Add GPT-neox model (#863)

llms/mlx_lm/models/gpt_neox.py

@@ -0,0 +1,227 @@
+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, create_additive_causal_mask
+
+# Based on the transformers implementation at:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    max_position_embeddings: int
+    hidden_size: int
+    num_attention_heads: int
+    num_hidden_layers: int
+    layer_norm_eps: float
+    vocab_size: int
+    rotary_emb_base: int
+    rotary_pct: float
+    num_key_value_heads: int = None
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        assert (
+            args.hidden_size % args.num_attention_heads == 0
+        ), "hidden_size must be divisible by num_attention_heads"
+
+        self.hidden_size = args.hidden_size
+        self.num_attention_heads = args.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_attention_heads
+
+        self.rope = nn.RoPE(
+            dims=int(self.head_dim * args.rotary_pct),
+            traditional=False,
+            base=args.rotary_emb_base,
+        )
+
+        self.scale = self.head_dim**-0.5
+
+        self.query_key_value = nn.Linear(
+            self.hidden_size, 3 * self.hidden_size, bias=True
+        )
+        self.dense = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
+
+    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
+
+        qkv = self.query_key_value(x)
+
+        new_qkv_shape = qkv.shape[:-1] + (self.num_attention_heads, 3 * self.head_dim)
+        qkv = qkv.reshape(*new_qkv_shape)
+
+        queries, keys, values = [x.transpose(0, 2, 1, 3) for x in qkv.split(3, -1)]
+
+        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.dense(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.hidden_size = args.hidden_size
+        self.dense_h_to_4h = nn.Linear(self.hidden_size, 4 * self.hidden_size)
+        self.dense_4h_to_h = nn.Linear(4 * self.hidden_size, self.hidden_size)
+
+    def __call__(self, x) -> mx.array:
+        # gelu_approx corresponds to FastGELUActivation in transformers.
+        return self.dense_4h_to_h(nn.gelu_approx(self.dense_h_to_4h(x)))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.hidden_size = args.hidden_size
+        self.layer_norm_eps = args.layer_norm_eps
+        self.attention = Attention(args)
+        self.mlp = MLP(args)
+        self.input_layernorm = nn.LayerNorm(
+            self.hidden_size,
+            eps=self.layer_norm_eps,
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            self.hidden_size, eps=self.layer_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        residual = x
+        # NeoX runs attention and feedforward network in parallel.
+        attn = self.attention(self.input_layernorm(x), mask, cache)
+        ffn = self.mlp(self.post_attention_layernorm(x))
+        out = attn + ffn + residual
+        return out
+
+
+class GPTNeoXModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.hidden_size = args.hidden_size
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        self.layer_norm_eps = args.layer_norm_eps
+        assert self.vocab_size > 0
+        self.embed_in = nn.Embedding(self.vocab_size, self.hidden_size)
+        self.embed_out = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+        self.h = [TransformerBlock(args=args) for _ in range(self.num_hidden_layers)]
+        self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        _, L = inputs.shape
+
+        hidden_states = self.embed_in(inputs)
+
+        mask = None
+        if hidden_states.shape[1] > 1:
+            mask = create_additive_causal_mask(
+                hidden_states.shape[1], cache[0].offset if cache is not None else 0
+            )
+            mask = mask.astype(hidden_states.dtype)
+
+        if cache is None:
+            cache = [None] * len(self.h)
+
+        for layer, c in zip(self.h, cache):
+            hidden_states = layer(hidden_states, mask, cache=c)
+
+        out = self.final_layer_norm(hidden_states)
+        out = self.embed_out(out)
+
+        return out
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = GPTNeoXModel(args)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        return out
+
+    def sanitize(self, weights):
+        new_weights = {}
+
+        for w_key, w_value in weights.items():
+            # Created through register_buffer in Pytorch, not needed here.
+            ignore_suffixes = [
+                ".attention.bias",
+                ".attention.masked_bias",
+                ".attention.rotary_emb.inv_freq",
+            ]
+
+            skip_weight = False
+            for ignored_suffix in ignore_suffixes:
+                if w_key.endswith(ignored_suffix):
+                    skip_weight = True
+                    break
+
+            if skip_weight:
+                continue
+
+            if not w_key.startswith("model."):
+                w_key = f"model.{w_key}"
+
+            w_key = w_key.replace(".gpt_neox.layers.", ".h.")
+            w_key = w_key.replace(".gpt_neox.", ".")
+
+            new_weights[w_key] = w_value
+
+        return new_weights
+
+    @property
+    def layers(self):
+        return self.model.h
+
+    @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/tuner/utils.py

@@ -111,6 +111,8 @@ def linear_to_lora_layers(
         keys = set(["attn.c_attn"])
     elif model.model_type == "gpt2":
         keys = set(["attn.c_attn"])
+    elif model.model_type == "gpt_neox":
+        keys = set(["attention.query_key_value"])
     elif model.model_type == "olmo":
         keys = set(["att_proj"])
     elif model.model_type == "openelm":

llms/tests/test_lora.py

@@ -22,7 +22,7 @@ class TestLora(unittest.TestCase):
     def tearDown(self):
         sys.stdout = sys.__stdout__
 
-    def test_to_lora(self):
+    def test_llama(self):
         from mlx_lm.models import llama
 
         args = llama.ModelArgs(
@@ -60,6 +60,28 @@ class TestLora(unittest.TestCase):
         params["keys"] = ["self_attn.k_proj"]
         check_config(params)
 
+    def test_gpt_neox(self):
+        from mlx_lm.models import gpt_neox
+
+        args = gpt_neox.ModelArgs(
+            model_type="gpt_neox",
+            max_position_embeddings=2048,
+            hidden_size=6144,
+            num_attention_heads=64,
+            num_hidden_layers=44,
+            layer_norm_eps=1e-5,
+            vocab_size=50432,
+            rotary_emb_base=10_000,
+            rotary_pct=0.25,
+        )
+
+        num_lora_layers = 4
+        params = {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0}
+
+        model = gpt_neox.Model(args)
+        model.freeze()
+        tuner.utils.linear_to_lora_layers(model, num_lora_layers, params)
+
 
 class TestScheduleConfig(unittest.TestCase):
     def test_join(self):

llms/tests/test_models.py

@@ -355,6 +355,25 @@ class TestModels(unittest.TestCase):
         model = gpt2.Model(args)
         self.model_test_runner(model, args.model_type, args.vocab_size, args.n_layer)
 
+    def test_gpt_neox(self):
+        from mlx_lm.models import gpt_neox
+
+        args = gpt_neox.ModelArgs(
+            model_type="gpt_neox",
+            max_position_embeddings=2048,
+            hidden_size=6144,
+            num_attention_heads=64,
+            num_hidden_layers=44,
+            layer_norm_eps=1e-5,
+            vocab_size=50432,
+            rotary_emb_base=10_000,
+            rotary_pct=0.25,
+        )
+        model = gpt_neox.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
     def test_openelm(self):
         from mlx_lm.models import openelm