Use flash-attn in gemma. (#2195)
* Use flash-attn in gemma. * Fix flash-attn for head dim 256.
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@ -193,6 +193,9 @@ struct Args {
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/// The model to use.
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#[arg(long, default_value = "2b")]
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which: Which,
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#[arg(long)]
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use_flash_attn: bool,
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}
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fn main() -> Result<()> {
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@ -270,7 +273,7 @@ fn main() -> Result<()> {
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DType::F32
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};
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
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let model = Model::new(&config, vb)?;
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let model = Model::new(args.use_flash_attn, &config, vb)?;
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println!("loaded the model in {:?}", start.elapsed());
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@ -42,6 +42,10 @@ void run_flash_fwd(Flash_fwd_params ¶ms, cudaStream_t stream) {
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// auto kernel = &flash_fwd_kernel<Kernel_traits, false, Is_causal, false, false, true, true, false>;
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// printf("IsEvenMNConst = %d, IsEvenKConst = %d, Is_local = %d, Is_causal = %d, ReturnSoftmaxConst = %d, Is_dropout = %d\n", int(IsEvenMNConst), int(IsEvenKConst), int(Is_local), int(Is_causal), int(ReturnSoftmaxConst), int(Is_dropout));
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// auto kernel = &flash_fwd_kernel<Kernel_traits, false, Is_causal, false, true, true, false>;
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if (smem_size >= 48 * 1024) {
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cudaFuncSetAttribute(
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kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
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}
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// int ctas_per_sm;
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// cudaError status_ = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
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// &ctas_per_sm, kernel, Kernel_traits::kNThreads, smem_size);
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@ -139,7 +139,9 @@ impl FlashAttn {
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let elem_count = out_shape.elem_count();
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let dst = unsafe { dev.alloc::<T>(elem_count) }.w()?;
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let softmax_lse = dev.alloc_zeros::<f32>(b_sz * num_heads * seqlen_q).w()?;
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let softmax_lse = dev
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.alloc_zeros::<f32>(b_sz * 128 * num_heads * seqlen_q)
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.w()?;
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let is_bf16 = if is_bf16 { 1 } else { 0 };
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@ -73,13 +73,6 @@ struct RotaryEmbedding {
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cos: Tensor,
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}
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fn rotate_half(xs: &Tensor) -> Result<Tensor> {
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let last_dim = xs.dim(D::Minus1)?;
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let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
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let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
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Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
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}
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impl RotaryEmbedding {
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fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
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let dim = cfg.head_dim;
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@ -94,7 +87,6 @@ impl RotaryEmbedding {
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.to_dtype(dtype)?
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.reshape((max_seq_len, 1))?;
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let freqs = t.matmul(&inv_freq)?;
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let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
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Ok(Self {
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sin: freqs.sin()?,
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cos: freqs.cos()?,
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@ -110,10 +102,8 @@ impl RotaryEmbedding {
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let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
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let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
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let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
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let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
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let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
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let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
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let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
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let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
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let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
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Ok((q_embed, k_embed))
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}
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}
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@ -163,10 +153,16 @@ struct Attention {
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head_dim: usize,
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rotary_emb: Arc<RotaryEmbedding>,
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kv_cache: Option<(Tensor, Tensor)>,
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use_flash_attn: bool,
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}
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impl Attention {
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fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
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fn new(
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rotary_emb: Arc<RotaryEmbedding>,
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use_flash_attn: bool,
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cfg: &Config,
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vb: VarBuilder,
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) -> Result<Self> {
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let hidden_sz = cfg.hidden_size;
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let num_heads = cfg.num_attention_heads;
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let num_kv_heads = cfg.num_key_value_heads;
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@ -188,6 +184,7 @@ impl Attention {
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head_dim,
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rotary_emb,
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kv_cache: None,
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use_flash_attn,
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})
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}
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@ -231,7 +228,14 @@ impl Attention {
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let value_states =
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crate::utils::repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
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let attn_output = {
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let attn_output = if self.use_flash_attn {
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// flash-attn expects (b_sz, seq_len, nheads, head_dim)
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let q = query_states.transpose(1, 2)?;
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let k = key_states.transpose(1, 2)?;
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let v = value_states.transpose(1, 2)?;
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let scale = 1f32 / (self.head_dim as f32).sqrt();
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flash_attn(&q, &k, &v, scale, attention_mask.is_some())?.transpose(1, 2)?
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} else {
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let scale = 1f64 / f64::sqrt(self.head_dim as f64);
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let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
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@ -253,6 +257,22 @@ impl Attention {
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}
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}
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#[cfg(feature = "flash-attn")]
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fn flash_attn(
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q: &Tensor,
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k: &Tensor,
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v: &Tensor,
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softmax_scale: f32,
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causal: bool,
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) -> Result<Tensor> {
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candle_flash_attn::flash_attn(q, k, v, softmax_scale, causal)
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}
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#[cfg(not(feature = "flash-attn"))]
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fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Tensor> {
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unimplemented!("compile with '--features flash-attn'")
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}
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#[derive(Debug, Clone)]
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struct DecoderLayer {
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self_attn: Attention,
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@ -262,8 +282,13 @@ struct DecoderLayer {
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}
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impl DecoderLayer {
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fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
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fn new(
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rotary_emb: Arc<RotaryEmbedding>,
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use_flash_attn: bool,
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cfg: &Config,
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vb: VarBuilder,
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) -> Result<Self> {
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let self_attn = Attention::new(rotary_emb, use_flash_attn, cfg, vb.pp("self_attn"))?;
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let mlp = MLP::new(cfg, vb.pp("mlp"))?;
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let input_layernorm =
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RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
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@ -312,7 +337,7 @@ pub struct Model {
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}
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impl Model {
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pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
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pub fn new(use_flash_attn: bool, cfg: &Config, vb: VarBuilder) -> Result<Self> {
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let vb_m = vb.pp("model");
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let embed_tokens =
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candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
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@ -320,7 +345,8 @@ impl Model {
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let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
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let vb_l = vb_m.pp("layers");
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for layer_idx in 0..cfg.num_hidden_layers {
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let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
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let layer =
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DecoderLayer::new(rotary_emb.clone(), use_flash_attn, cfg, vb_l.pp(layer_idx))?;
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layers.push(layer)
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}
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let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb_m.pp("norm"))?;
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