[TRTLLM-12982][chore] relocate torch_multi_arange#15416
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Signed-off-by: ixlmar <206748156+ixlmar@users.noreply.github.com>
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PR_Github #54587 [ run ] triggered by Bot. Commit: |
Funatiq
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Jun 16, 2026
tburt-nv
approved these changes
Jun 16, 2026
ixlmar
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HuiGao-NV,
ZhanruiSunCh,
schetlur-nv,
yiqingy0 and
yuxianq
Contributor
📝 WalkthroughWalkthrough
Changestorch_multi_arange relocation and multi_item_part_lens prepare() refactor
Sequence Diagram(s)sequenceDiagram
participant encode as llm.encode()
participant model_engine as _prepare_encoder_inputs
participant cuda_runner as EncoderCUDAGraphRunner
participant metadata as FlashInferAttentionMetadata
participant plan as FlashInferAttentionMetadata.plan()
encode->>encode: compute position_ids via torch_multi_arange
encode->>model_engine: inputs (multi_item_part_lens, position_ids)
model_engine->>cuda_runner: maybe_get_cuda_graph(inputs)
cuda_runner-->>model_engine: (None, None) — fallback to eager
model_engine->>metadata: prepare(multi_item_part_lens=...)
metadata->>metadata: _process_multi_item_part_lens() → _multi_item_params
model_engine->>plan: plan(...)
plan->>plan: PlanParams(multi_item_params=_multi_item_params)
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Possibly related PRs
Suggested reviewers
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 6
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@tensorrt_llm/_torch/attention_backend/flashinfer.py`:
- Around line 744-755: The code accesses req_part_lens[0] and req_part_lens[1:]
without validating that each req_part_lens in multi_item_part_lens has the
required structure, which can cause IndexError or ValueError when constructing
tensors for malformed entries like empty lists or lists with only a prefix_len.
Before constructing the prefix_len_ptr and max_item_len_ptr tensors, add
validation to ensure each req_part_lens has at least two elements (one for
prefix_len and at least one for scored items), and raise an API-level ValueError
with a descriptive message if any request part list fails this validation.
- Around line 762-770: The zip() call combining multi_item_part_lens and
token_pos_in_items_raw_lens needs to add strict=True parameter to document that
these iterables have the same length, which resolves the B905 lint finding.
Additionally, replace the list concatenation in the innermost for loop
(req_part_lens[1:] + [token_pos_in_items_len - token_pos_in_items_raw_len]) with
iterable unpacking syntax instead to resolve the RUF005 lint finding.
In `@tensorrt_llm/_torch/utils.py`:
- Around line 574-580: The variable repeats is initialized as an alias to the
ends tensor, and when starts is None, this alias is never broken before the
in-place multiplication operation repeats *= steps.sign() on line 579. This
mutates the caller's ends tensor. Fix this by using out-of-place arithmetic for
the repeat count calculation: instead of the in-place multiplication repeats *=
steps.sign(), use repeats = repeats * steps.sign() to create a new tensor and
avoid mutating the input.
- Around line 584-602: The prev_range_ends calculation using range_ends.roll(1)
doesn't account for empty ranges where repeats == 0. When a range is empty, its
nominal end value should not be used as the previous range end for the next
range; instead, the end of the last non-empty range should be carried forward.
Modify the logic that computes prev_range_ends to propagate the previous
non-empty range's end value through empty ranges, ensuring that jumps
calculations correctly reflect transitions only between actual non-empty ranges.
- Around line 541-557: Replace the assert statements in the function that
validates dtype, shape, and device compatibility between ends, steps, and starts
parameters with explicit ValueError exceptions that include descriptive error
messages. Additionally, add validation at the function entry to ensure that all
input tensors (starts, ends, and steps) are 1-D tensors, raising ValueError if
they are not, since the implementation later uses unsqueeze and torch.cat
operations that expect 1-D inputs.
In `@tensorrt_llm/llmapi/llm.py`:
- Around line 904-932: The code does not sufficiently validate the structure of
multi_item_part_lens before constructing starts_cuda and ends_cuda, allowing
malformed inputs like [prefix_len] with no item lengths to pass through and fail
later in FlashInfer. Add validation before the torch.tensor calls that construct
starts_cuda and ends_cuda to ensure that each multi_item_part_lens in
batch_multi_item_part_lens has length greater than 1 (meaning at least one item
length in addition to the prefix length) and that all length values are
non-negative. Reject the inputs early with a clear error message if these
conditions are not met.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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📒 Files selected for processing (18)
.pre-commit-config.yamllegacy-files.txtpyproject.tomlruff-legacy.tomltensorrt_llm/_torch/attention_backend/flashinfer.pytensorrt_llm/_torch/attention_backend/interface.pytensorrt_llm/_torch/attention_backend/star_flashinfer.pytensorrt_llm/_torch/attention_backend/trtllm.pytensorrt_llm/_torch/attention_backend/vanilla.pytensorrt_llm/_torch/modules/attention.pytensorrt_llm/_torch/pyexecutor/cuda_graph_runner.pytensorrt_llm/_torch/pyexecutor/model_engine.pytensorrt_llm/_torch/pyexecutor/sampler.pytensorrt_llm/_torch/pyexecutor/sampling_utils.pytensorrt_llm/_torch/utils.pytensorrt_llm/llmapi/llm.pytests/integration/test_lists/test-db/l0_a10.ymltests/unittest/_torch/test_torch_multi_arange.py
💤 Files with no reviewable changes (2)
- tensorrt_llm/_torch/pyexecutor/sampling_utils.py
- tensorrt_llm/_torch/modules/attention.py
Comment on lines
+744
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+755
| prefix_len_ptr = torch.tensor( | ||
| [req_part_lens[0] for req_part_lens in multi_item_part_lens], | ||
| pin_memory=prefer_pinned(), | ||
| dtype=torch.uint32, | ||
| ).to(device=device, non_blocking=True) | ||
| token_pos_in_items_raw_lens = [ # 'raw' lengths before padding | ||
| sum(req_part_lens[1:]) + len(req_part_lens) | ||
| for req_part_lens in multi_item_part_lens | ||
| ] | ||
| token_pos_in_items_len = max(token_pos_in_items_raw_lens) | ||
| max_item_len_ptr = torch.tensor( | ||
| [max(req_part_lens[1:]) for req_part_lens in multi_item_part_lens], |
Contributor
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Validate each request’s part list before indexing it.
req_part_lens[0] and max(req_part_lens[1:]) will raise IndexError/ValueError for malformed entries such as [] or [prefix_len], instead of the API-level ValueError used by the surrounding validation. Please reject entries with fewer than one scored item before constructing tensors.
Proposed validation
if len(multi_item_part_lens) != self.num_contexts:
raise ValueError(
"\"multi_item_part_lens\" needs to be provided for all requests."
)
+ if any(len(req_part_lens) < 2 for req_part_lens in multi_item_part_lens):
+ raise ValueError(
+ "\"multi_item_part_lens\" entries must contain a prefix length "
+ "followed by at least one item length."
+ )
prefix_len_ptr = torch.tensor(🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/_torch/attention_backend/flashinfer.py` around lines 744 - 755,
The code accesses req_part_lens[0] and req_part_lens[1:] without validating that
each req_part_lens in multi_item_part_lens has the required structure, which can
cause IndexError or ValueError when constructing tensors for malformed entries
like empty lists or lists with only a prefix_len. Before constructing the
prefix_len_ptr and max_item_len_ptr tensors, add validation to ensure each
req_part_lens has at least two elements (one for prefix_len and at least one for
scored items), and raise an API-level ValueError with a descriptive message if
any request part list fails this validation.
Comment on lines
+762
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+770
| range_ends = torch.tensor( | ||
| [ | ||
| item_len + 1 | ||
| for req_part_lens, token_pos_in_items_raw_len in zip( | ||
| multi_item_part_lens, token_pos_in_items_raw_lens) | ||
| for item_len in ( | ||
| req_part_lens[1:] + | ||
| [token_pos_in_items_len - token_pos_in_items_raw_len]) | ||
| ], |
Contributor
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🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
ruff check tensorrt_llm/_torch/attention_backend/flashinfer.py --select B905,RUF005Repository: NVIDIA/TensorRT-LLM
Length of output: 4828
🏁 Script executed:
#!/bin/bash
# Read the file to see the exact code context and verify the proposed fix
sed -n '760,775p' tensorrt_llm/_torch/attention_backend/flashinfer.py | cat -nRepository: NVIDIA/TensorRT-LLM
Length of output: 955
Fix the Ruff B905/RUF005 findings in the range construction.
The two iterables passed to zip() are derived together, so strict=True documents that invariant and satisfies B905. Replace the list concatenation with iterable unpacking to resolve RUF005.
Proposed lint fix
range_ends = torch.tensor(
[
item_len + 1
for req_part_lens, token_pos_in_items_raw_len in zip(
- multi_item_part_lens, token_pos_in_items_raw_lens)
+ multi_item_part_lens, token_pos_in_items_raw_lens, strict=True)
for item_len in (
- req_part_lens[1:] +
- [token_pos_in_items_len - token_pos_in_items_raw_len])
+ *req_part_lens[1:],
+ token_pos_in_items_len - token_pos_in_items_raw_len,
+ )
],🧰 Tools
🪛 Ruff (0.15.17)
[warning] 765-766: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
[warning] 768-769: Consider iterable unpacking instead of concatenation
Replace with iterable unpacking
(RUF005)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/_torch/attention_backend/flashinfer.py` around lines 762 - 770,
The zip() call combining multi_item_part_lens and token_pos_in_items_raw_lens
needs to add strict=True parameter to document that these iterables have the
same length, which resolves the B905 lint finding. Additionally, replace the
list concatenation in the innermost for loop (req_part_lens[1:] +
[token_pos_in_items_len - token_pos_in_items_raw_len]) with iterable unpacking
syntax instead to resolve the RUF005 lint finding.
Sources: Coding guidelines, Linters/SAST tools
Comment on lines
+541
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+557
| """Efficiently compute torch.cat([torch.arange(b, e, d) for b, e, d in zip(starts, ends, steps)]). | ||
|
|
||
| Starts, ends, steps need to share dtype and shape. Invalid ranges like range(1, 2, -1) are | ||
| silently discarded. 'steps' defaults to 1 and 'starts' defaults to 0. | ||
|
|
||
| Provide 'output_length' to avoid synchronization when using device tensors or pass | ||
| `ACCEPT_SYNC_COMPUTE` to explicitly accept the possibility of a device sync (for device tensors) | ||
| or when tensors are known to reside on the host. | ||
| """ | ||
| if steps is not None: | ||
| assert ends.dtype == steps.dtype | ||
| assert ends.shape == steps.shape | ||
| assert ends.device == steps.device | ||
| if starts is not None: | ||
| assert ends.dtype == starts.dtype | ||
| assert ends.shape == starts.shape | ||
| assert ends.device == starts.device |
Contributor
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#!/bin/bash
# Description: Demonstrate why assert should not guard public input validation.
python -O - <<'PY'
def validate():
assert False, "this is skipped with -O"
validate()
print("assertions are skipped under optimized mode")
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sed -n '530,570p' tensorrt_llm/_torch/utils.py | cat -n
else
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fiRepository: NVIDIA/TensorRT-LLM
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rg -A 50 "def torch_multi_arange" tensorrt_llm/_torch/utils.py --max-count=1Repository: NVIDIA/TensorRT-LLM
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# Check if there's any ndim validation elsewhere in the function or if unsqueeze is used
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Replace assert statements with explicit ValueError in public input validation.
The function uses assert to validate public inputs (dtype, shape, device compatibility), which are silently skipped under Python's optimized mode (-O flag). Additionally, the implementation expects 1-D range-parameter tensors (operations on lines 63 and 71 use unsqueeze(-1) followed by torch.cat(..., dim=1)), but this constraint is not validated at the function entry.
Per coding guidelines, use built-in exception types for public input validation. Raise ValueError instead of assert, and validate that input tensors are 1-D.
Suggested fix
or when tensors are known to reside on the host.
"""
+ if ends.ndim != 1:
+ raise ValueError(f"ends must be a 1-D tensor, got shape {tuple(ends.shape)}")
+
+ def _validate_like_ends(name: str, tensor: torch.Tensor) -> None:
+ if ends.dtype != tensor.dtype:
+ raise ValueError(f"{name}.dtype must match ends.dtype")
+ if ends.shape != tensor.shape:
+ raise ValueError(f"{name}.shape must match ends.shape")
+ if ends.device != tensor.device:
+ raise ValueError(f"{name}.device must match ends.device")
+
if steps is not None:
- assert ends.dtype == steps.dtype
- assert ends.shape == steps.shape
- assert ends.device == steps.device
+ _validate_like_ends("steps", steps)
if starts is not None:
- assert ends.dtype == starts.dtype
- assert ends.shape == starts.shape
- assert ends.device == starts.device
+ _validate_like_ends("starts", starts)🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/_torch/utils.py` around lines 541 - 557, Replace the assert
statements in the function that validates dtype, shape, and device compatibility
between ends, steps, and starts parameters with explicit ValueError exceptions
that include descriptive error messages. Additionally, add validation at the
function entry to ensure that all input tensors (starts, ends, and steps) are
1-D tensors, raising ValueError if they are not, since the implementation later
uses unsqueeze and torch.cat operations that expect 1-D inputs.
Source: Coding guidelines
Comment on lines
+574
to
+580
| repeats = ends # number of elements in each range | ||
| if starts is not None: | ||
| repeats = repeats.clone() | ||
| repeats -= starts | ||
| if steps is not None: | ||
| repeats *= steps.sign() | ||
| steps_abs = steps.abs() |
Contributor
There was a problem hiding this comment.
Avoid mutating the caller’s ends tensor.
When starts is None and steps is provided, repeats aliases ends, so Line 579 modifies the input tensor in place. Use out-of-place arithmetic for the repeat count.
Suggested fix
- repeats = ends # number of elements in each range
- if starts is not None:
- repeats = repeats.clone()
- repeats -= starts
+ repeats = ends - starts if starts is not None else ends
if steps is not None:
- repeats *= steps.sign()
+ repeats = repeats * steps.sign()
steps_abs = steps.abs()🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/_torch/utils.py` around lines 574 - 580, The variable repeats is
initialized as an alias to the ends tensor, and when starts is None, this alias
is never broken before the in-place multiplication operation repeats *=
steps.sign() on line 579. This mutates the caller's ends tensor. Fix this by
using out-of-place arithmetic for the repeat count calculation: instead of the
in-place multiplication repeats *= steps.sign(), use repeats = repeats *
steps.sign() to create a new tensor and avoid mutating the input.
Comment on lines
+584
to
+602
| range_ends = repeats - 1 # last element in each range | ||
| if steps is not None: | ||
| range_ends *= steps | ||
| if starts is not None: | ||
| range_ends += starts | ||
| prev_range_ends = range_ends.roll( | ||
| 1) # last element in preceding range (or 0) | ||
| prev_range_ends[0].fill_(0) | ||
| ones = torch.ones((), dtype=ends.dtype, device=ends.device) | ||
| zeros = torch.zeros((), dtype=ends.dtype, device=ends.device) | ||
| if steps is None: | ||
| steps = ones.broadcast_to(ends.shape) | ||
| jumps = -prev_range_ends # delta from one range to the next | ||
| if starts is not None: | ||
| jumps += starts | ||
| # NB: Apply correction for empty ranges | ||
| jumps_corrections = torch.where(repeats == 0, jumps, | ||
| zeros).cumsum(0, dtype=ends.dtype) | ||
| jumps += jumps_corrections |
Contributor
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Carry forward the previous non-empty range end.
Empty ranges are skipped in seq_repeats, but prev_range_ends still uses their nominal start - step end. For example, default starts/steps with ends=[0, 3] yields [1, 2, 3] instead of [0, 1, 2].
Suggested direction
range_ends = repeats - 1 # last element in each range
if steps is not None:
range_ends *= steps
- if starts is not None:
- range_ends += starts
- prev_range_ends = range_ends.roll(
- 1) # last element in preceding range (or 0)
- prev_range_ends[0].fill_(0)
ones = torch.ones((), dtype=ends.dtype, device=ends.device)
zeros = torch.zeros((), dtype=ends.dtype, device=ends.device)
+ start_values = starts if starts is not None else zeros.broadcast_to(ends.shape)
+ range_ends += start_values
+
+ non_empty = repeats > 0
+ range_indices = torch.arange(ends.numel(), device=ends.device)
+ last_non_empty = torch.where(
+ non_empty, range_indices, range_indices.new_full((), -1)
+ ).cummax(0).values
+ prev_non_empty = last_non_empty.roll(1)
+ prev_non_empty[0].fill_(-1)
+ prev_range_ends = torch.where(
+ prev_non_empty >= 0,
+ range_ends[prev_non_empty.clamp(min=0)],
+ zeros,
+ )
if steps is None:
steps = ones.broadcast_to(ends.shape)
- jumps = -prev_range_ends # delta from one range to the next
- if starts is not None:
- jumps += starts
- # NB: Apply correction for empty ranges
- jumps_corrections = torch.where(repeats == 0, jumps,
- zeros).cumsum(0, dtype=ends.dtype)
- jumps += jumps_corrections
+ jumps = start_values - prev_range_ends🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/_torch/utils.py` around lines 584 - 602, The prev_range_ends
calculation using range_ends.roll(1) doesn't account for empty ranges where
repeats == 0. When a range is empty, its nominal end value should not be used as
the previous range end for the next range; instead, the end of the last
non-empty range should be carried forward. Modify the logic that computes
prev_range_ends to propagate the previous non-empty range's end value through
empty ranges, ensuring that jumps calculations correctly reflect transitions
only between actual non-empty ranges.
Comment on lines
+904
to
+932
| # Scoring items have overlapping position IDs. Position IDs of delimiters | ||
| # are irrelevant. | ||
| starts_cuda = torch.tensor( | ||
| [ | ||
| start for multi_item_part_lens in batch_multi_item_part_lens | ||
| for start in [0] + [multi_item_part_lens[0]] * | ||
| (len(multi_item_part_lens) - 1) | ||
| ], | ||
| pin_memory=prefer_pinned(), | ||
| dtype=torch.int32, | ||
| ).to("cuda", non_blocking=True) # uses current device | ||
| ends_cuda = torch.tensor( | ||
| [ | ||
| end + 1 | ||
| for multi_item_part_lens in batch_multi_item_part_lens | ||
| for end in [multi_item_part_lens[0]] + [ | ||
| multi_item_part_lens[0] + item_len | ||
| for item_len in multi_item_part_lens[1:] | ||
| ] | ||
| ], | ||
| pin_memory=prefer_pinned(), | ||
| dtype=torch.int32, | ||
| ).to("cuda", non_blocking=True) | ||
| position_ids_cuda = torch_multi_arange( | ||
| starts=starts_cuda, | ||
| ends=ends_cuda, | ||
| output_length=len(flat_token_ids), | ||
| ) | ||
| forward_inputs["position_ids"] = position_ids_cuda |
Contributor
There was a problem hiding this comment.
Validate multi_item_part_lens shape before generating CUDA position IDs.
The length check accepts malformed inputs such as [prefix_len] with no item lengths, so this block can build position_ids successfully while FlashInfer later fails in _process_multi_item_part_lens() on max(req_part_lens[1:]). Reject lists without at least one item and negative lengths before constructing starts_cuda/ends_cuda.
Proposed validation tightening
prompt_token_ids = inp_tok["prompt_token_ids"]
if multi_item_part_lens is not None:
+ if len(multi_item_part_lens) < 2:
+ raise ValueError(
+ '"multi_item_part_lens" must contain a prefix length '
+ "followed by at least one item length"
+ )
+ if any(part_len < 0 for part_len in multi_item_part_lens):
+ raise ValueError(
+ '"multi_item_part_lens" must not contain negative lengths'
+ )
# validate lengths
if sum(multi_item_part_lens) + len(
multi_item_part_lens) != len(prompt_token_ids):
raise ValueError(
"\"multi_item_part_lens\" inconsistent with prompt length"🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tensorrt_llm/llmapi/llm.py` around lines 904 - 932, The code does not
sufficiently validate the structure of multi_item_part_lens before constructing
starts_cuda and ends_cuda, allowing malformed inputs like [prefix_len] with no
item lengths to pass through and fail later in FlashInfer. Add validation before
the torch.tensor calls that construct starts_cuda and ends_cuda to ensure that
each multi_item_part_lens in batch_multi_item_part_lens has length greater than
1 (meaning at least one item length in addition to the prefix length) and that
all length values are non-negative. Reject the inputs early with a clear error
message if these conditions are not met.
Collaborator
|
PR_Github #54587 [ run ] completed with state
|
yuxianq
reviewed
Jun 17, 2026
| tests/unittest/_torch/sampler/test_beam_search.py | ||
| tests/unittest/_torch/sampler/test_best_of_n.py | ||
| tests/unittest/_torch/sampler/test_torch_multi_arange.py | ||
| tests/unittest/_torch/test_torch_multi_arange.py |
Collaborator
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Use alphabetical order for path
yuxianq
reviewed
Jun 17, 2026
MartinMarciniszyn
approved these changes
Jun 17, 2026
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Description
Follow-up on #14693 (comment).
Commit 800c7ee is from #15413, which is to be merged before this PR.
Test Coverage
Covered by existing tests
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
If PR introduces API changes, an appropriate PR label is added - either
api-compatibleorapi-breaking. Forapi-breaking, includeBREAKINGin the PR title.Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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/bot help.Summary by CodeRabbit
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