Tencent HunYuan MOE model

llms/mlx_lm/models/hunyuan.py

@@ -0,0 +1,291 @@
+# Copyright © 2023-2024 Apple Inc.
+
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+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
+    vocab_size: int
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    num_key_value_heads: int
+    attention_bias: bool
+    moe_topk: int
+    num_experts: int
+    num_shared_expert: int
+    use_mixed_mlp_moe: bool
+    use_qk_norm: bool
+    rms_norm_eps: float
+    rope_theta: float
+    use_cla: bool
+    cla_share_factor: 2
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self):
+
+        if self.rope_scaling:
+            required_keys = {"factor", "type"}
+            if not all(key in self.rope_scaling for key in required_keys):
+                raise ValueError(f"rope_scaling must contain keys {required_keys}")
+
+
+class DynamicNTKAlphaRoPE(nn.Module):
+    def __init__(
+        self,
+        dims: int,
+        base: float = 10000,
+        scaling_alpha: float = 1.0,
+    ):
+        super().__init__()
+        self.dims = dims
+        base = base * scaling_alpha ** (dims / (dims - 2))
+        self._freqs = base ** (mx.arange(0, self.dims, 2) / self.dims)
+
+    def __call__(self, x, offset: int = 0):
+        return mx.fast.rope(
+            x,
+            self.dims,
+            traditional=False,
+            base=None,
+            scale=1.0,
+            offset=offset,
+            freqs=self._freqs,
+        )
+
+
+class Attention(nn.Module):
+    def __init__(self, kv_proj: bool, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        assert args.num_key_value_heads is not None
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+
+        head_dim = args.hidden_size // n_heads
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=args.attention_bias)
+        if kv_proj:
+            self.k_proj = nn.Linear(
+                dim, n_kv_heads * head_dim, bias=args.attention_bias
+            )
+            self.v_proj = nn.Linear(
+                dim, n_kv_heads * head_dim, bias=args.attention_bias
+            )
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=args.attention_bias)
+        self.use_qk_norm = args.use_qk_norm
+        if self.use_qk_norm:
+            self.query_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps)
+            self.key_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps)
+
+        self.rope = DynamicNTKAlphaRoPE(
+            head_dim,
+            base=args.rope_theta,
+            scaling_alpha=args.rope_scaling["alpha"],
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+        kv_states=None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries = self.q_proj(x)
+
+        if kv_states is None:
+            keys, values = self.k_proj(x), self.v_proj(x)
+            kv_states = keys, values
+        else:
+            keys, values = kv_states
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+
+        offset = cache.offset if cache else 0
+        queries = self.rope(queries, offset=offset)
+        keys = self.rope(keys, offset=offset)
+        if self.use_qk_norm:
+            queries = self.query_layernorm(queries)
+            keys = self.key_layernorm(keys)
+
+        if cache is not None:
+            keys, values = cache.update_and_fetch(keys, values)
+
+        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), kv_states
+
+
+class MLP(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 Gate(nn.Module):
+    def __init__(self, dim, num_experts):
+        super().__init__()
+        self.wg = nn.Linear(dim, num_experts, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        return self.wg(x)
+
+
+class MoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        dim = args.hidden_size
+        intermediate_size = args.intermediate_size
+        self.use_shared_mlp = args.use_mixed_mlp_moe
+
+        if args.use_mixed_mlp_moe:
+            self.shared_mlp = MLP(dim, intermediate_size * args.num_shared_expert)
+
+        self.num_experts = num_experts = args.num_experts
+        self.top_k = args.moe_topk
+
+        self.gate = Gate(dim, num_experts)
+        self.switch_mlp = SwitchGLU(dim, intermediate_size, num_experts)
+
+    def __call__(
+        self,
+        x: mx.array,
+    ):
+        gates = self.gate(x)
+        gates = mx.softmax(gates, axis=-1, precise=True)
+
+        k = self.top_k
+        inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k])
+        scores = mx.take_along_axis(gates, inds, axis=-1)
+
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+
+        if self.use_shared_mlp:
+            shared_expert_output = self.shared_mlp(x)
+            y = y + shared_expert_output
+
+        return y
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs, kv_proj: bool):
+        super().__init__()
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(kv_proj, args)
+        self.mlp = MoeBlock(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
+        )
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+        shared_kv_states: Optional[Tuple[mx.array, mx.array]] = None,
+    ):
+        r, shared_kv_states = self.self_attn(
+            self.input_layernorm(x), mask, cache, shared_kv_states
+        )
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out, shared_kv_states
+
+
+class HunYuanModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        assert self.vocab_size > 0
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            DecoderLayer(args=args, kv_proj=(i % args.cla_share_factor) == 0)
+            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=None,
+    ):
+        h = self.embed_tokens(inputs)
+
+        mask = create_attention_mask(h, cache)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for i, (layer, c) in enumerate(zip(self.layers, cache)):
+            if i % self.args.cla_share_factor == 0:
+                shared_kv_states = None
+            h, shared_kv_states = layer(h, mask, c, shared_kv_states)
+
+        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 = HunYuanModel(args)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        return self.model.embed_tokens.as_linear(out)
+
+    def sanitize(self, weights):
+        if "model.layers.0.mlp.experts.0.up_proj.weight" not in weights:
+            return weights
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}"
+            for n in ["up_proj", "down_proj", "gate_proj"]:
+                for k in ["weight", "scales", "biases"]:
+                    if f"{prefix}.mlp.experts.0.{n}.{k}" in weights:
+                        to_join = [
+                            weights.pop(f"{prefix}.mlp.experts.{e}.{n}.{k}")
+                            for e in range(self.args.num_experts)
+                        ]
+                        weights[f"{prefix}.mlp.switch_mlp.{n}.{k}"] = mx.stack(to_join)
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers

llms/mlx_lm/utils.py

@@ -61,8 +61,8 @@ def wired_limit(model: nn.Module, streams: Optional[List[mx.Stream]] = None):
         model_mb = model_bytes // 2**20
         max_rec_mb = max_rec_size // 2**20
         print(
-            "[WARNING] Generating with a model that requires {model_mb} MB "
-            "which is close to the maximum recommended size of {max_rec_mb} "
+            f"[WARNING] Generating with a model that requires {model_mb} MB "
+            f"which is close to the maximum recommended size of {max_rec_mb} "
             "MB. This can be slow. See the documentation for possible work-arounds: "
             "https://github.com/ml-explore/mlx-examples/tree/main/llms#large-models"
         )

llms/tests/test_models.py

@@ -760,6 +760,38 @@ def test_recurrent_gemma(self):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_hunyuan(self):
+        from mlx_lm.models import hunyuan
+
+        args = hunyuan.ModelArgs(
+            model_type="hunyuan",
+            hidden_size=128,
+            attention_bias=False,
+            intermediate_size=256,
+            num_attention_heads=4,
+            num_hidden_layers=4,
+            num_key_value_heads=2,
+            rms_norm_eps=1e-4,
+            rope_theta=1000,
+            vocab_size=1000,
+            moe_topk=2,
+            num_experts=2,
+            num_shared_expert=1,
+            use_mixed_mlp_moe=True,
+            use_qk_norm=True,
+            rope_scaling={
+                "alpha": 1000.0,
+                "factor": 1.0,
+                "type": "dynamic",
+            },
+            use_cla=True,
+            cla_share_factor=2,
+        )
+        model = hunyuan.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
 
 if __name__ == "__main__":
     unittest.main()