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add deepseek coder example (#172)
* feat: add example for deepseek coder * chore: remove hardcoded rope_scaling_factor * feat: add quantization support * chore: update readme * chore: clean up the rope scalling factor param in create cos sin theta * feat: add repetition_penalty * style /consistency changes to ease future integration * nits in README * one more typo --------- Co-authored-by: Awni Hannun <awni@apple.com>
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Anchen
309
llms/deepseek-coder/deepseek_coder.py
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309
llms/deepseek-coder/deepseek_coder.py
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import argparse
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import json
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import math
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from dataclasses import dataclass
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from pathlib import Path
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from typing import 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 transformers import AutoTokenizer
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@dataclass
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class ModelArgs:
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hidden_size: int = 4096
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num_attention_heads: int = 32
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num_hidden_layers: int = 32
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num_key_value_heads: int = 32
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max_position_embeddings: int = 16384
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rms_norm_eps: float = 1e-6
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intermediate_size: int = 11008
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rope_theta: float = 100000
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rope_scaling_factor: float = 4.0
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vocab_size: int = 32256
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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 LinearScalingRoPE(nn.RoPE):
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def __init__(
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self, dims: int, rope_scaling_factor: float = 4.0, base: float = 10000
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):
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super().__init__(dims)
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self.base = base
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self.rope_scaling_factor = rope_scaling_factor
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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 = LinearScalingRoPE.create_cos_sin_theta(
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N,
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self.dims,
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offset=offset,
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base=self.base,
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rope_scaling_factor=self.rope_scaling_factor,
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dtype=x.dtype,
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)
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rx = self._compute_rope(costheta, sintheta, x)
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return mx.reshape(rx, shape)
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@staticmethod
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def create_cos_sin_theta(
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N: int,
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D: int,
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offset: int = 0,
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base: float = 10000,
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rope_scaling_factor: float = 1.0,
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dtype=mx.float32,
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):
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D = D // 2
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positions = mx.arange(offset, N, dtype=dtype)
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positions = positions / rope_scaling_factor
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freqs = mx.exp(-mx.arange(0.0, D, dtype=dtype) * (math.log(base) / D))
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theta = mx.reshape(positions, (-1, 1)) * mx.reshape(freqs, (1, -1))
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return mx.cos(theta), mx.sin(theta)
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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.num_attention_heads: int = args.num_attention_heads
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self.num_key_value_heads: int = args.num_key_value_heads
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self.repeats = self.num_attention_heads // self.num_key_value_heads
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self.head_dim = args.hidden_size // args.num_attention_heads
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self.scale = self.head_dim**-0.5
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self.wq = nn.Linear(
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args.hidden_size, args.num_attention_heads * self.head_dim, bias=False
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)
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self.wk = nn.Linear(
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args.hidden_size, args.num_key_value_heads * self.head_dim, bias=False
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)
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self.wv = nn.Linear(
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args.hidden_size, args.num_key_value_heads * self.head_dim, bias=False
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)
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self.wo = nn.Linear(
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args.num_attention_heads * self.head_dim, args.hidden_size, bias=False
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)
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self.rope = LinearScalingRoPE(
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self.head_dim,
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rope_scaling_factor=args.rope_scaling_factor,
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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.num_attention_heads, -1).transpose(
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0, 2, 1, 3
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)
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keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
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0, 2, 1, 3
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)
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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.num_attention_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.hidden_size, args.intermediate_size, bias=False)
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self.w2 = nn.Linear(args.intermediate_size, args.hidden_size, bias=False)
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self.w3 = nn.Linear(args.hidden_size, args.intermediate_size, 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.attention = Attention(args)
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self.feed_forward = FeedForward(args=args)
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self.attention_norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.ffn_norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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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 DeepseekCoder(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.hidden_size)
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self.layers = [
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TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
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]
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self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.output = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(self, x, mask=None, cache=None):
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x = self.tok_embeddings(x)
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mask = None
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T = x.shape[1]
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if T > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
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mask = mask.astype(x.dtype)
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if cache is None:
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cache = [None] * len(self.layers)
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for e, layer in enumerate(self.layers):
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x, cache[e] = layer(x, mask, cache[e])
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x = self.norm(x)
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return self.output(x), cache
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def generate(
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prompt: mx.array,
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model: DeepseekCoder,
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temp: float = 0.0,
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):
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def sample(logits):
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if temp == 0:
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return mx.argmax(logits, axis=-1)
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else:
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return mx.random.categorical(logits * (1 / temp))
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y = prompt
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cache = None
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while True:
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logits, cache = model(y[None], cache=cache)
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logits = logits[:, -1, :]
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y = sample(logits)
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yield y
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def load_model(model_path: str):
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model_path = Path(model_path)
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with open(model_path / "config.json", "r") as f:
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config = json.load(f)
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config.pop("model_type")
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quantization = config.pop("quantization", None)
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model_args = ModelArgs(**config)
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model = DeepseekCoder(model_args)
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weights = mx.load(str(model_path / "weights.npz"))
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if quantization := config.get("quantization", False):
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nn.QuantizedLinear.quantize_module(model, **quantization)
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model.update(tree_unflatten(list(weights.items())))
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Deepseek coder inference script")
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parser.add_argument(
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"--model-path",
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type=str,
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default="mlx_model",
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help="The path to the mlx model weights, tokenizer, and config",
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)
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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",
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default="### Instruction: \nwrite a quick sort algorithm in python.\n### Response: \n",
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)
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parser.add_argument(
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"--max-tokens",
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"-m",
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type=int,
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default=100,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp",
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help="The sampling temperature.",
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type=float,
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default=0.6,
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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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model, tokenizer = load_model(args.model_path)
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prompt = tokenizer(args.prompt, return_tensors="np", return_attention_mask=False,)[
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"input_ids"
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][0]
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prompt = mx.array(prompt)
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print(args.prompt, end="", flush=True)
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tokens = []
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skip = 0
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for token, _ in zip(
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generate(prompt, model, args.temp),
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range(args.max_tokens),
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):
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if token == tokenizer.eos_token_id:
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break
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tokens.append(token.item())
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s = tokenizer.decode(tokens)
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print(s[skip:], end="", flush=True)
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skip = len(s)
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print(tokenizer.decode(tokens)[skip:], flush=True)
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