[Bugfix][Model] Fix DiffusionGemma self-conditioning with tensor parallelism

[shubhamprshr27]

Purpose

Fix DiffusionGemma self-conditioning when the LM head/embedding table is tensor-parallel sharded.

Fixes - #45719

Previously, the sampler computed soft self-conditioning embeddings as:

soft_embeds = probs @ embed_weight

This assumes embed_weight contains the full vocabulary. Under tensor parallelism, each rank only owns a local vocab shard, so the full-vocab probability tensor is incompatible with the rank-local embedding table.

This PR moves the soft-embedding computation into a helper that:

1. preserves the existing full-vocab path for non-TP execution,
2. slices the full-vocab probabilities into each rank's local vocab shard,
3. handles both original and added vocab ranges using embed_tokens.shard_indices,
4. computes each rank's local hidden contribution,
5. all-reduces the hidden contribution across tensor-parallel ranks.

Conceptually, instead of requiring every rank to compute:
P @ W

each TP rank computes:
P_r @ W_r

and vLLM sums the result across TP ranks:
sum_r(P_r @ W_r) == P @ W

Test Plan

Added ests/model_executor/test_diffusion_gemma.py covering:

1. full-vocab / non-sharded behavior,
2. tensor-parallel shard probability slicing,
3. error handling for vocab mismatch without shard metadata.

Local checks

python -m py_compile vllm/model_executor/models/diffusion_gemma.py tests/model_executor/test_diffusion_gemma.py
git diff --check

CI should run:

pytest tests/model_executor/test_diffusion_gemma.py

Test Result

Local checks passed:

python -m py_compile vllm/model_executor/models/diffusion_gemma.py tests/model_executor/test_diffusion_gemma.py
git diff --check

I also validated the behavior locally with an 8-way tensor-parallel DiffusionGemma smoke test:

Before this change, the unpatched path fails at the old self-conditioning matmul because probs is full-vocab while embed_weight is rank-local:

RuntimeError: a and b must have the same reduction dim ... X [32768, 2816]

from user code:
  File "vllm/model_executor/models/diffusion_gemma.py", in _compiled_sample_step
    soft_embeds = torch.matmul(probs.to(embed_weight.dtype), embed_weight) * normalizer

After this change, the same TP configuration initializes and completes successfully.

Disclosure: This PR was developed with AI assistance. I reviewed the changed code and validated the behavior with the checks listed above.

---

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[shubhamprshr27]

cc @LucasWilkinson since you added the original DiffusionGemma support in #45163. This fixes a TP self-conditioning issue where full-vocab probabilities were multiplied with a rank-local embedding shard.

Also cc @martin-kukla in case you have context on the DiffusionGemma implementation.

[shubhamprshr27]

Looks like this PR and my #46212 are addressing the same DiffusionGemma TP self-conditioning issue via the same sharded probs @ embed_weight identity plus all-reduce. I’m happy to close mine in favor of this if maintainers prefer this PR. I also validated the fix pattern with an 8-way TP smoke test, so I can share that result or add any additional test coverage if useful.

[LucasWilkinson]

should we maybe just try ReplicatedLinear?

[shubhamprshr27]

Hi Lucas,

I looked into ReplicatedLinear. It would work conceptually as a replicated vocab-to-hidden projection, but it would require storing a full transposed copy of the embedding matrix on every TP rank and special handling in load_weights to copy embed_tokens.weight.T into the layer.

Happy to switch to ReplicatedLinear if that is preferred for code simplicity, but I think the current approach is more memory-efficient.

[martin-kukla]

@shubhamprshr27 Thanks a lot for working on this - this is probably a right direction on how to specify TP for the model. It looks like there is a speed regression in single GPU setup. I can send some replicating scripts tomorrow, but the perf hit is around 10%

[shubhamprshr27]

Hi Martin,

Glad you found the approach useful, please send me the replicating scripts, I will have a look and try to fix the issue.

[martin-kukla]

Here are the commands based on @LucasWilkinson's gist: https://gist.github.com/LucasWilkinson/89185e4dc05d300df33a4ce030973911

Run the server:
vllm serve --model RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic --max-num-seqs 4 --max-model-len 8192 --trust-remote-code --diffusion-config '{"canvas_length":256,"max_denoising_steps":16}' --hf-overrides '{"diffusion_sampler":"entropy_bound","diffusion_entropy_bound":0.1,"diffusion_confidence_threshold":0.0}'

Run bench serve:
vllm bench serve --backend vllm --base-url http://localhost:8000 --model "RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic" --dataset-name random --random-input-len 1024 --random-output-len 1024 --ignore-eos --num-prompts 100 --max-concurrency 1 --save-result --save-detailed --result-filename results/diffusion.json

Results without the TP support (single H100):

============ Serving Benchmark Result ============
Successful requests:                     100       
Failed requests:                         0         
Maximum request concurrency:             1         
Benchmark duration (s):                  102.01    
Total input tokens:                      102400    
Total generated tokens:                  102400    
Request throughput (req/s):              0.98      
Output token throughput (tok/s):         1003.79   
Peak output token throughput (tok/s):    5.00      
Peak concurrent requests:                3.00      
Total token throughput (tok/s):          2007.59   
---------------Time to First Token----------------
Mean TTFT (ms):                          344.85    
Median TTFT (ms):                        285.45    
P99 TTFT (ms):                           384.45    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          0.66      
Median TPOT (ms):                        0.66      
P99 TPOT (ms):                           0.72      
---------------Inter-token Latency----------------
Mean ITL (ms):                           225.02    
Median ITL (ms):                         225.89    
P99 ITL (ms):                            252.02    
----------------Diffusion Decoding----------------
Committed throughput (tok/s):            1003.79   
Denoising steps per canvas:              15.99     
Committed per denoising step:            16.01     
Committed tokens:                        102400    
Denoising steps:                         6398      
Canvas positions evaluated:              1740288   
==================================================

Results with the TP support:

============ Serving Benchmark Result ============
Successful requests:                     100       
Failed requests:                         0         
Maximum request concurrency:             1         
Benchmark duration (s):                  107.64    
Total input tokens:                      102400    
Total generated tokens:                  102400    
Request throughput (req/s):              0.93      
Output token throughput (tok/s):         951.28    
Peak output token throughput (tok/s):    5.00      
Peak concurrent requests:                3.00      
Total token throughput (tok/s):          1902.56   
---------------Time to First Token----------------
Mean TTFT (ms):                          346.14    
Median TTFT (ms):                        309.40    
P99 TTFT (ms):                           390.13    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          0.71      
Median TPOT (ms):                        0.71      
P99 TPOT (ms):                           0.78      
---------------Inter-token Latency----------------
Mean ITL (ms):                           243.36    
Median ITL (ms):                         245.08    
P99 ITL (ms):                            269.99    
----------------Diffusion Decoding----------------
Committed throughput (tok/s):            951.28    
Denoising steps per canvas:              16.00     
Committed per denoising step:            16.00     
Committed tokens:                        102400    
Denoising steps:                         6400      
Canvas positions evaluated:              1740800   
==================================================

It's best to look at Mean ITL to spot speed regresion: it goes from 225.02 to 243.36.
I suspect that your change results in some GPU bubbles during sampling even in single GPU setup, but I didn't have time to look into it. I will try to do so later on

[LucasWilkinson]

I think #46177 is actually the better approach (apologies for the delay). Any objections to that approach?

[shubhamprshr27]

As I have commented on the PR, I am fine either way.

@LucasWilkinson @martin-kukla, please let me know if you prefer that PR. I can close this one and help with the testing and triaging of that.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @shubhamprshr27.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork