adding Kwai-Klear/Klear-46B-A2.5B-Instruct (ml-explore#437)

* in. com.

* clean up

* sanitize

* fix

* nits

* making it trainable

* format

* upd. ackn

* rebase + nits

* rebase + nits

---------

Co-authored-by: Awni Hannun <[email protected]>

Author: Goekdeniz-Guelmez

ACKNOWLEDGMENTS.md

@@ -8,5 +8,5 @@ with a short description of your contribution(s) below. For example:
 MLX LM was developed with contributions from the following individuals:
 
 - Shunta Saito: Added support for PLaMo models.
-- Gökdeniz Gülmez: Added support for the following architectures: OpenBMB's `MiniCPM` and `MiniCPM3`, Kyutai's `Helium`, State-Space's`Mamba v1`, Z.ai & THUKEG's `GLM4`, Rednote `dots.llm1`, Baisu's `Ernie4.5 MoE`, inclusionAI's `Bailing MoE e.g. Ling-family`, IBM's `Granite MoE`, Meituan's `LongCat`, Nvidia's `Nemotron H`, Swiss-AI's `Apertus`, and Allenai's `OLMoE`; Added support for the following training algorithms: `Full Weight Fine-Tuning`, and the `Muon` optimizer; Added support for the following other features: `Multiple Optimizers to choose for training`, and `reporting training metrics to WandB (Weights & Biases)`.
+- Gökdeniz Gülmez: Added support for the following architectures: OpenBMB's `MiniCPM` and `MiniCPM3`, Kyutai's `Helium`, State-Space's`Mamba v1`, Z.ai & THUKEG's `GLM4`, Rednote `dots.llm1`, Baisu's `Ernie4.5 MoE`, inclusionAI's `Bailing MoE e.g. Ling-family`, Klear team - Kuaishou Technology's `Klear`, IBM's `Granite MoE`, Meituan's `LongCat`, Nvidia's `Nemotron H`, Swiss-AI's `Apertus`, and Allenai's `OLMoE`; Added support for the following training algorithms: `Full Weight Fine-Tuning`, and the `Muon` optimizer; Added support for the following other features: `Multiple Optimizers to choose for training`, and `reporting training metrics to WandB (Weights & Biases)`.
 - Prince Canuma: Helped add support for the following model architectures: HuggingFace's `Starcoder2`, Cohere's `Cohere (1 and 2)`, Alibaba Qwen's `Qwen (2, 3 and MoE)`, Microsoft's `Phi (3 and 3.5 MoE)`, `BitNet1.58`, Meta's `Llama (3 and 4)`, Google DeepMind's `Gemma 3`, and InterLM's `InternLM 2.5`.

mlx_lm/models/Klear.py

@@ -0,0 +1,262 @@
+# 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
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    attention_bias: bool
+    mlp_only_layers: List[int]
+    num_experts: int
+    num_experts_per_tok: int
+    decoder_sparse_step: int
+    n_shared_experts: int
+    moe_intermediate_size: int
+    rms_norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int
+    rope_theta: float
+    max_position_embeddings: int
+    norm_topk_prob: bool
+
+
+class KlearAttention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.num_key_value_heads = args.num_key_value_heads
+
+        self.head_dim = args.hidden_size // args.num_attention_heads
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(
+            args.hidden_size,
+            self.num_attention_heads * self.head_dim,
+            bias=args.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            args.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=args.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            args.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=args.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            self.num_attention_heads * self.head_dim,
+            args.hidden_size,
+            bias=args.attention_bias,
+        )
+
+        self.q_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+        self.k_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+
+        self.rope = nn.RoPE(
+            self.head_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)
+
+        queries = self.q_norm(
+            queries.reshape(B, L, self.num_attention_heads, -1)
+        ).transpose(0, 2, 1, 3)
+        keys = self.k_norm(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 KlearMLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class KlearSparseMoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.norm_topk_prob = args.norm_topk_prob
+        self.num_experts = args.num_experts
+        self.top_k = args.num_experts_per_tok
+
+        self.gate = nn.Linear(args.hidden_size, args.num_experts, bias=False)
+        self.experts = SwitchGLU(
+            args.hidden_size, args.moe_intermediate_size, args.num_experts
+        )
+        self.shared_experts = KlearMLP(
+            args.hidden_size,
+            hidden_dim=args.moe_intermediate_size * args.n_shared_experts,
+        )
+        self.coefficient = nn.Linear(args.hidden_size, 2)
+        self.expert_bias = mx.zeros((self.num_experts,), dtype=mx.float32)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        routing_weights = mx.sigmoid(self.gate(x).astype(mx.float32))
+        biased_weights = routing_weights + self.expert_bias.reshape((1, 1, -1))
+        k = self.top_k
+        inds = mx.argpartition(-biased_weights, kth=k - 1, axis=-1)[..., :k]
+        scores = mx.take_along_axis(routing_weights, inds, axis=-1)
+        if self.norm_topk_prob:
+            scores = scores / mx.sum(scores, axis=-1, keepdims=True)
+        scores = scores.astype(x.dtype)
+        expert_out = self.experts(x, inds)
+        y_experts = (expert_out * scores[..., None]).sum(axis=-2)
+        coef = mx.softmax(self.coefficient(x), axis=-1, precise=True)
+        shared = self.shared_experts(x)
+        y = y_experts * coef[..., :1] + shared * coef[..., 1:]
+        return y
+
+
+class KlearDecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.self_attn = KlearAttention(args)
+
+        if (layer_idx not in args.mlp_only_layers) and (
+            args.num_experts > 0 and (layer_idx + 1) % args.decoder_sparse_step == 0
+        ):
+            self.mlp = KlearSparseMoeBlock(args)
+        else:
+            self.mlp = KlearMLP(args.hidden_size, args.intermediate_size)
+
+        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 = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out
+
+
+class KlearModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            KlearDecoderLayer(args=args, layer_idx=i)
+            for i in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        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 = KlearModel(args)
+        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ):
+        out = self.model(inputs, cache)
+        return self.lm_head(out)
+
+    def sanitize(self, weights):
+        if "model.layers.0.mlp.experts.0.gate_proj.weight" not in weights:
+            return weights
+
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}.mlp.experts"
+            for name in ["gate_proj", "up_proj", "down_proj"]:
+                stacked = [
+                    weights.pop(f"{prefix}.{e}.{name}.weight")
+                    for e in range(self.args.num_experts)
+                ]
+                weights[f"{prefix}.{name}.weight"] = mx.stack(stacked)
+
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def quant_predicate(self):
+        def predicate(path, _):
+            if path.endswith("mlp.gate"):
+                return {"group_size": 64, "bits": 8}
+            return True
+
+        return predicate
+
+    @property
+    def cast_predicate(self):
+        def predicate(k):
+            return "expert_bias" not in k
+
+        return predicate

mlx_lm/tuner/utils.py

@@ -128,14 +128,15 @@ def to_lora(layer):
         "longcat_flash",
         "seed_oss",
         "apertus",
+        "Klear",
     }:
         keys = {"self_attn.q_proj", "self_attn.v_proj"}
         if model.model_type in ["mixtral", "phimoe"]:
             keys.add("block_sparse_moe.gate")
         if model.model_type == "qwen2_moe":
             keys.add("mlp.gate")
             keys.add("mlp.shared_expert_gate")
-        if model.model_type in ["olmoe", "qwen3_moe", "dots1"]:
+        if model.model_type in ["olmoe", "qwen3_moe", "dots1", "Klear"]:
             keys.add("mlp.gate")
         if model.model_type in ["longcat_flash"]:
             keys.add("mlp.router.classifier")

tests/test_models.py

@@ -1651,6 +1651,26 @@ def test_all_models(self):
                 "num_key_value_heads": 2,
                 "head_dim": 64,
             },
+            {
+                "model_type": "Klear",
+                "hidden_size": 128,
+                "num_hidden_layers": 4,
+                "intermediate_size": 128,
+                "num_attention_heads": 4,
+                "attention_bias": False,
+                "mlp_only_layers": [0],
+                "num_experts": 4,
+                "num_experts_per_tok": 2,
+                "decoder_sparse_step": 2,
+                "n_shared_experts": 1,
+                "moe_intermediate_size": 128,
+                "rms_norm_eps": 1e-5,
+                "vocab_size": 1000,
+                "num_key_value_heads": 4,
+                "rope_theta": 1000.0,
+                "max_position_embeddings": 1000,
+                "norm_topk_prob": True,
+            },
         ]
         for config in test_configs:
             model_type = config["model_type"]