Add Phi-3.5-MoE (#946)

* add phimoe

* add phimoe to tunner

* add switch_mlp

* fix SuScaled args

* nits

---------

Co-authored-by: Awni Hannun <awni@apple.com>

llms/mlx_lm/models/phimoe.py

@@ -0,0 +1,218 @@
+# Copyright © 2024 Apple Inc.
+import math
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask
+from .su_rope import SuScaledRotaryEmbedding
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "phimoe"
+    vocab_size: int = 32064
+    hidden_size: int = 4096
+    intermediate_size: int = 6400
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 131072
+    original_max_position_embeddings: int = 4096
+    rms_norm_eps: float = 1e-6
+    rope_scaling: Dict[str, Union[float, List[float]]] = None
+    num_local_experts: int = 16
+    num_experts_per_tok: int = 2
+    rope_theta: float = 10000.0
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        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=True)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=True)
+
+        self.rope = SuScaledRotaryEmbedding(
+            head_dim,
+            base=args.rope_theta,
+            max_position_embeddings=args.max_position_embeddings,
+            original_max_position_embeddings=args.original_max_position_embeddings,
+            short_factor=args.rope_scaling["short_factor"],
+            long_factor=args.rope_scaling["long_factor"],
+            short_mscale=args.rope_scaling["short_mscale"],
+            long_mscale=args.rope_scaling["long_mscale"],
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache=None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        # 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)
+
+        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 = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class PhiMoESparseMoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.hidden_dim = args.hidden_size
+        self.ffn_dim = args.intermediate_size
+        self.num_experts = args.num_local_experts
+        self.top_k = args.num_experts_per_tok
+
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+        self.switch_mlp = SwitchGLU(self.hidden_dim, self.ffn_dim, self.num_experts)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        gates = self.gate(x)
+
+        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)
+        scores = mx.softmax(scores, axis=-1, precise=True)
+
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+
+        return y
+
+
+class PhiMoEDecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.hidden_size = args.hidden_size
+
+        self.self_attn = Attention(args)
+        self.block_sparse_moe = PhiMoESparseMoeBlock(args)
+        self.input_layernorm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache=None,
+    ) -> mx.array:
+        residual = x
+        hidden_states = self.input_layernorm(x)
+        hidden_states = self.self_attn(hidden_states, mask=mask, cache=cache)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class PhiMoEModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [PhiMoEDecoderLayer(args) for _ in range(args.num_hidden_layers)]
+        self.norm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ) -> mx.array:
+        h = self.embed_tokens(inputs)
+
+        mask = create_attention_mask(h, cache)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        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 = PhiMoEModel(args)
+        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=True)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        return self.lm_head(out)
+
+    def sanitize(self, weights):
+        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}"
+            for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
+                for k in ["weight", "scales", "biases"]:
+                    if f"{prefix}.block_sparse_moe.experts.0.{n}.{k}" in weights:
+                        to_join = [
+                            weights.pop(
+                                f"{prefix}.block_sparse_moe.experts.{e}.{n}.{k}"
+                            )
+                            for e in range(self.args.num_local_experts)
+                        ]
+                        weights[f"{prefix}.block_sparse_moe.switch_mlp.{m}.{k}"] = (
+                            mx.stack(to_join)
+                        )
+
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def head_dim(self):
+        return self.args.hidden_size // self.args.num_attention_heads
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads

llms/mlx_lm/models/su_rope.py

@@ -16,6 +16,8 @@ class SuScaledRotaryEmbedding(nn.Module):
         original_max_position_embeddings: int = 4096,
         short_factor: Union[List[float], float] = 1.0,
         long_factor: Union[List[float], float] = 1.0,
+        short_mscale: float = None,
+        long_mscale: float = None,
     ):
         """
         Phi3Su Scaled Rotary Embedding layer for Phi-3 models.
@@ -37,12 +39,14 @@ class SuScaledRotaryEmbedding(nn.Module):
             long_factor (float or list[float], optional): List of scaling
               factors for sequences of length greater than
               ``original_max_position_embeddings``.  Default: ``1.0``.
+            short_mscale (float, optional): Scale the input prior to embedding.
+            long_mscale (float, optional): Scale the input prior to embedding.
         """
         super().__init__()
         freqs = base ** (mx.arange(0, dims, 2, dtype=mx.float32) / dims)
         self._freqs = mx.array(long_factor, dtype=mx.float32) * freqs
         self.original_max_position_embeddings = original_max_position_embeddings
-        self.scale = math.sqrt(
+        self.scale = long_mscale or math.sqrt(
             1
             + math.log(max_position_embeddings / original_max_position_embeddings)
             / math.log(original_max_position_embeddings)

llms/mlx_lm/tuner/utils.py

@@ -96,6 +96,7 @@ def linear_to_lora_layers(
         "stablelm",
         "qwen2",
         "qwen2_moe",
+        "phimoe",
         "gemma",
         "gemma2",
         "starcoder2",
@@ -104,7 +105,7 @@ def linear_to_lora_layers(
         "deepseek",
     ]:
         keys = set(["self_attn.q_proj", "self_attn.v_proj"])
-        if model.model_type == "mixtral":
+        if model.model_type in ["mixtral", "phimoe"]:
             keys.add("block_sparse_moe.gate")
         if model.model_type == "qwen2_moe":
             keys.add("mlp.gate")