicing/reshaping or reuse the ConcatExpertsIntoDenseFFN logic where
appropriate), so the child init no longer falls through to the "Unsupported
mlp_init_mode" error for MoEChannelPruning.

In `@tests/gpu/torch/puzzletron/test_puzzletron.py`:
- Around line 236-245: The printer currently outputs only rank-local
pruning_scores causing incomplete EXPECTED_PRUNING_VALUES for multi-GPU runs;
modify the logic so rank 0 aggregates pruning data from all ranks before
printing: collect and merge per-rank pruning_scores (or load all rank_{rank}.pth
files) into a global pruning_scores for each layer_name, compute the global
score and channels (e.g., combine/average or gather channel indices across
ranks) respecting total_layers, and then have rank 0 iterate over layer_names
using the aggregated values when printing the block that uses total_layers and
prints the EXPECTED_PRUNING_VALUES snippet.

---

Nitpick comments:
In `@examples/puzzletron/main.py`:
- Around line 154-167: Update run_mip_only to compute the total steps like
run_full_puzzletron by calling _total_steps(hydra_cfg) and use that N when
formatting the progress messages instead of hardcoded "7/8" and "8/8";
specifically, replace the two mprint calls around the conditional that currently
show "Puzzletron Progress 7/8" and "8/8" with dynamic messages using N (e.g.,
f"Puzzletron Progress {current_step}/{N}: ...") and ensure current_step
increments are correct for both the sweep branch (sweep.run_mip_sweep) and the
mip branch (mip_and_realize_models.launch_mip_and_realize_model) so progress
displays consistently with _total_steps.

In `@modelopt/torch/puzzletron/bypass_distillation/stitched_model_factory.py`:
- Around line 548-556: Remove the unused computation of num_trainable_params:
delete the sum(...) assignment that iterates over
student_stitched_module.named_parameters() checking p.requires_grad and
submodule_name in p_name (and the "dummy_param" exclusion). Keep the surrounding
explanatory comment about dummy params if still relevant, but eliminate the dead
variable and its associated needless iteration to avoid wasted computation and
clarify stitched_model_factory.py.

In `@modelopt/torch/puzzletron/tools/bypassed_training/child_init.py`:
- Around line 804-806: The current override function (override) treats
item_overrides == None as "keep original", which prevents callers from
explicitly clearing a value to None via JSON/YAML; change the logic to use a
distinct sentinel (e.g., a new unique object like NO_OVERRIDE) to represent "no
override" and reserve None in item_overrides to mean "set to None"/clear the
field, updating the override function to check against the sentinel
(NO_OVERRIDE) instead of None and adjust any callers that construct overrides to
use the sentinel when they mean "leave original".

In `@modelopt/torch/puzzletron/utils/data/dataset.py`:
- Around line 123-130: The fallback that builds sample when
getattr(self.tokenizer, "chat_template", None) is None should preserve role
markers instead of joining only message["content"]; update the else branch in
dataset.py (the block that currently sets sample = "\n".join(m["content"] for m
in sample)) to join messages using a lightweight role-prefixed format like
"{role}: {content}" so conversation turns (system/user/assistant) are retained;
keep using the same sample variable and ensure this mirrors the structure
expected by downstream code that consumes apply_chat_template outputs.

In `@modelopt/torch/puzzletron/utils/parsing.py`:
- Around line 337-345: The current filtering silently drops any NaN in
losses_dict (and prunes best_steps_dict/best_values_dict to match), which hides
diverging trainable blocks; instead, update the logic around losses_dict,
best_steps_dict and best_values_dict so you only drop entries whose keys match
known skipped block types (e.g., the explicit list of no-op block names like
"Mamba"), and for any other NaN values emit a warning/error (via the existing
logger) that a trainable block produced NaN rather than removing it; ensure
best_steps_dict and best_values_dict are only pruned to match the filtered
losses_dict after this selective filtering and warning behavior.

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@modelopt/torch/puzzletron/bypass_distillation/bypass_checkpoint_utils.py`:
- Around line 113-125: The checkpoint load/save is missing GradScaler state so
when use_grad_scaling=True resumed runs lose scaler state; update the save and
load paths around the StitchedModuleDescriptor handling to persist
grad_scaler.state_dict() (e.g., save to
stitched/{stitched_module_name}.grad_scaler.pth) when grad_scaler is not None
and on load (in the blocks that currently load optimizer state and in the
similar 165-171 block) call grad_scaler.load_state_dict(...) after constructing
or retrieving the module’s grad_scaler, using map_location=device, and guard
with the use_grad_scaling flag so scaler state is restored only when applicable.

In `@modelopt/torch/puzzletron/bypass_distillation/stitched_model_factory.py`:
- Around line 597-600: Log currently prints an empty block name because
submodule_name is never reassigned; update the mprint call in
stitched_model_factory.py (the one that prints Block {submodule_name}: ...) to
use a valid identifier such as student_stitched_module_name (or module_name) so
the block name is meaningful; locate the mprint invocation and replace
submodule_name with student_stitched_module_name (ensuring
student_stitched_module_name is in scope) and keep the rest of the
message/parameter counting intact.
- Around line 417-428: The code assumes owned_block_indexes is non-empty before
calling min()/max(), which will raise ValueError if a rank owns no blocks; in
the block around min_owned_index/max_owned_index in stitched_model_factory.py,
first check if not owned_block_indexes and handle it defensively (e.g., set
prev_rank and next_rank to None or raise a clear, explanatory error) instead of
calling min()/max(); update the logic that computes prev_rank and next_rank
using model_blocks_process_ownership and all_block_indices to only run when
owned_block_indexes is non-empty so misconfiguration yields a clear message or
safe defaults.

In `@modelopt/torch/puzzletron/bypass_distillation/training_loop.py`:
- Around line 837-838: The code reads source_datasets_to_discard from cfg.bypass
root but the new config nests it under bypass.data; update the dataloader calls
that set source_datasets_to_discard (and any similar occurrences) to read from
cfg.bypass.data.get("source_datasets_to_discard", tuple()) instead of
cfg.bypass.get(...), leaving bos_rate as cfg.bypass.data.bos_rate; search for
the occurrences that set source_datasets_to_discard (the two places mentioned
around the calls that also use bos_rate) and replace them to use cfg.bypass.data
so the discard list becomes configurable.
- Around line 252-253: The parameter skip_first_batches is never applied: after
creating the batch iterator from the ConstantLengthDataset/dataloader you must
advance that iterator by skip_first_batches before entering the training loop
(e.g., consume the iterator with next(...) in a short loop or use
itertools.islice to drop the first N items); update the code paths where
skip_first_batches is accepted (the occurrences around skip_first_batches in
training_loop.py and the second occurrence at lines ~329-330) to consume the
iterator accordingly so resumed runs do not replay from batch 0.
- Around line 349-350: The loop exit condition uses a 1-based counter and
currently uses >=, causing it to stop one step too early; update the check in
the training loop that references cfg.bypass.step_num and
cfg.bypass.training.max_steps so it breaks only once step_num has passed the
budget (use > instead of >=) so the final scheduled step runs.
- Around line 103-107: The AutoConfig.from_pretrained call inside
run_bypassed_training bypasses the earlier trust_remote_code decision; update
the AutoConfig.from_pretrained invocation in training_loop.py (the block that
imports HFAutoConfig and sets teacher_hf_cfg/teacher_intermediate_size) to pass
the same trust_remote_code flag you query earlier (the
descriptor/trust_remote_code value used by run_bypassed_training) so
remote-code-required models load consistently and safely. Locate the
AutoConfig.from_pretrained usage and add the trust_remote_code argument (sourced
from the existing hydra_cfg/descriptor/trust_remote_code variable) when calling
from_pretrained, ensuring the call mirrors the trusted-remote-code decision made
elsewhere.

In `@tests/gpu/torch/puzzletron/test_puzzletron.py`:
- Around line 209-215: The test incorrectly computes per-rank FFN counts from
hidden layer count (total_layers = max(2, size)); instead compute the actual
number of prunable FFN blocks (e.g., scan the model's layer names or modules to
count FFN/prunable blocks rather than using hidden-layer count) into
total_ffn_blocks, then compute layers_this_rank = total_ffn_blocks // size + (1
if rank < total_ffn_blocks % size else 0) and assert len(layer_names) ==
layers_this_rank (allowing 0 for ranks that only own Mamba blocks); update the
variables total_layers/layers_this_rank and reference layer_names when making
this change.

---

Outside diff comments:
In `@modelopt/torch/puzzletron/tools/bypassed_training/child_init.py`:
- Around line 811-843: The dataclass_override loop special-case instantiates a
nested dataclass even when the provided override is None, causing a crash;
modify the block in dataclass_override that handles "previous_value is None and
_is_dataclass_type(item_type)" to first check if item_overrides is None and in
that case set new_value = None (or call override(previous_value,
item_overrides)), otherwise instantiate with
_get_dataclass_type(item_type)(**item_overrides); keep the subsequent
check_type(new_value, item_type) and existing symbols (override,
dataclass_override, _is_dataclass_type, _get_dataclass_type, check_type) so
optional nested dataclass overrides that are null no longer raise.

---

Nitpick comments:
In `@modelopt/torch/puzzletron/bypass_distillation/stitched_model_factory.py`:
- Around line 377-381: The current lookup for block_loss_func in
stitched_model_factory.py uses a direct dict index which raises an opaque
KeyError when cfg.model_factory.block_loss_func is invalid; replace it with a
guarded lookup: retrieve via dict.get or check membership first and raise a
ValueError with a clear message that includes the invalid value and the allowed
options (e.g., "normalized_mse_loss", "vectorwise_normalized_mse_loss",
"batched_normalized_mse_loss"); update the code around the block_loss_func
assignment (the dict and its use) so callers get a descriptive error instead of
a raw KeyError.

In `@modelopt/torch/puzzletron/tools/bypassed_training/child_init.py`:
- Around line 96-115: The loop over pruning mixins currently does
layer_out_state_dict.update(_layer_out) which allows later mixins to silently
overwrite keys from earlier ones; change this to detect overlapping keys and
fail fast: for each _mixin when you get _layer_out from prune_single_layer,
compute intersection = set(layer_out_state_dict.keys()) & set(_layer_out.keys())
and if intersection is non-empty raise a ValueError (or AssertionError) listing
the conflicting keys and the mixin identity (use _mixin or its type) instead of
updating; only call layer_out_state_dict.update(_layer_out) when intersection is
empty to ensure deterministic, non-overlapping outputs from prune_single_layer
across mixins.

In `@modelopt/torch/puzzletron/tools/kd_model.py`:
- Around line 38-39: Add a unit test in
tests/unit/torch/puzzletron/test_bypass_losses.py (e.g.,
test_kd_loss_zero_and_near_zero_target) that imports the kd loss implementation
from modelopt.torch.puzzletron.tools.kd_model, constructs both a zero target and
a near-zero target tensor, calls the code path that computes loss using the
expression containing F.mse_loss(input, target, reduction=reduction) /
(F.mse_loss(target, torch.zeros_like(target), reduction=reduction) + epsilon),
and asserts the loss is finite and behaves stably (no division-by-zero, not
NaN/Inf) for both cases; use the same input tensor for both and check that
adding the epsilon in the denominator prevents regressions.

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@examples/puzzletron/main.py`:
- Around line 99-101: The docstring parameter name is incorrect — replace the
documented `config_path` with the actual function parameter `hydra_config_path`
and update its description to match (e.g., "Path to the YAML configuration
file") so the `hydra_config_path` argument in the function signature and the
docstring are consistent; locate the docstring in examples/puzzletron/main.py
near the function that accepts `hydra_config_path` and make this single-name
correction.

---

Outside diff comments:
In `@examples/puzzletron/main.py`:
- Around line 102-135: The current flow calls dist.cleanup() after running
mtn.convert and mtn.search but if mtn.convert/mtn.search (or any subsequent
step) raises an exception the cleanup is skipped; wrap the multi-GPU pipeline
(from _setup through mtn.search/mtn.sweep/mtn.MIP calls around lines that create
PuzzletronModel, call mtn.convert and mtn.search) in a try/finally block so
dist.cleanup() always runs, and apply the same try/finally pattern to the other
runner block referenced around lines 147-163; ensure the try encompasses all
work that requires the distributed group and the finally calls dist.cleanup()
unconditionally.

---

Nitpick comments:
In `@modelopt/torch/puzzletron/bypass_distillation/stitched_model_factory.py`:
- Around line 377-381: The current lookup for block_loss_func using a dict keyed
by cfg.model_factory.block_loss_func can raise a cryptic KeyError; update the
code around block_loss_func (in stitched_model_factory.py) to explicitly
validate cfg.model_factory.block_loss_func against the allowed names
("normalized_mse_loss", "vectorwise_normalized_mse_loss",
"batched_normalized_mse_loss") and raise a clear ValueError that includes the
invalid value and the list of valid options; reference the existing functions
normalized_mse_loss, vectorwise_normalized_mse_loss, and
batched_normalized_mse_loss when constructing the mapping and error message so
users can quickly see the supported choices.