GPT2 Support (#798)

* GPT-2 model support

* Add test for gpt2 model

* Fix weight sanitizing for quantization

* use approx gelu

---------

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

llms/mlx_lm/models/gpt2.py

@@ -0,0 +1,207 @@
+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
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    n_ctx: int
+    n_embd: int
+    n_head: int
+    n_layer: int
+    n_positions: int
+    layer_norm_epsilon: float
+    vocab_size: int
+    num_key_value_heads: int = None
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.n_head
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        assert args.n_embd % args.n_head == 0, "n_embd must be divisible by n_head"
+
+        self.n_embd = args.n_embd
+        self.n_head = args.n_head
+        self.head_dim = self.n_embd // self.n_head
+
+        self.scale = self.head_dim**-0.5
+
+        self.c_attn = nn.Linear(self.n_embd, 3 * self.n_embd, bias=True)
+        self.c_proj = nn.Linear(self.n_embd, self.n_embd, 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.c_attn(x)
+        queries, keys, values = mx.split(qkv, 3, axis=-1)
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            keys, values = cache.update_and_fetch(keys, values)
+
+        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.c_proj(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.n_embd = args.n_embd
+        self.c_fc = nn.Linear(self.n_embd, 4 * self.n_embd)
+        self.c_proj = nn.Linear(4 * self.n_embd, self.n_embd)
+
+    def __call__(self, x) -> mx.array:
+        return self.c_proj(nn.gelu_approx(self.c_fc(x)))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.n_head = args.n_head
+        self.n_embd = args.n_embd
+        self.layer_norm_epsilon = args.layer_norm_epsilon
+        self.attn = Attention(args)
+        self.mlp = MLP(args)
+        self.ln_1 = nn.LayerNorm(
+            self.n_embd,
+            eps=self.layer_norm_epsilon,
+        )
+        self.ln_2 = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        r = self.attn(self.ln_1(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.ln_2(h))
+        out = h + r
+        return out
+
+
+class GPT2Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.n_embd = args.n_embd
+        self.n_positions = args.n_positions
+        self.vocab_size = args.vocab_size
+        self.n_layer = args.n_layer
+        self.layer_norm_epsilon = args.layer_norm_epsilon
+        assert self.vocab_size > 0
+        self.wte = nn.Embedding(self.vocab_size, self.n_embd)
+        self.wpe = nn.Embedding(self.n_positions, self.n_embd)
+        self.h = [TransformerBlock(args=args) for _ in range(self.n_layer)]
+        self.ln_f = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        _, L = inputs.shape
+
+        hidden_states = self.wte(inputs)
+
+        mask = None
+        if hidden_states.shape[1] > 1:
+
+            position_ids = mx.array(np.arange(L))
+            hidden_states += self.wpe(position_ids)
+
+            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)
+
+        return self.ln_f(hidden_states)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = GPT2Model(args)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        out = self.model.wte.as_linear(out)
+        return out
+
+    def sanitize(self, weights):
+        new_weights = {}
+        for i in range(self.args.n_layer):
+            if f"h.{i}.attn.bias" in weights:
+                del weights[f"h.{i}.attn.bias"]
+            if f"h.{i}.attn.c_attn.weight" in weights:
+                weights[f"h.{i}.attn.c_attn.weight"] = weights[
+                    f"h.{i}.attn.c_attn.weight"
+                ].transpose(1, 0)
+            if f"h.{i}.attn.c_proj.weight" in weights:
+                weights[f"h.{i}.attn.c_proj.weight"] = weights[
+                    f"h.{i}.attn.c_proj.weight"
+                ].transpose(1, 0)
+            if f"h.{i}.mlp.c_fc.weight" in weights:
+                weights[f"h.{i}.mlp.c_fc.weight"] = weights[
+                    f"h.{i}.mlp.c_fc.weight"
+                ].transpose(1, 0)
+            if f"h.{i}.mlp.c_proj.weight" in weights:
+                weights[f"h.{i}.mlp.c_proj.weight"] = weights[
+                    f"h.{i}.mlp.c_proj.weight"
+                ].transpose(1, 0)
+        for weight in weights:
+            if not weight.startswith("model."):
+                new_weights[f"model.{weight}"] = weights[weight]
+            else:
+                new_weights[weight] = weights[weight]
+        return new_weights
+
+    @property
+    def layers(self):
+        return self.model.h
+
+    @property
+    def head_dim(self):
+        return self.args.n_embd // self.args.n_head
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads

llms/mlx_lm/tuner/utils.py

@@ -108,6 +108,8 @@ def linear_to_lora_layers(
 
     elif model.model_type == "gpt_bigcode":
         keys = set(["attn.c_attn"])
+    elif model.model_type == "gpt2":
+        keys = set(["attn.c_attn"])
     elif model.model_type == "olmo":
         keys = set(["att_proj"])
     elif model.model_type == "openelm":

llms/tests/test_models.py

@@ -339,6 +339,22 @@ class TestModels(unittest.TestCase):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_gpt2(self):
+        from mlx_lm.models import gpt2
+
+        args = gpt2.ModelArgs(
+            model_type="gpt2",
+            n_ctx=1024,
+            n_embd=768,
+            n_head=12,
+            n_layer=12,
+            n_positions=1024,
+            layer_norm_epsilon=1e-5,
+            vocab_size=50256,
+        )
+        model = gpt2.Model(args)
+        self.model_test_runner(model, args.model_type, args.vocab_size, args.n_layer)
+
     def test_openelm(self):
         from mlx_lm.models import openelm