feat: Add support of scheduling attention dp request #6246
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📝 WalkthroughWalkthroughThis change introduces a new Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant LLMAPI
participant Executor
participant Worker
participant RequestQueue
Client->>LLMAPI: generate / generate_async(..., scheduling_params)
LLMAPI->>Executor: generate_async(..., scheduling_params)
Executor->>RequestQueue: enqueue request (with scheduling_params)
Worker->>RequestQueue: process request (attention DP logic)
RequestQueue->>RequestQueue: filter & schedule requests by attention DP rank/capacity
RequestQueue-->>Worker: scheduled requests
Worker-->>Executor: scheduled requests
Executor-->>LLMAPI: results
LLMAPI-->>Client: results
Estimated code review effort🎯 4 (Complex) | ⏱️ ~40 minutes Possibly related PRs
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🧹 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
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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.
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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):
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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.
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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
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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)
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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):
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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
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🧠 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.
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♻️ 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
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tensorrt_llm/_torch/pyexecutor/executor_request_queue.py(5 hunks)tensorrt_llm/executor/worker.py(1 hunks)
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🧠 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)
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🔇 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.
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♻️ 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)
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🧠 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
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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)
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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
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tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
267-270: Use local variable instead of instance variable.Storing active request counts in an instance variable can lead to unexpected side effects if methods are called in different orders.
# Get active request counts across all ranks - self.all_ranks_num_active_requests = [] + all_ranks_num_active_requests = [] responses_list = self.dist.tp_allgather(num_active_requests) for num_active_requests in responses_list: - self.all_ranks_num_active_requests.append(num_active_requests) + all_ranks_num_active_requests.append(num_active_requests)Then pass
all_ranks_num_active_requestsas a parameter to methods that need it.
303-309: Fix incorrect sorting for relax mode prioritization.The comment says "Prioritize the requests that are not in relax mode" but
reverse=Trueactually puts relax=True requests first.- # Prioritize the requests that are not in relax mode + # Prioritize the requests that are not in relax mode (relax=False first) def get_relax_value(req_item): if req_item.request.py_scheduling_params is None: return True return req_item.request.py_scheduling_params.attention_dp_relax - new_requests = sorted(new_requests, key=get_relax_value, reverse=True) + new_requests = sorted(new_requests, key=get_relax_value)
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tensorrt_llm/scheduling_params.py (1)
7-12: Fix line length and improve docstring format.The docstring line exceeds the 120 character limit and should follow Google style format for better consistency.
class SchedulingParams: - """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 + """Scheduling parameters for attention data parallelism. + + Attributes: + attention_dp_rank: The rank of target attention dp. + attention_dp_relax: Whether to allow the request to be scheduled to other + attention dp ranks for better throughput. """
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tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (2)
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tests/unittest/api_stability/references/llm.yaml (1)
129-131: LGTM! API changes maintain backward compatibility.The addition of the optional
scheduling_paramsparameter to bothgenerateandgenerate_asyncmethods is well-designed with proper type annotations and default values, ensuring existing code continues to work without modifications.Also applies to: 141-143
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
86-119: Well-implemented attention DP filtering logic.The method correctly handles attention DP request filtering while maintaining FIFO order with
extendleft(reversed()). Good use ofcopy()to avoid side effects on the shared state.
198-236: Clean integration of attention DP flag.The method correctly propagates the
enable_attention_dpflag to the waiting queue processing logic.
390-442: Good update to merge pre-scheduled requests.The method signature change and logic update correctly handle merging requests that were already scheduled to the current rank with newly balanced requests.
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tests/unittest/_torch/test_executor_request_queue.py (2)
339-351: Consider using a more descriptive initialization pattern.The fixture is well-structured, but the hardcoded active request counts could be more self-documenting.
def attention_dp_queue(mock_dist_attention_dp): """Create an ExecutorRequestQueue instance for attention DP testing.""" queue = ExecutorRequestQueue(dist=mock_dist_attention_dp, enable_attention_dp=True, max_batch_size=4, max_beam_width=2, max_num_active_requests=8, enable_iter_perf_stats=True, is_disaggregated=False) - # Initialize all_ranks_num_active_requests - queue.all_ranks_num_active_requests = [2, 1, 3, 0] # 4 ranks + # Initialize all_ranks_num_active_requests with test values + # Rank 0: 2 active, Rank 1: 1 active, Rank 2: 3 active, Rank 3: 0 active + queue.all_ranks_num_active_requests = [2, 1, 3, 0] return queue
667-696: Fix typo in variable name.attention_dp_queue.all_ranks_num_active_requests = [5, 6, 3, 7] attention_dp_queue.waiting_queue.extend(req_list) - avaiable_active_requests = attention_dp_queue.max_num_active_requests * 4 - sum( + available_active_requests = attention_dp_queue.max_num_active_requests * 4 - sum( attention_dp_queue.all_ranks_num_active_requests) result = attention_dp_queue._get_from_waiting_queue( attention_dp_queue.waiting_queue, - avaiable_active_requests, + available_active_requests, enable_attention_dp=True) - assert len(result) == avaiable_active_requests + assert len(result) == available_active_requests
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tests/unittest/_torch/test_executor_request_queue.py (4)
319-336: LGTM!The fixture properly sets up a mock distributed environment with 4 tensor parallel ranks for testing attention DP functionality.
354-373: LGTM!The helper function properly creates mock requests with optional scheduling parameters, handling both cases when parameters are provided and when they're None.
377-396: LGTM!The test correctly verifies that requests scheduled for the current rank are returned and the active request count is properly updated.
398-417: LGTM!The test properly verifies that requests scheduled for other ranks are not returned but their active counts are updated.
|
@Superjomn this is touching the LLM API, could you please review? Thanks. |
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]>
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tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (1)
276-311: Fix undefined variable error.The variable
num_new_requests_all_ranksis used on line 306 but is never defined in this method.Either remove these lines if the counters are not needed:
- # Update counters - self.num_fetch_requests += num_new_requests_all_ranks - self.num_fetch_requests_cur_rank += len(new_requests_cur_rank)Or define the variable before using it:
# Schedule attention dp requests all_ranks_new_requests = self._schedule_attention_dp_requests( new_requests, all_ranks_num_active_requests) new_requests_cur_rank = all_ranks_new_requests[self.dist.tp_rank] + + # Calculate total number of new requests across all ranks + num_new_requests_all_ranks = sum(len(reqs) for reqs in all_ranks_new_requests.values()) # Update performance metrics
♻️ Duplicate comments (5)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (4)
136-136: Fix incorrect attribute name.The attribute should be
py_schedule_paramsinstead ofpy_scheduling_params.- scheduling_params = req_item.request.py_scheduling_params + scheduling_params = req_item.request.py_schedule_params
326-328: Fix incorrect attribute name in scheduling method.The attribute should be
py_schedule_paramsinstead ofpy_scheduling_params.- if req_item.request.py_scheduling_params is None: + if req_item.request.py_schedule_params is None: return True - return req_item.request.py_scheduling_params.attention_dp_relax + return req_item.request.py_schedule_params.attention_dp_relax
330-330: Fix sorting logic to match the comment.The comment says "Prioritize the requests that are not in relax mode" but the current sorting puts relax=True first.
- new_requests = sorted(new_requests, key=get_relax_value, reverse=True) + new_requests = sorted(new_requests, key=get_relax_value)
337-337: Fix another incorrect attribute name reference.- target_dp_rank = req_item.request.py_scheduling_params.attention_dp_rank + target_dp_rank = req_item.request.py_schedule_params.attention_dp_ranktests/unittest/_torch/test_executor_request_queue.py (1)
672-697: Simplify boolean comparisons in assertions.Avoid explicit comparisons to
TrueandFalse.- 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]) req_relax = RequestQueueItem( 2, create_mock_request_with_py_schedule_params(attention_dp_rank=0, attention_dp_relax=True)) - 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]) req_target = RequestQueueItem( 3, create_mock_request_with_py_schedule_params(attention_dp_rank=1, attention_dp_relax=False)) all_ranks = [0, 0, 0, 0] - 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) assert all_ranks[1] == 1 req_no_capacity = RequestQueueItem( 4, create_mock_request_with_py_schedule_params(attention_dp_rank=0, attention_dp_relax=False)) all_ranks_full = [8, 0, 0, 0] # Rank 0 is at capacity - 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)
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tensorrt_llm/scheduling_params.py (1)
11-11: Fix line length to comply with 120 character limit.The docstring line exceeds the maximum line length of 120 characters.
- 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 throughput
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cpp/tensorrt_llm/common/tllmException.cppcpp/include/tensorrt_llm/common/tllmException.h
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cpp/tensorrt_llm/common/tllmException.cppcpp/include/tensorrt_llm/common/tllmException.htensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/scheduling_params.py
**/*.{h,hpp}
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Use a preprocessor guard in header files. The guard name must have prefix TRTLLM_ followed by the filename, all in caps, and no trailing underscore.
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cpp/include/tensorrt_llm/common/tllmException.h
**/*.py
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tensorrt_llm/_torch/pyexecutor/executor_request_queue.pytests/unittest/_torch/test_executor_request_queue.pytensorrt_llm/scheduling_params.py
🧠 Learnings (1)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
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.
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: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-07-29T23:14:44.448Z
Learning: Applies to **/*.py : For interfaces that may be used outside a file, prefer docstrings over comments in Python.
🧬 Code Graph Analysis (2)
cpp/tensorrt_llm/common/tllmException.cpp (1)
cpp/include/tensorrt_llm/common/tllmException.h (1)
RequestSpecificException(63-78)
tensorrt_llm/_torch/pyexecutor/executor_request_queue.py (3)
tests/unittest/_torch/test_executor_request_queue.py (1)
all_ranks_num_active_requests(354-355)tensorrt_llm/_torch/distributed/communicator.py (1)
tp_size(46-47)tensorrt_llm/functional.py (1)
sum(3253-3275)
🪛 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/scheduling_params.py
11-11: Line too long (123 > 120)
(E501)
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🔇 Additional comments (1)
cpp/include/tensorrt_llm/common/tllmException.h (1)
35-78: Well-structured exception class and error code enum.The implementation follows C++ best practices with proper use of
[[nodiscard]],noexcept, explicit constructor, and strongly-typed enum. The error code categorization with reserved ranges is a good design choice.
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/bot run --disable-fail-fast |
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PR_Github #13500 [ run ] triggered by Bot |
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I would also cherry-pick this commit to fix the issue with attention dp balancing: pcastonguay@72b2d00
| #include <stdexcept> | ||
| #include <string> | ||
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| #define NEW_TLLM_EXCEPTION(...) \ | ||
| tensorrt_llm::common::TllmException(__FILE__, __LINE__, tensorrt_llm::common::fmtstr(__VA_ARGS__).c_str()) | ||
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| #define NEW_TLLM_REQUEST_SPECIFIC_EXCEPTION_WITH_ERROR_CODE(requestID, errorCode, ...) \ |
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Is this needed in this MR? If not, let's move to a separate MR about error handling. We should try to make MR well-scoped.
Shunkangz
removed
Documentation
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PR_Github #13500 [ run ] completed with state |
Summary by CodeRabbit
New Features
SchedulingParamsclass for specifying attention data parallel rank and relax options.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
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