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Add llms subdir + update README (#145)
* add llms subdir + update README * nits * use same pre-commit as mlx * update readmes a bit * format
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Awni Hannun
390
llms/llama/llama.py
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390
llms/llama/llama.py
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# Copyright © 2023 Apple Inc.
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import argparse
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import json
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import List, Optional, Tuple
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.utils import tree_unflatten
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from sentencepiece import SentencePieceProcessor
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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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rope_theta: float
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rope_traditional: bool = True
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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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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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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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class RoPE(nn.RoPE):
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def __init__(self, dims: int, traditional: bool = False, base: float = 10000):
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super().__init__(dims, traditional)
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self.base = base
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def __call__(self, x, offset: int = 0):
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shape = x.shape
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x = mx.reshape(x, (-1, shape[-2], shape[-1]))
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N = x.shape[1] + offset
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costheta, sintheta = RoPE.create_cos_sin_theta(
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N, self.dims, offset=offset, base=self.base, dtype=x.dtype
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)
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rope = (
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self._compute_traditional_rope if self.traditional else self._compute_rope
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)
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rx = rope(costheta, sintheta, x)
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return mx.reshape(rx, 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 = RoPE(
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args.head_dim, traditional=args.rope_traditional, base=args.rope_theta
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)
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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, 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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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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keys = self.rope(keys, offset=key_cache.shape[2])
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keys = mx.concatenate([key_cache, keys], axis=2)
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values = mx.concatenate([value_cache, values], axis=2)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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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 += 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 FeedForward(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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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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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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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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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__(self, args: ModelArgs):
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super().__init__()
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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.tok_embeddings.weight.dtype)
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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.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.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.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.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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# Since MLX is lazily evaluated nothing is computed yet.
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# Calling y.item() would force the computation to happen at
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# this point but we can also choose not to do that and let the
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# user choose when to start the computation.
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yield y
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# Now we parsed the prompt and generated the first token we
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# need to feed it back into the model and loop to generate the
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# rest.
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while True:
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# Unsqueezing the last dimension to add a sequence length
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# dimension of 1
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x = y[:, None]
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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.output(x[:, -1])
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y = mx.random.categorical(y * (1 / temp))
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yield y
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def tic():
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return time.time()
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def toc(msg, start):
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end = time.time()
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return f"[INFO] {msg}: {end - start:.3f} s"
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def generate(args):
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input("Press enter to start generation")
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print("------")
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print(args.prompt)
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x = mx.array([[tokenizer.bos_id()] + tokenizer.encode(args.prompt)])
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skip = 0
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prompt_processing = None
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tokens = []
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start = tic()
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for token in model.generate(x, args.temp):
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tokens.append(token)
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if len(tokens) == 1:
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# Actually perform the computation to measure the prompt processing time
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mx.eval(token)
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prompt_processing = toc("Prompt processing", start)
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if len(tokens) >= args.max_tokens:
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break
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elif (len(tokens) % args.write_every) == 0:
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# It is perfectly ok to eval things we have already eval-ed.
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s[skip:], end="", flush=True)
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skip = len(s)
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mx.eval(tokens)
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full_gen = toc("Full generation", start)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s[skip:], flush=True)
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print("------")
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print(prompt_processing)
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print(full_gen)
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def few_shot_generate(args):
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def possible_end(s):
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word = "[Instruction]"
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for i in range(len(word) - 1, 0, -1):
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if s[-i:] == word[:i]:
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return 0
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if s[-len(word) :] == word:
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return 1
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return -1
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def generate(question):
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x = mx.array([[tokenizer.bos_id()] + tokenizer.encode(question)])
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skip = 0
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prompt_processing = None
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tokens = []
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start = tic()
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for token in model.generate(x, args.temp):
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tokens.append(token)
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if len(tokens) == 1:
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# Actually perform the computation to measure the prompt processing time
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mx.eval(token)
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prompt_processing = toc("Prompt processing", start)
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if len(tokens) >= args.max_tokens:
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break
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mx.eval(tokens)
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token_list = [t.item() for t in tokens]
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s = tokenizer.decode(token_list)
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end = possible_end(s)
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if end == 0:
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continue
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if end == 1:
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skip = len(s)
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break
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print(s[skip:], end="", flush=True)
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skip = len(s)
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if token_list[-1] == tokenizer.eos_id():
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break
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mx.eval(tokens)
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full_gen = toc("Full generation", start)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s[skip:], end="", flush=True)
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print("[INFO] Loading few-shot examples from: {}".format(args.few_shot))
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prompt = open(args.few_shot).read().strip()
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while True:
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question = input("Ask a question: ")
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generate(prompt.replace("{}", question))
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print()
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def load_model(model_path):
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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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if "rope_theta" not in config:
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config["rope_theta"] = 10000
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unused = ["multiple_of", "ffn_dim_multiplier"]
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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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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(
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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(
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"--prompt",
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help="The message to be processed by the model. Ignored when --few-shot is provided.",
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default="In the beginning the Universe was created.",
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)
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parser.add_argument(
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"--few-shot",
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help="Read a few shot prompt from a file (as in `sample_prompt.txt`).",
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)
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parser.add_argument(
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"--max-tokens", "-m", type=int, default=100, help="How many tokens to generate"
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)
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parser.add_argument(
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"--write-every", type=int, default=1, help="After how many tokens to detokenize"
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)
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parser.add_argument(
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"--temp", type=float, default=0.8, help="The sampling temperature"
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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args = parser.parse_args()
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mx.random.seed(args.seed)
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tokenizer = SentencePieceProcessor(model_file=args.tokenizer)
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print("[INFO] Loading model from disk.")
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model = load_model(args.model)
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if args.few_shot:
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few_shot_generate(args)
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else:
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generate(args)
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