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https://github.com/ml-explore/mlx-examples.git
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llama v2 with sharded weights
This commit is contained in:
Awni Hannun
206
llama/llama.py
206
llama/llama.py
@@ -1,8 +1,10 @@
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# Copyright © 2023 Apple Inc.
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import argparse
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import math
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import numpy as np
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from dataclasses import dataclass
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import json
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from pathlib import Path
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from typing import Optional, Tuple, List
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from sentencepiece import SentencePieceProcessor
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import time
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@@ -11,33 +13,71 @@ import mlx.nn as nn
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from mlx.utils import tree_unflatten
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class LlamaAttention(nn.Module):
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def __init__(self, dims: int, num_heads: int):
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@dataclass
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class ModelArgs:
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dim: int
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n_layers: int
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head_dim: int
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hidden_dim: int
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n_heads: int
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n_kv_heads: int
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norm_eps: float
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vocab_size: int
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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self.num_heads = num_heads
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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self.rope = nn.RoPE(dims // num_heads, traditional=True)
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self.query_proj = nn.Linear(dims, dims, bias=False)
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self.key_proj = nn.Linear(dims, dims, bias=False)
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self.value_proj = nn.Linear(dims, dims, bias=False)
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self.out_proj = nn.Linear(dims, dims, bias=False)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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def __call__(self, queries, keys, values, mask=None, cache=None):
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queries = self.query_proj(queries)
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keys = self.key_proj(keys)
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values = self.value_proj(values)
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# Extract some shapes
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num_heads = self.num_heads
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B, L, D = queries.shape
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.n_heads: int = args.n_heads
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self.n_kv_heads: int = args.n_kv_heads
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self.repeats = self.n_heads // self.n_kv_heads
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self.scale = self.args.head_dim**-0.5
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self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False)
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self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
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self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
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self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False)
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self.rope = nn.RoPE(args.head_dim, traditional=True)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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B, L, D = x.shape
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queries, keys, values = self.wq(x), self.wk(x), self.wv(x)
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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def repeat(a):
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a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
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return a.reshape([B, self.n_heads, L, -1])
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keys, values = map(repeat, (keys, values))
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# Add RoPE to the queries and keys and combine them with the cache
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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@@ -48,86 +88,87 @@ class LlamaAttention(nn.Module):
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queries = self.rope(queries)
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keys = self.rope(keys)
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# Finally perform the attention computation
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scale = math.sqrt(1 / queries.shape[-1])
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scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
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scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores, axis=-1)
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values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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# Note that we return the keys and values to possibly be used as a cache
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return self.out_proj(values_hat), (keys, values)
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scores += mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.wo(output), (keys, values)
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class LlamaEncoderLayer(nn.Module):
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def __init__(self, dims: int, mlp_dims: int, num_heads: int):
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class FeedForward(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.attention = LlamaAttention(dims, num_heads)
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self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False)
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self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False)
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self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False)
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self.norm1 = nn.RMSNorm(dims)
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self.norm2 = nn.RMSNorm(dims)
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def __call__(self, x) -> mx.array:
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return self.w2(nn.silu(self.w1(x)) * self.w3(x))
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self.linear1 = nn.Linear(dims, mlp_dims, bias=False)
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self.linear2 = nn.Linear(dims, mlp_dims, bias=False)
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self.linear3 = nn.Linear(mlp_dims, dims, bias=False)
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def __call__(self, x, mask=None, cache=None):
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y = self.norm1(x)
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y, cache = self.attention(y, y, y, mask, cache)
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x = x + y
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.n_heads = args.n_heads
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self.dim = args.dim
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self.attention = Attention(args)
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self.feed_forward = FeedForward(args=args)
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self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
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self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
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self.args = args
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y = self.norm2(x)
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a = self.linear1(y)
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b = self.linear2(y)
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y = a * mx.sigmoid(a) * b
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y = self.linear3(y)
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x = x + y
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return x, cache
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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r, cache = self.attention(self.attention_norm(x), mask, cache)
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h = x + r
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r = self.feed_forward(self.ffn_norm(h))
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out = h + r
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return out, cache
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class Llama(nn.Module):
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def __init__(
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self, num_layers: int, vocab_size: int, dims: int, mlp_dims: int, num_heads: int
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):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.embedding = nn.Embedding(vocab_size, dims)
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self.layers = [
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LlamaEncoderLayer(dims, mlp_dims, num_heads) for _ in range(num_layers)
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]
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self.norm = nn.RMSNorm(dims)
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self.out_proj = nn.Linear(dims, vocab_size, bias=False)
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self.args = args
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self.vocab_size = args.vocab_size
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self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
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self.layers = [TransformerBlock(args=args) for _ in range(args.n_layers)]
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self.norm = RMSNorm(args.dim, eps=args.norm_eps)
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self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
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def __call__(self, x):
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(self.embedding.weight.dtype)
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mask = mask.astype(self.tok_embeddings.weight.dtype)
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x = self.embedding(x)
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x = self.tok_embeddings(x)
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for l in self.layers:
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x, _ = l(x, mask)
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x = self.norm(x)
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return self.out_proj(x)
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return self.output(x)
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def generate(self, x, temp=1.0):
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cache = []
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# Make an additive causal mask. We will need that to process the prompt.
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(self.embedding.weight.dtype)
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mask = mask.astype(self.tok_embeddings.weight.dtype)
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# First we process the prompt x the same was as in __call__ but
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# save the caches in cache
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x = self.embedding(x)
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x = self.tok_embeddings(x)
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for l in self.layers:
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x, c = l(x, mask=mask)
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# We store the per layer cache in a simple python list
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cache.append(c)
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x = self.norm(x)
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# We only care about the last logits that generate the next token
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y = self.out_proj(x[:, -1])
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y = self.output(x[:, -1])
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y = mx.random.categorical(y * (1 / temp))
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# y now has size [1]
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@@ -145,14 +186,14 @@ class Llama(nn.Module):
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# dimension of 1
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x = y[:, None]
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x = self.embedding(x)
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x = self.tok_embeddings(x)
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for i in range(len(cache)):
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# We are overwriting the arrays in the cache list. When
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# the computation will happen, MLX will be discarding the
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# old cache the moment it is not needed anymore.
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x, cache[i] = self.layers[i](x, mask=None, cache=cache[i])
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x = self.norm(x)
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y = self.out_proj(x[:, -1])
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y = self.output(x[:, -1])
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y = mx.random.categorical(y * (1 / temp))
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yield y
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@@ -261,20 +302,33 @@ def few_shot_generate(args):
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def load_model(model_path):
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weights = mx.load(model_path)
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mlp_dims, dims = weights["layers.0.linear1.weight"].shape
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num_heads = dims // 128
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num_layers = max(int(l.split(".")[1]) for l in weights.keys() if "layers" in l) + 1
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vocab_size = weights["out_proj.weight"].shape[-1]
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model = Llama(num_layers, vocab_size, dims, mlp_dims, num_heads)
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model_path = Path(model_path)
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weights = mx.load(str(model_path / "weights.npz"))
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with open(model_path / "params.json", "r") as f:
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config = json.loads(f.read())
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n_heads = config["n_heads"]
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if "n_kv_heads" not in config:
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config["n_kv_heads"] = n_heads
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if "head_dim" not in config:
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config["head_dim"] = config["dim"] // n_heads
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if "hidden_dim" not in config:
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config["hidden_dim"] = weights["layers.0.feed_forward.w1.weight"].shape[0]
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if config.get("vocab_size", -1) < 0:
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config["vocab_size"] = weights["output.weight"].shape[-1]
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unused = ["multiple_of", "ffn_dim_multiplie"]
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for k in unused:
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if k in config:
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config.pop(k)
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model = Llama(ModelArgs(**config))
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model.update(tree_unflatten(list(weights.items())))
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mx.eval(model.parameters())
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return model
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Llama inference script")
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parser.add_argument("model", help="The model file containing MLX weights")
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parser.add_argument(
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"model", help="Path to the model directory containing the MLX weights"
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)
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parser.add_argument("tokenizer", help="The sentencepiece tokenizer")
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parser.add_argument("prompt", help="The message to be processed by the model")
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parser.add_argument(
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