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Add support for qwen2moe (#640)

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* add sparsemoe block and update decoder logic

* update file name to match HF

* update name

* Code formatting

* update gates calculation

* add support for Qwen2MoE.

* fix pytest

* code formatting and fix missing comma in utils

* Remove decoder sparse step.

Co-authored-by: bozheng-hit <dsoul0621@gmail.com>

* remove gate layer anti-quantisation

* remove unused argument

---------

Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
This commit is contained in:
Prince Canuma
2024-04-02 20:33:29 +02:00
committed by GitHub
parent 78c431dc25
commit d661440dbb
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llms/mlx_lm/models/qwen2_moe.py Normal file
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from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import mlx.core as mx
import mlx.nn as nn
import numpy as np
from .base import BaseModelArgs
@dataclass
class ModelArgs(BaseModelArgs):
model_type: str
hidden_size: int
num_hidden_layers: int
intermediate_size: int
num_attention_heads: int
num_experts_per_tok: int
num_experts: int
moe_intermediate_size: int
shared_expert_intermediate_size: int
rms_norm_eps: float
vocab_size: int
num_key_value_heads: int = None
rope_theta: float = 1000000
rope_traditional: bool = False
rope_scaling: Optional[Dict[str, Union[float, str]]] = None
tie_word_embeddings: bool = False
def __post_init__(self):
if self.num_key_value_heads is None:
self.num_key_value_heads = self.num_attention_heads
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}")
if self.rope_scaling["type"] != "linear":
raise ValueError("rope_scaling 'type' currently only supports 'linear'")
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=False)
self.rope = nn.RoPE(
head_dim,
traditional=args.rope_traditional,
base=args.rope_theta,
)
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = 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:
key_cache, value_cache = cache
queries = self.rope(queries, offset=key_cache.shape[2])
keys = self.rope(keys, offset=key_cache.shape[2])
keys = mx.concatenate([key_cache, keys], axis=2)
values = mx.concatenate([value_cache, values], axis=2)
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), (keys, values)
class Qwen2MoeMLP(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 Qwen2MoeSparseMoeBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
dim = args.hidden_size
intermediate_size = args.moe_intermediate_size
shared_expert_intermediate_size = args.shared_expert_intermediate_size
self.num_experts = num_experts = args.num_experts
self.top_k = args.num_experts_per_tok
# gating
self.gate = nn.Linear(dim, num_experts, bias=False)
self.experts = [
Qwen2MoeMLP(dim, intermediate_size) for _ in range(self.num_experts)
]
self.shared_expert = Qwen2MoeMLP(dim, shared_expert_intermediate_size)
self.shared_expert_gate = nn.Linear(dim, 1, bias=False)
def __call__(
self,
x: mx.array,
):
ne = self.top_k
B, L, D = x.shape
x = x.reshape(-1, D)
# router_logits: (batch * sequence_length, n_experts)
gates = self.gate(x)
gates = mx.softmax(gates.astype(mx.float32), axis=-1)
inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne, axis=-1)[:, :ne])
scores = mx.take_along_axis(gates, inds, axis=-1).astype(x.dtype)
if self.training:
inds = np.array(inds)
y = mx.zeros((B, ne, D), x.dtype)
for e, expert in enumerate(self.experts):
idx1, idx2 = map(mx.array, np.where(inds == e))
if idx1.size == 0:
continue
y[idx1, idx2] = expert(x[idx1])
y = (y * scores[:, :, None]).sum(axis=1)
else:
y = []
for xt, st, it in zip(x, scores, inds.tolist()):
yt = mx.stack([self.experts[e](xt) for e in it], axis=-1)
yt = (yt * st).sum(axis=-1)
y.append(yt)
y = mx.stack(y, axis=0)
shared_expert_output = self.shared_expert(x)
shared_expert_output = (
mx.sigmoid(self.shared_expert_gate(x)) * shared_expert_output
)
y += shared_expert_output
return y.reshape(B, L, -1)
class Qwen2MoeDecoderLayer(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.hidden_size = args.hidden_size
self.self_attn = Attention(args)
self.mlp = Qwen2MoeSparseMoeBlock(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[Tuple[mx.array, mx.array]] = None,
) -> mx.array:
r, cache = 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, cache
class Qwen2MoeModel(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 = [
Qwen2MoeDecoderLayer(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,
cache=None,
):
h = self.embed_tokens(inputs)
mask = None
if h.shape[1] > 1:
mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
mask = mask.astype(h.dtype)
if cache is None:
cache = [None] * len(self.layers)
for e, layer in enumerate(self.layers):
h, cache[e] = layer(h, mask, cache[e])
return self.norm(h), cache
class Model(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.model_type = args.model_type
self.model = Qwen2MoeModel(args)
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
def __call__(
self,
inputs: mx.array,
cache=None,
):
out, cache = self.model(inputs, cache)
return self.lm_head(out), cache
def sanitize(self, weights):
if self.args.tie_word_embeddings and "lm_head.weight" not in weights:
weights["lm_head.weight"] = weights["model.embed_tokens.weight"]
# Remove unused precomputed rotary freqs
return {
k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
}
@property
def layers(self):
return self.model.layers