[Model] New model support for microsoft/Phi-4-mini-flash-reasoning

[congcongchen123]

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Purpose

New Model for https://huggingface.co/microsoft/Phi-4-mini-flash-reasoning

co-author: @aatkinson and @renll

Test Plan

Test Result

(Optional) Documentation Update

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[gemini-code-assist]

Summary of Changes

Hello @congcongchen123, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces full support for the microsoft/Phi-4-mini-flash-reasoning model, which utilizes a novel hybrid architecture integrating both FlashAttention and Mamba (State Space Model) layers. The changes involve implementing a new 'Differential FlashAttention' backend, integrating Mamba's selective scan and causal convolution operations, and enhancing the KV cache management system to support cross-layer sharing, optimizing memory usage for this complex model.

Highlights

• New Model Support: Adds comprehensive support for the microsoft/Phi-4-mini-flash-reasoning model, which features a hybrid architecture combining FlashAttention and Mamba (SSM) layers.
• Differential FlashAttention Backend: Introduces a specialized attention backend (DifferentialFlashAttentionBackend) designed for the Phi-4 model, implementing a unique 'differential' attention mechanism with split QKV and KV cache handling.
• Mamba (SSM) Integration: Implements the Phi4Mamba module to support the State Space Model components of the Phi-4 architecture, including causal convolution and selective scan operations, with specific handling for KV cache sharing (yoco_kv, yoco_cross).
• Cross-Layer KV Cache Sharing: Enhances the KV cache management system to support cross-layer KV cache sharing, allowing certain attention layers to reuse the KV cache from other layers, which can lead to significant memory savings for the Phi-4 model's hybrid architecture.
• Dynamic SSM z Parameter: Modifies the Mamba selective scan CUDA kernel and its Python wrapper to dynamically handle the z parameter, making it optional rather than a mandatory input, which improves flexibility.
• Conditional Hidden State Pruning: Adds a new parameter to the LogitsProcessor to conditionally prune hidden states, enabling optimization for specific model architectures like Phi-4 where intermediate states might be truncated.

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[gemini-code-assist]

Code Review

The pull request introduces a new model architecture, Phi4Flash, with a hybrid Mamba/Attention mechanism and cross-layer KV cache sharing. The changes involve modifications to CUDA kernels, attention backends, model layers, and configuration files. The review focuses on ensuring the correctness, maintainability, and performance of the new implementation, particularly concerning the handling of conditional logic, error conditions, and integration with existing vLLM features.

[gemini-code-assist]

The condition if i == self.config.num_hidden_layers // 2 + 2: is used to conditionally execute code during a profile run. Ensure that this condition accurately identifies the intended layer for profiling and that the profiling logic does not inadvertently affect the behavior of other layers or the overall model execution.

[gemini-code-assist]

The NotImplementedError for cross-layer KV sharing in V0 has been commented out. Confirm that cross-layer KV sharing is indeed correctly handled or disabled elsewhere, and that this change does not inadvertently enable unsupported functionality.

[heheda12345]

I like the current kv sharing implementation. It is quite clean. But not sure whether cross_layer_shared_block_tables is necessary.

Please add the required test as mentioned here https://docs.vllm.ai/en/latest/contributing/model/tests.html#required-tests

[luccafong]

Is this runnable in V1?
@sarckk can we try with V1 on this model with your PR #19719 and see if any gains?

[heheda12345]

can we try with V1 on this model with your PR #19719 and see if any gains?

You can use gemma-3n to test #19719 . This PR is not runable with v1 now and I think it needs some effort to enable v1.

[ZV-Liu]

After installing vllm through source code and starting it with the following command, the following error still occurs.
Thanks.
vllm-0.10.0rc2.dev35+g32142b3c6.cu124.dist-info

vllm serve /openbayes/input/input0/Phi-4-mini-flash-reasoning   --host 0.0.0.0   --port 8000   --gpu-memory-utilization 0.9   --max-num-seqs 8   --max-model-len 512   --tensor-parallel-size 1   --served-model-name Phi-4-mini-flash-reasoning   --trust-remote-code

  File "/output/vllm/vllm/model_executor/models/phi4flash.py", line 519, in <lambda>
    lambda prefix: SambaYDecoderLayer(config,
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/output/vllm/vllm/model_executor/models/phi4flash.py", line 452, in __init__
    self.attn = SambaYAttention(config,
                ^^^^^^^^^^^^^^^^^^^^^^^
  File "/output/vllm/vllm/model_executor/models/phi4flash.py", line 165, in __init__
    self.attn = Attention(
                ^^^^^^^^^^
  File "/output/vllm/vllm/attention/layer.py", line 167, in __init__
    self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: FlashAttentionImpl.__init__() got an unexpected keyword argument 'differential_flash_attention_config'

[ccongcong321]

Hi @ZV-Liu , you need to set VLLM_ATTENTION_BACKEND to be DIFFERENTIAL_FLASH_ATTN​​​.

[hmellor]

@ccongcong321 should that not have happened automatically? I see it being set in this PR

[ccongcong321]

@hmellor , it is not selected automatically for microsoft/Phi-4-mini-flash-reasoning.
The current auto backend selection logic in vLLM is not designed to gracefully support selecting a specific backend for individual models. As a result, if we would like to support that for this model, we have to take an intrusive approach, modifying the code directly and adding a series of if-else conditions.

[ZV-Liu]

ccongcong321

When using vllm, is there a switch similar to the enable_thinking of the Qwen3 series models when calling the API?

[hmellor]

Ah my mistake, it's only auto-selected in the test in tests/models/test_initialization.py

[ZV-Liu]

AssertionError: Phi4Flash currently does not support prefix cachingI have set VLLM_ATTENTION_BACKEND to DIFFERENTIAL_FLASH_ATTN and successfully started the service. I remember using the V1 engine. However, when I tried to start it again, the following error occurred:

"Phi4Flash currently does not support prefix caching."

[renll]

AssertionError: Phi4Flash currently does not support prefix cachingI have set VLLM_ATTENTION_BACKEND to DIFFERENTIAL_FLASH_ATTN and successfully started the service. I remember using the V1 engine. However, when I tried to start it again, the following error occurred:

"Phi4Flash currently does not support prefix caching."

Unfortunately, we do not support prefix caching because of Mamba layers. And we currently only support V0 engine. cc. @congcongchen123

[sarckk]

@congcongchen123 @renll is V1 support planned?

[ccongcong321]

@sarckk , yes, we are planning to support v1 once we get more bandwidth.

[tdoublep]

@ccongcong321 Was there a specific reason why this model didn't re-use MambaMixer2 layer? The reason I'm asking is it would be much easier to enable this model in V1 if it did not have a custom mamba layer. We are looking to strip out V0 code in the very near future.

[aatkinson]

Good point. Likely because development of this model pre-dated Bamba when MambaMixer2 was introduced. Rebasing was more approachable than reimplementing everything and ensured parity. Though there may be other reasons.