[TRTLLM-17210][feat] Qualify tied word embeddings for the MX Qwen3 dense profile by moraxu · Pull Request #20767 · NVIDIA/TensorRT-LLM

moraxu · 2026-10-21T19:41:45Z

Description

Every ModelExpress (MX) post-transform profile pinned tied_word_embeddings to False, so tied Qwen3 checkpoints (Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B, Qwen3-4B-Instruct-2507) always fell back to Hugging Face loading. This PR qualifies them through a sibling Qwen3 profile, with Qwen3-4B as the canary.

Sibling profile with its own ABI. A new exact profile, qwen3-for-causal-lm-bf16-tied-target-v1, sits next to the untied qwen3-for-causal-lm-bf16-target-v1. It uses a new immutable transform-layout ABI, trtllm-qwen3-dense-tied-target-layout-v1, and its constraints are the Qwen3 BF16 dense constraints with tied_word_embeddings pinned to {True}. The untied profile, its ABI and the shared dense constraints are unchanged, so existing untied publications stay byte-identical, and PR 3's FP8 Qwen3 sibling, which derives from the untied constraints, cannot inherit the tie. The registry already accepts several profiles per root as long as their runtime constraints are disjoint. When a run matches none of them, it reports the closest profile, and the untied one wins ties because it is listed first.

Why the ABI is not reused:

Tying changes the set of transferred tensor names and adds an alias relationship. The ABI rules in model-express.md list both as reasons for a new ID, and [TRTLLM-14880][feat] qualify Qwen3 dense for MX #17142 created the Qwen3 ID for an untied head.
Under IdentityCheckPolicy.ENFORCE, SourceIdentity compares only the format version and the ABI (source_identity.py, ENFORCE path), so the model and shard fingerprints that carry the tie are skipped. With a shared ABI, a tied source and an untied receiver could pair under ENFORCE. A separate ABI keeps them apart under every policy; the new tests check this.

No model change. DecoderModelForCausalLM.__init__ already makes lm_head.weight an alias of model.embed_tokens.weight when the config ties them, after asserting that both use the same TP size and mode. This happens on every role, before qualification, before SourceIdentity and before MX discovers tensors. setup_aliases() plays no part. ModelExpress 0.5.1 builds its tensor catalog from named_parameters(remove_duplicate=False), skips runtime aliases and keeps the first name per (device, data_ptr), so the shared table is registered, published and received once, as model.embed_tokens.weight. On the real loader path, meta-init _apply leaves lm_head and embed_tokens as two Parameter objects over one memoized allocation (torch 2.14), so every check in this PR compares memory, never object identity.

Realized-tie check (separate commit). PostTransformRuntimeConfig.from_model_config(..., model=...) now accepts the declared tie_word_embeddings flag only when the constructed model has exactly one lm_head and one embed_tokens weight, and those weights are the same tensor (or identical views of the same memory) exactly when the flag says so. Otherwise the dimension is None, and every profile, all of which constrain the tie, rejects the load. A config flag therefore cannot claim a tie that the model lacks, or hide one that it has. The helper is duck-typed, never raises, and leaves the declared value unchanged when no model is passed. The reviewed outline deferred this check to the Qwen3.5 PR, where composite configs make the top-level flag less trustworthy. It is kept here as its own commit, so it can be dropped without touching the rest.

E2E rows and CI.

qwen3-tied-bf16-tp1 goes in the post-merge 2-GPU block and qwen3-tied-bf16-tp2 in the on-demand 4-GPU block, both on Qwen3/Qwen3-4B with the TRTLLM_MX_QWEN3_TIED_MODEL override.
Compared with the Qwen3-8B canary, Qwen3-4B differs only in hidden and intermediate size and in the tie. It stores no lm_head.weight, so any accidental head load or transfer fails loudly.
In its own last commit, the donor/receiver test now also requires that no receiver rank's RDMA transfer complete record moves more tensors or bytes than the distinct parameter views in that rank's transfer manifest. Without this, CI would stay green if the shared table crossed the wire twice. Empty parameters count once each, so the bound never fails when MX merges them under the null data pointer.

Docs. model-express.md gains:

the new support-table row
a Qwen3 paragraph covering both profiles, the canaries, the single transfer and why the tied profile has its own ABI
the realized-tie rule
the new row IDs and override
"The other TP=1 rows" wording in the stage table, and the same wording in the l0_model_express.yml comment
the tied Qwen3 profile added to the accuracy section's list of profiles that rely on smoke rows, since Qwen3-4B has no bare BF16 GSM8K reference

Evidence (single H200, PostMerge wheel at `0829689` plus this branch as a Python overlay)

Probe: construct the model exactly as ModelLoader.load does (resolved config, MetaInitMode), qualify it, then materialize it with the loader's memo:

Checkpoint	Before (main)	After (this PR)
Qwen3-4B, TP1	`runtime_config_not_supported`, `qwen3-for-causal-lm-bf16-target-v1`, `['tied_word_embeddings']`	`qualified`, `qwen3-for-causal-lm-bf16-tied-target-v1`, `trtllm-qwen3-dense-tied-target-layout-v1`
Qwen3-4B, TP2 rank 0 and rank 1 (meta only)	`runtime_config_not_supported`	`qualified` (tied profile); head and table are the same `(75968, 2560)` shard
Qwen3-8B, TP1 (untied control)	`qualified` (untied profile)	`qualified`, `qwen3-for-causal-lm-bf16-target-v1`; MX catalog 291 tensors, 16,381,470,720 B, `lm_head.weight` present
Qwen3-0.6B, TP1	`runtime_config_not_supported`	`qualified` (tied profile); MX catalog 226 tensors, 1,192,099,840 B

Qwen3-4B TP1 after materialization:

Same Parameter object: False; same storage: True. The realized tie and the runtime config still report True.
MX catalog: 290 tensors, 8,044,936,192 B (8.04 GB), with no lm_head.weight. A separate head would be 291 tensors and 8.82 GB.
Allocated delta: 8,101,708,800 B, about 57 MB above the catalog bytes. A second copy of the table would add 0.78 GB.

A full LLM(..., checkpoint_format="MX") load of Qwen3-4B with no MX server and TLLM_LOG_LEVEL=INFO:

Main: exit 0; logs MX loading unavailable, then Skipping MX post-transform publish for Qwen3ForCausalLM: qualification reason=runtime_config_not_supported, unsupported_runtime_dimensions=tied_word_embeddings.
This PR: exit 0; logs MX loading unavailable, with no skip line, so the load qualified.
In both runs the final weight manifest records the alias group [["lm_head.weight", "model.embed_tokens.weight"]] over 291 parameter entries. The new transfer bound computed from that manifest is exactly 290 tensors and 8,044,936,192 B.

Notes for reviewers

[None][feat] Add lm_head_output_dtype for FP32 LM head logits #19850 (FP32 LM-head output) changes only the dtype of the logits GEMM output. The weights stay in the model dtype, so the head stays tied, and MX needs no new runtime dimension.
get_rank_model_storage deduplicates by Parameter object, so the Use X GB for model weights log line counts the tied table twice (8.22 GiB for Qwen3-4B). It is used only for logging and predates this PR.
After a partial RDMA write, TrtllmAdapter.reinit_for_retry relies on the native HF loader to rewrite the whole canonical catalog. For a tied model the HF loader skips lm_head, and the catalog holds only model.embed_tokens.weight, so the invariant still holds.
keep_embed_tokens is set at construction only for the last PP rank. It matters only with PP, which every MX profile pins to 1.
Follow-ups, not in this PR:
- a tied Qwen2/Qwen2.5 sibling profile (Qwen2.5-0.5B, 1.5B and 3B-Instruct are tied)
- tied Qwen3.5 rows once the Qwen3.5 profile lands
- deduplicating get_rank_model_storage by storage
- a tie-specific check for the tied E2E rows, for example asserting tie_word_embeddings in the resolved checkpoint's config.json before the roles launch. Today the rows would pass silently if TRTLLM_MX_QWEN3_TIED_MODEL pointed at an untied checkpoint.

Test Coverage

Unit tests, tests/unittest/_torch/executor/test_model_loader_mx.py:

cpu_only (CPU-Generic stages):
- test_public_support_table_matches_qualified_profile_registry (the doc row is rendered from the registry)
- test_bf16_dense_profiles_scope_tied_embeddings[llama|qwen2|qwen3|mistral]: only Qwen3 qualifies a tie, an unknown tie is rejected everywhere
- test_qwen3_tied_profile_rejects_unqualified_runtime_variants[...]: every unqualified variant on top of the tied profile
- test_bf16_dense_profiles_reject_wrong_realized_dimension[qwen3-tied-fused-rope]
- test_bf16_dense_profiles_reject_unqualified_runtime_variants, whose shared tied-embeddings variant moves to the scope test
GPU stages:
- test_qwen3_dense_tied_profile_qualifies_full_staged_lifecycle
- test_qwen3_dense_tied_profile_qualifies_tp2_rank_lifecycle[0|1]: the receiver is zeroed and gets only the MX catalog, so an uncovered head would fail
- test_mx_canonical_catalog_holds_tied_embedding_once[tp1|tp2-rank0|tp2-rank1]
- test_staged_qwen3_tied_finalization_preserves_mx_tensor_catalog
- test_qwen3_tied_and_untied_source_identities_never_pair: real tiny models; model and shard fingerprints and the ABI differ, and ENFORCE refuses on the ABI alone
- test_mx_staged_receiver_load_keeps_tied_qwen3_table_in_one_allocation: CUDA; the real ModelLoader.load() with real MetaInitMode, memoized _apply and SourceIdentity
- test_qwen3_dense_profiles_reject_tie_the_model_does_not_realize[declared-tie-not-realized|realized-tie-not-declared]

Other unit tests:

tests/unittest/_torch/weight_sharing/test_post_transform_profiles.py (A10 stage): test_runtime_config_realizes_tied_word_embeddings_from_model[...] (shared Parameter, shared memory after a split, distinct, meta, different view, no head, ambiguous heads), test_runtime_config_tie_needs_a_tensor_weight, test_runtime_config_keeps_declared_tie_without_model.
tests/unittest/_torch/weight_sharing/test_source_identity.py (cpu_only): the tied ABI in test_from_model_config_binds_transform_abi, plus test_qwen3_tied_transform_abi_shares_only_with_the_same_abi[warn_fallback|enforce x same-tied|qwen3-untied|missing].
tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py (cpu_only): test_mx_transfer_upper_bound_counts_each_shared_view_once, test_receiver_transfer_must_stay_within_its_manifest[...].

E2E rows:

model_express/test_model_express.py::test_mx_donor_receiver[qwen3-tied-bf16-tp1] runs in DGX_H100-2_GPUs-PyTorch-ModelExpress-Post-Merge-1.
model_express/test_model_express.py::test_mx_donor_receiver[qwen3-tied-bf16-tp2] runs in DGX_H100-4_GPUs-PyTorch-ModelExpress-OnDemand-1.

Expected receiver evidence:

TP1: RDMA transfer complete: 290 tensors, 8.04 GB. TP2: 290 tensors and about 4.02 GB per rank.
The staged-path line names qwen3-for-causal-lm-bf16-tied-target-v1 and trtllm-qwen3-dense-tied-target-layout-v1.
The alias group lm_head.weight / model.embed_tokens.weight is identical for baseline, donor and receiver at both manifest tiers.

Every MX stage needs the ci: full pre-merge approved label. A bare /bot run never schedules the new rows, and runs started with --stage-list do not update the GitHub check.

Merge-gating run: the default pre-merge set, the auto-added multi-GPU stages, and all three MX stages:

/bot run --extra-stage "DGX_H100-2_GPUs-PyTorch-ModelExpress-1, DGX_H100-2_GPUs-PyTorch-ModelExpress-Post-Merge-1, DGX_H100-4_GPUs-PyTorch-ModelExpress-OnDemand-1"

Cheap rerun of only the MX stages (for example to get the E2E evidence first):

/bot run --stage-list "DGX_H100-2_GPUs-PyTorch-ModelExpress-1, DGX_H100-2_GPUs-PyTorch-ModelExpress-Post-Merge-1, DGX_H100-4_GPUs-PyTorch-ModelExpress-OnDemand-1"

Stages that hold the MX unit tests only (no label needed):

/bot run --stage-list "CPU-Generic-x86-1, CPU-Generic-arm-1, DGX_H100-PyTorch-1, DGX_H100-PyTorch-2, DGX_H100-PyTorch-3, DGX_H100-PyTorch-4, DGX_H100-PyTorch-5, DGX_H100-PyTorch-6, A10-PyTorch-1, A10-PyTorch-2, A10-PyTorch-3"

PR Checklist

Please review the following before submitting your PR:

PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.

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Dev Engineer Review

Adds a dedicated tied Qwen3 MX profile and layout ABI.
Validates realized embedding aliasing against tie_word_embeddings.
Adds transfer bounds based on distinct parameter views and bytes.
Preserves separation between tied and untied source identities.
No test execution results or additional review findings were supplied.

QA Engineer Review

Adds unit coverage for profile qualification, alias detection, ABI identity, transfer bounds, TP behavior, and rejection paths.
Adds Qwen3 tied ModelExpress cases for TP1 and TP2.
Lists the integration cases in tests/integration/test_lists/test-db/l0_model_express.yml.
Coverage verdict: sufficient based on the supplied test scope. Execution status is unavailable.

Per-File QA Perspective

docs/source/features/model-express.md: Documents tied-profile matching, aliasing, transfer behavior, qualification rows, and smoke-test limitations.
tensorrt_llm/_torch/pyexecutor/model_loader.py: Adds tied-profile matching. Verify tied and untied checkpoint qualification.
tensorrt_llm/_torch/weight_sharing/__init__.py: Exports the new ABI constant. Verify import compatibility.
tensorrt_llm/_torch/weight_sharing/post_transform_profiles.py: Adds runtime alias detection and declaration validation. Verify shared, distinct, missing, and invalid weight cases.
tests/integration/defs/model_express/mx_harness.py: Validates transfer counts and bytes against manifests, including shared-storage deduplication.
tests/integration/defs/model_express/test_model_express.py: Covers tied Qwen3 donor/receiver flows for TP1 and TP2. The cases are listed in the ModelExpress CI test list.
tests/integration/test_lists/test-db/l0_model_express.yml: Adds tied Qwen3 TP1 and TP2 CI entries.
tests/unittest/_torch/executor/test_model_loader_mx.py: Covers qualification, materialization, TP behavior, lifecycle equivalence, identity separation, and rejection paths. Test-list coverage is not supplied.
tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py: Covers transfer bounds, aliases, empty parameters, and over-limit transfers. Test-list coverage is not supplied.
tests/unittest/_torch/weight_sharing/test_post_transform_profiles.py: Covers realized embedding alias detection and inconsistent declarations. Test-list coverage is not supplied.
tests/unittest/_torch/weight_sharing/test_source_identity.py: Covers same-ABI sharing and cross-ABI rejection. Test-list coverage is not supplied.

…nse profile Add a sibling `qwen3-for-causal-lm-bf16-tied-target-v1` post-transform profile for tied Qwen3 checkpoints. It has its own transform-layout ABI, `trtllm-qwen3-dense-tied-target-layout-v1`, and runtime constraints that pin `tied_word_embeddings` to True. The untied Qwen3 profile, its ABI and the shared dense constraints are unchanged. Tests cover the tied tiny Qwen3 lifecycle at TP1 and on both TP2 ranks, with a receiver that gets only the MX tensor catalog, the catalog itself, staged finalization, tie scoping across the dense roots, the tied profile's rejected runtime variants, ABI pairing under every identity policy, real-model SourceIdentity separation, and a CUDA test through the real meta-init `ModelLoader.load()` path that keeps the shared table in one allocation. Signed-off-by: Michal Guzek <mguzek@nvidia.com>

…declared flag `PostTransformRuntimeConfig.from_model_config` now checks the declared `tie_word_embeddings` flag against a constructed model. The flag counts only when the model's one `lm_head` weight and one `embed_tokens` weight are the same tensor, or identical views of the same memory, exactly when the flag says they are. Otherwise the dimension is unknown, and every profile that constrains it rejects the load. The check never raises. Signed-off-by: Michal Guzek <mguzek@nvidia.com>

Add `qwen3-tied-bf16-tp1` to the post-merge 2-GPU block and `qwen3-tied-bf16-tp2` to the on-demand 4-GPU block. Both load the tied Qwen3-4B checkpoint, with the `TRTLLM_MX_QWEN3_TIED_MODEL` override, and the qualification-test docs name them. Signed-off-by: Michal Guzek <mguzek@nvidia.com>

The donor/receiver test now requires that no receiver rank's RDMA completion record moves more tensors or bytes than the distinct parameter views in that rank's transfer manifest. A storage shared by several parameters, such as a tied LM head, must therefore cross the wire once. Empty parameters count once each, so the bound never rejects ModelExpress merging them under the null data pointer. Signed-off-by: Michal Guzek <mguzek@nvidia.com>

moraxu · 2026-10-21T23:34:48Z

@CodeRabbit fullreview

coderabbitai · 2026-10-21T23:35:01Z

@moraxu I will trigger a full review of all changes in this PR.

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coderabbitai · 2026-10-21T23:39:12Z

Walkthrough

The change adds a tied-embedding Qwen3 MX profile with a distinct layout ABI. Runtime configuration validates declared tying against model weights. ModelExpress tests cover tied Qwen3 at TP=1 and TP=2 and check receiver transfer counts and bytes against weight manifests.

Changes

Qwen3 tied-embedding MX support

Layer / File(s)	Summary
Tied-embedding ABI and runtime validation `tensorrt_llm/_torch/weight_sharing/post_transform_profiles.py`, `tensorrt_llm/_torch/weight_sharing/__init__.py`, `tests/unittest/_torch/weight_sharing/test_post_transform_profiles.py`, `tests/unittest/_torch/weight_sharing/test_source_identity.py`	Adds a tied-layout ABI and detects whether model weights share memory. Runtime configuration retains the declared tie state only when it matches the constructed model. Tests cover alias detection and source identity compatibility.
Qwen3 tied profile and staged loading `tensorrt_llm/_torch/pyexecutor/model_loader.py`, `tests/unittest/_torch/executor/test_model_loader_mx.py`, `docs/source/features/model-express.md`	Adds tied Qwen3 runtime constraints and a post-transform profile. Tests cover profile selection, staged loading, tied storage, catalog contents, TP ranks, and rejection of unsupported or unrealized ties.
Transfer bounds and ModelExpress qualification `tests/integration/defs/model_express/*`, `tests/integration/test_lists/test-db/l0_model_express.yml`, `tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py`, `docs/source/features/model-express.md`	Checks receiver RDMA transfer counts and bytes against manifest-derived bounds. Adds tied Qwen3 TP=1 and TP=2 ModelExpress cases and updates qualification documentation and test selection.

Priority: ⬇️ Low

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Qwen3Model
  participant RuntimeConfig
  participant MXProfileRegistry
  participant MXReceiver
  Qwen3Model->>RuntimeConfig: Provide declared tie setting and model weights
  RuntimeConfig->>RuntimeConfig: Check embedding and lm_head weight aliasing
  RuntimeConfig->>MXProfileRegistry: Provide validated runtime constraints
  MXProfileRegistry-->>RuntimeConfig: Select tied Qwen3 profile and layout ABI
  MXReceiver->>MXReceiver: Rebuild lm_head alias from embed_tokens.weight

Suggested reviewers: qijune, chienchunhung

Merge Risk: ⚪ Minimal · up to da97a

This change adds a separate qualified profile for tied Qwen3 checkpoints, validates the declared embedding tie against the built model, and extends tests and documentation. No merge-blocking risk was found in the reviewed material; the multi-GPU ModelExpress rows still need to run on suitable hardware in CI.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 25.49% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 51 functions across 9 files. (2 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly and concisely identifies the ticket, feature type, and primary change: qualifying tied word embeddings for the MX Qwen3 dense profile.
Description check		The description is complete and relevant. It explains the problem, implementation, ABI rationale, validation, documentation, test coverage, E2E coverage, and merge-gating commands. The final checklist…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Description check

Explanation

The description is complete and relevant. It explains the problem, implementation, ABI rationale, validation, documentation, test coverage, E2E coverage, and merge-gating commands. The final checklist checkbox is not marked, but this is non-critical because the required content is otherwise provided.

Full details: Docstring Coverage

Explanation

Docstring coverage is 25.49% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 51 functions across 9 files. (2 skipped: 2 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

✨ Finishing Touches: 💡 1

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🧹 Nitpick comments: 1

tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py: 1
15-26: 🧹 Nitpick | 🔵 Trivial

Test coverage summary for the MX transfer-bound change.

Files modified:

tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py

tests/integration/defs/model_express/test_model_express.py

tests/integration/test_lists/test-db/l0_model_express.yml

Test cases added:

test_mx_transfer_upper_bound_counts_each_shared_view_once.

test_receiver_transfer_must_stay_within_its_manifest, with ids empty-parameters-merged, bound, shared-storage-sent-twice, too-many-bytes.

test_mx_donor_receiver[qwen3-tied-bf16-tp1].

test_mx_donor_receiver[qwen3-tied-bf16-tp2].

Behavior covered:

Aliased non-empty storage counts once.

Empty parameters count individually.

The tensor-count limit and the byte limit each fail the check when exceeded.

The real E2E rows call assert_transfer_within_manifests after the manifest checks.

Test-list coverage:

l0_model_express.yml lists qwen3-tied-bf16-tp2 in the 4-GPU pre-merge group.

It lists qwen3-tied-bf16-tp1 in the 2-GPU post-merge group.

Both selectors match the new parameter ids.

Verdict: sufficient. I found no assertion that can pass silently, no flaky pattern, and no missing capability gate.

Also applies to: 51-56, 229-273
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Review comment at
@tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py around lines
15 - 26:
No implementation change is requested: the review confirms the coverage around
test_mx_transfer_upper_bound_counts_each_shared_view_once,
test_receiver_transfer_must_stay_within_its_manifest, and the MX donor/receiver
integration cases is sufficient. Leave the current test and harness code
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Review comments at
@tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py:
- Around line 15-26: No implementation change is requested: the review confirms
the coverage around test_mx_transfer_upper_bound_counts_each_shared_view_once,
test_receiver_transfer_must_stay_within_its_manifest, and the MX donor/receiver
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📒 Files selected for processing: 11

docs/source/features/model-express.md
tensorrt_llm/_torch/pyexecutor/model_loader.py
tensorrt_llm/_torch/weight_sharing/__init__.py
tensorrt_llm/_torch/weight_sharing/post_transform_profiles.py
tests/integration/defs/model_express/mx_harness.py
tests/integration/defs/model_express/test_model_express.py
tests/integration/test_lists/test-db/l0_model_express.yml
tests/unittest/_torch/executor/test_model_loader_mx.py
tests/unittest/_torch/weight_sharing/test_mx_harness_artifacts.py
tests/unittest/_torch/weight_sharing/test_post_transform_profiles.py
tests/unittest/_torch/weight_sharing/test_source_identity.py

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moraxu added 4 commits October 21, 2026 12:38

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[TRTLLM-17210][feat] Qualify tied word embeddings for the MX Qwen3 dense profile - #20767

[TRTLLM-17210][feat] Qualify tied word embeddings for the MX Qwen3 dense profile#20767
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Evidence (single H200, PostMerge wheel at 0829689 plus this branch as a Python overlay)

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moraxu commented Oct 21, 2026

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moraxu commented Oct 21, 2026 •

edited by coderabbitai Bot

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Evidence (single H200, PostMerge wheel at `0829689` plus this branch as a Python overlay)

coderabbitai Bot commented Oct 21, 2026 •

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coderabbitai Bot commented Oct 21, 2026 •

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