Add mimo v2 flash by Aznix07 · Pull Request #43020 · huggingface/transformers

Aznix07 · 2025-12-23T13:02:30Z

What does this PR do?

This PR adds support for the MiMo-V2-Flash architecture from Xiaomi (reference: XiaomiMiMo/MiMo-V2-Flash).

MiMo-V2-Flash is a large-scale Mixture-of-Experts (MoE) model (309B params / 15B active) that introduces several architectural innovations:

Hybrid Attention: A specific pattern of alternating Full Attention and Sliding Window Attention layers.
Asymmetric Head Dimensions: The Value (V) heads have a different dimension (v_head_dim=128) than Query/Key (
Q,K) heads (head_dim=192).
Partial Rotary Embeddings: RoPE is applied only to a fraction of the head dimension (approx 33%).
Sigmoid-based MoE Router: Uses a Sigmoid scoring function with Top-K normalization, distinct from the Softmax routers in models like Mixtral.

Implementation Details

Configuration: Added MiMoV2FlashConfig.
Modeling: Implemented MiMoV2FlashModel and MiMoV2FlashForCausalLM.
- MiMoV2FlashAttention: Handles the dimension mismatch and partial RoPE.
- MiMoV2FlashMoE: Implements the Sigmoid-based routing logic.
Integration: Registered the model in AutoConfig, AutoModel, and AutoModelForCausalLM.
Conversion: Added convert_mimo_v2_flash_weights_to_hf.py to handle sharded weights and key remapping from the original repo.
Testing: Added a model test suite in tests/models/mimo_v2_flash/.

Fixes #42954

Before Submitting

This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
Did you read the contributor guideline, Pull Request section?
Was this discussed/approved via a Github issue or the forum? Please add a link to it if that's the case.
Did you make sure to update the documentation with your changes? Here are the documentation guidelines, and here are tips on formatting docstrings.
Did you write any new necessary tests?

Who Can Review?

Models: @ArthurZucker @Cyrilvallez @SunMarc

Rocketknight1 · 2026-01-12T13:27:01Z

Hey! Thank you for the PR, but can you convert to modular style? It'll make it a lot easier to review, and should cut down on the amount of code you need too!

vasqu

Some initial comments but it's like @Rocketknight1 said, we should change the implementation to a modular one

Also we are missing docs and tests!

vasqu · 2026-01-12T17:13:41Z

+        head_dim (`int`, *optional*, defaults to 192):
+            The attention head dimension for Q and K.
+        v_head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension for V. This is specific to the MiMo-V2 architecture.


If we have this explicit difference between the head dims, I'd prefer we follow an existing notation like in deepseek, e.g.

transformers/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py

Lines 69 to 70 in 2aa7b65

v_head_dim (`int`, *optional*, defaults to 128):

Dimension of the value heads.

transformers/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py

Line 204 in 2aa7b65

self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim

(meaning the qk_head_dim

vasqu · 2026-01-12T17:16:05Z

+        rope_theta (`float`, *optional*, defaults to 5000000.0):
+            The base period of the RoPE embeddings.


Does not exist anymore, you can set default_theta

transformers/src/transformers/models/minimax_m2/modular_minimax_m2.py

Line 151 in 2aa7b65

default_theta = 5000000.0

vasqu · 2026-01-12T17:19:03Z

+        partial_rotary_factor (`float`, *optional*, defaults to 0.334):
+            Percentage of the hidden dimension to apply RoPE to.


We incorporate RoPE related parameters into a separate dict like object, see

transformers/src/transformers/models/minimax_m2/modular_minimax_m2.py

Line 177 in 2aa7b65

rope_parameters: RopeParameters | dict[RopeParameters] | None = None,

You can customize the initialization like here

transformers/src/transformers/models/minimax_m2/modular_minimax_m2.py

Lines 209 to 228 in 2aa7b65

def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):

rope_scaling = kwargs.pop("rope_scaling", None)

self.rope_parameters = rope_scaling or self.rope_parameters

self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

# Standardize and validate the correctness of rotary position embeddings parameters

# Model uses non-standard naming for rope params, overwrite!

self.rope_parameters.setdefault("rope_theta", self.default_theta)

self.rope_parameters["partial_rotary_factor"] = (

kwargs.pop("rotary_dim", self.head_dim // 2) / self.head_dim

) # Default to `0.5`

self.standardize_rope_params()

if ignore_keys_at_rope_validation is None:

ignore_keys_at_rope_validation = {"partial_rotary_factor"}

else:

ignore_keys_at_rope_validation |= {"partial_rotary_factor"}

self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)

return kwargs

Meaning partial_rotary_factor in this case mostly.

vasqu · 2026-01-12T17:20:09Z

+        hybrid_layer_pattern (`List[int]`, *optional*):
+            Pattern defining which layers use full attention (0) and which use sliding window attention (1).


We use a list of strings nowadays, see

transformers/src/transformers/models/gemma3/configuration_gemma3.py

Lines 190 to 195 in 2aa7b65

if self.layer_types is None:

self.layer_types = [

"sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention"

for i in range(self.num_hidden_layers)

]

layer_type_validation(self.layer_types, self.num_hidden_layers)

This is more explicit and allows for more layer types (as they grew over time for other models)

vasqu · 2026-01-12T17:23:38Z

+        scoring_func (`str`, *optional*, defaults to `"sigmoid"`):
+            The scoring function used for the MoE router.


If it's always sigmoid, we can simply leave it out

vasqu · 2026-01-12T17:46:22Z

+        return outputs
+
+
+class MiMoV2FlashModel(MiMoV2FlashPreTrainedModel):


In general might be completely inheritable

vasqu · 2026-01-12T17:46:58Z

+        )
+
+
+class MiMoV2FlashForCausalLM(MiMoV2FlashPreTrainedModel):


Inherit from another model as well

vasqu · 2026-01-12T17:47:26Z

+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(


Should not be needed, looks like very outdated code that is not used on our side anymore

vasqu · 2026-01-12T17:47:35Z

+        return model_inputs
+
+
+def _prepare_4d_causal_attention_mask(


See my comment about the masks

vasqu · 2026-01-12T17:48:45Z

Might need to define the tokenizer in auto as well, would need a double check

Depends on if it works with tokenizers backend or really the qwen2 tokenizer 👀

… notation

github-actions · 2026-01-15T12:08:47Z

[For maintainers] Suggested jobs to run (before merge)

run-slow: auto, mimo_v2_flash

Aznix07 · 2026-01-15T12:26:53Z

Thank you @Rocketknight1 and @vasqu for the thorough review and also appreciating your efforts to give me the proper guidance! 🫡

I have completed the full refactor to the Modular Style and addressed all architectural feedback. Here's the detailed breakdown of the changes:

Configuration (configuration_mimo_v2_flash.py)

Deepseek Notation: Adopted qk_head_dim and v_head_dim as requested.
RoPE Parameters: Moved rope_theta and partial_rotary_factor into a rope_parameters dictionary to align with newer models (e.g., MiniMax).
Layer Types: Switched from integer patterns to a string list (["full_attention", "sliding_attention"]).
Cleanup: Removed unused arguments and updated the copyright year to 2025 :(.

Modular Structure (modular_mimo_v2_flash.py)

Renamed: Changed the modeling file to modular_mimo_v2_flash.py and updated __init__.py registration.
Standard Components: Replaced the custom RMSNorm with LlamaRMSNorm from the Llama definitions.
Cleanup: Removed prepare_inputs_for_generation and other outdated flags (_supports_flash_attn_2, etc.) to rely on standard inheritance where possible.

Modeling Logic

MoE Implementation: Implemented the specific Sigmoid Router logic with normalized Top-K weights, as defined in the config.
RoPE Logic: Updated MiMoV2FlashAttention to calculate rotary_dim based on qk_head_dim and ensure it remains even.
Outputs: Ensured forward properly returns BaseModelOutputWithPast and CausalLMOutputWithPast with all fields (hidden_states, attentions) populated.
Attention Mask: I re-introduced _prepare_4d_causal_attention_mask inside the forward pass.
- Reason: Since MiMoV2FlashModel currently inherits from PreTrainedModel (and not a specific upstream model class yet), the mask broadcasting was failing during tests ([batch, seq] vs [batch, heads, seq, seq]). Using the standard utility fixed the broadcasting errors.

Verification

Updated tests/models/mimo_v2_flash/test_modeling_mimo_v2_flash.py to match the new string-based config.
Status: Local tests (Config, Model, and CausalLM forward passes) are Passing.

vasqu · 2026-01-19T16:25:08Z

@Aznix07 It still doesn't use modular at all, could you take the comments into account? LLM agents are powerful but it seems like it isn't working here

Furthermore, some details seem to be missing like sink attention (looking at #42995) cc @Aaraviitkgp (sorry about noticing it just now); it's hard to keep track of multiple PRs at times. I'd like to give this PR another chance but please properly work on this, don't blindly trust the agent to "just" work.

There are other issues like the tests not even running, wrong import structure, very old outdated patterns we no longer use etc.

Aaraviitkgp · 2026-02-01T23:39:24Z

@Aznix07 If possible we can work together on this, if you are ok with it ?

Aznix07 · 2026-02-16T05:11:28Z

@Aaraviitkgp, Yeah we can do it if you dont mind.

Aaraviitkgp · 2026-02-16T13:19:53Z

@Aznix07 Add me as collaborator to your fork.

Aaraviitkgp · 2026-03-03T09:16:21Z

@Aznix07 any update ?

Aznix07 added 2 commits December 21, 2025 13:13

Add MiMo-V2-Flash model skeleton and configuration

91c1ec1

Fix RoPE dimensions and Output types, register model in init files

bc06429

ArthurZucker added the New model label Jan 5, 2026

vasqu reviewed Jan 12, 2026

View reviewed changes

Aznix07 added 7 commits January 14, 2026 12:00

Refactor Config

1cc813c

Refactor Structure & Rename modeling files to modular

8ddfec5

Fix Tests - Update test config to use string-based layer_types

506cbce

Refactor Model - Implement modular style, clean inheritence, and RoPE…

56f1da0

… notation

Update year to 2025

3488051

Fixes notation issues

5c7a44c

Refactor Model: Modular style, clean inheritence, fix logic and tests

3aa5652

casinca mentioned this pull request Mar 31, 2026

Add Xiaomi MiMo-V2 #45144

Open

6 tasks

	v_head_dim (`int`, optional, defaults to 128):
	Dimension of the value heads.

		rope_theta (`float`, optional, defaults to 5000000.0):
		The base period of the RoPE embeddings.

		partial_rotary_factor (`float`, optional, defaults to 0.334):
		Percentage of the hidden dimension to apply RoPE to.

	def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
	rope_scaling = kwargs.pop("rope_scaling", None)
	self.rope_parameters = rope_scaling or self.rope_parameters
	self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

	# Standardize and validate the correctness of rotary position embeddings parameters
	# Model uses non-standard naming for rope params, overwrite!
	self.rope_parameters.setdefault("rope_theta", self.default_theta)
	self.rope_parameters["partial_rotary_factor"] = (
	kwargs.pop("rotary_dim", self.head_dim // 2) / self.head_dim
	) # Default to `0.5`
	self.standardize_rope_params()

	if ignore_keys_at_rope_validation is None:
	ignore_keys_at_rope_validation = {"partial_rotary_factor"}
	else:
	ignore_keys_at_rope_validation \|= {"partial_rotary_factor"}

	self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
	return kwargs

		hybrid_layer_pattern (`List[int]`, optional):
		Pattern defining which layers use full attention (0) and which use sliding window attention (1).

	if self.layer_types is None:
	self.layer_types = [
	"sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention"
	for i in range(self.num_hidden_layers)
	]
	layer_type_validation(self.layer_types, self.num_hidden_layers)

		scoring_func (`str`, optional, defaults to `"sigmoid"`):
		The scoring function used for the MoE router.

		return outputs


		class MiMoV2FlashModel(MiMoV2FlashPreTrainedModel):

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Aznix07 commented Dec 23, 2025

What does this PR do?

Implementation Details

Before Submitting

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github-actions bot commented Jan 15, 2026

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Aznix07 commented Jan 15, 2026

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vasqu commented Jan 19, 2026

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Aaraviitkgp commented Feb 1, 2026

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Aznix07 commented Feb 16, 2026

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Aaraviitkgp commented Feb 16, 2026

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Aaraviitkgp commented Mar 3, 2026

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