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feat: DeepSeek MoE v1 (#942)
* feat: deepseek v1
DeepSeek is still releasing models on the DeepSeek V1 architecture.
```sh
mlx_lm.convert --hf-path deepseek-ai/DeepSeek-Prover-V1.5-RL --mlx-path DeepSeek-Prover-V1.5-RL-8bit --q-bits 8 -q
mlx_lm.generate --model DeepSeek-Prover-V1.5-RL-8bit --ignore-chat-template --max-tokens 512 --prompt 'import Mathlib
import Aesop
set_option maxHeartbeats 0
open BigOperators Real Nat Topology Rat
/-- The second and fourth terms of a geometric sequence are $2$ and $6$. Which of the following is a possible first term?
Show that it is $\frac{2\sqrt{3}}{3}$.-/
theorem amc12b_2003_p6 (a r : ℝ) (u : ℕ → ℝ) (h₀ : ∀ k, u k = a * r ^ k) (h₁ : u 1 = 2)
(h₂ : u 3 = 6) : u 0 = 2 / Real.sqrt 3 ∨ u 0 = -(2 / Real.sqrt 3) := by'
```
* nits
* nits
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
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llms/mlx_lm/models/deepseek.py
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266
llms/mlx_lm/models/deepseek.py
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@ -0,0 +1,266 @@
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from dataclasses import dataclass
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from typing import Dict, Optional
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs, KVCache, create_attention_mask
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from .switch_layers import SwitchGLU
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "deepseek"
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vocab_size: int = 102400
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hidden_size: int = 4096
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intermediate_size: int = 11008
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moe_intermediate_size: int = 1407
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num_hidden_layers: int = 30
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num_attention_heads: int = 32
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num_key_value_heads: int = 32
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n_shared_experts: Optional[int] = None
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n_routed_experts: Optional[int] = None
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num_experts_per_tok: Optional[int] = None
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moe_layer_freq: int = 1
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first_k_dense_replace: int = 0
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max_position_embeddings: int = 2048
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rms_norm_eps: float = 1e-6
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rope_theta: float = 10000.0
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rope_scaling: Optional[Dict] = None
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attention_bias: bool = False
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class DeepseekAttention(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.num_kv_heads = config.num_key_value_heads
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self.head_dim = config.hidden_size // config.num_attention_heads
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self.scale = self.head_dim**-0.5
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attention_bias = getattr(config, "attention_bias", False)
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self.q_proj = nn.Linear(
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self.hidden_size,
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config.num_attention_heads * self.head_dim,
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bias=attention_bias,
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)
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self.k_proj = nn.Linear(
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self.hidden_size,
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config.num_key_value_heads * self.head_dim,
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bias=attention_bias,
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)
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self.v_proj = nn.Linear(
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self.hidden_size,
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config.num_key_value_heads * self.head_dim,
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bias=attention_bias,
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)
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self.o_proj = nn.Linear(
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self.hidden_size,
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config.num_attention_heads * self.head_dim,
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bias=attention_bias,
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)
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rope_scale = 1.0
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if config.rope_scaling and config.rope_scaling["type"] == "linear":
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assert isinstance(config.rope_scaling["factor"], float)
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rope_scale = 1 / config.rope_scaling["factor"]
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self.rope = nn.RoPE(
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self.head_dim,
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base=config.rope_theta,
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scale=rope_scale,
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)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[KVCache] = None,
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) -> mx.array:
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B, L, _ = x.shape
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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queries = queries.reshape(B, L, self.num_attention_heads, -1).transpose(
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0, 2, 1, 3
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)
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keys = keys.reshape(B, L, self.num_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.num_kv_heads, -1).transpose(0, 2, 1, 3)
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if cache is not None:
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queries = self.rope(queries, offset=cache.offset)
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keys = self.rope(keys, offset=cache.offset)
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keys, values = cache.update_and_fetch(keys, values)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output)
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class DeepseekMLP(nn.Module):
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def __init__(
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self,
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config: ModelArgs,
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hidden_size: int | None = None,
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intermediate_size: int | None = None,
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):
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super().__init__()
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self.config = config
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self.hidden_size = hidden_size or config.hidden_size
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self.intermediate_size = intermediate_size or config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = nn.silu
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def __call__(self, x: mx.array) -> mx.array:
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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class MoEGate(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.top_k = config.num_experts_per_tok
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self.n_routed_experts = config.n_routed_experts
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self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
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def __call__(self, x):
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gates = x @ self.weight.T
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scores = mx.softmax(gates, axis=-1, precise=True)
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k = self.top_k
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inds = mx.stop_gradient(mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k])
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scores = mx.take_along_axis(scores, inds, axis=-1)
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return inds, scores
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class DeepseekMoE(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.switch_mlp = SwitchGLU(
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config.hidden_size, config.moe_intermediate_size, config.n_routed_experts
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)
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self.gate = MoEGate(config)
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if config.n_shared_experts is not None:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekMLP(
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config=config, intermediate_size=intermediate_size
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)
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def __call__(self, x):
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inds, scores = self.gate(x)
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y = self.switch_mlp(x, inds)
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y = (y * scores[..., None]).sum(axis=-2)
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if self.config.n_shared_experts is not None:
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y = y + self.shared_experts(x)
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return y
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class DeepseekDecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs, layer_idx: int):
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super().__init__()
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self.self_attn = DeepseekAttention(config)
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self.mlp = (
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DeepseekMoE(config)
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if (
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config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace
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and layer_idx % config.moe_layer_freq == 0
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)
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else DeepseekMLP(config)
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)
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self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = nn.RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[KVCache] = None,
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) -> mx.array:
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r = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out
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class DeepseekModel(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = [
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DeepseekDecoderLayer(config, idx) for idx in range(config.num_hidden_layers)
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]
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self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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x: mx.array,
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cache: Optional[KVCache] = None,
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) -> mx.array:
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h = self.embed_tokens(x)
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mask = create_attention_mask(h, cache)
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if cache is None:
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cache = [None] * len(self.layers)
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for layer, c in zip(self.layers, cache):
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h = layer(h, mask, c)
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return self.norm(h)
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class Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.args = config
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self.model_type = config.model_type
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self.model = DeepseekModel(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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def __call__(
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self,
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inputs: mx.array,
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cache: Optional[KVCache] = None,
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):
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out = self.model(inputs, cache)
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return self.lm_head(out)
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def sanitize(self, weights):
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for l in range(self.args.num_hidden_layers):
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prefix = f"model.layers.{l}"
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for m in ["gate_proj", "down_proj", "up_proj"]:
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for k in ["weight", "scales", "biases"]:
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if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
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to_join = [
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weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
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for e in range(self.args.n_routed_experts)
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]
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weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)
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return weights
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@property
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def layers(self):
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return self.model.layers
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@property
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def head_dim(self):
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return self.args.hidden_size // self.args.num_attention_heads
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@property
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def n_kv_heads(self):
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return self.args.num_key_value_heads
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@ -101,6 +101,7 @@ def linear_to_lora_layers(
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"starcoder2",
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"cohere",
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"minicpm",
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"deepseek",
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]:
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keys = set(["self_attn.q_proj", "self_attn.v_proj"])
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if model.model_type == "mixtral":
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