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unify cargo version and add some ci rules

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This commit is contained in:
Yijun Zhao
2025-10-15 21:03:49 +08:00
parent 0fd3c7d935
commit 9b9a8f2c73
40 changed files with 873 additions and 836 deletions
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src/models/voxcpm/model.rs
+14 -10
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@@ -7,7 +7,7 @@ use candle_transformers::models::deepseek2::SplitOp;
use crate::{
models::voxcpm::{
audio_vae::{AudioVAE},
audio_vae::AudioVAE,
config::{CfmConfig, VoxCPMConfig, VoxMiniCPM4Config},
minicpm4::MiniCPMModel,
tokenizer::SingleChineseTokenizer,
@@ -67,7 +67,7 @@ impl SinusoidalPosEmb {
let half_dim = self.dim / 2;
let dif = 10000.0_f64.ln() / (half_dim - 1) as f64;
let emb = Tensor::arange(0.0, half_dim as f32, x.device())?
.affine(-1.0 * dif, 0.0)?
.affine(-dif, 0.0)?
.exp()?
.to_dtype(x.dtype())?;
@@ -94,8 +94,8 @@ impl TimestepEmbedding {
out_dim: Option<usize>,
) -> Result<Self> {
let linear_1 = linear(in_channels, time_embed_dim, vb.pp("linear_1"))?;
let time_embed_dim_out = if out_dim.is_some() {
out_dim.unwrap()
let time_embed_dim_out = if let Some(out_dim) = out_dim {
out_dim
} else {
time_embed_dim
};
@@ -104,7 +104,7 @@ impl TimestepEmbedding {
}
pub fn forward(&self, sample: &Tensor) -> Result<Tensor> {
let sample = self.linear_1.forward(&sample)?.silu()?;
let sample = self.linear_1.forward(sample)?.silu()?;
let sample = self.linear_2.forward(&sample)?;
Ok(sample)
}
@@ -199,7 +199,7 @@ pub struct UnifiedCFM {
impl UnifiedCFM {
pub fn new(
in_channels: usize,
cfm_params: CfmConfig,
_cfm_params: CfmConfig,
estimator: VoxCPMLocDiT,
mean_mode: bool,
) -> Result<Self> {
@@ -270,7 +270,7 @@ impl UnifiedCFM {
let mut sol = Vec::new();
let t_span_len = t_span.dim(0)?;
let zero_init_steps = max(1, (t_span_len as f32 * 0.04) as usize);
let mut dphi_dt = Tensor::zeros(1, t_span.dtype(), t_span.device())?;
let mut dphi_dt;
let mut x = x.clone();
for step in 1..t_span_len {
if use_cfg_zero_star && step <= zero_init_steps {
@@ -307,7 +307,7 @@ impl UnifiedCFM {
let cfg = cfg_dphi_dt.broadcast_mul(&st_star)?;
dphi_dt = cfg.add(&dphi_dt.sub(&cfg)?.affine(cfg_value, 0.0)?)?; // step步的预测噪声
}
x = x.broadcast_sub(&dphi_dt.broadcast_mul(&dt)?)?; // 逐步去噪
x = x.broadcast_sub(&dphi_dt.broadcast_mul(&dt)?)?; // 逐步去噪
t = t.sub(&dt)?;
sol.push(x.clone());
if step < t_span_len - 1 {
@@ -644,7 +644,9 @@ impl VoxCPMModel {
let mut pred_feat_seq = Vec::new();
let mut position_id = 0;
let mut seq_len = t;
let enc_outputs = self.base_lm.forward_with_cache(&combined_embed, position_id)?;
let enc_outputs = self
.base_lm
.forward_with_cache(&combined_embed, position_id)?;
let enc_outputs = self
.fsq_layer
.forward(&enc_outputs)?
@@ -655,7 +657,9 @@ impl VoxCPMModel {
let input_embeds =
enc_outputs.add(&feat_mask.unsqueeze(D::Minus1)?.broadcast_mul(&feat_embed)?)?;
let residual_enc_outputs = self.residual_lm.forward_with_cache(&input_embeds, position_id)?;
let residual_enc_outputs = self
.residual_lm
.forward_with_cache(&input_embeds, position_id)?;
let mut residual_hidden = residual_enc_outputs.i((.., t - 1, ..))?;
for i in 0..max_len {