[ROCm] Add AMD GPU support on Deepseek v3.2 and SparseMLA #26670

ganyi1996ppo · 2025-10-13T02:56:30Z

Purpose

The PR add Deepseek v3.2 support on ROCm platforms. The main change in this PR include:

Replace all hardcode float8_e4m3fn to platform supported fp8 dtype, and add FP8_E4M3FNUZ enum to cpp kernels.
Add torch impl to deepgemm
Add rocm_aiter_mla_sparse backend and dispatch it in rocm platform

Test Plan

Verify its accuracy on gsm8k and wikitext

Test Result

# wikitext
| Tasks  |Version|Filter|n-shot|    Metric     |   |Value |   |Stderr|
|--------|------:|------|-----:|---------------|---|-----:|---|------|
|wikitext|      2|none  |     0|bits_per_byte  |↓  |0.3691|±  |   N/A|
|        |       |none  |     0|byte_perplexity|↓  |1.2915|±  |   N/A|
|        |       |none  |     0|word_perplexity|↓  |3.9272|±  |   N/A|

# gsm8k

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9629|±  |0.0052|
|     |       |strict-match    |     5|exact_match|↑  |0.9621|±  |0.0053|

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
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gemini-code-assist

Code Review

This pull request adds support for Deepseek v3.2 and SparseMLA on ROCm platforms. The changes are well-structured and cover adding a new FP8 data type for ROCm, a new attention backend for sparse MLA, and PyTorch fallbacks for some custom operations. The code modifications correctly abstract away platform-specific details. I have one suggestion to improve performance in the newly added sparse MLA backend file by avoiding unnecessary tensor initializations.

gemini-code-assist · 2025-10-13T02:58:55Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+    out = torch.randn([qs, qh, kv_lora_rank], dtype=torch.bfloat16, device=q.device)
+    block_table = torch.empty_like(topk_indices)    # no use
+    num_seq = cu_seqlens_q.size(0) - 1
+    seqused_k = torch.randn([num_seq], device=q.device)  # no use


Using torch.randn to initialize tensors that are either output parameters (out) or unused (seqused_k) is inefficient. It populates the tensors with random values, which is an unnecessary computation that can impact performance. It's more performant to use torch.empty, which only allocates uninitialized memory.

Suggested change

out = torch.randn([qs, qh, kv_lora_rank], dtype=torch.bfloat16, device=q.device)

block_table = torch.empty_like(topk_indices) # no use

num_seq = cu_seqlens_q.size(0) - 1

seqused_k = torch.randn([num_seq], device=q.device) # no use

out = torch.empty([qs, qh, kv_lora_rank], dtype=torch.bfloat16, device=q.device)

block_table = torch.empty_like(topk_indices) # no use

num_seq = cu_seqlens_q.size(0) - 1

seqused_k = torch.empty([num_seq], device=q.device) # no use

same question

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chatgpt-codex-connector · 2025-10-13T03:01:00Z

vllm/utils/deep_gemm.py

+    mask_lo = torch.arange(0, seq_len_kv, device='cuda')[None, :] >= cu_seqlen_ks[:, None]
+    mask_hi = torch.arange(0, seq_len_kv, device='cuda')[None, :] < cu_seqlen_ke[:, None]
+    mask = mask_lo & mask_hi


Use input device instead of 'cuda' in DeepGEMM torch fallback

The new torch implementations of FP8 MQA logits create their temporary tensors with device='cuda' (e.g., the arange masks at fp8_mqa_logits_torch). When the caller runs on a non‑default device such as cuda:1 or HIP, these tensors land on a different device than q/kv, so subsequent ops like mask_lo & mask_hi or matrix multiplies raise a device‑mismatch error. The fallback is meant to run when the compiled kernel is unavailable, so it should follow the device of the inputs (q.device or kv.device) instead of assuming the global CUDA device. The same hardcoded device constant appears again in fp8_paged_mqa_logits_torch and the ROCm branch of fp8_paged_mqa_logits, so all temporaries there should be created on the input device as well.

Useful? React with 👍 / 👎.

HAIAI

Please revert the FP8_E4M3FNUZ part for kv cache, we had uniformed the fp8 KV cache format since 3.2024, and OCP is the only format we support from Quark as well.

HAIAI

lint: few more comments
And please format accordingly.

HAIAI · 2025-10-16T05:52:45Z

vllm/v1/attention/backends/mla/indexer.py

    @classmethod
    def get_supported_kernel_block_size(cls) -> list[int | MultipleOf]:
+        if current_platform.is_rocm():
+            return [MultipleOf(1)]


I have the same question

The current triton kernel deepgemm_fp8_paged_mqa_logits_stage1 we used to replace deepgemm only support block-size=1 case and so dose our MLA kernel, although it not used in deepseekv3.2. We already have a solution on this, but not in this PR.

HAIAI · 2025-10-16T05:53:47Z

vllm/platforms/rocm.py

            )
+            if use_sparse:
+                if kv_cache_dtype.startswith("fp8"):
+                    raise ValueError(f"ROCMAiterMLASparseBackend dose not support kv_cache_dtype == fp8.")


HAIAI · 2025-10-16T06:03:22Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+
+
+
+def ref_convert_to_gloabl(req_id: torch.Tensor,


HAIAI · 2025-10-16T06:04:37Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+    out = torch.randn([qs, qh, kv_lora_rank], dtype=torch.bfloat16, device=q.device)
+    block_table = torch.empty_like(topk_indices)    # no use
+    num_seq = cu_seqlens_q.size(0) - 1
+    seqused_k = torch.randn([num_seq], device=q.device)  # no use


same question

mergify · 2025-10-16T06:06:39Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @ganyi1996ppo.

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

SageMoore · 2025-10-16T15:26:11Z

vllm/platforms/rocm.py

            )
+            if use_sparse:
+                if kv_cache_dtype.startswith("fp8"):
+                    raise ValueError(f"ROCMAiterMLASparseBackend dose not support kv_cache_dtype == fp8.")


Nit: This looks like a RuntimeError

Hi, this ValueError is meant to tell the users that we don't support fp8 kv cache for now, so we throw value error here.

SageMoore · 2025-10-16T15:44:09Z

vllm/v1/attention/backends/mla/indexer.py

    @classmethod
    def get_supported_kernel_block_size(cls) -> list[int | MultipleOf]:
+        if current_platform.is_rocm():
+            return [MultipleOf(1)]


I have the same question

SageMoore · 2025-10-16T15:49:41Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+      kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.view(
+          -1, 1, kv_c_and_k_pe_cache.shape[-1])
+
+      # NOTE(Chen): kernel requires num_local_head to be a multiple of


Nit: Let's update the comment or remove it.

Done, irrelevant comments have been removed.

tjtanaa · 2025-10-20T08:41:04Z

vllm/envs.py

+    # By default is enabled.
    "VLLM_ROCM_USE_TRITON_ROPE": lambda: (
-        os.getenv("VLLM_ROCM_USE_TRITON_ROPE", "False").lower() in ("true", "1")
+        os.getenv("VLLM_ROCM_USE_TRITON_ROPE", "True").lower() in ("true", "1")


@ganyi1996ppo
To actually call the AITER triton rope code path, we also have to set both VLLM_ROCM_USE_TRITON_ROPE=1 and --compilation-config '{"custom_ops": ["+rotary_embedding"]}' . Only by setting the custom_ops that the forward_hip function will be called, else it will call forward_native by default. I think you will encounter a bug with the DeepSeekRotary Embedding class when trying to use the AITER triton rope.

For my understanding, this AITER triton rope has not been validated for all ROPE classes. It was introduced and tested on Llama-3-405B only. #25135 . So it is better to set it to False for now. Or open another PR to bugfix this and validate on all ROPE classes.

This PR only enables eager mode for dsv3.2, we haven't test graph mode for now. The interesting facts is that Mi300/Mi350(we only test these 2) can only get correct result when using triton rope.

And of course we will investigate this case, and since rope will be broke up to small ops and get fused by torch.inductor in graph mode, this change actually won't effect the default path in graph mode.

FYI, piecewise cudagraph is now supported

Agree with @tjtanaa , we should set False by default.

ganyi1996ppo · 2025-10-20T12:28:09Z

Please revert the FP8_E4M3FNUZ part for kv cache, we had uniformed the fp8 KV cache format since 3.2024, and OCP is the only format we support from Quark as well.

@HAIAI Thanks for the advices, all fp8_e4m3fnuz related code have been removed from this PR, the fp8_e4m3fnuz specific part in cpp is now using the __gfx942__ macro to handle

SageMoore · 2025-10-21T15:25:44Z

vllm/v1/attention/backends/mla/indexer.py

-            self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
-                seq_lens, self.kv_cache_spec.block_size, self.num_sms
-            )
+            if current_platform.is_cuda():


Looking at the code, it appears that we only need this when running with DeepGemm? Can you update the check to if has_deep_gemm()

I'll refactor this place, thanks~

SageMoore · 2025-10-21T15:43:12Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+
+        if output_scale is not None or output_block_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported for MLACommonImpl"


Nit: Can you update the string to reference this class

Sure, thanks to ping that out~

SageMoore · 2025-10-21T15:44:59Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+
+        topk_indices = self.topk_indices_buffer[:num_actual_toks]
+
+        # Note: the above triton kernel may triggers some strange unexpected


What kernel is this comment referring to?

This refers to this triton kernel triton_convert_req_index_to_global_index, I'm using a reference torch path to replace it now. But I think we should go triton_convert_req_index_to_global_index eventually for performance consideration, and I'm working on it now.

Hi @SageMoore , triton crash issue have already been fixed in newest commit, please take a look.

SageMoore · 2025-10-21T15:55:25Z

vllm/utils/deep_gemm.py

    _lazy_init()
    if _fp8_mqa_logits_impl is None:
-        return _missing()
+        return fp8_mqa_logits_torch(q, kv, weights, cu_seqlen_ks, cu_seqlen_ke)[0]


Doing this dispatching inside of a file that's just for wrapping Deep Gemm seems incorrect. Let's move this dispatching somewhere else. Looks like indexer.py could be a good spot? CC @LucasWilkinson

Sorry for the delay. Yes, I think we can make it a custom_op and dispatch it outside rather than put it inside the deep_gemm.py

I have isolate the sparse_attn_indexer function out of the deepseek modeling file. This should look clean than previous version, please take a look again.

SageMoore · 2025-10-21T15:55:51Z

vllm/utils/deep_gemm.py

        `torch.float32`.
    """
    _lazy_init()
+    if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER:


Same idea here. Let's get this out of deep_gemm.py

rasmith · 2025-10-21T22:07:14Z

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

+    )
+
+    def dynamic_per_batched_tensor_quant(
+        x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn


Why not torch.dtype = current_platform.fp8_dtype()?

Sorry for the delay, got caught up by something else. Yes, there should be using platform related dtype, thanks to point that out!

This function is unnecessary, removed.

tjtanaa · 2025-11-18T10:22:45Z

Validated and reproduced results.

Server command:

export VLLM_USE_V1=1
export SAFETENSORS_FAST_GPU=1
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_MOE=1
export NCCL_DEBUG=WARN
export VLLM_LOGGING_LEVEL=DEBUG
export VLLM_RPC_TIMEOUT=18000000

model_path="deepseek-ai/DeepSeek-V3.2-Exp"

vllm serve $model_path \
  --tensor-parallel-size 8 \
  --data-parallel-size 1 \
  --max-num-batched-tokens 32768 \
  --trust-remote-code \
  --no-enable-prefix-caching \
  --disable-log-requests \
  --kv-cache-dtype bfloat16 \
  --gpu_memory_utilization 0.85 \
  --compilation-config '{"cudagraph_mode": "FULL_AND_PIECEWISE"}' \
  --block-size 1 \
2>&1 | tee server-deepseek-ai_DeepSeek-V3.2-Exp.log

lm_eval command

lm_eval --model local-completions  \
   --tasks wikitext  \
   --output_path ./results  \
   --log_samples \
   --model_args model=deepseek-ai/DeepSeek-V3.2-Exp,base_url=http://localhost:8000/v1/completions,num_concurrent=2,max_retries=3,timeout=3000,seed=1234,temperature=0 \
   | tee lmeval_server-deepseek-ai_DeepSeek-V3.2-Exp.log 2>&1

lm_eval \
--model local-completions \
--tasks gsm8k \
--model_args model=deepseek-ai/DeepSeek-V3.2-Exp,base_url=http://127.0.0.1:8000/v1/completions \
--batch_size 1 \
> lmeval_server-deepseek-ai_DeepSeek-V3.2-Exp-gsm8k.log 2>&1

Reproduced.

local-completions (model=deepseek-ai/DeepSeek-V3.2-Exp,base_url=http://localhost:8000/v1/completions,num_concurrent=2,max_retries=3,timeout=3000,seed=1234,temperature=0), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
| Tasks  |Version|Filter|n-shot|    Metric     |   |Value |   |Stderr|
|--------|------:|------|-----:|---------------|---|-----:|---|------|
|wikitext|      2|none  |     0|bits_per_byte  |↓  |0.3654|±  |   N/A|
|        |       |none  |     0|byte_perplexity|↓  |1.2882|±  |   N/A|
|        |       |none  |     0|word_perplexity|↓  |3.8745|±  |   N/A|

local-completions (model=deepseek-ai/DeepSeek-V3.2-Exp,base_url=http://127.0.0.1:8000/v1/completions), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|_  |0.9598|_  |0.0054|
|     |       |strict-match    |     5|exact_match|_  |0.9591|_  |0.0055|

tjtanaa · 2025-11-19T00:47:55Z

@ganyi1996ppo can you try rebasing?

tjtanaa · 2025-11-20T02:35:23Z

@ganyi1996ppo can you try rebasing again. Thank you so much.

ganyi1996ppo · 2025-11-20T02:59:40Z

@ganyi1996ppo can you try rebasing again. Thank you so much.

Sure, but the error seems mainly related to llama and oom issue, Should not caused by this PR right?

Signed-off-by: ganyi <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]> Signed-off-by: LuminolT <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]> Signed-off-by: Runkai Tao <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]> Signed-off-by: Xingyu Liu <[email protected]>

…ct#26670) Signed-off-by: ganyi <[email protected]>

ganyi1996ppo requested review from LucasWilkinson, WoosukKwon, alexm-redhat, comaniac, gshtras, njhill, robertgshaw2-redhat and ywang96 as code owners October 13, 2025 02:56

mergify bot added deepseek Related to DeepSeek models rocm Related to AMD ROCm v1 labels Oct 13, 2025

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wuhuikx mentioned this pull request Oct 18, 2025

Enable Deepseek V3.2 ROCm/vllm#742

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ganyi1996ppo force-pushed the ganyi/dsv3.2_rocm_support branch from 1327e75 to 99df437 Compare November 19, 2025 01:46

ganyi1996ppo added 8 commits November 20, 2025 03:00

deepseekv3.2 support for rocm platform

abf85c4

Signed-off-by: ganyi <[email protected]>

adopt in sparse mla backend, and add ref path to mqa logits

8fdafbc

Signed-off-by: ganyi <[email protected]>

fix lint

f6dd0c8

Signed-off-by: ganyi <[email protected]>

make indexer support block-size 1 if platform is rocm

e684acb

Signed-off-by: ganyi <[email protected]>

add assertion for block-size in SparseMLA and fix pa_mqa import bug

100f965

Signed-off-by: ganyi <[email protected]>

add lru cache on module check

68882cc

Signed-off-by: ganyi <[email protected]>

add comments

406fbdb

Signed-off-by: ganyi <[email protected]>

remove paged_mqa_logits module check

acec771

Signed-off-by: ganyi <[email protected]>

ganyi1996ppo force-pushed the ganyi/dsv3.2_rocm_support branch from 4468193 to acec771 Compare November 20, 2025 03:00

Merge branch 'main' into ganyi/dsv3.2_rocm_support

47c64db

vllm-bot merged commit 06c20c9 into vllm-project:main Nov 20, 2025
86 of 88 checks passed

MatthewBonanni mentioned this pull request Nov 20, 2025

[Attention] Add ROCM_AITER_MLA_SPARSE to attention backend registry #29103

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[ROCm] Add AMD GPU support on Deepseek v3.2 and SparseMLA (vllm-proje…

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huangye123 mentioned this pull request Dec 9, 2025

Request to compile and package a cuda12.9-vllm0.12.0 docker image. gpustack/gpustack#3789

Open


		topk_indices = self.topk_indices_buffer[:num_actual_toks]

		# Note: the above triton kernel may triggers some strange unexpected

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[ROCm] Add AMD GPU support on Deepseek v3.2 and SparseMLA #26670

[ROCm] Add AMD GPU support on Deepseek v3.2 and SparseMLA #26670

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ganyi1996ppo commented Oct 13, 2025 •

edited by github-actions bot

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tjtanaa Oct 20, 2025 •

edited

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ganyi1996ppo commented Oct 20, 2025 •

edited

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tjtanaa commented Nov 18, 2025 •

edited

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