feat(mlx_lm): Nemotron (#949)

* feat: Nemotron

https://huggingface.co/nvidia/Minitron-4B-Base

This is basically Llama with partial RoPE and LayerNorm instead of
BatchNorm. Also they add 1 to the LayerNorm weight for some reason.

* fixup! feat: Nemotron

* nits

---------

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

llms/mlx_lm/models/nemotron.py

@@ -0,0 +1,227 @@
+# Copyright © 2024 Apple Inc.
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Dict, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, KVCache, create_attention_mask
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    hidden_act: str
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int
+    head_dim: Optional[int] = None
+    max_position_embeddings: Optional[int] = None
+    attention_bias: bool = False
+    mlp_bias: bool = False
+    partial_rotary_factor: float = 0.5
+    rope_theta: float = 10000.0
+    rope_traditional: bool = False
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self):
+        if self.rope_scaling:
+            if not "factor" in self.rope_scaling:
+                raise ValueError(f"rope_scaling must contain 'factor'")
+            rope_type = self.rope_scaling.get("type") or self.rope_scaling.get(
+                "rope_type"
+            )
+            if rope_type is None:
+                raise ValueError(
+                    f"rope_scaling must contain either 'type' or 'rope_type'"
+                )
+            if rope_type not in ["linear"]:
+                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
+
+
+@partial(mx.compile, shapeless=True)
+def relu_squared(x):
+    return nn.relu(x).square()
+
+
+class NemotronLayerNorm1P(nn.LayerNorm):
+    def __call__(self, x):
+        weight = self.weight + 1 if "weight" in self else None
+        bias = self.bias if "bias" in self else None
+        return mx.fast.layer_norm(x, weight, bias, self.eps)
+
+
+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
+
+        self.head_dim = head_dim = args.head_dim or args.hidden_size // n_heads
+        self.partial_rotary_factor = args.partial_rotary_factor
+
+        self.scale = head_dim**-0.5
+        if hasattr(args, "attention_bias"):
+            attention_bias = args.attention_bias
+        else:
+            attention_bias = False
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
+
+        rope_scale = 1.0
+        if args.rope_scaling and args.rope_scaling["type"] == "linear":
+            assert isinstance(args.rope_scaling["factor"], float)
+            rope_scale = 1 / args.rope_scaling["factor"]
+        self.rope = nn.RoPE(
+            int(self.partial_rotary_factor * self.head_dim),
+            base=args.rope_theta,
+            scale=rope_scale,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        B, L, _ = 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 MLP(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        hidden_dim = args.intermediate_size
+        mlp_bias = args.mlp_bias
+
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(relu_squared(self.up_proj(x)))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args)
+        self.input_layernorm = NemotronLayerNorm1P(args.hidden_size, eps=args.norm_eps)
+        self.post_attention_layernorm = NemotronLayerNorm1P(
+            args.hidden_size, eps=args.norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        r = 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
+
+
+class NemotronModel(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 = [
+            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+        ]
+        self.norm = NemotronLayerNorm1P(args.hidden_size, eps=args.norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        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, cache=c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = NemotronModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def head_dim(self):
+        return (
+            self.args.head_dim or 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

@@ -93,6 +93,7 @@ def to_lora(layer):
         "llama",
         "phi",
         "mixtral",
+        "nemotron",
         "stablelm",
         "qwen2",
         "qwen2_moe",