From fc93c557238e9441835afe2748fd170016cb068b Mon Sep 17 00:00:00 2001 From: L Date: Thu, 29 Aug 2024 21:08:57 -0700 Subject: [PATCH] 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 --- llms/mlx_lm/models/nemotron.py | 227 +++++++++++++++++++++++++++++++++ llms/mlx_lm/tuner/utils.py | 1 + 2 files changed, 228 insertions(+) create mode 100644 llms/mlx_lm/models/nemotron.py diff --git a/llms/mlx_lm/models/nemotron.py b/llms/mlx_lm/models/nemotron.py new file mode 100644 index 00000000..ef55d1d7 --- /dev/null +++ b/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 diff --git a/llms/mlx_lm/tuner/utils.py b/llms/mlx_lm/tuner/utils.py index 1a54a925..71fbfaab 100644 --- a/llms/mlx_lm/tuner/utils.py +++ b/llms/mlx_lm/tuner/utils.py @@ -93,6 +93,7 @@ def linear_to_lora_layers( "llama", "phi", "mixtral", + "nemotron", "stablelm", "qwen2", "qwen2_moe",