Add Minimax-M2 (#568)

* add minimax m2

* fix dequant and decoder

* remove unused

* remove unused

* normalize scores

* refactor

* fix minimax

* fix

---------

Co-authored-by: awni <[email protected]>

Author: Blaizzy

mlx_lm/models/minimax.py

@@ -0,0 +1,287 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, List, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    intermediate_size: int
+    num_attention_heads: int
+    num_key_value_heads: int
+    max_position_embeddings: int
+    num_experts_per_tok: int
+    num_local_experts: int
+    shared_intermediate_size: int
+    num_hidden_layers: int
+    rms_norm_eps: float
+    rope_theta: float
+    rotary_dim: int
+    vocab_size: int
+    tie_word_embeddings: bool = False
+    scoring_func: str = "sigmoid"
+    head_dim: Optional[int] = None
+    use_qk_norm: bool = True
+
+
+class MiniMaxAttention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.hidden_dim = hidden_size = args.hidden_size
+
+        self.num_attention_heads = args.num_attention_heads
+        self.num_key_value_heads = args.num_key_value_heads
+        self.head_dim = head_dim = (
+            args.head_dim or hidden_size // args.num_attention_heads
+        )
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(
+            args.hidden_size, self.num_attention_heads * head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            args.hidden_size, self.num_key_value_heads * head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            args.hidden_size, self.num_key_value_heads * head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            self.num_attention_heads * head_dim, args.hidden_size, bias=False
+        )
+
+        self.use_qk_norm = args.use_qk_norm if hasattr(args, "use_qk_norm") else False
+        if self.use_qk_norm:
+            self.q_norm = nn.RMSNorm(
+                head_dim * self.num_attention_heads, eps=args.rms_norm_eps
+            )
+            self.k_norm = nn.RMSNorm(
+                head_dim * self.num_key_value_heads, eps=args.rms_norm_eps
+            )
+
+        self.rope = nn.RoPE(args.rotary_dim, traditional=False, base=args.rope_theta)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        if self.use_qk_norm:
+            queries = self.q_norm(queries)
+            keys = self.k_norm(keys)
+
+        queries = queries.reshape(B, L, self.num_attention_heads, -1).transpose(
+            0, 2, 1, 3
+        )
+        keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
+            0, 2, 1, 3
+        )
+
+        if cache is not None:
+            queries = self.rope(queries, offset=cache.offset)
+            keys = self.rope(keys, offset=cache.offset)
+            keys, values = cache.update_and_fetch(keys, values)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        output = scaled_dot_product_attention(
+            queries, keys, values, cache=cache, scale=self.scale, mask=mask
+        )
+
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.o_proj(output)
+
+
+class MiniMaxSparseMoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_experts_per_tok = args.num_experts_per_tok
+
+        self.gate = nn.Linear(args.hidden_size, args.num_local_experts, bias=False)
+        self.switch_mlp = SwitchGLU(
+            args.hidden_size, args.intermediate_size, args.num_local_experts
+        )
+        self.e_score_correction_bias = mx.zeros((args.num_local_experts,))
+
+    def __call__(self, x: mx.array) -> mx.array:
+        gates = self.gate(x.astype(mx.float32))
+
+        scores = mx.sigmoid(gates)
+        orig_scores = scores
+        scores = scores + self.e_score_correction_bias
+
+        k = self.num_experts_per_tok
+        inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
+        scores = mx.take_along_axis(orig_scores, inds, axis=-1)
+
+        scores = scores / (mx.sum(scores, axis=-1, keepdims=True) + 1e-20)
+        scores = scores.astype(x.dtype)
+
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+        return y
+
+
+class MiniMaxDecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.self_attn = MiniMaxAttention(args)
+
+        self.block_sparse_moe = MiniMaxSparseMoeBlock(args)
+
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        r = x + self.self_attn(self.input_layernorm(x), mask, cache)
+        r = r + self.block_sparse_moe(self.post_attention_layernorm(r))
+        return r
+
+
+class MiniMaxModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+
+        self.layers = [
+            MiniMaxDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
+        ]
+
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        h = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        mask = create_attention_mask(h, cache[0])
+
+        for layer, c in zip(self.layers, cache):
+            h = layer(h, mask, c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = MiniMaxModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ):
+        out = self.model(inputs=inputs, mask=mask, cache=cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    def sanitize(self, weights):
+        """Dequantize FP8 weights and restructure MoE experts."""
+
+        def dequant(weight, scale_inv):
+            dtype = weight.dtype
+            bs = 128  # block size
+            m, n = weight.shape
+            pad_bottom = (-m) % bs
+            pad_side = (-n) % bs
+            weight = mx.pad(weight, ((0, pad_bottom), (0, pad_side)))
+            weight = weight.reshape(
+                ((m + pad_bottom) // bs, bs, (n + pad_side) // bs, bs)
+            )
+            weight = (weight * scale_inv[:, None, :, None]).reshape(
+                m + pad_bottom, n + pad_side
+            )
+            return weight[:m, :n].astype(dtype)
+
+        # Dequantize
+        new_weights = {}
+        for k, v in weights.items():
+            if "weight_scale_inv" in k:
+                scale_inv = v
+                wk = k.replace("_scale_inv", "")
+                weight = weights[wk]
+                weight = dequant(weight, scale_inv)
+                new_weights[wk] = weight
+            elif k not in new_weights:
+                new_weights[k] = v
+        weights = new_weights
+
+        # Step 2: Handle MoE expert weights restructuring
+        if "model.layers.0.block_sparse_moe.experts.0.w1.weight" not in weights:
+            return weights
+
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}"
+            mapping = {"w1": "gate_proj", "w2": "down_proj", "w3": "up_proj"}
+            for orig_name, new_name in mapping.items():
+                if f"{prefix}.block_sparse_moe.experts.0.{orig_name}.weight" in weights:
+                    to_join = [
+                        weights.pop(
+                            f"{prefix}.block_sparse_moe.experts.{e}.{orig_name}.weight"
+                        )
+                        for e in range(self.args.num_local_experts)
+                    ]
+                    weights[
+                        f"{prefix}.block_sparse_moe.switch_mlp.{new_name}.weight"
+                    ] = mx.stack(to_join)
+
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def cast_predicate(self):
+        def predicate(k):
+            return "e_score_correction_bias" not in k
+
+        return predicate
+
+    @property
+    def quant_predicate(self):
+        def predicate(path, _):
+            if path.endswith("block_sparse_moe.gate"):
+                return {"group_size": 64, "bits": 8}
+            return True
+
+        return predicate

tests/test_models.py

@@ -1939,6 +1939,22 @@ def test_all_models(self):
                 "vocab_size": 32,
                 "intermediate_size": 128,
             },
+            {
+                "model_type": "minimax",
+                "hidden_size": 128,
+                "intermediate_size": 128,
+                "num_attention_heads": 8,
+                "num_key_value_heads": 8,
+                "max_position_embeddings": 1000,
+                "num_experts_per_tok": 2,
+                "num_local_experts": 8,
+                "shared_intermediate_size": 128,
+                "num_hidden_layers": 4,
+                "rms_norm_eps": 1e-4,
+                "rope_theta": 1000,
+                "rotary_dim": 16,
+                "vocab_size": 1000,
+            },
         ]
         for config in test_configs:
             model_type = config["model_type"]