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Add the phi-3 model. (#2120)

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* Add the phi-3 model.

* Faster rope.

* Bugfix.

* Fix the detokenization.
This commit is contained in:
Laurent Mazare
2024-04-24 09:48:13 +02:00
committed by GitHub
parent 9d3f1c8af5
commit 11d4a3c588
3 changed files with 391 additions and 20 deletions
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75
candle-examples/examples/phi/main.rs
View File

@ -7,11 +7,13 @@ extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
use candle_transformers::models::phi::{Config as PhiConfig, Model as Phi};
use candle_transformers::models::phi3::{Config as Phi3Config, Model as Phi3};
use candle_transformers::models::quantized_mixformer::MixFormerSequentialForCausalLM as QMixFormer;
use candle::{DType, Device, Tensor};
use candle::{DType, Device, IndexOp, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
@ -20,13 +22,14 @@ use tokenizers::Tokenizer;
enum Model {
MixFormer(MixFormer),
Phi(Phi),
Phi3(Phi3),
Quantized(QMixFormer),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: Tokenizer,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
@ -49,7 +52,7 @@ impl TextGeneration {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
@ -61,7 +64,11 @@ impl TextGeneration {
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
println!("starting the inference loop");
let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
let tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?;
if tokens.is_empty() {
anyhow::bail!("Empty prompts are not supported in the phi model.")
}
@ -73,13 +80,14 @@ impl TextGeneration {
}
let mut tokens = tokens.get_ids().to_vec();
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
Some(token) => *token,
let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
Some(token) => token,
None => anyhow::bail!("cannot find the endoftext token"),
};
print!("{prompt}");
std::io::stdout().flush()?;
let start_gen = std::time::Instant::now();
let mut pos = 0;
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
@ -88,6 +96,7 @@ impl TextGeneration {
Model::MixFormer(m) => m.forward(&input)?,
Model::Phi(m) => m.forward(&input)?,
Model::Quantized(m) => m.forward(&input)?,
Model::Phi3(m) => m.forward(&input, pos)?.i((.., 0, ..))?,
};
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
@ -107,10 +116,12 @@ impl TextGeneration {
if next_token == eos_token {
break;
}
let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
print!("{token}");
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
pos += context_size;
}
let dt = start_gen.elapsed();
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
@ -128,6 +139,8 @@ enum WhichModel {
V1_5,
#[value(name = "2")]
V2,
#[value(name = "3")]
V3,
#[value(name = "2-old")]
V2Old,
PuffinPhiV2,
@ -236,6 +249,7 @@ fn main() -> Result<()> {
WhichModel::V1 => "microsoft/phi-1".to_string(),
WhichModel::V1_5 => "microsoft/phi-1_5".to_string(),
WhichModel::V2 | WhichModel::V2Old => "microsoft/phi-2".to_string(),
WhichModel::V3 => "microsoft/Phi-3-mini-4k-instruct".to_string(),
WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
"lmz/candle-quantized-phi".to_string()
}
@ -253,9 +267,10 @@ fn main() -> Result<()> {
WhichModel::V1 => "refs/pr/8".to_string(),
WhichModel::V1_5 => "refs/pr/73".to_string(),
WhichModel::V2Old => "834565c23f9b28b96ccbeabe614dd906b6db551a".to_string(),
WhichModel::V2 | WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
"main".to_string()
}
WhichModel::V2
| WhichModel::V3
| WhichModel::PuffinPhiV2
| WhichModel::PhiHermes => "main".to_string(),
}
}
}
@ -264,9 +279,11 @@ fn main() -> Result<()> {
let tokenizer_filename = match args.tokenizer {
Some(file) => std::path::PathBuf::from(file),
None => match args.model {
WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 | WhichModel::V2Old => {
repo.get("tokenizer.json")?
}
WhichModel::V1
| WhichModel::V1_5
| WhichModel::V2
| WhichModel::V2Old
| WhichModel::V3 => repo.get("tokenizer.json")?,
WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
repo.get("tokenizer-puffin-phi-v2.json")?
}
@ -282,14 +299,19 @@ fn main() -> Result<()> {
WhichModel::V2 | WhichModel::V2Old => vec![repo.get("model-v2-q4k.gguf")?],
WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2-q4k.gguf")?],
WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B-q4k.gguf")?],
WhichModel::V3 => anyhow::bail!(
"use the quantized or quantized-phi examples for quantized phi-v3"
),
}
} else {
match args.model {
WhichModel::V1 | WhichModel::V1_5 => vec![repo.get("model.safetensors")?],
WhichModel::V2 | WhichModel::V2Old => candle_examples::hub_load_safetensors(
WhichModel::V2 | WhichModel::V2Old | WhichModel::V3 => {
candle_examples::hub_load_safetensors(
&repo,
"model.safetensors.index.json",
)?,
)?
}
WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2.safetensors")?],
WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B.safetensors")?],
}
@ -306,6 +328,9 @@ fn main() -> Result<()> {
WhichModel::V2 | WhichModel::V2Old => Config::v2(),
WhichModel::PuffinPhiV2 => Config::puffin_phi_v2(),
WhichModel::PhiHermes => Config::phi_hermes_1_3b(),
WhichModel::V3 => {
panic!("use the quantized or quantized-phi examples for quantized phi-v3")
}
};
let device = candle_examples::device(args.cpu)?;
let model = if args.quantized {
@ -320,7 +345,12 @@ fn main() -> Result<()> {
};
Model::Quantized(model)
} else {
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
let dtype = if args.model == WhichModel::V3 && device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
match args.model {
WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 => {
let config_filename = repo.get("config.json")?;
@ -329,6 +359,13 @@ fn main() -> Result<()> {
let phi = Phi::new(&config, vb)?;
Model::Phi(phi)
}
WhichModel::V3 => {
let config_filename = repo.get("config.json")?;
let config = std::fs::read_to_string(config_filename)?;
let config: Phi3Config = serde_json::from_str(&config)?;
let phi3 = Phi3::new(&config, vb)?;
Model::Phi3(phi3)
}
WhichModel::V2Old => {
let config = config();
Model::MixFormer(MixFormer::new_v2(&config, vb)?)
@ -421,6 +458,10 @@ fn mmlu<P: AsRef<std::path::Path>>(
m.clear_kv_cache();
m.forward(&input)?
}
Model::Phi3(m) => {
m.clear_kv_cache();
m.forward(&input, 0)?
}
Model::Quantized(m) => {
m.clear_kv_cache();
m.forward(&input)?

1
candle-transformers/src/models/mod.rs
View File

@ -28,6 +28,7 @@ pub mod moondream;
pub mod mpt;
pub mod persimmon;
pub mod phi;
pub mod phi3;
pub mod quantized_blip;
pub mod quantized_blip_text;
pub mod quantized_llama;

329
candle-transformers/src/models/phi3.rs Normal file
View File

@ -0,0 +1,329 @@
// This implementation is based on:
// https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/modeling_phi3.py
use crate::models::with_tracing::{linear_no_bias as linear, Linear, RmsNorm};
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
use std::sync::Arc;
// https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/config.json
#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
pub vocab_size: usize,
pub hidden_act: candle_nn::Activation,
pub hidden_size: usize,
pub intermediate_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub num_key_value_heads: usize,
pub rms_norm_eps: f64,
pub rope_theta: f64,
pub bos_token_id: Option<u32>,
pub eos_token_id: Option<u32>,
pub rope_scaling: Option<String>,
pub max_position_embeddings: usize,
}
impl Config {
fn head_dim(&self) -> usize {
self.hidden_size / self.num_attention_heads
}
}
#[derive(Debug, Clone)]
struct RotaryEmbedding {
sin: Tensor,
cos: Tensor,
}
impl RotaryEmbedding {
fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
let dim = cfg.head_dim();
let max_seq_len = cfg.max_position_embeddings;
let inv_freq: Vec<_> = (0..dim)
.step_by(2)
.map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
.collect();
let inv_freq_len = inv_freq.len();
let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
.to_dtype(dtype)?
.reshape((max_seq_len, 1))?;
let freqs = t.matmul(&inv_freq)?;
Ok(Self {
sin: freqs.sin()?,
cos: freqs.cos()?,
})
}
fn apply_rotary_emb_qkv(
&self,
q: &Tensor,
k: &Tensor,
seqlen_offset: usize,
) -> Result<(Tensor, Tensor)> {
let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
Ok((q_embed, k_embed))
}
}
#[derive(Debug, Clone)]
struct Attention {
qkv_proj: Linear,
o_proj: Linear,
num_heads: usize,
num_kv_heads: usize,
num_kv_groups: usize,
head_dim: usize,
rotary_emb: Arc<RotaryEmbedding>,
kv_cache: Option<(Tensor, Tensor)>,
}
impl Attention {
fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads;
let head_dim = cfg.head_dim();
let op_size = num_heads * head_dim + 2 * num_kv_heads * head_dim;
let qkv_proj = linear(cfg.hidden_size, op_size, vb.pp("qkv_proj"))?;
let o_proj = linear(num_heads * head_dim, cfg.hidden_size, vb.pp("o_proj"))?;
Ok(Self {
qkv_proj,
o_proj,
rotary_emb,
kv_cache: None,
num_heads,
num_kv_heads,
num_kv_groups: num_heads / num_kv_heads,
head_dim,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Tensor> {
let (b_sz, q_len, _) = xs.dims3()?;
let qkv = self.qkv_proj.forward(xs)?;
let query_pos = self.num_heads * self.head_dim;
let query_states = qkv.narrow(D::Minus1, 0, query_pos)?;
let key_states = qkv.narrow(D::Minus1, query_pos, self.num_kv_heads * self.head_dim)?;
let value_states = qkv.narrow(
D::Minus1,
query_pos + self.num_kv_heads * self.head_dim,
self.num_kv_heads * self.head_dim,
)?;
let query_states = query_states
.reshape((b_sz, q_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?;
let key_states = key_states
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let value_states = value_states
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let (query_states, key_states) =
self.rotary_emb
.apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
let (key_states, value_states) = match &self.kv_cache {
None => (key_states, value_states),
Some((prev_k, prev_v)) => {
let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
(key_states, value_states)
}
};
self.kv_cache = Some((key_states.clone(), value_states.clone()));
let key_states = crate::utils::repeat_kv(key_states, self.num_kv_groups)?.contiguous()?;
let value_states =
crate::utils::repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
let attn_output = {
let scale = 1f64 / f64::sqrt(self.head_dim as f64);
let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
let attn_weights = match attention_mask {
None => attn_weights,
Some(mask) => attn_weights.broadcast_add(mask)?,
};
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
attn_weights.matmul(&value_states)?
};
attn_output
.transpose(1, 2)?
.reshape((b_sz, q_len, ()))?
.apply(&self.o_proj)
}
fn clear_kv_cache(&mut self) {
self.kv_cache = None
}
}
#[derive(Debug, Clone)]
struct Mlp {
gate_up_proj: Linear,
down_proj: Linear,
act_fn: candle_nn::Activation,
i_size: usize,
}
impl Mlp {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let i_size = cfg.intermediate_size;
let gate_up_proj = linear(hidden_size, 2 * i_size, vb.pp("gate_up_proj"))?;
let down_proj = linear(i_size, hidden_size, vb.pp("down_proj"))?;
Ok(Self {
gate_up_proj,
down_proj,
act_fn: cfg.hidden_act,
i_size,
})
}
}
impl Module for Mlp {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let up_states = xs.apply(&self.gate_up_proj)?;
let gate = up_states.narrow(D::Minus1, 0, self.i_size)?;
let up_states = up_states.narrow(D::Minus1, self.i_size, self.i_size)?;
let up_states = (up_states * gate.apply(&self.act_fn))?;
up_states.apply(&self.down_proj)
}
}
#[derive(Debug, Clone)]
struct DecoderLayer {
self_attn: Attention,
mlp: Mlp,
input_layernorm: RmsNorm,
post_attention_layernorm: RmsNorm,
}
impl DecoderLayer {
fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
let mlp = Mlp::new(cfg, vb.pp("mlp"))?;
let input_layernorm =
RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
let post_attention_layernorm = RmsNorm::new(
cfg.hidden_size,
cfg.rms_norm_eps,
vb.pp("post_attention_layernorm"),
)?;
Ok(Self {
self_attn,
mlp,
input_layernorm,
post_attention_layernorm,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Tensor> {
let residual = xs;
let xs = self.input_layernorm.forward(xs)?;
let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
let xs = (xs + residual)?;
let residual = &xs;
let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
residual + xs
}
fn clear_kv_cache(&mut self) {
self.self_attn.clear_kv_cache()
}
}
#[derive(Debug, Clone)]
pub struct Model {
embed_tokens: candle_nn::Embedding,
layers: Vec<DecoderLayer>,
norm: RmsNorm,
lm_head: Linear,
device: Device,
dtype: DType,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let vb_m = vb.pp("model");
let embed_tokens =
candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_l = vb_m.pp("layers");
for layer_idx in 0..cfg.num_hidden_layers {
let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
layers.push(layer)
}
let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
Ok(Self {
embed_tokens,
layers,
norm,
lm_head,
device: vb.device().clone(),
dtype: vb.dtype(),
})
}
fn prepare_decoder_attention_mask(
&self,
b_size: usize,
tgt_len: usize,
seqlen_offset: usize,
) -> Result<Tensor> {
let mask: Vec<_> = (0..tgt_len)
.flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0. }))
.collect();
let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
let mask = if seqlen_offset > 0 {
let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
Tensor::cat(&[&mask0, &mask], D::Minus1)?
} else {
mask
};
mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
.to_dtype(self.dtype)
}
pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
let (b_size, seq_len) = input_ids.dims2()?;
let attention_mask = if seq_len <= 1 {
None
} else {
let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
Some(mask)
};
let mut xs = self.embed_tokens.forward(input_ids)?;
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
}
xs.narrow(1, seq_len - 1, 1)?
.apply(&self.norm)?
.apply(&self.lm_head)
}
pub fn clear_kv_cache(&mut self) {
for layer in self.layers.iter_mut() {
layer.clear_kv_cache()
}
}
}