#![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
use std::sync::Arc;
use candle_core::{DType, Device, Module, Result, Tensor};
use candle_nn::{linear_no_bias, VarBuilder};
use mistralrs_quant::{QuantMethod, QuantMethodConfig, UnquantLinear};
use crate::{
amoe::{AnyMoeBaseModelMixin, AnyMoeTrainableLayer, MlpLayer, MoeMlp},
attention::SdpaParams,
device_map::DeviceMapper,
get_delta_from_lora_ab,
layers::{Activation, CausalMasker, MatMul, RmsNorm, Sdpa},
layers_masker::PastKvLenCache,
paged_attention::{AttentionImplementation, ModelConfigMetadata, PagedAttention},
pipeline::{
extract_logits,
text_models_inputs_processor::{FlashParams, PagedAttentionInputMetadata},
Cache, EitherCache, IsqModel, NormalLoadingMetadata, NormalModel,
},
utils::progress::NiceProgressBar,
AnyMoeConfig, AnyMoeExpertType,
};
use super::{LLaVALLM, OrdinaryRoPE};
use crate::models::mistral::Config;
#[derive(Clone)]
#[allow(clippy::upper_case_acronyms)]
struct MLP {
gate_proj: Arc<dyn QuantMethod>,
up_proj: Arc<dyn QuantMethod>,
down_proj: Arc<dyn QuantMethod>,
act_fn: Activation,
params: Vec<usize>,
}
impl MLP {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden_sz = cfg.hidden_size;
let intermediate_sz = cfg.intermediate_size;
let gate_proj = mistralrs_quant::linear_no_bias(
hidden_sz,
intermediate_sz,
&cfg.quantization_config,
vb.pp("gate_proj"),
)?;
let up_proj = mistralrs_quant::linear_no_bias(
hidden_sz,
intermediate_sz,
&cfg.quantization_config,
vb.pp("up_proj"),
)?;
let down_proj = mistralrs_quant::linear_no_bias(
intermediate_sz,
hidden_sz,
&cfg.quantization_config,
vb.pp("down_proj"),
)?;
Ok(Self {
gate_proj,
up_proj,
down_proj,
act_fn: cfg.hidden_act,
params: vec![hidden_sz, intermediate_sz],
})
}
}
impl AnyMoeTrainableLayer for MLP {}
impl MlpLayer for MLP {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let original_dtype = xs.dtype();
let mut xs = xs.clone();
if let Some(t) = self.gate_proj.quantized_act_type() {
xs = xs.to_dtype(t)?;
}
let lhs = MatMul
.qmethod_matmul(&xs, &*self.gate_proj)?
.apply(&self.act_fn)?;
let rhs = MatMul.qmethod_matmul(&xs, &*self.up_proj)?;
let mut res = MatMul.qmethod_matmul(&(lhs * rhs)?, &*self.down_proj)?;
if self.gate_proj.quantized_act_type().is_some() {
res = res.to_dtype(original_dtype)?;
}
Ok(res)
}
fn get_isq_layers(&mut self) -> Vec<&mut Arc<dyn QuantMethod>> {
vec![&mut self.gate_proj, &mut self.up_proj, &mut self.down_proj]
}
fn clone(&self) -> Box<dyn MlpLayer> {
Box::new(Clone::clone(self))
}
fn get_params(&self) -> &[usize] {
&self.params
}
fn new_added_delta(&self, deltas: Vec<Option<Tensor>>) -> Result<Box<dyn MlpLayer>> {
let gate_proj = if let Some(ref delta) = deltas[0] {
self.gate_proj.add_delta_w(delta)?
} else {
self.gate_proj.clone()
};
let up_proj = if let Some(ref delta) = deltas[1] {
self.up_proj.add_delta_w(delta)?
} else {
self.up_proj.clone()
};
let down_proj = if let Some(ref delta) = deltas[2] {
self.down_proj.add_delta_w(delta)?
} else {
self.down_proj.clone()
};
Ok(Box::new(Self {
gate_proj,
up_proj,
down_proj,
act_fn: self.act_fn,
params: self.params.clone(),
}))
}
fn dtype_device(&self) -> (DType, Device) {
self.gate_proj.dtype_and_device()
}
}
struct Attention {
q_proj: Arc<dyn QuantMethod>,
k_proj: Arc<dyn QuantMethod>,
v_proj: Arc<dyn QuantMethod>,
o_proj: Arc<dyn QuantMethod>,
num_heads: usize,
num_kv_heads: usize,
head_dim: usize,
sliding_window: Option<usize>,
paged_attn: Option<PagedAttention>,
sdpa_params: SdpaParams,
}
impl Attention {
fn new(cfg: &Config, vb: VarBuilder, paged_attn: Option<PagedAttention>) -> Result<Self> {
let hidden_sz = cfg.hidden_size;
let num_heads = cfg.num_attention_heads;
let num_kv_heads = cfg.num_key_value_heads;
let head_dim = cfg.head_dim();
let q_proj = mistralrs_quant::linear_no_bias(
hidden_sz,
num_heads * head_dim,
&cfg.quantization_config,
vb.pp("q_proj"),
)?;
let k_proj = mistralrs_quant::linear_no_bias(
hidden_sz,
num_kv_heads * head_dim,
&cfg.quantization_config,
vb.pp("k_proj"),
)?;
let v_proj = mistralrs_quant::linear_no_bias(
hidden_sz,
num_kv_heads * head_dim,
&cfg.quantization_config,
vb.pp("v_proj"),
)?;
let o_proj = mistralrs_quant::linear_no_bias(
num_heads * head_dim,
hidden_sz,
&cfg.quantization_config,
vb.pp("o_proj"),
)?;
Ok(Self {
q_proj,
k_proj,
v_proj,
o_proj,
num_heads,
num_kv_heads,
head_dim,
sliding_window: cfg.sliding_window,
paged_attn,
sdpa_params: SdpaParams {
n_kv_groups: num_heads / num_kv_heads,
use_flash_attn: cfg.use_flash_attn,
softcap: None,
softmax_scale: 1.0 / (head_dim as f32).sqrt(),
sliding_window: cfg.sliding_window,
},
})
}
#[allow(clippy::too_many_arguments)]
fn forward(
&self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offsets: &[usize],
_start_offsets_kernel: Tensor,
kv_cache: &mut Option<(Tensor, Tensor)>,
rope_parameter: (&Tensor, &Tensor),
metadata: Option<((Tensor, Tensor), &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
let (b_sz, q_len, _) = xs.dims3()?;
let original_dtype = xs.dtype();
let mut xs = xs.clone();
if let Some(t) = self.q_proj.quantized_act_type() {
xs = xs.to_dtype(t)?;
}
let mut q = MatMul.qmethod_matmul(&xs, &*self.q_proj)?;
let mut k = MatMul.qmethod_matmul(&xs, &*self.k_proj)?;
let mut v = MatMul.qmethod_matmul(&xs, &*self.v_proj)?;
if self.q_proj.quantized_act_type().is_some() {
q = q.to_dtype(original_dtype)?;
k = k.to_dtype(original_dtype)?;
v = v.to_dtype(original_dtype)?;
}
let mut q = q
.reshape((b_sz, q_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()?;
let mut k = k
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()?;
q = OrdinaryRoPE::forward(&q, seqlen_offsets[0], rope_parameter.0, rope_parameter.1)?;
k = OrdinaryRoPE::forward(&k, seqlen_offsets[0], rope_parameter.0, rope_parameter.1)?;
let v = v
.reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?;
let mut attn_output = match &self.paged_attn {
Some(paged_attn) => match metadata {
Some(((key_cache, value_cache), input_metadata)) => paged_attn.forward(
&q,
&k,
&v,
attention_mask,
Some(key_cache),
Some(value_cache),
input_metadata,
None,
)?,
None => {
let mut input_metadata = PagedAttentionInputMetadata::dummy(q.device())?;
assert!(attention_mask.is_some());
paged_attn.forward(
&q,
&k,
&v,
attention_mask,
None,
None,
&mut input_metadata,
None,
)?
}
},
None => {
let (k, v, attn_mask) = Cache::update_kv_cache_sliding_window(
kv_cache,
k,
v,
attention_mask,
self.sliding_window,
false,
)?;
Sdpa.run_attention(
&q,
&k,
&v,
attn_mask.as_ref(),
Some(flash_params),
&self.sdpa_params,
)?
}
};
if let Some(t) = self.q_proj.quantized_act_type() {
attn_output = attn_output.to_dtype(t)?;
}
attn_output = if attention_mask.is_some() {
attn_output.transpose(1, 2)?.reshape((b_sz, q_len, ()))?
} else {
attn_output.reshape((b_sz, q_len, ()))?
};
let mut res = MatMul.qmethod_matmul(&attn_output, &*self.o_proj)?;
if self.q_proj.quantized_act_type().is_some() {
res = res.to_dtype(original_dtype)?;
}
Ok(res)
}
}
struct DecoderLayer {
self_attn: Attention,
mlp: Box<dyn MlpLayer>,
input_layernorm: RmsNorm,
post_attention_layernorm: RmsNorm,
}
impl DecoderLayer {
fn new(
cfg: &Config,
vb: VarBuilder,
mapper: &dyn DeviceMapper,
layer_idx: usize,
loading_isq: bool,
paged_attn: Option<PagedAttention>,
) -> Result<Self> {
let self_attn = Attention::new(
cfg,
mapper.set_device(layer_idx, vb.pp("self_attn"), loading_isq),
paged_attn,
)?;
let mlp = MLP::new(cfg, mapper.set_device(layer_idx, vb.pp("mlp"), loading_isq))?;
let input_layernorm = RmsNorm::new(
cfg.hidden_size,
cfg.rms_norm_eps,
mapper.set_device(layer_idx, vb.pp("input_layernorm"), false),
)?;
let post_attention_layernorm = RmsNorm::new(
cfg.hidden_size,
cfg.rms_norm_eps,
mapper.set_device(layer_idx, vb.pp("post_attention_layernorm"), false),
)?;
Ok(Self {
self_attn,
mlp: Box::new(mlp),
input_layernorm,
post_attention_layernorm,
})
}
#[allow(clippy::too_many_arguments)]
fn forward(
&self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
seqlen_offsets: &[usize],
start_offsets_kernel: Tensor,
kv_cache: &mut Option<(Tensor, Tensor)>,
rope_parameter: (&Tensor, &Tensor),
metadata: Option<((Tensor, Tensor), &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
let residual = xs;
let mut xs = self.input_layernorm.forward(xs)?;
xs = self.self_attn.forward(
&xs,
attention_mask,
seqlen_offsets,
start_offsets_kernel,
kv_cache,
rope_parameter,
metadata,
flash_params,
)?;
xs = (xs + residual)?;
let residual = &xs;
let xs = self
.mlp
.forward(&xs.apply(&self.post_attention_layernorm)?)?;
residual + xs
}
}
pub struct Model {
embed_tokens: candle_nn::Embedding,
layers: Vec<DecoderLayer>,
norm: RmsNorm,
lm_head: Arc<dyn QuantMethod>,
sliding_window: Option<usize>,
device: Device,
cache: EitherCache,
max_seq_len: usize,
mapper: Box<dyn DeviceMapper + Send + Sync>,
rope_parameters: (Tensor, Tensor),
cfg: ModelConfigMetadata,
}
impl Model {
pub fn new(
cfg: &Config,
vb: VarBuilder,
is_gptx: bool,
normal_loading_metadata: NormalLoadingMetadata,
attention_mechanism: AttentionImplementation,
) -> Result<Self> {
let vb_m = vb.pp("model");
let vb_lm_head = vb.pp("lm_head");
Self::new_inner(
cfg,
vb_m,
vb_lm_head,
is_gptx,
normal_loading_metadata,
attention_mechanism,
)
}
pub fn new_inner(
cfg: &Config,
vb_m: VarBuilder,
vb_lm_head: VarBuilder,
_is_gptx: bool,
normal_loading_metadata: NormalLoadingMetadata,
attention_mechanism: AttentionImplementation,
) -> Result<Self> {
let mapper = normal_loading_metadata.mapper;
let embed_tokens = candle_nn::embedding(
cfg.vocab_size,
cfg.hidden_size,
mapper.set_nm_device(vb_m.pp("embed_tokens"), false),
)?;
let head_dim = cfg.hidden_size / cfg.num_attention_heads;
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_l = vb_m.pp("layers");
let rope_parameters = OrdinaryRoPE::create_parameters(
head_dim,
cfg.max_position_embeddings,
cfg.rope_theta as f32,
vb_m.dtype(),
&normal_loading_metadata.real_device,
)?;
for layer_idx in
NiceProgressBar::<_, 'b'>(0..cfg.num_hidden_layers, "Loading repeating layers")
{
let paged_attn = match &attention_mechanism {
AttentionImplementation::Eager => None,
AttentionImplementation::PagedAttention => Some(PagedAttention::new(
cfg.num_attention_heads,
head_dim,
(1.0 / (head_dim as f64).sqrt()) as f32,
Some(cfg.num_key_value_heads),
cfg.sliding_window,
&normal_loading_metadata.real_device,
None,
)?),
};
let layer = DecoderLayer::new(
cfg,
vb_l.pp(layer_idx),
&*mapper,
layer_idx,
normal_loading_metadata.loading_isq,
paged_attn,
)?;
layers.push(layer)
}
let norm = RmsNorm::new(
cfg.hidden_size,
cfg.rms_norm_eps,
mapper.set_nm_device(vb_m.pp("norm"), false),
)?;
let lm_head = linear_no_bias(
cfg.hidden_size,
cfg.vocab_size,
mapper.set_nm_device(vb_lm_head, normal_loading_metadata.loading_isq),
)?;
Ok(Self {
embed_tokens,
layers,
norm,
lm_head: Arc::new(UnquantLinear::new(QuantMethodConfig::Unquantized(lm_head))?),
sliding_window: cfg.sliding_window,
device: normal_loading_metadata.real_device,
cache: EitherCache::Full(Cache::new(cfg.num_hidden_layers, false)),
max_seq_len: cfg.max_position_embeddings,
mapper,
rope_parameters,
cfg: ModelConfigMetadata {
num_layers: cfg.num_hidden_layers,
hidden_size: cfg.hidden_size,
num_kv_heads: cfg.num_key_value_heads,
num_attn_heads: cfg.num_attention_heads,
sliding_window: cfg.sliding_window,
k_head_dim: None,
v_head_dim: None,
},
})
}
pub fn get_input_embeddings(&self, input_ids: &Tensor) -> Result<Tensor> {
self.embed_tokens.forward(input_ids)
}
pub fn forward(
&self,
input_ids: &Tensor,
seqlen_offsets: &[usize],
start_offsets_kernel: Tensor,
context_lens: Vec<(usize, usize)>,
metadata: Option<(Vec<(Tensor, Tensor)>, &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
self.forward_embeds(
input_ids,
self.embed_tokens.forward(input_ids)?,
seqlen_offsets,
start_offsets_kernel,
context_lens,
metadata,
flash_params,
)
}
#[allow(clippy::too_many_arguments)]
pub fn forward_embeds(
&self,
input_ids: &Tensor,
input_embeds: Tensor,
seqlen_offsets: &[usize],
start_offsets_kernel: Tensor,
context_lens: Vec<(usize, usize)>,
mut metadata: Option<(Vec<(Tensor, Tensor)>, &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
let mut xs = input_embeds;
let mut cache = self.cache.full().lock();
let attention_mask = CausalMasker.make_sliding_window_causal_mask_matrix(
input_ids,
metadata
.as_ref()
.map(|(_, _)| &seqlen_offsets as &dyn PastKvLenCache)
.unwrap_or(&*cache as &dyn PastKvLenCache),
self.sliding_window,
xs.dtype(),
self.cfg.num_attn_heads,
)?;
for (i, layer) in self.layers.iter().enumerate() {
xs = self.mapper.map(xs, i)?;
xs = layer.forward(
&xs,
attention_mask
.as_ref()
.map(|m| m.to_device(xs.device()).unwrap())
.as_ref(),
seqlen_offsets,
start_offsets_kernel.clone(),
&mut cache[i],
(&self.rope_parameters.0, &self.rope_parameters.1),
metadata
.as_mut()
.map(|(kv_cache, metadata)| (kv_cache[i].clone(), &mut **metadata)),
flash_params,
)?;
}
xs = xs.to_device(&self.device)?;
xs = xs.apply(&self.norm)?;
if let Some(t) = self.lm_head.quantized_act_type() {
xs = xs.to_dtype(t)?;
}
extract_logits(&MatMul.qmethod_matmul(&xs, &*self.lm_head)?, context_lens)
}
}
impl IsqModel for Model {
fn get_layers(
&mut self,
) -> (
Vec<(&mut Arc<dyn QuantMethod>, Option<usize>)>,
&dyn DeviceMapper,
) {
let mut tensors = Vec::new();
tensors.push((&mut self.lm_head, None));
for (i, layer) in self.layers.iter_mut().enumerate() {
tensors.push((&mut layer.self_attn.q_proj, Some(i)));
tensors.push((&mut layer.self_attn.k_proj, Some(i)));
tensors.push((&mut layer.self_attn.v_proj, Some(i)));
tensors.push((&mut layer.self_attn.o_proj, Some(i)));
tensors.extend(
layer
.mlp
.get_isq_layers()
.into_iter()
.map(|m| (m, Some(i)))
.collect::<Vec<_>>(),
);
}
(tensors, &*self.mapper)
}
fn residual_tensors(&self) -> Vec<(String, Tensor)> {
Vec::new()
}
}
impl LLaVALLM for Model {
fn embed(&self, input_ids: &Tensor) -> Result<Tensor> {
self.get_input_embeddings(input_ids)
}
fn forward_input_embed(
&self,
input_ids: &Tensor,
input_embed: Tensor,
seqlen_offsets: &[usize],
start_offsets_kernel: Tensor,
context_lens: Vec<(usize, usize)>,
metadata: Option<(Vec<(Tensor, Tensor)>, &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
self.forward_embeds(
input_ids,
input_embed,
seqlen_offsets,
start_offsets_kernel,
context_lens,
metadata,
flash_params,
)
}
}
impl NormalModel for Model {
fn forward(
&self,
input_ids: &Tensor,
seqlen_offsets: &[usize],
start_offsets_kernel: Tensor,
context_lens: Vec<(usize, usize)>,
_position_ids: Vec<usize>,
metadata: Option<(Vec<(Tensor, Tensor)>, &mut PagedAttentionInputMetadata)>,
flash_params: &FlashParams,
) -> Result<Tensor> {
self.forward(
input_ids,
seqlen_offsets,
start_offsets_kernel,
context_lens,
metadata,
flash_params,
)
}
fn xlora_forward(
&self,
_input_ids: &Tensor,
_input_ids_full: &Tensor,
_seqlen_offsets: &[usize],
_seqlen_offsets_full: &[usize],
_start_offsets_kernel: Tensor,
_start_offsets_kernel_full: Tensor,
_no_kv_cache: bool,
_non_granular_state: &Option<crate::xlora_models::NonGranularState>,
_context_lens: Vec<(usize, usize)>,
_position_ids: Vec<usize>,
_flash_params: &FlashParams,
_flash_params_full: &FlashParams,
) -> Result<Tensor> {
unimplemented!()
}
fn cache(&self) -> &EitherCache {
&self.cache
}
fn cache_mut(&mut self) -> &mut EitherCache {
&mut self.cache
}
fn device(&self) -> &Device {
&self.device
}
fn is_xlora(&self) -> bool {
false
}
fn max_seq_len(&self) -> usize {
self.max_seq_len
}
fn config(&self) -> &ModelConfigMetadata {
&self.cfg
}
}
impl AnyMoeBaseModelMixin for Model {
fn get_mlps(&self) -> Vec<&dyn MlpLayer> {
let mut mlps = Vec::new();
for layer in &self.layers {
mlps.push(&*layer.mlp);
}
mlps
}
fn get_mlps_mut(&mut self) -> Vec<&mut Box<dyn MlpLayer>> {
let mut mlps = Vec::new();
for layer in &mut self.layers {
mlps.push(&mut layer.mlp);
}
mlps
}
fn create_anymoe_layers(
&mut self,
additional_vbs: Vec<VarBuilder>,
config: AnyMoeConfig,
(prefix, mlp): (String, String),
mut layers: Vec<usize>,
expert_type: AnyMoeExpertType,
gate_vb: Option<VarBuilder>,
) -> Result<()> {
let mut experts: Vec<Vec<Box<dyn MlpLayer>>> = Vec::new();
if layers.is_empty() {
layers = (0..self.layers.len()).collect::<Vec<_>>();
}
for _ in 0..layers.len() {
experts.push(Vec::new());
}
for vb in additional_vbs {
let vb = vb.pp(&prefix);
for (layer, row) in experts.iter_mut().enumerate() {
if !layers.contains(&layer) {
continue;
}
let intermediate_size = self.layers[layer].mlp.get_params()[1];
let hidden_size = self.layers[layer].mlp.get_params()[0];
match expert_type {
AnyMoeExpertType::FineTuned => {
let (dtype, device) = self.layers[layer].mlp.dtype_device();
row.push(Box::new(MLP::new(
&Config {
intermediate_size: self.layers[layer].mlp.get_params()[1],
hidden_size: self.layers[layer].mlp.get_params()[0],
..Default::default()
},
vb.pp(layer).pp(&mlp).set_dtype(dtype).set_device(device),
)?));
}
AnyMoeExpertType::LoraAdapter {
rank,
alpha,
ref target_modules,
} => {
let vb_mlp = vb.pp(layer).pp(&mlp);
let gate_proj_delta = if target_modules.contains(&"gate_proj".to_string()) {
Some(get_delta_from_lora_ab!(
vb_mlp,
rank,
alpha,
(hidden_size, intermediate_size),
"gate_proj"
))
} else {
None
};
let up_proj_delta = if target_modules.contains(&"up_proj".to_string()) {
Some(get_delta_from_lora_ab!(
vb_mlp,
rank,
alpha,
(hidden_size, intermediate_size),
"up_proj"
))
} else {
None
};
let down_proj_delta = if target_modules.contains(&"down_proj".to_string()) {
Some(get_delta_from_lora_ab!(
vb_mlp,
rank,
alpha,
(intermediate_size, hidden_size),
"down_proj"
))
} else {
None
};
row.push(self.layers[layer].mlp.new_added_delta(vec![
gate_proj_delta,
up_proj_delta,
down_proj_delta,
])?);
}
}
}
}
for (layer, expert) in layers.into_iter().zip(experts) {
let mut experts_all = vec![self.layers[layer].mlp.clone()];
experts_all.extend(expert);
let (dtype, device) = self.layers[layer].mlp.dtype_device();
self.layers[layer].mlp = Box::new(MoeMlp::new(
experts_all,
config.clone(),
dtype,
&device,
layer,
gate_vb.as_ref(),
)?);
}
Ok(())
}
fn amoe_supported(&self) -> bool {
true
}
}