[TRTLLM-6342][fix] Fixed triggering BMM sharding #7389
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Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>
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📝 WalkthroughWalkthroughUpdates default sharding detection config to use ['tp','ep','bmm'] and enables post-sharding shape propagation. Adds a matching test-time detect_sharding override to specify "bmm" in the BMM sharding unit test. No other logic or files changed. Changes
Sequence Diagram(s)sequenceDiagram
participant User as Test Runner
participant Detect as DetectSharding
participant Exec as ShardingTransformExecutor
participant Graph as ModelGraph
User->>Detect: detect_sharding(sharding_dims=['bmm'])
Detect->>Graph: Analyze ops for BMM sharding
Detect-->>User: Sharding plan (BMM)
User->>Exec: apply_sharding(plan, run_shape_prop=true)
Exec->>Graph: Apply sharding
Exec->>Graph: Propagate shapes (post-sharding)
Exec-->>User: Transformed graph
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Actionable comments posted: 1
🧹 Nitpick comments (3)
tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_bmm_sharding.py (3)
67-75: Also enable shape propagation in the test config to avoid overriding the default.Passing a partial transform config can override default.yaml. To be safe, set run_shape_prop: True here as well.
"sharding_transform_executor": { "stage": "sharding", + "run_shape_prop": True, },
1-1: Add NVIDIA copyright header (2025).Repository guidelines require the header on .py files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. """ Tests for basic graph sharding. """
13-13: Preserve module namespaces per Python import guidelines.Import the module and reference the symbol via the namespace.
-from tensorrt_llm._torch.auto_deploy.transform.library.sharding import BMMShardingInfo +from tensorrt_llm._torch.auto_deploy.transform.library import sharding as sharding_lib- expected_transformations.append( - BMMShardingInfo( + expected_transformations.append( + sharding_lib.BMMShardingInfo( target_node=node.name, rank=rank, world_size=world_size, start_idx=start_idx, end_idx=end_idx, ) )Also applies to: 116-122
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tensorrt_llm/_torch/auto_deploy/config/default.yaml(1 hunks)tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_bmm_sharding.py(1 hunks)
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tensorrt_llm/_torch/auto_deploy/config/default.yaml (1)
61-63: Enabling post-sharding shape propagation looks good.This aligns with the TODO and should reduce shape-related follow-up issues.
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3 participants
Commit 2101d incorrectly triggered BMM sharding (relevant for e.g., LLama4). This commit fixes it.
Bug:
Cause:
sharding_dims:dpFix:
sharding_dims: 'bmm` to trigger the BMM sharding.Summary by CodeRabbit
Description
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