[FEAT] [ROCm]: Add AITER CK 2 Stages MoE support #17110

tjtanaa · 2025-04-24T13:34:40Z

Description

This PR introduces the use of ck_moe_2stages from ROCm/aiter.

Performance gain

meta-llama/Llama-4-Maverick-17B-128E-Instruct

V1 Engine
Input: Output = 1000:1000

Metric	No AITER	AITER 1 Stage	AITER 2 Stage	% Gain (2 Stage vs Baseline)
Benchmark Duration (s)	67.39	64.84	59.52	11.7%
Request Throughput (req/s)	7.42	7.71	8.40	13.2%
Request Goodput (req/s)	0.61	0.71	1.01	65.6%
Output Token Throughput (tok/s)	6,135.14	6,437.38	6,898.15	12.4%
Total Token Throughput (tok/s)	13,469.17	14,059.20	15,201.54	12.9%
Mean TTFT (ms)	7,285.70	7,133.57	6,187.27	15.1%
Median TTFT (ms)	7,231.37	6,962.17	6,006.79	16.9%
Mean TPOT (ms)	52.90	50.15	47.16	10.9%
Median ITL (ms)	34.30	31.46	29.33	14.5%

amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV

V1 Engine
Input: Output = 1000:1000

Metric	No AIter	AIter 2Stage	Improvement
Benchmark duration (s)	80.38	76.48	4.85% faster
Total generated tokens	436,389	482,066	10.47% more
Request throughput (req/s)	6.22	6.54	5.14% higher
Output token throughput (tok/s)	5,429.41	6,303.20	16.09% higher
Total token throughput (tok/s)	11,578.38	12,765.36	10.25% higher
Median TTFT (ms)	9,477.47	9,574.82	1.03% slower
Median TPOT (ms)	50.20	46.73	6.91% faster
Median ITL (ms)	39.45	36.85	6.59% faster

mistralai/Mixtral-8x7B-v0.1

V1 Engine
Input: Output = 1000:1000

Metric	No AITER	AITER 1-Stage	AITER 2-Stage	% Gain (2-Stage vs No AITER)	% Gain (2-Stage vs 1-Stage)
Benchmark Duration (s)	108.38	122.76	100.61	7.2%	18.0%
Request Throughput (req/s)	4.61	4.07	4.97	7.8%	22.1%
Request Goodput (req/s)	0.02	0.01	0.02	0.0%	100.0%
Output Token Throughput (tok/s)	4445.47	3907.71	4773.95	7.4%	22.2%
Total Token Throughput (tok/s)	9005.44	7933.76	9686.44	7.6%	22.1%
Mean TTFT (ms)	13133.12	20749.40	12677.77	3.5%	38.9%
Median TTFT (ms)	12911.29	20420.47	12429.12	3.7%	39.1%
P99 TTFT (ms)	27070.03	41767.87	25574.30	5.5%	38.8%
Mean TPOT (ms)	83.69	104.94	78.76	5.9%	24.9%
Median TPOT (ms)	64.66	73.77	61.07	5.6%	17.2%
P99 TPOT (ms)	399.80	642.01	393.91	1.5%	38.6%
Mean ITL (ms)	62.21	70.75	58.46	6.0%	17.4%
Median ITL (ms)	52.15	55.18	48.90	6.2%	11.4%
P99 ITL (ms)	403.70	645.63	396.17	1.9%	38.6%

Lm_eval

Dataset: GSM8K
Engine: V1

AITER 1 stage amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV fallback to 2 stage moe

vllm (pretrained=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV,tensor_parallel_size=2,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.6528	±	0.0131
		strict-match	5	exact_match	↑	0.6490	±	0.0131

AITER 2 stage amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV

[2025-04-30 02:18:36] INFO evaluation_tracker.py:272: Output path not provided, skipping saving results aggregated
vllm (pretrained=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV,tensor_parallel_size=2,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.6422	±	0.0132
		strict-match	5	exact_match	↑	0.6391	±	0.0132

AITER 1 stage meta-llama/Llama-4-Scout-17B-16E-Instruct

vllm (pretrained=meta-llama/Llama-4-Scout-17B-16E-Instruct,tensor_parallel_size=8,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.9143	±	0.0077
		strict-match	5	exact_match	↑	0.8961	±	0.0084

AITER 2 stage meta-llama/Llama-4-Scout-17B-16E-Instruct

vllm (pretrained=meta-llama/Llama-4-Scout-17B-16E-Instruct,tensor_parallel_size=8,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.9227	±	0.0074
		strict-match	5	exact_match	↑	0.9060	±	0.0080

AITER 1 stage mistralai/Mixtral-8x7B-Instruct-v0.1 FP16

[2025-05-05 09:29:26] INFO evaluation_tracker.py:272: Output path not provided, skipping saving results aggregated
vllm (pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,tensor_parallel_size=2,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.6429	±	0.0132
		strict-match	5	exact_match	↑	0.6399	±	0.0132

AITER 2 stage mistralai/Mixtral-8x7B-Instruct-v0.1FP16

vllm (pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,tensor_parallel_size=2,max_model_len=10000,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.6513	±	0.0131
		strict-match	5	exact_match	↑	0.6475	±	0.0132

AITER 1 stage mistralai/Mixtral-8x7B-Instruct-v0.1 Dynamic FP8

vllm (pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,tensor_parallel_size=2,max_model_len=10000,quantization=fp8,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.5777	±	0.0136
		strict-match	5	exact_match	↑	0.5701	±	0.0136

AITER 2 stage mistralai/Mixtral-8x7B-Instruct-v0.1 Dynamic FP8 (fall back to 1 stage)

vllm (pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,tensor_parallel_size=2,max_model_len=10000,quantization=fp8,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.5792	±	0.0136
		strict-match	5	exact_match	↑	0.5701	±	0.0136

Updated unit tests

Given that the rocm_aiter_fused_experts has been removed from the function dispatch_fused_experts_func in vllm/model_executor/layers/fused_moe/fused_moe.py.

Future TODO:

# TODO: remove @cache to allow the use case
# where there are multiple instances of LLM
# running on the same process/program with different
# configurations. Blockage is that dispatch_topk_func()
# in vllm/model_executor/layers/fused_moe/fused_moe.py
# uses this function in critical path.
@cache
def is_rocm_aiter_moe_enabled() -> bool:
    return current_platform.is_rocm() \
        and envs.VLLM_ROCM_USE_AITER_MOE \
        and envs.VLLM_ROCM_USE_AITER

Co-authored-by: Gregory Shtrasberg <[email protected]> Signed-off-by: tjtanaa <[email protected]>

github-actions · 2025-04-24T13:34:49Z

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

mergify · 2025-05-05T16:41:03Z

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

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

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

SageMoore

In general I would like us to contain all of the 1 stage vs 2 stage dispatching logic in the rocm_aiter_fused_experts function if posible

SageMoore · 2025-05-08T15:59:22Z

vllm/model_executor/layers/quantization/fp8.py

+            is_rocm_aiter_moe_enabled, shuffle_weights)
+
+        self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
+        self.rocm_aiter_2stage_moe_enabled = is_rocm_aiter_2stage_moe_enabled()


It looks like this variable is only used to warn that we are falling back to a different implementation. Let's remove it to simplify the code.

Same comment as below
#17110 (comment)

There is an RFC that highlights Accessing envs.ENV is very costly. RFC Issue #17067 .
Thus, all the env are only invoked and stored as a property of the class during initialization stage.

SageMoore · 2025-05-08T16:01:27Z

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

+    a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
+    allow_deep_gemm: bool = False,
+    use_ck_moe_2stages: bool = False,


Instead of passing this boolean around, can you check the environment variable here?

@SageMoore There is an RFC that highlights Accessing envs.ENV is very costly. RFC Issue #17067 .
Thus, all the env are only invoked and stored as a property of the class during initialization stage.

SageMoore · 2025-05-08T16:15:04Z

vllm/model_executor/layers/fused_moe/fused_moe.py

                  block_shape: Optional[List[int]] = None,
-                  allow_deep_gemm: bool = False) -> torch.Tensor:
+                  allow_deep_gemm: bool = False,
+                  use_ck_moe_2stages: bool = False) -> torch.Tensor:


Nit: I doesn't look like you need this?

agreed. this is not needed

I have removed the argument allow_deep_gemm. use_ck_moe_2stages is kept as there is an RFC that highlights Accessing envs.ENV is very costly. RFC Issue #17067 .
Thus, all the env are only invoked and stored as a property of the class during initialization stage.

To pass the pre-commit tests of file vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py , we have adjusted the logic of assignment of fused_experts function to fused_experts_func to become rocm_aiter_fused_experts_func, following the approach in vllm/attention/backends/rocm_flash_attn.py, where the attention functions are assigned to different property name: self.fa_attn_func , self.sdpa_attn_func and self.triton_attn_func

This also allows us to clean up the unused arguments of the function in rocm_aiter_fused_experts (vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py). Note that the expert_map has been removed as a bugfix, expert_map (integer ID of the experts) in vLLM and expert_mask (boolean mask of active experts on current GPU) in the AITER ops are different. The current rocm_aiter_fused_experts has removed the expert_map argument and encourage to add it back when enabling EP using AITER in future PR.

hongxiayang

Verified with llama4 bf16 128e model.
LGTM in general. Agreed the environment variable part can be simplified.

hongxiayang · 2025-05-08T22:15:04Z

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

+def is_rocm_aiter_2stage_moe_enabled() -> bool:
+    return current_platform.is_rocm() \
+        and envs.VLLM_ROCM_USE_AITER_2STAGE_MOE \
+        and envs.VLLM_ROCM_USE_AITER_MOE \
+        and envs.VLLM_ROCM_USE_AITER


This seems too much to check 3 environment variables. envs.VLLM_ROCM_USE_AITER_2STAGE_MOE is enough as it is only used when the other two are already true.

The is_rocm_aiter_2stage_moe_enabled() has been removed since envs.VLLM_ROCM_USE_AITER_2STAGE_MOE is being called in the layer class during initialization only, not in the forward pass.

…argument clean up Signed-off-by: tjtanaa <[email protected]>

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

SageMoore

I'd like to propose an alternative implementation. Instead of passing this new environment variable around, let's just keep the existing VLLM_ROCM_USE_AITER_MOE variable and add heuristics to the code that decide when to use the one stage vs the two stage implementation.

It looks like the two stage kernel is generally faster but doesn't support dynamic quantization? Let's rework this PR so that we always use the two stage implementation when it's supported and fallback to the one stage implementation when it isn't. We don't need any logging when this fallback occurs.

If we make the change that I suggested, it looks like you can simplify the changes in fp8.py down to just some layout changes for the weights. fused_moe.py should get a bit simpler as well.

What do you think?

hongxiayang · 2025-05-12T22:37:12Z

I'd like to propose an alternative implementation. Instead of passing this new environment variable around, let's just keep the existing VLLM_ROCM_USE_AITER_MOE variable and add heuristics to the code that decide when to use the one stage vs the two stage implementation.

It looks like the two stage kernel is generally faster but doesn't support dynamic quantization? Let's rework this PR so that we always use the two stage implementation when it's supported and fallback to the one stage implementation when it isn't. We don't need any logging when this fallback occurs.

If we make the change that I suggested, it looks like you can simplify the changes in fp8.py down to just some layout changes for the weights. fused_moe.py should get a bit simpler as well.

What do you think?

@SageMoore Can we get this merged and do the rework as a follow up PR?

I'd like to propose an alternative implementation. Instead of passing this new environment variable around, let's just keep the existing VLLM_ROCM_USE_AITER_MOE variable and add heuristics to the code that decide when to use the one stage vs the two stage implementation.

It looks like the two stage kernel is generally faster but doesn't support dynamic quantization? Let's rework this PR so that we always use the two stage implementation when it's supported and fallback to the one stage implementation when it isn't. We don't need any logging when this fallback occurs.

If we make the change that I suggested, it looks like you can simplify the changes in fp8.py down to just some layout changes for the weights. fused_moe.py should get a bit simpler as well.

What do you think?

@SageMoore It would be better to do a different implementation in a follow-up PR. We would like to have this PR merged as this improves performance for a bunch of models.

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

mergify · 2025-05-13T11:19:40Z

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

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

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

tjtanaa · 2025-05-13T13:00:05Z

I'd like to propose an alternative implementation. Instead of passing this new environment variable around, let's just keep the existing VLLM_ROCM_USE_AITER_MOE variable and add heuristics to the code that decide when to use the one stage vs the two stage implementation.

It looks like the two stage kernel is generally faster but doesn't support dynamic quantization? Let's rework this PR so that we always use the two stage implementation when it's supported and fallback to the one stage implementation when it isn't. We don't need any logging when this fallback occurs.

If we make the change that I suggested, it looks like you can simplify the changes in fp8.py down to just some layout changes for the weights. fused_moe.py should get a bit simpler as well.

What do you think?

@SageMoore Thank you for the suggestion, we had already cleared up this env variable and made simplifications to the fused moe functions.

SageMoore

Looks reasonable. Thanks for cleaning up the dispatching logic!

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

mergify · 2025-05-14T02:49:58Z

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

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

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

mergify · 2025-05-14T05:05:16Z

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

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

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

Signed-off-by: tjtanaa <[email protected]> Co-authored-by: Gregory Shtrasberg <[email protected]> Signed-off-by: Yuqi Zhang <[email protected]>

Add AITER CK 2 Stages MoE support

4972bcd

Co-authored-by: Gregory Shtrasberg <[email protected]> Signed-off-by: tjtanaa <[email protected]>

tjtanaa mentioned this pull request Apr 23, 2025

[Feature] [ROCm]: AITER Kernel Integration #14964

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61 tasks

tjtanaa added 7 commits April 25, 2025 06:49

merge with main to get V1 fused moe abstraction

1e83ec8

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

fix naming convention of rocm_aiter_fused_moe

9f5de58

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

Merge remote-tracking branch 'origin/main' into aiter-ck-moe-2-stage

4a10295

Merge remote-tracking branch 'origin/main' into aiter-ck-moe-2-stage

f5ec370

fix code that are using aiter shuffle

92fc4de

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

add fallback for dynamic quantization

7a8e063

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fix pre-commit lint

17146df

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tjtanaa marked this pull request as ready for review May 5, 2025 13:01

tjtanaa requested review from mgoin, robertgshaw2-redhat and tlrmchlsmth as code owners May 5, 2025 13:01

hongxiayang added the rocm Related to AMD ROCm label May 5, 2025

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merge with main to resolve merge conflict

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SageMoore suggested changes May 8, 2025

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tjtanaa added 3 commits May 9, 2025 10:30

Merge remote-tracking branch 'origin/main' into aiter-ck-moe-2-stage

24dd605

remove is_rocm_aiter_2stage_moe_enabled and rocm_aiter_fused_experts …

5efe74d

…argument clean up Signed-off-by: tjtanaa <[email protected]>

fix pre-commit by cleaning up the rocm_aiter_fused_experts arguments

8cd674c

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remove stage2 flag, simplify the aiter fused moe kernel selection

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tjtanaa added 2 commits May 13, 2025 11:34

clean up fused_moe api

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sync with main; main changed linter

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Trigger CI build

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fix test_moe

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sync with main

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sync with main

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tjtanaa added 2 commits May 14, 2025 06:10

fix the UnquantizedFusedMoEMethod for unit tests to pass

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Annotate type none

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vllm-bot merged commit 612c2ed into vllm-project:main May 14, 2025
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vllmellm mentioned this pull request Aug 26, 2025

[Feature] [ROCm]: AITER Kernel Integration vllmellm/vllm#51

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[FEAT] [ROCm]: Add AITER CK 2 Stages MoE support #17110

[FEAT] [ROCm]: Add AITER CK 2 Stages MoE support #17110

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tjtanaa commented Apr 24, 2025 • edited by github-actions bot Loading Uh oh! There was an error while loading. Please reload this page.

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Description

Performance gain

meta-llama/Llama-4-Maverick-17B-128E-Instruct

amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV

mistralai/Mixtral-8x7B-v0.1

Lm_eval

AITER 1 stage amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV fallback to 2 stage moe

AITER 2 stage amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV

AITER 1 stage meta-llama/Llama-4-Scout-17B-16E-Instruct

AITER 2 stage meta-llama/Llama-4-Scout-17B-16E-Instruct

AITER 1 stage mistralai/Mixtral-8x7B-Instruct-v0.1 FP16

AITER 2 stage mistralai/Mixtral-8x7B-Instruct-v0.1FP16

AITER 1 stage mistralai/Mixtral-8x7B-Instruct-v0.1 Dynamic FP8

AITER 2 stage mistralai/Mixtral-8x7B-Instruct-v0.1 Dynamic FP8 (fall back to 1 stage)

Updated unit tests

Future TODO:

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tjtanaa commented Apr 24, 2025 •

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