[None][feat] Add support of scheduling attention dp request #6246
Conversation
📝 WalkthroughWalkthroughThis change introduces a new Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant LLM_API
participant Executor
participant Worker
participant RequestQueue
User->>LLM_API: generate/generate_async(..., scheduling_params)
LLM_API->>Executor: generate_async(..., scheduling_params)
Executor->>RequestQueue: enqueue request (with scheduling_params)
Worker->>RequestQueue: _enqueue_request (attach py_scheduling_params)
RequestQueue->>RequestQueue: _get_from_waiting_queue (filter/schedule based on attention DP)
RequestQueue->>RequestQueue: _schedule_attention_dp_requests (balance requests across ranks)
RequestQueue-->>Worker: scheduled requests for current rank
Worker-->>Executor: process scheduled requests
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
Suggested labels
Suggested reviewers
Note ⚡️ Unit Test Generation is now available in beta!Learn more here, or try it out under "Finishing Touches" below. ✨ Finishing Touches
🧪 Generate unit tests
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
Documentation and Community
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (1)
tensorrt_llm/schedule_params.py (1)
10-11: Fix line length to comply with coding standards.The docstring line exceeds the 120-character limit. Consider reformatting for better readability.
- attention_dp_rank (int): The rank of target attention dp - attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp for better throughput + attention_dp_rank (int): The rank of target attention dp + attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp + for better throughput
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (6)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(1 hunks)tensorrt_llm/executor/executor.py(3 hunks)tensorrt_llm/executor/request.py(3 hunks)tensorrt_llm/executor/worker.py(1 hunks)tensorrt_llm/llmapi/llm.py(5 hunks)tensorrt_llm/schedule_params.py(1 hunks)
🧠 Learnings (1)
tensorrt_llm/executor/worker.py (1)
Learnt from: amitz-nv
PR: #5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.374Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks is_adapter_in_cpu_cache() and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
🪛 Ruff (0.12.2)
tensorrt_llm/schedule_params.py
11-11: Line too long (123 > 120)
(E501)
🧰 Additional context used
🧠 Learnings (1)
tensorrt_llm/executor/worker.py (1)
Learnt from: amitz-nv
PR: #5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.374Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks is_adapter_in_cpu_cache() and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
🪛 Ruff (0.12.2)
tensorrt_llm/schedule_params.py
11-11: Line too long (123 > 120)
(E501)
🔇 Additional comments (12)
tensorrt_llm/executor/request.py (1)
13-13: Clean integration of ScheduleParams parameter.The addition of
ScheduleParamsimport and integration into theGenerationRequestconstructor follows the established pattern for other optional parameters. The parameter is properly typed and stored as an instance attribute.Also applies to: 90-90, 115-115
tensorrt_llm/executor/worker.py (1)
511-513: Proper conditional attachment of schedule parameters.The implementation correctly follows the existing pattern for PyTorch backend-specific parameters (like
py_multimodal_dataandpy_logits_post_processors). The conditional checks ensure that schedule parameters are only attached when using the PyTorch backend and when they are actually provided.tensorrt_llm/schedule_params.py (1)
5-15: Well-designed dataclass with optimization features.The implementation uses
slots=Truefor memory efficiency andkw_only=Trueto enforce keyword-only arguments, which is excellent for API clarity. The optional typing and clear docstring make the parameters self-documenting.tensorrt_llm/llmapi/llm.py (3)
33-33: Proper import of ScheduleParams.The import is correctly placed with other parameter-related imports.
239-240: Consistent parameter handling in synchronous generate method.The
schedule_paramsparameter is properly integrated into the method signature and correctly handled in the batching logic using the_item_athelper function, following the established pattern for other optional parameters.Also applies to: 288-288
314-314: Clean integration in asynchronous generate method.The parameter is properly typed and forwarded through the call chain to the executor's
generate_asyncmethod, maintaining the parameter propagation pattern throughout the system.Also applies to: 425-425
tensorrt_llm/executor/executor.py (3)
32-32: LGTM: Clean import additionThe import of
ScheduleParamsis properly placed with other related imports.
124-124: LGTM: Consistent parameter additionThe
schedule_paramsparameter follows the established pattern of other optional parameters in the method signature.
147-148: LGTM: Proper parameter propagationThe
schedule_paramsis correctly passed to theGenerationRequestconstructor, maintaining the parameter flow through the system.tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
247-249: LGTM: Clean method call delegationThe replacement of direct balancing logic with a dedicated scheduling method improves code organization and readability.
257-257: LGTM: Consistent counter updateThe counter update properly tracks the total number of new requests processed.
264-344: Ignore the logic-error suggestion for attention_dp_rankThe comparison
req_item.request.schedule_params.attention_dp_rank == self.dist.tp_rankis intentional—“dp” here refers to the dimension-parallel (attention) split, which is driven by the tensor-parallel rank (
tp_rank). There is no separatedp_rankin thedistcontext, so the existing check is correct.Likely an incorrect or invalid review comment.
Shunkangz
force-pushed
the
attention_dp_rank_route
branch
from
ac0d750 to
fb1dd7f
Compare
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (2)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
312-317: Optimize communication overhead.The padding approach for allgather creates unnecessary communication overhead as noted in the TODO. Consider using variable-length communication or a more efficient serialization approach.
Would you like me to suggest an alternative implementation that reduces communication overhead?
327-330: Simplify the condition logic.The nested condition can be simplified for better readability.
- elif req_item.request.schedule_params is None or \ - req_item.request.schedule_params is not None and \ - req_item.request.schedule_params.attention_dp_rank is None: + elif (req_item.request.schedule_params is None or + req_item.request.schedule_params.attention_dp_rank is None):
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(4 hunks)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (3)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
247-249: LGTM! Clean refactoring of scheduling logic.The extraction of scheduling logic into a dedicated
_schedule_attention_dp_requestsmethod improves code organization and readability. The method call and parameter passing are correct.Also applies to: 257-257
406-406: LGTM! Clean integration of pre-scheduled requests.The method signature update to accept
new_requests_cur_rankand the subsequent merging logic correctly integrate pre-scheduled requests with the load balancing algorithm. The variable renaming tonew_scheduled_requestsimproves code clarity.Also applies to: 410-410, 424-424, 457-457, 462-462, 464-465
274-275: DP rank comparison is correct
TheExecutorRequestQueueuses the tensor-parallel group as the attention-DP group—there is nodist.dp_rank. Comparingschedule_params.attention_dp_rankagainstself.dist.tp_rankand coordinating viatp_allgatheris the intended behavior.Likely an incorrect or invalid review comment.
There was a problem hiding this comment.
Actionable comments posted: 1
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
tests/unittest/_torch/test_executor_request_queue.py(1 hunks)
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
835-835: Local variable req_id is assigned to but never used
Remove assignment to unused variable req_id
(F841)
🧰 Additional context used
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
835-835: Local variable req_id is assigned to but never used
Remove assignment to unused variable req_id
(F841)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
|
/bot run --disable-fail-fast |
|
PR_Github #12549 [ run ] triggered by Bot |
|
PR_Github #12549 [ run ] completed with state |
Shunkangz
force-pushed
the
attention_dp_rank_route
branch
from
8b7483b to
537de6d
Compare
|
/bot run --disable-fail-fast |
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (2)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
305-305: Fix potential request order reversal.Using
extendleftwill reverse the order of waiting requests. If maintaining FIFO order is important, consider usingextendand reversing the list first, or use a different approach.- self.waiting_queue.extendleft(new_requests_cur_rank_waiting) + # Maintain FIFO order by extending from the right + self.waiting_queue.extend(reversed(new_requests_cur_rank_waiting))tests/unittest/_torch/test_executor_request_queue.py (1)
835-835: Remove unused variable assignment.The variable
req_idis assigned but never used in this test.- req_id = schedule_queue.enqueue_request(mock_request) + schedule_queue.enqueue_request(mock_request)
🧹 Nitpick comments (2)
tensorrt_llm/schedule_params.py (1)
11-11: Fix line length to comply with style guide.Line exceeds the 120 character limit.
- attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp for better throughput + attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp for better + throughputtensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
327-330: Simplify complex conditional logic.The nested conditions for identifying non-scheduled requests can be simplified for better readability.
- elif req_item.request.py_schedule_params is None or \ - req_item.request.py_schedule_params is not None and \ - req_item.request.py_schedule_params.attention_dp_rank is None: + elif (req_item.request.py_schedule_params is None or + req_item.request.py_schedule_params.attention_dp_rank is None):
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (8)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(4 hunks)tensorrt_llm/executor/executor.py(3 hunks)tensorrt_llm/executor/request.py(3 hunks)tensorrt_llm/executor/worker.py(1 hunks)tensorrt_llm/llmapi/llm.py(5 hunks)tensorrt_llm/schedule_params.py(1 hunks)tests/unittest/_torch/test_executor_request_queue.py(1 hunks)tests/unittest/api_stability/references/llm.yaml(1 hunks)
🧠 Learnings (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
Learnt from: yiqingy0
PR: #5198
File: jenkins/mergeWaiveList.py:0-0
Timestamp: 2025-07-22T08:33:49.076Z
Learning: In the TensorRT-LLM waive list merging system, removed lines are always located at the end of the merge waive lists, which is why the mergeWaiveList.py script uses reverse traversal - it's an optimization for this specific domain constraint.
🧬 Code Graph Analysis (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
tensorrt_llm/_torch/distributed/communicator.py (2)
tp_allgather(138-139)tp_size(46-47)
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
835-835: Local variable req_id is assigned to but never used
Remove assignment to unused variable req_id
(F841)
tensorrt_llm/schedule_params.py
11-11: Line too long (123 > 120)
(E501)
🚧 Files skipped from review as they are similar to previous changes (4)
- tensorrt_llm/executor/request.py
- tensorrt_llm/executor/worker.py
- tensorrt_llm/executor/executor.py
- tensorrt_llm/llmapi/llm.py
🧰 Additional context used
🧠 Learnings (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
Learnt from: yiqingy0
PR: #5198
File: jenkins/mergeWaiveList.py:0-0
Timestamp: 2025-07-22T08:33:49.076Z
Learning: In the TensorRT-LLM waive list merging system, removed lines are always located at the end of the merge waive lists, which is why the mergeWaiveList.py script uses reverse traversal - it's an optimization for this specific domain constraint.
🧬 Code Graph Analysis (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
tensorrt_llm/_torch/distributed/communicator.py (2)
tp_allgather(138-139)tp_size(46-47)
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
835-835: Local variable req_id is assigned to but never used
Remove assignment to unused variable req_id
(F841)
tensorrt_llm/schedule_params.py
11-11: Line too long (123 > 120)
(E501)
🔇 Additional comments (3)
tests/unittest/api_stability/references/llm.yaml (1)
138-140: LGTM!The addition of the optional
schedule_paramsparameter togenerate_asyncmaintains backward compatibility while enabling the new scheduling functionality.tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
274-276: Ignore incorrect renaming suggestion:.py_schedule_paramsis intentional
Thepy_schedule_paramsattribute is explicitly set on the executor request intensorrt_llm/executor/worker.py:if self._is_pytorch_backend and request.schedule_params is not None: executor_request.py_schedule_params = request.schedule_paramsAll usages of
req_item.request.py_schedule_paramsintensorrt_llm/_torch/pyexecutor/executor_request_queue.pyare therefore correct and should remain unchanged.Likely an incorrect or invalid review comment.
tests/unittest/_torch/test_executor_request_queue.py (1)
459-857: Excellent test coverage!The comprehensive test suite effectively covers all major scheduling scenarios including rank filtering, capacity limits, relax flag prioritization, edge cases, and integration with the LLM API. The use of mocked distributed operations and helper functions makes the tests clear and maintainable.
|
PR_Github #12641 [ run ] triggered by Bot |
|
PR_Github #12641 [ run ] completed with state |
There was a problem hiding this comment.
Actionable comments posted: 1
♻️ Duplicate comments (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
301-301: Fix potential request order reversal.Using
extendleftwill reverse the order of pending requests, potentially breaking FIFO ordering when these requests are processed in the next scheduling round.- self.waiting_queue.extendleft(pending_requests) + # Maintain FIFO order by extending from the right + self.waiting_queue.extend(reversed(pending_requests))
🧹 Nitpick comments (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
324-331: Optimize request removal for better performance.Using
remove()in a loop over a potentially large list is inefficient (O(n²) complexity). Consider using a more efficient approach.- # Try to put the unscheduled requests to the target dp rank until the max_num_active_requests is reached - for req_item in unscheduled_requests[:]: - if req_item.request.py_schedule_params is not None: - target_dp_rank = req_item.request.py_schedule_params.attention_dp_rank - if self.all_ranks_num_active_requests[ - target_dp_rank] < self.max_num_active_requests: - self.all_ranks_num_active_requests[target_dp_rank] += 1 - # Ensure all ranks have the same unscheduled requests - unscheduled_requests.remove(req_item) - - # If the target dp rank is the current rank, add it to the new_requests_cur_rank - if target_dp_rank == self.dist.tp_rank: - new_requests_cur_rank.append(req_item) + # Try to put the unscheduled requests to the target dp rank until the max_num_active_requests is reached + remaining_unscheduled = [] + for req_item in unscheduled_requests: + if req_item.request.py_schedule_params is not None: + target_dp_rank = req_item.request.py_schedule_params.attention_dp_rank + if self.all_ranks_num_active_requests[ + target_dp_rank] < self.max_num_active_requests: + self.all_ranks_num_active_requests[target_dp_rank] += 1 + + # If the target dp rank is the current rank, add it to the new_requests_cur_rank + if target_dp_rank == self.dist.tp_rank: + new_requests_cur_rank.append(req_item) + else: + remaining_unscheduled.append(req_item) + else: + remaining_unscheduled.append(req_item) + unscheduled_requests[:] = remaining_unscheduled
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(5 hunks)tensorrt_llm/executor/worker.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- tensorrt_llm/executor/worker.py
🧰 Additional context used
🧠 Learnings (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
Learnt from: amitz-nv
PR: #5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks is_adapter_in_cpu_cache() and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: yiqingy0
PR: #5198
File: jenkins/mergeWaiveList.py:0-0
Timestamp: 2025-07-22T08:33:49.109Z
Learning: In the TensorRT-LLM waive list merging system, removed lines are always located at the end of the merge waive lists, which is why the mergeWaiveList.py script uses reverse traversal - it's an optimization for this specific domain constraint.
🪛 Ruff (0.12.2)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py
316-316: Line too long (133 > 120)
(E501)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (3)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
223-225: LGTM! Improved performance metrics accuracy.Moving the performance metrics update to the specific fetch methods ensures metrics are calculated after all filtering and scheduling steps, providing more accurate measurements.
235-241: LGTM! Necessary refactoring for scheduling logic.Converting to an instance variable allows the scheduling methods to access and modify active request counts across ranks. The allgather operation correctly synchronizes state across all ranks.
400-459: LGTM! Proper integration of pre-scheduled requests.The method signature change correctly allows merging of pre-scheduled attention DP requests with load-balanced requests. The heap-based balancing logic is preserved and the final merge ensures all scheduled requests are returned together.
There was a problem hiding this comment.
Actionable comments posted: 6
♻️ Duplicate comments (3)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
303-303: Fix potential request order reversal.Using
extendleftwill reverse the order of waiting requests. If maintaining FIFO order is important, consider usingextendand reversing the list first, or use a different approach.- self.waiting_queue.extendleft(pending_requests) + # Maintain FIFO order by extending from the right + self.waiting_queue.extend(reversed(pending_requests))
315-319: Strengthen error handling for scheduling assertion.The assertion provides good validation but could fail unexpectedly if there are bugs in the filtering logic. Consider adding more descriptive error handling.
- assert self.all_ranks_num_active_requests[target_dp_rank] <= self.max_num_active_requests, \ - f"The number of active requests on rank {target_dp_rank} is {self.all_ranks_num_active_requests[target_dp_rank]}, " \ - f"which is greater than the max_num_active_requests {self.max_num_active_requests}" + if self.all_ranks_num_active_requests[target_dp_rank] > self.max_num_active_requests: + raise RuntimeError( + f"Scheduling inconsistency: rank {target_dp_rank} has " + f"{self.all_ranks_num_active_requests[target_dp_rank]} active requests, " + f"exceeding limit of {self.max_num_active_requests}. " + f"This indicates a bug in the filtering logic." + )tests/unittest/_torch/test_executor_request_queue.py (1)
698-699: Remove unused variable assignment.The variable
resultis assigned but never used in this test.- result = attention_dp_queue._schedule_attention_dp_requests( - scheduled_requests, unscheduled_requests) + attention_dp_queue._schedule_attention_dp_requests( + scheduled_requests, unscheduled_requests)
🧹 Nitpick comments (1)
tests/unittest/_torch/test_executor_request_queue.py (1)
726-728: Improve test documentation.The comment doesn't accurately describe what's being tested. The test is checking that an assertion is raised when trying to schedule a request to a rank that's already over capacity.
- # Should raise assertion error because we're trying to schedule a request when rank is at capacity + # Should raise assertion error because rank 0 already exceeds max capacity (9 > 8)
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(5 hunks)tests/unittest/_torch/test_executor_request_queue.py(1 hunks)
🧰 Additional context used
🧠 Learnings (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
Learnt from: amitz-nv
PR: #5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks is_adapter_in_cpu_cache() and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Learnt from: yiqingy0
PR: #5198
File: jenkins/mergeWaiveList.py:0-0
Timestamp: 2025-07-22T08:33:49.109Z
Learning: In the TensorRT-LLM waive list merging system, removed lines are always located at the end of the merge waive lists, which is why the mergeWaiveList.py script uses reverse traversal - it's an optimization for this specific domain constraint.
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
677-677: Local variable result is assigned to but never used
Remove assignment to unused variable result
(F841)
698-698: Local variable result is assigned to but never used
Remove assignment to unused variable result
(F841)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py
318-318: Line too long (133 > 120)
(E501)
🔇 Additional comments (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
460-460: Consider the order when merging scheduled requests.The order of extending
new_scheduled_requestswithnew_requests_cur_rankmay affect processing order. Ensure this aligns with the expected behavior - typically, pre-scheduled requests should be processed before newly balanced ones.- new_scheduled_requests.extend(new_requests_cur_rank) + # Process pre-scheduled requests first, then newly balanced ones + result = new_requests_cur_rank + new_scheduled_requests + return result
Shunkangz
force-pushed
the
attention_dp_rank_route
branch
from
d01a546 to
f783e2c
Compare
There was a problem hiding this comment.
Actionable comments posted: 2
🔭 Outside diff range comments (1)
tensorrt_llm/schedule_params.py (1)
1-16: Add NVIDIA copyright header.All TensorRT-LLM source files must contain an NVIDIA copyright header that includes the current year.
Add the copyright header at the beginning of the file:
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from dataclasses import dataclass from typing import Optional
♻️ Duplicate comments (8)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (5)
249-254: Fix duplicate performance metrics update.The performance metrics are updated twice for the same requests, causing inflated latency measurements as noted in past reviews.
Remove the duplicate metric updates:
- # Update performance metrics - # TODO: Check whether we should update the performance metrics for all ranks - if self.enable_iter_perf_stats and self.dist.rank == 0: - self._update_new_active_requests_queue_latency(scheduled_requests) - self._update_new_active_requests_queue_latency(unscheduled_requests) -
280-281: Fix misleading comment about copying.The comment says "avoid modifying the original" but the code doesn't create a proper copy.
- # Create a copy to avoid modifying the original all_ranks_num_active_requests - all_ranks_num_active_requests = self.all_ranks_num_active_requests.copy() + # Create a copy to avoid modifying the original all_ranks_num_active_requests + all_ranks_num_active_requests = self.all_ranks_num_active_requests.copy()
302-302: Fix potential request order reversal with extendleft.Using
extendleftreverses the order of pending requests, breaking FIFO order.- self.waiting_queue.extendleft(pending_requests) + # Maintain FIFO order by extending from the right + self.waiting_queue.extend(reversed(pending_requests))
315-319: Replace assertion with proper error handling.The assertion could fail unexpectedly and should be replaced with descriptive error handling.
- assert self.all_ranks_num_active_requests[target_dp_rank] <= self.max_num_active_requests, \ - f"The number of active requests on rank {target_dp_rank}" \ - f"is {self.all_ranks_num_active_requests[target_dp_rank]}, " \ - f"which is greater than the max_num_active_requests {self.max_num_active_requests}" + if self.all_ranks_num_active_requests[target_dp_rank] > self.max_num_active_requests: + raise RuntimeError( + f"Scheduling inconsistency: rank {target_dp_rank} has " + f"{self.all_ranks_num_active_requests[target_dp_rank]} active requests, " + f"exceeding limit of {self.max_num_active_requests}. " + f"This indicates a bug in the filtering logic." + )
233-236: Consider using local variable for active request counts.The instance variable
self.all_ranks_num_active_requestscould lead to side effects if methods are called in different orders.Consider passing the active request counts as a parameter between methods instead of storing as an instance variable to avoid potential side effects and improve thread safety.
tests/unittest/_torch/test_executor_request_queue.py (3)
677-679: Remove unused variable assignment.The variable
resultis assigned but never used in this test.- result = attention_dp_queue._schedule_attention_dp_requests( - scheduled_requests, unscheduled_requests) + attention_dp_queue._schedule_attention_dp_requests( + scheduled_requests, unscheduled_requests)
764-766: Add assertion to verify scheduling result.The test schedules requests but doesn't verify the final result.
result = attention_dp_queue._schedule_attention_dp_requests( scheduled, unscheduled) + + # Verify that all requests were scheduled successfully + assert len(unscheduled) == 0 # All should have been scheduled + assert req_no_params not in result # Only requests for current rank
698-700: Remove another unused variable assignment.The variable
resultis assigned but never used in this test.- result = attention_dp_queue._schedule_attention_dp_requests( - scheduled_requests, unscheduled_requests) + attention_dp_queue._schedule_attention_dp_requests( + scheduled_requests, unscheduled_requests)
🧹 Nitpick comments (4)
tensorrt_llm/schedule_params.py (1)
7-12: Improve docstring format and clarity.The docstring could benefit from following Google style more closely and providing clearer parameter descriptions.
- """Schedule parameters. - - Args: - attention_dp_rank (int): The rank of target attention dp - attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp for better throughput - """ + """Parameters for controlling attention data parallel scheduling behavior. + + Args: + attention_dp_rank: The target rank for attention data parallelism. If None, the request + can be scheduled to any available rank. + attention_dp_relax: Whether to allow relaxed scheduling. If True, the request can be + scheduled to other attention dp ranks for better throughput when the target rank + is at capacity. If None or False, strict scheduling is enforced. + """tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
265-266: Clarify counter semantics for filtered requests.The counter
num_fetch_requestsincludes all new requests but some may be filtered out and put back to waiting queue. Consider tracking scheduled vs unscheduled separately.# Update counters self.num_fetch_requests += len(new_requests) self.num_fetch_requests_cur_rank += len(new_requests_cur_rank) + # TODO: Consider tracking num_scheduled_requests and num_pending_requests separatelytests/unittest/_torch/test_executor_request_queue.py (2)
319-805: Consider adding edge case tests.The test coverage is comprehensive but could benefit from additional edge cases.
Consider adding tests for:
- Concurrent access scenarios with multiple threads
- Very large request queues to test scalability
- Invalid attention_dp_rank values (negative, >= tp_size)
- Mixed relax=True/False requests for the same rank
- Boundary conditions when all ranks are at capacity
349-351: Remove hardcoded initialization of all_ranks_num_active_requests.The test fixture hardcodes
all_ranks_num_active_requestswhich might not reflect real scenarios where this is populated via allgather.Consider creating a helper method to properly initialize this based on test requirements:
- # Initialize all_ranks_num_active_requests - queue.all_ranks_num_active_requests = [2, 1, 3, 0] # 4 ranks + # Initialize via a helper method that simulates allgather + queue.all_ranks_num_active_requests = [] # Will be set per test
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (7)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(5 hunks)tensorrt_llm/executor/executor.py(3 hunks)tensorrt_llm/executor/request.py(3 hunks)tensorrt_llm/executor/worker.py(1 hunks)tensorrt_llm/llmapi/llm.py(5 hunks)tensorrt_llm/schedule_params.py(1 hunks)tests/unittest/_torch/test_executor_request_queue.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (4)
- tensorrt_llm/executor/worker.py
- tensorrt_llm/executor/request.py
- tensorrt_llm/llmapi/llm.py
- tensorrt_llm/executor/executor.py
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: Python code should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL).
Python constants should use upper snake_case (e.g., MY_CONSTANT).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a Python file, prefer docstrings over comments.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/schedule_params.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/schedule_params.py
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
677-677: Local variable result is assigned to but never used
Remove assignment to unused variable result
(F841)
698-698: Local variable result is assigned to but never used
Remove assignment to unused variable result
(F841)
tensorrt_llm/schedule_params.py
11-11: Line too long (123 > 120)
(E501)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
Superjomn
changed the title
Shunkangz
changed the title
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (1)
tensorrt_llm/llmapi/llm.py (1)
331-331: Fix line length violation in documentation.The documentation line exceeds the 120-character limit. Consider breaking it into multiple lines.
- scheduling_params (tensorrt_llm.scheduling_params.SchedulingParams, optional): Scheduling parameters. Defaults to None. + scheduling_params (tensorrt_llm.scheduling_params.SchedulingParams, optional): + Scheduling parameters. Defaults to None.
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
tensorrt_llm/llmapi/llm.py(7 hunks)tests/unittest/api_stability/references/llm.yaml(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- tests/unittest/api_stability/references/llm.yaml
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: Python code should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a Python file, prefer docstrings over comments.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tensorrt_llm/llmapi/llm.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. The block should be prepended to the top of all files, including .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/llmapi/llm.py
🧬 Code Graph Analysis (1)
tensorrt_llm/llmapi/llm.py (1)
tensorrt_llm/scheduling_params.py (1)
SchedulingParams(6-15)
🪛 Ruff (0.12.2)
tensorrt_llm/llmapi/llm.py
331-331: Line too long (131 > 120)
(E501)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (6)
tensorrt_llm/llmapi/llm.py (6)
33-33: LGTM: Import statement follows established patterns.The import of
SchedulingParamsis correctly placed and follows the same pattern as other parameter imports in the file.
240-241: LGTM: Method signature follows established patterns.The
scheduling_paramsparameter addition is consistent with other optional batched parameters in the method signature.
260-261: LGTM: Documentation follows established format.The parameter documentation is consistent with other parameters and provides appropriate type and description information.
291-291: LGTM: Parameter forwarding follows established pattern.The
scheduling_paramsforwarding uses the same_item_athelper pattern as other batched parameters, maintaining consistency.
317-317: LGTM: Async method signature is correctly updated.The
scheduling_paramsparameter is properly typed as a single optional parameter, which is appropriate for the async method that handles individual requests.
429-429: LGTM: Parameter forwarding to executor is correct.The
scheduling_paramsparameter is properly forwarded to the executor'sgenerate_asyncmethod, maintaining consistency with other parameter forwarding.
|
PR_Github #13773 [ run ] triggered by Bot |
|
/bot kill |
Signed-off-by: Shunkang <[email protected]>
Signed-off-by: Shunkang <[email protected]>
Signed-off-by: Shunkang <[email protected]>
Signed-off-by: Patrice Castonguay <[email protected]>
Signed-off-by: Patrice Castonguay <[email protected]>
Signed-off-by: Patrice Castonguay <[email protected]>
Signed-off-by: Shunkang <[email protected]>
Signed-off-by: Patrice Castonguay <[email protected]>
Signed-off-by: Shunkang <[email protected]>
Signed-off-by: Shunkang <[email protected]>
pcastonguay
force-pushed
the
attention_dp_rank_route
branch
from
4e872d2 to
d7bde53
Compare
|
/bot run --disable-fail-fast --add-multi-gpu-test |
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (6)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (5)
136-137: Fix incorrect attribute name.The attribute name should be
py_schedule_params, notpy_scheduling_params. This was flagged in previous reviews.- scheduling_params = getattr(req_item.request, 'py_scheduling_params', - None) + scheduling_params = getattr(req_item.request, 'py_schedule_params', + None)
325-333: Fix incorrect sorting logic for relax mode prioritization.The sorting with
reverse=Trueputs relax=True requests first, which contradicts the comment. This was flagged in previous reviews.- new_requests = sorted(new_requests, key=get_relax_value, reverse=True) + new_requests = sorted(new_requests, key=get_relax_value)
327-328: Fix incorrect attribute name.Same issue as previous - should use
py_schedule_paramsinstead ofpy_scheduling_params.
339-340: Fix incorrect attribute name.Same issue as previous - should use
py_schedule_paramsinstead ofpy_scheduling_params.
375-376: Fix incorrect attribute name in broadcasting.Should use
py_schedule_paramsinstead ofpy_scheduling_paramsfor consistency.- py_scheduling_params = self._collect_py_objects_from_requests( - new_requests, "py_scheduling_params") + py_scheduling_params = self._collect_py_objects_from_requests( + new_requests, "py_schedule_params")tests/unittest/_torch/test_executor_request_queue.py (1)
371-373: Fix incorrect attribute name in mock request creation.The mock should use
py_schedule_paramsto match the actual implementation, notpy_scheduling_params. This was flagged in previous reviews.- mock_request.py_scheduling_params = mock_schedule_params + mock_request.py_schedule_params = mock_schedule_params else: - mock_request.py_scheduling_params = None + mock_request.py_schedule_params = None
🧹 Nitpick comments (7)
tensorrt_llm/scheduling_params.py (1)
10-11: Fix line length violation in docstring.The docstring line exceeds the 120-character limit specified in the coding guidelines.
- attention_dp_rank (int): The rank of target attention dp - attention_dp_relax (bool): Whether to allow the request to be scheduled to other attention dp for better throughput + attention_dp_rank (int): The rank of target attention dp. + attention_dp_relax (bool): Whether to allow the request to be scheduled to + other attention dp for better throughput.tensorrt_llm/llmapi/llm.py (1)
317-317: Fix line length violation in parameter list.The parameter addition is correct, but line 331 exceeds the 120-character limit.
- scheduling_params (tensorrt_llm.scheduling_params.SchedulingParams, optional): Scheduling parameters. Defaults to None. + scheduling_params (tensorrt_llm.scheduling_params.SchedulingParams, optional): + Scheduling parameters. Defaults to None.Also applies to: 331-331
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
378-381: Fix variable name for consistency.The variable should be named
py_schedule_paramsfor consistency with the correct attribute name.py_request_objects = tuple( filter(None, [ py_logits_post_processors, py_multimodal_data, - py_scheduling_params + py_schedule_params ]))tests/unittest/_torch/test_executor_request_queue.py (4)
672-673: Simplify boolean comparison.Avoid explicit comparison to
True. Use the boolean expression directly.- assert attention_dp_queue._can_process_attention_dp_request( - req_no_params, [0, 0, 0, 0]) == True + assert attention_dp_queue._can_process_attention_dp_request( + req_no_params, [0, 0, 0, 0])
679-680: Simplify boolean comparison.Avoid explicit comparison to
True. Use the boolean expression directly.- assert attention_dp_queue._can_process_attention_dp_request( - req_relax, [0, 0, 0, 0]) == True + assert attention_dp_queue._can_process_attention_dp_request( + req_relax, [0, 0, 0, 0])
687-688: Simplify boolean comparison.Avoid explicit comparison to
True. Use the boolean expression directly.- assert attention_dp_queue._can_process_attention_dp_request( - req_target, all_ranks) == True + assert attention_dp_queue._can_process_attention_dp_request( + req_target, all_ranks)
696-697: Simplify boolean comparison.Avoid explicit comparison to
False. Usenotfor false checks.- assert attention_dp_queue._can_process_attention_dp_request( - req_no_capacity, all_ranks_full) == False + assert not attention_dp_queue._can_process_attention_dp_request( + req_no_capacity, all_ranks_full)
📜 Review details
Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (8)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(8 hunks)tensorrt_llm/executor/executor.py(3 hunks)tensorrt_llm/executor/request.py(3 hunks)tensorrt_llm/executor/worker.py(1 hunks)tensorrt_llm/llmapi/llm.py(7 hunks)tensorrt_llm/scheduling_params.py(1 hunks)tests/unittest/_torch/test_executor_request_queue.py(3 hunks)tests/unittest/api_stability/references/llm.yaml(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (4)
- tensorrt_llm/executor/request.py
- tensorrt_llm/executor/worker.py
- tests/unittest/api_stability/references/llm.yaml
- tensorrt_llm/executor/executor.py
🧰 Additional context used
📓 Path-based instructions (2)
**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile = ...).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL = ...).
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a class in the constructor in Python.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for classes and functions in Python, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without it.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/llmapi/llm.pytensorrt_llm/scheduling_params.py
**/*.{cpp,h,hpp,cc,cxx,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/llmapi/llm.pytensorrt_llm/scheduling_params.py
🧠 Learnings (3)
📚 Learning: in the tensorrt-llm waive list merging system, removed lines are always located at the end of the me...
Learnt from: yiqingy0
PR: NVIDIA/TensorRT-LLM#5198
File: jenkins/mergeWaiveList.py:0-0
Timestamp: 2025-07-22T08:33:49.109Z
Learning: In the TensorRT-LLM waive list merging system, removed lines are always located at the end of the merge waive lists, which is why the mergeWaiveList.py script uses reverse traversal - it's an optimization for this specific domain constraint.
Applied to files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py
📚 Learning: in tensorrt_llm/executor/worker.py, the lora adapter cache optimization logic that checks `is_adapte...
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py
📚 Learning: applies to **/*.py : for interfaces that may be used outside a file, prefer docstrings over comments...
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-01T07:34:42.734Z
Learning: Applies to **/*.py : For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Applied to files:
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py
🪛 Ruff (0.12.2)
tests/unittest/_torch/test_executor_request_queue.py
672-673: Avoid equality comparisons to True; use ...: for truth checks
Replace comparison
(E712)
679-680: Avoid equality comparisons to True; use ...: for truth checks
Replace comparison
(E712)
687-688: Avoid equality comparisons to True; use ...: for truth checks
Replace comparison
(E712)
696-697: Avoid equality comparisons to False; use not ...: for false checks
Replace comparison
(E712)
tensorrt_llm/llmapi/llm.py
331-331: Line too long (131 > 120)
(E501)
tensorrt_llm/scheduling_params.py
11-11: Line too long (123 > 120)
(E501)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Pre-commit Check
🔇 Additional comments (12)
tensorrt_llm/scheduling_params.py (1)
5-15: LGTM! Well-structured dataclass implementation.The dataclass is properly implemented with appropriate use of
slots=Truefor performance andkw_only=Truefor API clarity. The optional attributes with proper type hints align well with the attention data parallelism scheduling requirements.tensorrt_llm/llmapi/llm.py (4)
33-33: LGTM! Proper import of SchedulingParams.The import follows the established pattern and maintains namespace consistency as required by the coding guidelines.
240-241: LGTM! Consistent parameter addition to synchronous generate method.The
scheduling_paramsparameter is properly added to both the method signature and docstring, following the same pattern as other optional parameters.Also applies to: 260-261
291-291: LGTM! Proper parameter forwarding in batch processing.The
scheduling_paramsis correctly extracted using the_item_athelper function and forwarded togenerate_async, maintaining consistency with other batched parameters.
437-437: LGTM! Proper parameter forwarding to executor.The
scheduling_paramsis correctly passed through to the executor'sgenerate_asyncmethod, completing the parameter propagation chain.tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
90-91: LGTM: Method signature follows previous feedback.The addition of
enable_attention_dpandall_ranks_num_active_requestsparameters aligns with past review suggestions to extend the existing method rather than creating separate methods.
213-215: LGTM: Consistent parameter addition.The method signature correctly includes the attention DP parameters, maintaining consistency with the overall design approach.
420-474: LGTM: Improved method flexibility.The refactoring of
_balance_requests_across_ranksto accept a dictionary of pre-scheduled requests and merge balanced requests improves the method's flexibility and makes the scheduling logic more modular.tests/unittest/_torch/test_executor_request_queue.py (4)
319-356: LGTM: Well-designed test fixtures.The test fixtures provide appropriate mocking for distributed environments and create clean test isolation. The multi-rank setup properly simulates the attention DP environment.
739-845: LGTM: Comprehensive parameterized test coverage.The parameterized test cases provide excellent coverage of various attention DP scheduling scenarios, including balanced distribution, capacity limits, rank targeting, and edge cases. The test data is well-structured and validates the scheduling logic thoroughly.
358-377: LGTM: Well-designed helper function.The helper function provides good abstraction for creating mock requests with scheduling parameters. The logic correctly handles both cases with and without scheduling parameters.
575-625: LGTM: Valuable integration tests.The integration tests effectively verify end-to-end behavior by combining filtering and scheduling operations. They test realistic scenarios with proper capacity constraints and mixed request types.
|
PR_Github #13821 [ kill ] triggered by Bot |
|
PR_Github #13773 [ run ] completed with state |
|
PR_Github #13821 [ kill ] completed with state |
|
PR_Github #13822 [ run ] triggered by Bot |
|
PR_Github #13822 [ run ] completed with state |
) Signed-off-by: Shunkang <[email protected]> Signed-off-by: Patrice Castonguay <[email protected]> Co-authored-by: Shunkang <[email protected]> Co-authored-by: Patrice Castonguay <[email protected]> Signed-off-by: Lanyu Liao <[email protected]>
) Signed-off-by: Shunkang <[email protected]> Signed-off-by: Patrice Castonguay <[email protected]> Co-authored-by: Shunkang <[email protected]> Co-authored-by: Patrice Castonguay <[email protected]>
Summary by CodeRabbit
New Features
scheduling_paramsoption for generation methods, allowing finer control over request scheduling.Bug Fixes
Tests
Documentation
scheduling_paramsparameter in generation methods.Description
In this PR, I add the feature of scheduling attention dp requests. In this implementation, we have three choice.
The workflow is as follows: each rank selects the requests routed to it. It then attempts to retrieve requests until it reaches the
max_num_active_requestsconstraint. If a request has theattention_dp_relaxflag enabled, the scheduler will try to assign it to other ranks to improve overall throughput. In contrast, requests without the relax flag will be placed back at the front of the waiting queue to await the next round of scheduling.Test Coverage
GitHub Bot Help
/bot [-h] ['run', 'kill', 'skip', 'reuse-pipeline'] ...Provide a user friendly way for developers to interact with a Jenkins server.
Run
/bot [-h|--help]to print this help message.See details below for each supported subcommand.
run [--reuse-test (optional)pipeline-id --disable-fail-fast --skip-test --stage-list "A10-PyTorch-1, xxx" --gpu-type "A30, H100_PCIe" --test-backend "pytorch, cpp" --add-multi-gpu-test --only-multi-gpu-test --disable-multi-gpu-test --post-merge --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx" --detailed-log --debug(experimental)]Launch build/test pipelines. All previously running jobs will be killed.
--reuse-test (optional)pipeline-id(OPTIONAL) : Allow the new pipeline to reuse build artifacts and skip successful test stages from a specified pipeline or the last pipeline if no pipeline-id is indicated. If the Git commit ID has changed, this option will be always ignored. The DEFAULT behavior of the bot is to reuse build artifacts and successful test results from the last pipeline.--disable-reuse-test(OPTIONAL) : Explicitly prevent the pipeline from reusing build artifacts and skipping successful test stages from a previous pipeline. Ensure that all builds and tests are run regardless of previous successes.--disable-fail-fast(OPTIONAL) : Disable fail fast on build/tests/infra failures.--skip-test(OPTIONAL) : Skip all test stages, but still run build stages, package stages and sanity check stages. Note: Does NOT update GitHub check status.--stage-list "A10-PyTorch-1, xxx"(OPTIONAL) : Only run the specified test stages. Examples: "A10-PyTorch-1, xxx". Note: Does NOT update GitHub check status.--gpu-type "A30, H100_PCIe"(OPTIONAL) : Only run the test stages on the specified GPU types. Examples: "A30, H100_PCIe". Note: Does NOT update GitHub check status.--test-backend "pytorch, cpp"(OPTIONAL) : Skip test stages which don't match the specified backends. Only support [pytorch, cpp, tensorrt, triton]. Examples: "pytorch, cpp" (does not run test stages with tensorrt or triton backend). Note: Does NOT update GitHub pipeline status.--only-multi-gpu-test(OPTIONAL) : Only run the multi-GPU tests. Note: Does NOT update GitHub check status.--disable-multi-gpu-test(OPTIONAL) : Disable the multi-GPU tests. Note: Does NOT update GitHub check status.--add-multi-gpu-test(OPTIONAL) : Force run the multi-GPU tests in addition to running L0 pre-merge pipeline.--post-merge(OPTIONAL) : Run the L0 post-merge pipeline instead of the ordinary L0 pre-merge pipeline.--extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx"(OPTIONAL) : Run the ordinary L0 pre-merge pipeline and specified test stages. Examples: --extra-stage "H100_PCIe-TensorRT-Post-Merge-1, xxx".--detailed-log(OPTIONAL) : Enable flushing out all logs to the Jenkins console. This will significantly increase the log volume and may slow down the job.--debug(OPTIONAL) : Experimental feature. Enable access to the CI container for debugging purpose. Note: Specify exactly one stage in thestage-listparameter to access the appropriate container environment. Note: Does NOT update GitHub check status.For guidance on mapping tests to stage names, see
docs/source/reference/ci-overview.mdand the
scripts/test_to_stage_mapping.pyhelper.kill
killKill all running builds associated with pull request.
skip
skip --comment COMMENTSkip testing for latest commit on pull request.
--comment "Reason for skipping build/test"is required. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.reuse-pipeline
reuse-pipelineReuse a previous pipeline to validate current commit. This action will also kill all currently running builds associated with the pull request. IMPORTANT NOTE: This is dangerous since lack of user care and validation can cause top of tree to break.