#![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
use std::sync::Arc;
use candle_core::quantized::QMatMul;
use candle_core::quantized::QTensor;
use candle_core::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{Embedding, LayerNorm};
use mistralrs_quant::GgufMatMul;
use mistralrs_quant::QuantMethod;
use mistralrs_quant::QuantMethodConfig;
use crate::attention::SdpaParams;
use crate::device_map::DeviceMapper;
use crate::gguf::Content;
use crate::layers::MatMul;
use crate::layers::Sdpa;
use crate::layers::{CausalMasker, QLinear};
use crate::layers_masker::PastKvLenCache;
use crate::paged_attention::AttentionImplementation;
use crate::paged_attention::PagedAttention;
use crate::pipeline::extract_logits;
use crate::pipeline::text_models_inputs_processor::PagedAttentionInputMetadata;
use crate::pipeline::EitherCache;
use crate::pipeline::KvCache;
use crate::pipeline::NormalCache;
use crate::utils::gguf_metadata::ContentMetadata;
use crate::utils::model_config as ModelConfig;
use crate::utils::progress::NiceProgressBar;
use crate::DeviceMapMetadata;
use crate::Topology;
pub const MAX_SEQ_LEN: usize = 4096;
#[derive(Clone)]
struct Mlp {
ffn_up: Arc<dyn QuantMethod>,
ffn_down: Arc<dyn QuantMethod>,
}
impl Module for Mlp {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
MatMul.qmethod_matmul(&MatMul.qmethod_matmul(xs, &*self.ffn_up)?, &*self.ffn_down)
}
}
struct LayerWeights {
attn_qkv: Arc<dyn QuantMethod>,
attn_output: Arc<dyn QuantMethod>,
attn_norm: LayerNorm,
mlp: Mlp,
n_head: usize,
head_dim: usize,
cos: Tensor,
sin: Tensor,
rope_dim: usize,
paged_attn: Option<PagedAttention>,
sdpa_params: SdpaParams,
dtype: DType,
}
impl LayerWeights {
fn forward(&self, xs: &Tensor, start_offsets: &[usize]) -> Result<Tensor> {
let (_b_sz, _n_head, seq_len, _n_embd) = xs.dims4()?;
let xs_rot = xs.i((.., .., .., ..self.rope_dim))?;
let xs_pass = xs.i((.., .., .., self.rope_dim..))?;
let mut chunks = Vec::new();
for (b, offset) in (0..xs.dim(0)?).zip(start_offsets) {
let cos = self.cos.narrow(0, *offset, seq_len)?;
let sin = self.sin.narrow(0, *offset, seq_len)?;
let xs_rot =
candle_nn::rotary_emb::rope(&xs_rot.i(b)?.unsqueeze(0)?.contiguous()?, &cos, &sin)?;
chunks.push(Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)?);
}
Tensor::cat(&chunks, 0)?.contiguous()
}
fn forward_attn(
&self,
x: &Tensor,
mask: Option<&Tensor>,
seqlen_offsets: &[usize],
kv_cache: &mut KvCache,
metadata: Option<((Tensor, Tensor), &mut PagedAttentionInputMetadata)>,
) -> Result<Tensor> {
let (b_sz, seq_len, n_embd) = x.dims3()?;
let qkv = self
.attn_qkv
.forward(x)?
.reshape((b_sz, seq_len, 3, self.n_head, self.head_dim))?
.to_dtype(self.dtype)?;
let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
let v = v.contiguous()?;
let q = self.forward(&q, seqlen_offsets)?.contiguous()?;
let k = self.forward(&k, seqlen_offsets)?;
let y = match &self.paged_attn {
Some(paged_attn) => {
let ((key_cache, value_cache), input_metadata) = metadata.unwrap();
paged_attn.forward(
&q,
&k,
&v,
mask,
Some(key_cache),
Some(value_cache),
input_metadata,
None,
)?
}
None => {
let (k, v) = kv_cache.append(&k, &v)?;
Sdpa.run_attention(&q, &k, &v, mask, None, &self.sdpa_params)?
}
};
let y = if mask.is_some() {
y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?
} else {
y.reshape(&[b_sz, seq_len, n_embd])?
};
let y = self.attn_output.forward(&y.to_dtype(x.dtype())?)?;
Ok(y)
}
}
pub struct ModelWeights {
tok_embeddings: Embedding,
layers: Vec<LayerWeights>,
output_norm: LayerNorm,
output: QLinear,
pub device: Device,
pub cache: EitherCache,
pub max_seq_len: usize,
mapper: Box<dyn DeviceMapper + Send + Sync>,
dtype: DType,
}
fn precomput_freqs_cis(
head_dim: usize,
freq_base: f32,
device: &Device,
max_seq_len: usize,
dtype: DType,
) -> Result<(Tensor, Tensor)> {
let theta: Vec<_> = (0..head_dim)
.step_by(2)
.map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
.collect();
let theta = Tensor::new(theta.as_slice(), device)?;
let idx_theta = Tensor::arange(0, max_seq_len as u32, device)?
.to_dtype(DType::F32)?
.reshape((max_seq_len, 1))?
.matmul(&theta.reshape((1, theta.elem_count()))?)?;
let cos = idx_theta.cos()?.to_dtype(dtype)?;
let sin = idx_theta.sin()?.to_dtype(dtype)?;
Ok((cos, sin))
}
fn layer_norm(w: QTensor, b: QTensor, eps: f64) -> Result<LayerNorm> {
let w = w.dequantize(&w.device())?;
let b = b.dequantize(&b.device())?;
let ln = LayerNorm::new(w, b, eps);
Ok(ln)
}
struct PropsGGUF {
head_count: usize,
head_count_kv: usize,
block_count: usize,
embedding_length: usize,
rope_dim: usize,
ln_eps: f64,
max_seq_len: usize,
}
impl TryFrom<ContentMetadata<'_>> for PropsGGUF {
type Error = anyhow::Error;
fn try_from(c: ContentMetadata) -> std::result::Result<Self, Self::Error> {
c.verify_arch("phi2")?;
let required = [
"attention.head_count",
"attention.head_count_kv",
"block_count",
"embedding_length",
"rope.dimension_count",
"attention.layer_norm_rms_epsilon",
"context_length",
];
c.has_required_keys(&required)?;
let props = Self {
head_count: c.get_value::<u32>("attention.head_count")? as usize,
head_count_kv: c.get_value::<u32>("attention.head_count_kv")? as usize,
block_count: c.get_value::<u32>("block_count")? as usize,
embedding_length: c.get_value::<u32>("embedding_length")? as usize,
rope_dim: c.get_value::<u32>("rope.dimension_count")? as usize,
ln_eps: c.get_value::<f32>("attention.layer_norm_rms_epsilon")? as f64,
max_seq_len: c
.get_value::<u64>("context_length")
.ok()
.unwrap_or(MAX_SEQ_LEN as u64) as usize,
};
Ok(props)
}
}
impl ModelConfig::FromGGUF for ModelWeights {
fn from_gguf<R: std::io::Seek + std::io::Read>(
mut ct: Content<'_, R>,
device: &Device,
mapper: DeviceMapMetadata,
topology: Option<&'_ Topology>,
attention_mechanism: AttentionImplementation,
dtype: DType,
) -> Result<Self> {
let metadata = ContentMetadata {
path_prefix: "phi2",
metadata: ct.get_metadata(),
};
let PropsGGUF {
head_count,
head_count_kv,
block_count,
embedding_length,
rope_dim,
ln_eps,
max_seq_len,
} = PropsGGUF::try_from(metadata).or_else(|err| candle_core::bail!("{err}"))?;
let (cos, sin) = precomput_freqs_cis(rope_dim, 10_000., device, max_seq_len, dtype)?;
let tok_embeddings = ct.tensor("token_embd.weight", device)?;
let tok_embeddings = tok_embeddings.dequantize(device)?;
let output_norm = layer_norm(
ct.tensor("output_norm.weight", device)?,
ct.tensor("output_norm.bias", device)?,
ln_eps,
)?;
let output = QLinear::new(&mut ct, "output", device)?;
let mut layers = Vec::with_capacity(block_count);
let head_dim = embedding_length / head_count;
let mapper = mapper.into_mapper(block_count, device, topology)?;
for layer_idx in NiceProgressBar::<_, 'b'>(0..block_count, "Loading repeating layers") {
let prefix = format!("blk.{layer_idx}");
let device = mapper.device_for(layer_idx, false).unwrap_or(device);
let ffn_up = QLinear::new(&mut ct, &format!("{prefix}.ffn_up"), device)?;
let ffn_down = QLinear::new(&mut ct, &format!("{prefix}.ffn_down"), device)?;
let QMatMul::QTensor(ffn_up_w) = ffn_up.inner_ref().clone() else {
unreachable!()
};
let QMatMul::QTensor(ffn_down_w) = ffn_down.inner_ref().clone() else {
unreachable!()
};
let mlp = Mlp {
ffn_up: Arc::new(GgufMatMul::new(QuantMethodConfig::Gguf {
q_weight: ffn_up_w,
b: ffn_up.bias().cloned(),
})?),
ffn_down: Arc::new(GgufMatMul::new(QuantMethodConfig::Gguf {
q_weight: ffn_down_w,
b: ffn_down.bias().cloned(),
})?),
};
let attn_norm = layer_norm(
ct.tensor(&format!("{prefix}.attn_norm.weight"), device)?,
ct.tensor(&format!("{prefix}.attn_norm.bias"), device)?,
ln_eps,
)?;
let paged_attn = match &attention_mechanism {
AttentionImplementation::Eager => None,
AttentionImplementation::PagedAttention => Some(PagedAttention::new(
head_count,
head_dim,
(1.0 / (head_dim as f64).sqrt()) as f32,
Some(head_count_kv),
None, device,
None,
)?),
};
let qkv = QLinear::new(&mut ct, &format!("{prefix}.attn_qkv"), device)?;
let out = QLinear::new(&mut ct, &format!("{prefix}.attn_output"), device)?;
let QMatMul::QTensor(qkv_w) = qkv.inner_ref().clone() else {
unreachable!()
};
let QMatMul::QTensor(out_w) = out.inner_ref().clone() else {
unreachable!()
};
layers.push(LayerWeights {
attn_qkv: Arc::new(GgufMatMul::new(QuantMethodConfig::Gguf {
q_weight: qkv_w,
b: qkv.bias().cloned(),
})?),
attn_output: Arc::new(GgufMatMul::new(QuantMethodConfig::Gguf {
q_weight: out_w,
b: out.bias().cloned(),
})?),
attn_norm,
mlp,
n_head: head_count,
head_dim,
cos: cos.clone().to_device(device)?,
sin: sin.clone().to_device(device)?,
rope_dim,
paged_attn,
sdpa_params: SdpaParams {
n_kv_groups: head_count / head_count_kv,
use_flash_attn: false,
softcap: None,
softmax_scale: 1.0 / (head_dim as f32).sqrt(),
sliding_window: None,
},
dtype,
})
}
Ok(Self {
tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
layers,
output_norm,
output,
device: device.clone(),
cache: EitherCache::Normal(NormalCache::new(block_count, max_seq_len)),
max_seq_len,
mapper,
dtype,
})
}
}
impl ModelWeights {
pub fn forward(
&self,
input_ids: &Tensor,
seqlen_offsets: &[usize],
context_lens: Vec<(usize, usize)>,
mut metadata: Option<(Vec<(Tensor, Tensor)>, &mut PagedAttentionInputMetadata)>,
) -> Result<Tensor> {
let mut xs = self.tok_embeddings.forward(input_ids)?;
let cache = &mut self.cache.normal().0;
let mask = CausalMasker.make_causal_mask_matrix(
input_ids,
metadata
.as_ref()
.map(|(_, _)| &seqlen_offsets as &dyn PastKvLenCache)
.unwrap_or(cache as &dyn PastKvLenCache),
self.dtype,
self.layers[0].n_head,
)?;
for (i, layer) in self.layers.iter().enumerate() {
xs = self.mapper.map(xs, i)?;
let residual = &xs;
let xs_norm = xs.apply(&layer.attn_norm)?;
let attn_outputs = layer.forward_attn(
&xs_norm,
mask.as_ref()
.map(|m| m.to_device(xs.device()).unwrap())
.as_ref(),
seqlen_offsets,
&mut cache[i],
metadata
.as_mut()
.map(|(kv_cache, metadata)| (kv_cache[i].clone(), &mut **metadata)),
)?;
let feed_forward_hidden_states = layer.mlp.forward(&xs_norm)?;
xs = (attn_outputs + feed_forward_hidden_states + residual)?
}
let xs = xs.to_device(&self.device)?;
let xs = extract_logits(&xs.apply(&self.output_norm)?, context_lens)?;
self.output.forward(&xs)
}
}