Skip to content

Fix FlashQwen3 #650

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 24, 2025
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 9 additions & 6 deletions backends/candle/src/models/flash_qwen3.rs
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ struct Qwen3Attention {
impl Qwen3Attention {
pub fn load(vb: VarBuilder, config: &Qwen3Config) -> Result<Self> {
if config.use_sliding_window {
candle::bail!("Sliding window is not supported for Qwen3",);
candle::bail!("Sliding window is not supported for Qwen3");
}

let num_attention_heads = config.num_attention_heads;
Expand Down Expand Up @@ -143,8 +143,8 @@ impl Qwen3Attention {
)?;

// Apply normalization layers
let (q, _res) = self.q_norm.forward(&q, None)?;
let (k, _res) = self.k_norm.forward(&k, None)?;
let (q, _) = self.q_norm.forward(&q, None)?;
let (k, _) = self.k_norm.forward(&k, None)?;

apply_rotary_inplace(&q, &k, &cos, &sin, true)?;

Expand All @@ -158,7 +158,7 @@ impl Qwen3Attention {
max_s,
max_s,
self.softmax_scale,
false,
true,
None,
None,
)?;
Expand Down Expand Up @@ -215,8 +215,8 @@ impl Qwen3MLP {
let up_states = gate_up_states.narrow(1, self.intermediate_size, self.intermediate_size)?;

let gate_states = self.act.forward(&gate_states)?;
let r = self.down_proj.forward(&(gate_states * up_states)?);
r

self.down_proj.forward(&(gate_states * up_states)?)
}
}

Expand Down Expand Up @@ -266,12 +266,15 @@ impl Qwen3Layer {
let _enter = self.span.enter();

let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, residual)?;

let attn_output =
self.attention
.forward(&normed_hidden_states, cu_seqlens, cos, sin, max_s)?;

let (normed_attn_res_output, attn_res) = self
.post_attention_layer_norm
.forward(&attn_output, Some(&res))?;

let mlp_output = self.mlp.forward(&normed_attn_res_output)?;

Ok((mlp_output, attn_res))
Expand Down
Loading
Loading