[https://nvbugs/5647400] [fix] Enlarged the AllReduce workspace size to 64MB. Added AllReduce strategy to AD config.

[MrGeva]

1. Enlarged the allreduce workspace size to 64MB because the 8MB that was before caused hangs and crashes (see the bug linked to the title). the problem surfaced due to this commit eeb56c2. Ideally the workspace calculation could be calculated dynamically based on the model config, however this is a bigger change to be considered.
2. Added fallback from one shot to two shot kernel in case of a too short seq len that one shot does not support.
3. Added all_reduce strategy arg to AutoDeploy's config
4. Added a test for all strategies

Passing the allreduce strategy param:
This change makes allreduce_strategy a mandatory parameter throughout the AutoDeploy sharding pipeline. The strategy is configured in the detect_sharding transform YAML config, which sets it on the ShardingConfig. Since ShardingTransformInfo instances are immutable (frozen Pydantic models) and created without the strategy at ~10 call sites, I implemented automatic injection: when transforms are added via ShardingConfig.add(), it uses model_dump() to re-instantiate them with the strategy injected from the parent config. All custom ops (torch_dist_all_reduce, fused_linear_all_reduce, fused_fp8_linear_all_reduce) now require the strategy parameter (no defaults), and runtime validation checks ensure it's never None when helper functions execute. All direct .append() calls were replaced with .add() calls to trigger the injection mechanism.

Summary by CodeRabbit

• New Features

  • Added configurable allreduce strategy selection with AUTO, ONESHOT, TWOSHOT, MIN_LATENCY, NCCL, and other modes for distributed operations.
  • Introduced automatic strategy selection mode that optimizes based on runtime constraints.

• Configuration

  • Added allreduce_strategy parameter to sharding and operation configurations.

• Improvements

  • Enhanced workspace size management with environment variable override support.
  • Added strategy validation and runtime constraints with fallback behavior.
  • Improved logging for strategy configuration visibility.

• Tests

  • Added multi-GPU allreduce strategy validation tests.

Description

Test Coverage

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

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[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request adds comprehensive allreduce strategy support to TensorRT-LLM's auto-deploy system. Strategy parameters are introduced as configuration fields and propagated through custom ops APIs, distributed operations, and sharding utilities. A centralized validator normalizes strategy values (strings/enums to AllReduceStrategy type). Runtime enforcement in C++ validates strategy feasibility. A new multi-GPU test suite validates end-to-end strategy behavior.

Changes

Cohort / File(s)	Summary
Configuration & Defaults tensorrt_llm/_torch/auto_deploy/config/default.yaml	Added new config field allreduce_strategy: 'AUTO' under detect_sharding transform.
Custom Operations APIs tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py, linear.py, quant.py	Added mandatory strategy: str parameter to all_reduce(), all_reduce_fake(), fused_linear_all_reduce(), fused_linear_all_reduce_fake(), fused_fp8_linear_all_reduce(), and corresponding fake variants. Strategy is forwarded to TRT-LLM allreduce ops when available.
Distributed Operations tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py	Updated trtllm_allreduce() to accept strategy: str parameter, convert to AllReduceStrategy enum with validation, and use in cache key. Updated fused_allreduce_residual_rmsnorm() signatures (real and fake) to include strategy: str = "AUTO" parameter and propagate through to trtllm_allreduce().
Sharding Configuration tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py	Added public field allreduce_strategy: AllReduceStrategy to ShardingTransformConfig with default AUTO and field validator _validate_allreduce_strategy() for enum conversion.
Sharding Utilities tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py	Introduced centralized validate_allreduce_strategy() validator. Added mandatory allreduce_strategy: AllReduceStrategy parameter to _insert_sharded_mamba(), _shard_parameter_node(), _insert_sharded_moe(), and _insert_sharded_mxfp4_mlp_ep(). Added allreduce_strategy field to ShardingTransformInfo, ParameterUpdateInfo, and ShardingConfig with validators. Injected strategy into transforms lacking it.
Transform Library tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py	Updated calls to torch.ops.auto_deploy.torch_dist_all_reduce() in _allreduce_residual_rmsnorm_pattern and _allreduce_residual_rmsnorm_pattern2 to pass "AUTO" strategy as second argument.
Executor Runtime tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py	Added logging of allreduce strategy from ad_config.transforms (detect_sharding) during create_autodeploy_executor() initialization when non-AUTO strategy is configured.
Workspace Configuration tensorrt_llm/plugin/plugin.py	Enhanced max_workspace_size_auto() to support environment override TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE with logging. Updated default to 64 MiB and added detailed docstring on lamport-buffer workspace calculation.
TensorRT C++ Runtime cpp/tensorrt_llm/thop/allreduceOp.cpp	Added runtime guard in AllreduceOp::selectImplementation(): when non-AUTO strategy (TWOSHOT, ONESHOT, etc.) is specified, validates that seq_len >= group_size. Logs warning and falls back to ONESHOT if constraint violated.
Multi-GPU Test Suite tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py	New test module with parameterized test test_allreduce_strategies() covering AUTO, ONESHOT, TWOSHOT, MIN_LATENCY, NCCL strategies on 2-GPU setup. Includes timeout context manager, dataset fixture, and per-run YAML config generation with strategy injection.

Sequence Diagram

sequenceDiagram
    participant Config as Config<br/>(default.yaml)
    participant ShardingCfg as ShardingTransformConfig<br/>(validate_allreduce_strategy)
    participant SharUtils as Sharding Utils<br/>(_insert_sharded_*)
    participant OpsAPI as Custom Ops API<br/>(dist, linear, quant)
    participant DistOps as Distributed Ops<br/>(trtllm_allreduce)
    participant CppRT as C++ Runtime<br/>(allreduceOp)
    
    Config->>ShardingCfg: allreduce_strategy: 'AUTO'
    Note over ShardingCfg: Validate & convert<br/>string → enum
    ShardingCfg->>SharUtils: allreduce_strategy: AllReduceStrategy
    rect rgb(200, 220, 255)
        Note over SharUtils: Propagate to sharding<br/>transforms (_insert_sharded_*)
        SharUtils->>OpsAPI: strategy parameter
    end
    OpsAPI->>DistOps: strategy: str
    rect rgb(220, 200, 220)
        Note over DistOps: Convert to enum,<br/>validate, cache key,<br/>construct with strategy
    end
    DistOps->>CppRT: strategy in AllReduceStrategy
    rect rgb(255, 220, 200)
        Note over CppRT: Runtime guard:<br/>if non-AUTO, validate<br/>seq_len ≥ group_size<br/>else fallback to ONESHOT
    end

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

• Strategy propagation pattern: The core change—adding strategy parameter through APIs—is repetitive across dist.py, linear.py, quant.py, trtllm.py, and sharding utilities. This homogeneous pattern reduces effort.
• Centralized validation: The validate_allreduce_strategy() utility consolidates enum conversion logic, reducing per-file complexity.
• Areas requiring extra attention:
  • C++ runtime guard (cpp/tensorrt_llm/thop/allreduceOp.cpp): Verify the constraint logic (seq_len >= tp_size) and fallback behavior match TensorRT-LLM allreduce kernel capabilities and is correctly applied across TWOSHOT and other non-AUTO strategies.
  • ShardingConfig injection logic (tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py): Confirm that transforms receiving injected allreduce_strategy preserve backward compatibility and that None-to-AUTO conversion doesn't break existing callers or introduce unintended defaults.
  • Test timeout and fixture (tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py): Validate dataset fixture stability across runs, timeout duration (5 minutes) is sufficient for all strategies on 2-GPU setup, and test isolation (per-run config files) prevents cross-test interference.

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 57.14% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly summarizes the main changes: enlarging AllReduce workspace to 64MB and adding AllReduce strategy to AD config, matching the PR's primary objectives.
Description check	✅ Passed	PR description provides substantial context on all four changes (workspace size increase, kernel fallback, strategy arg, tests) and detailed implementation approach for parameter passing.

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Actionable comments posted: 0

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (1)

588-598: Inconsistent handling of .add() return values.

The code shows inconsistent patterns when calling sharding_config.add():

Correctly checks return value:

• Lines 588-598, 600-610, 644-654, 663-673: if sharding_config.add(...): num_x += 1

Unconditionally increments despite not checking return:

• Lines 612-622 (mamba config): Calls .add() but then unconditionally increments num_row_col_shards on line 623
• Lines 633-642 (local_colwise): Calls .add() without checking return
• Lines 678-687 (fallback case): Calls .add() without checking return

According to the PR description, .add() returns True when the transform is successfully applied and False when the node is already sharded. Unconditional increments could lead to inaccurate counter values if transforms are rejected.

For consistency and accuracy, consider checking the return value in all cases:

 elif config == "mamba":
-    sharding_config.add(
+    if sharding_config.add(
         WeightShardingInfo.from_node(
             lin_node,
             split_dim=SplitDimension.COLUMN,
             rank=rank,
             world_size=world_size,
             dist_op=None,
             min_local_shape=min_local_shape,
             layer_type=LayerType.MAMBA,
         )
-    )
-    num_row_col_shards += 1
+    ):
+        num_row_col_shards += 1

Apply similar fixes to lines 633-642 and 678-687.

Also applies to: 600-610, 612-623, 633-642, 644-654, 663-673, 678-687

🧹 Nitpick comments (3)

tensorrt_llm/plugin/plugin.py (1)

609-616: Consider adding validation for the environment variable.

The current implementation could raise a ValueError if TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE contains a non-numeric value, or could accept invalid values (negative, zero, or unreasonably large).

Consider adding error handling:

 # Allow override via environment variable for edge cases
 workspace_size_env = os.getenv("TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE")
 if workspace_size_env:
-    size = int(workspace_size_env)
+    try:
+        size = int(workspace_size_env)
+        if size <= 0:
+            logger.warning(
+                f"Invalid TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE={size}. Must be positive. Using default."
+            )
+        else:
+            logger.info(
+                f"Using custom allreduce fusion workspace size: {size} bytes ({size / (1024**2):.1f} MiB)"
+            )
+            return size
+    except ValueError:
+        logger.warning(
+            f"Invalid TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE='{workspace_size_env}'. Must be numeric. Using default."
+        )
-    logger.info(
-        f"Using custom allreduce fusion workspace size: {size} bytes ({size / (1024**2):.1f} MiB)"
-    )
-    return size

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (1)

25-33: Broaden strategy parsing to accept enums

Several call sites already traffic AllReduceStrategy objects (for example, the sharding config now stores enums); with the current getattr path, handing one of those directly into trtllm_allreduce raises a TypeError. We can make the conversion resilient by accepting enums (and ints) up front, while still raising a clear ValueError for bad inputs.

-        # Convert string strategy to enum
-        try:
-            strategy_enum = getattr(AllReduceStrategy, strategy)
-        except AttributeError:
-            raise ValueError(
-                f"Invalid allreduce strategy: {strategy}. "
-                f"Valid options: AUTO, NCCL, ONESHOT, TWOSHOT, MIN_LATENCY, "
-                f"LOWPRECISION, UB, MNNVL, NCCL_SYMMETRIC"
-            )
+        if isinstance(strategy, AllReduceStrategy):
+            strategy_enum = strategy
+        else:
+            try:
+                strategy_enum = (
+                    AllReduceStrategy[strategy]
+                    if isinstance(strategy, str)
+                    else AllReduceStrategy(strategy)
+                )
+            except (KeyError, ValueError, TypeError) as err:
+                valid = ", ".join(opt.name for opt in AllReduceStrategy)
+                raise ValueError(
+                    f"Invalid allreduce strategy: {strategy}. Valid options: {valid}"
+                ) from err

tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py (1)

1347-1352: Use model_copy(update=...) to clone frozen transforms

Since these Pydantic models are frozen, model_copy(update=...) communicates intent better than dumping to dict and re-instantiating with type(transform), and it preserves any Pydantic-internal metadata automatically.

-            transform_dict = transform.model_dump()
-            transform_dict["allreduce_strategy"] = self.allreduce_strategy
-            transform = type(transform)(**transform_dict)
+            transform = transform.model_copy(
+                update={"allreduce_strategy": self.allreduce_strategy}
+            )

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📥 Commits

Reviewing files that changed from the base of the PR and between 9ef7eb7 and 3334637.

📒 Files selected for processing (12)

• cpp/tensorrt_llm/thop/allreduceOp.cpp (1 hunks)
• tensorrt_llm/_torch/auto_deploy/config/default.yaml (1 hunks)
• tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py (1 hunks)
• tensorrt_llm/_torch/auto_deploy/custom_ops/linear.py (1 hunks)
• tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py (3 hunks)
• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (4 hunks)
• tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py (1 hunks)
• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py (2 hunks)
• tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (16 hunks)
• tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py (22 hunks)
• tensorrt_llm/plugin/plugin.py (1 hunks)
• tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py (1 hunks)

🧰 Additional context used

🧠 Learnings (14)

📓 Common learnings

Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149
Timestamp: 2025-10-13T19:45:03.518Z
Learning: In test_nccl_device.py, the NCCL device AllReduce implementation compares the entire residual tensor on each rank, unlike the UB implementation which compares per-rank chunks. The residual chunking calculations in the test are intentionally overridden to reflect this design difference.

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7520
File: tensorrt_llm/_torch/pyexecutor/resource_manager.py:605-613
Timestamp: 2025-09-24T03:31:28.908Z
Learning: In TensorRT-LLM Ray orchestrator mode, ProcessGroups are initialized with both Gloo and NCCL backends (e.g., "cuda:nccl,cpu:gloo"), allowing PyTorch distributed to automatically route CPU tensors through Gloo and GPU tensors through NCCL. This eliminates the need for manual device placement when performing allreduce operations on base types.

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device allreduce implementation (cpp/tensorrt_llm/thop/allreduceOp.cpp), the goto pattern in runNCCLAllReduceDeviceFusion is intentionally used for future extensibility, allowing multiple switch cases to fallback to the default handler. While not aesthetically ideal, this pattern supports adding more fusion cases later that can reuse the same fallback logic.

Applied to files:

• cpp/tensorrt_llm/thop/allreduceOp.cpp
• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py
• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

📚 Learning: 2025-08-14T06:36:40.701Z

Learnt from: timlee0212
Repo: NVIDIA/TensorRT-LLM PR: 6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.701Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.

Applied to files:

• cpp/tensorrt_llm/thop/allreduceOp.cpp
• tensorrt_llm/_torch/auto_deploy/config/default.yaml
• tensorrt_llm/plugin/plugin.py
• tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py
• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py
• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.

Applied to files:

• cpp/tensorrt_llm/thop/allreduceOp.cpp
• tensorrt_llm/plugin/plugin.py
• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py
• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

📚 Learning: 2025-08-20T06:56:02.889Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:577-579
Timestamp: 2025-08-20T06:56:02.889Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, maxSequenceLength is now enforced as a non-optional argument in the BlockManager constructor, so concerns about std::nullopt defaulting to 0 are not applicable. When windowSize > maxSequenceLength, a warning should be added instead of handling optional parameter cases.

Applied to files:

• cpp/tensorrt_llm/thop/allreduceOp.cpp

📚 Learning: 2025-08-14T23:23:27.449Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.

Applied to files:

• cpp/tensorrt_llm/thop/allreduceOp.cpp

📚 Learning: 2025-08-08T04:10:19.038Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6728
File: cpp/tensorrt_llm/plugins/mixtureOfExperts/mixtureOfExpertsPlugin.cpp:966-966
Timestamp: 2025-08-08T04:10:19.038Z
Learning: TensorRT plugins currently don't support padding functionality, and TensorRT is not getting new features (in maintenance mode). This means that duplicating parameters like mExpertHiddenSize in function calls, even with TODO comments, can be acceptable as pragmatic solutions within these constraints.

Applied to files:

• tensorrt_llm/plugin/plugin.py

📚 Learning: 2025-08-21T00:16:56.457Z

Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.

Applied to files:

• tensorrt_llm/plugin/plugin.py

📚 Learning: 2025-08-19T03:35:20.866Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.

Applied to files:

• tensorrt_llm/plugin/plugin.py

📚 Learning: 2025-10-13T19:45:03.518Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149
Timestamp: 2025-10-13T19:45:03.518Z
Learning: In test_nccl_device.py, the NCCL device AllReduce implementation compares the entire residual tensor on each rank, unlike the UB implementation which compares per-rank chunks. The residual chunking calculations in the test are intentionally overridden to reflect this design difference.

Applied to files:

• tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py
• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py
• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py

📚 Learning: 2025-10-20T17:09:21.560Z

Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py:180-182
Timestamp: 2025-10-20T17:09:21.560Z
Learning: In tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py, the _gated_rmsnorm_replacement function does not need to cast the output of torch.ops.auto_deploy.torch_rmsnorm_gated back to the input dtype, even though the custom op returns fp32. The dtype handling is managed elsewhere or the fp32 output is acceptable for downstream consumers.

Applied to files:

• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py

📚 Learning: 2025-10-20T16:54:09.824Z

Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py:6-6
Timestamp: 2025-10-20T16:54:09.824Z
Learning: In tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py, the import `from ...modules.mamba.layernorm_gated import _layer_norm_fwd` is correct and should not be changed to modules.fla.layernorm_gated. The _layer_norm_fwd function exists in both modules/mamba/layernorm_gated.py and modules/fla/layernorm_gated.py, but the mamba version is the intended implementation for this use case.

Applied to files:

• tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py

📚 Learning: 2025-08-14T15:38:01.771Z

Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.

Applied to files:

• tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

🧬 Code graph analysis (7)

tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py (1)

tests/integration/defs/conftest.py (1)

• llm_root (192-193)

tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py (3)

tensorrt_llm/functional.py (1)

• AllReduceStrategy (3876-3885)

tensorrt_llm/builder.py (1)

• default (45-50)

tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (1)

• _validate_allreduce_strategy (163-165)

tensorrt_llm/_torch/auto_deploy/custom_ops/linear.py (2)

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (3)

• is_trtllm_op_available (90-92)
• trtllm_allreduce (21-44)
• trtllm_allreduce (84-85)

tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py (1)

• all_reduce (29-43)

tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (2)

tensorrt_llm/functional.py (1)

• AllReduceStrategy (3876-3885)

tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py (7)

• validate_allreduce_strategy (33-60)
• _validate_allreduce_strategy (589-593)
• _validate_allreduce_strategy (1316-1318)
• add (1339-1369)
• EPShardingInfo (1178-1201)
• from_node (654-659)
• from_node (1185-1190)

tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py (1)

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (2)

• trtllm_allreduce (21-44)
• trtllm_allreduce (84-85)

tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py (1)

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (3)

• is_trtllm_op_available (90-92)
• trtllm_allreduce (21-44)
• trtllm_allreduce (84-85)

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py (3)

cpp/tensorrt_llm/thop/allreduceOp.cpp (3)

• op (1036-1036)
• op (1075-1075)
• rank (814-925)

tensorrt_llm/functional.py (1)

• AllReduceStrategy (3876-3885)

tensorrt_llm/_torch/distributed/ops.py (1)

• AllReduce (554-710)

🪛 Ruff (0.14.4)

tests/unittest/_torch/auto_deploy/unit/multigpu/test_ad_allreduce_strategies.py

11-11: Unused noqa directive (non-enabled: F401)

Remove unused noqa directive

(RUF100)

---

33-33: Unused function argument: signum

(ARG001)

---

33-33: Unused function argument: frame

(ARG001)

---

34-34: Avoid specifying long messages outside the exception class

(TRY003)

---

86-86: subprocess call: check for execution of untrusted input

(S603)

---

90-90: Avoid specifying long messages outside the exception class

(TRY003)

---

107-107: Unused function argument: llm_root

(ARG001)

tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py

54-57: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

---

54-57: Avoid specifying long messages outside the exception class

(TRY003)

---

297-299: Avoid specifying long messages outside the exception class

(TRY003)

---

458-460: Avoid specifying long messages outside the exception class

(TRY003)

---

1021-1021: Avoid specifying long messages outside the exception class

(TRY003)

---

1139-1141: Avoid specifying long messages outside the exception class

(TRY003)

tensorrt_llm/_torch/auto_deploy/custom_ops/linear.py

48-48: Unused function argument: strategy

(ARG001)

tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py

260-260: Unused function argument: strategy

(ARG001)

tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py

47-47: Unused function argument: strategy

(ARG001)

tensorrt_llm/_torch/auto_deploy/distributed/trtllm.py

29-33: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

---

29-33: Avoid specifying long messages outside the exception class

(TRY003)

---

71-71: Unused function argument: residual

(ARG001)

---

72-72: Unused function argument: norm_weight

(ARG001)

---

73-73: Unused function argument: eps

(ARG001)

---

74-74: Unused function argument: strategy

(ARG001)

---

84-84: Unused function argument: tensor

(ARG001)

---

84-84: Unused function argument: op

(ARG001)

---

84-84: Unused function argument: strategy

(ARG001)

---

84-84: Unused function argument: all_reduce_params

(ARG001)

🔇 Additional comments (11)

tensorrt_llm/plugin/plugin.py (2)

584-603: Excellent documentation for workspace calculation.

The docstring clearly explains the lamport buffer sizing, triple buffering mechanism, TP scaling, and provides a concrete example. This will help users understand when they need to adjust the workspace size.

---

618-621: LGTM - workspace size increase addresses critical issues.

The explicit 64 MiB default (8x increase from the previous 8 MB) aligns with the PR objectives to fix hangs and crashes. The clear comment provides guidance for users with larger models who may need to override via the environment variable.

tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py (1)

242-242: LGTM: Strategy parameter added correctly.

The strategy parameter has been properly added to both the real and fake implementations of fused_fp8_linear_all_reduce. The real implementation (line 251) correctly forwards the strategy to trtllm_allreduce when the TRT-LLM op is available.

The static analysis warning about unused strategy in the fake function (line 260) is a false positive—fake functions maintain signature parity for PyTorch tracing/export without executing the actual operation.

Also applies to: 260-260

tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py (1)

328-332: LGTM: Good observability enhancement.

The logging of the configured allreduce strategy provides valuable runtime visibility without affecting execution behavior. The check for non-AUTO strategies ensures the log is only emitted when users have explicitly configured a specific strategy.

tensorrt_llm/_torch/auto_deploy/config/default.yaml (1)

83-83: LGTM: Configuration field added appropriately.

The allreduce_strategy: 'AUTO' field extends the detect_sharding configuration with a sensible default that enables automatic strategy selection. This aligns with the AllReduceStrategy.AUTO enum and integrates well with the validation and propagation logic in the sharding pipeline.

cpp/tensorrt_llm/thop/allreduceOp.cpp (1)

962-968: LGTM: Good defensive fallback for TWOSHOT constraint.

The runtime check gracefully handles cases where TWOSHOT strategy is requested but seq_len < tp_size, falling back to ONESHOT with a clear warning message. This prevents the operation from hitting the TORCH_CHECK at line 603 and throwing an error, allowing execution to continue with a valid strategy.

This defensive programming aligns with the PR's stated objective of adding a fallback when sequence length is too short for the one-shot kernel.

tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py (1)

31-31: LGTM: Pattern functions updated with explicit strategy parameter.

The addition of "AUTO" as the strategy argument to torch_dist_all_reduce in both pattern functions correctly aligns with the updated API signature in dist.py. This ensures pattern matching captures the new parameter structure while maintaining automatic strategy selection.

Also applies to: 55-55

tensorrt_llm/_torch/auto_deploy/custom_ops/dist.py (1)

29-43: LGTM: Strategy parameter integrated correctly.

The strategy parameter has been properly added as a mandatory argument to all_reduce, with clear documentation indicating it's required. The implementation correctly forwards the strategy to trtllm_allreduce when the TRT-LLM op is available, while falling back to standard all_reduce otherwise.

The static analysis warning about unused strategy in the fake function (line 47) is a false positive—fake implementations maintain API parity for tracing without executing the actual distributed operation.

tensorrt_llm/_torch/auto_deploy/custom_ops/linear.py (1)

37-44: LGTM: Fused linear all-reduce updated with strategy parameter.

The strategy parameter has been properly integrated into both the signature and implementation of fused_linear_all_reduce. The docstring correctly documents that strategy is mandatory, and the real implementation forwards it to trtllm_allreduce when available.

The static analysis warning about unused strategy in the fake function (line 48) is a false positive—fake implementations maintain interface parity for PyTorch's tracing/export system.

tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (2)

154-165: LGTM: AllReduce strategy configuration with proper validation.

The allreduce_strategy field has been correctly added to ShardingTransformConfig with:

• Appropriate default value (AllReduceStrategy.AUTO)
• Clear description of available options
• Pre-validation using the shared validate_allreduce_strategy function to convert string/int inputs to the enum

This establishes a clean configuration path for allreduce strategy selection.

---

213-213: LGTM: Strategy propagation to sharding config.

The allreduce_strategy is correctly propagated from the transform config to the shared sharding_config (line 213), ensuring the strategy setting flows through the sharding pipeline and is available for downstream operations.

[greg-kwasniewski1]

One larger comment - I'd move all_reduce_strategy from parent ShardingTransformInfo class to WeightShardingInfo chilld class.

[lucaslie]

Design looks good overall. Just a few comments to refine the PR

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #24682 [ run ] triggered by Bot. Commit: 34aa1a8

[tensorrt-cicd]

PR_Github #24682 [ run ] completed with state FAILURE. Commit: 34aa1a8
/LLM/main/L0_MergeRequest_PR pipeline #18637 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #24684 [ run ] triggered by Bot. Commit: 34aa1a8

[tensorrt-cicd]

PR_Github #24684 [ run ] completed with state SUCCESS. Commit: 34aa1a8
/LLM/main/L0_MergeRequest_PR pipeline #18639 completed with status: 'FAILURE'

[MrGeva]

/bot run

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #24779 [ run ] triggered by Bot. Commit: 34aa1a8

[tensorrt-cicd]

PR_Github #24779 [ run ] completed with state SUCCESS. Commit: 34aa1a8
/LLM/main/L0_MergeRequest_PR pipeline #18696 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #24863 [ run ] triggered by Bot. Commit: 34aa1a8

[tensorrt-cicd]

PR_Github #24863 [ run ] completed with state SUCCESS. Commit: 34aa1a8
/LLM/main/L0_MergeRequest_PR pipeline #18767 completed with status: 'FAILURE'

[tensorrt-cicd]

PR_Github #25499 [ run ] triggered by Bot. Commit: 301b977

[tensorrt-cicd]

PR_Github #25499 [ run ] completed with state SUCCESS. Commit: 301b977
/LLM/main/L0_MergeRequest_PR pipeline #19310 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #25526 [ run ] triggered by Bot. Commit: 301b977

[tensorrt-cicd]

PR_Github #25526 [ run ] completed with state SUCCESS. Commit: 301b977
/LLM/main/L0_MergeRequest_PR pipeline #19331 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #25554 [ run ] triggered by Bot. Commit: 301b977

[tensorrt-cicd]

PR_Github #25554 [ run ] completed with state SUCCESS. Commit: 301b977
/LLM/main/L0_MergeRequest_PR pipeline #19353 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #25580 [ run ] triggered by Bot. Commit: 92b0944

[tensorrt-cicd]

PR_Github #25580 [ run ] completed with state SUCCESS. Commit: 92b0944
/LLM/main/L0_MergeRequest_PR pipeline #19377 completed with status: 'FAILURE'

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #25607 [ run ] triggered by Bot. Commit: 92b0944

[tensorrt-cicd]

PR_Github #25607 [ run ] completed with state FAILURE. Commit: 92b0944
LLM/main/L0_MergeRequest_PR #19399 (Blue Ocean) completed with status: ABORTED

[MrGeva]

/bot run

[MrGeva]

/bot run

[tensorrt-cicd]

PR_Github #25672 [ run ] triggered by Bot. Commit: 92b0944

[tensorrt-cicd]

PR_Github #25673 [ run ] triggered by Bot. Commit: 92b0944

[tensorrt-cicd]

PR_Github #25672 [ run ] completed with state ABORTED. Commit: 92b0944

[MrGeva]

@lucaslie can you please approve it?
I got an approval on slack from Yilin Zhang

[tensorrt-cicd]

PR_Github #25673 [ run ] completed with state SUCCESS. Commit: 92b0944
/LLM/main/L0_MergeRequest_PR pipeline #19458 completed with status: 'SUCCESS'