tract-cuda 0.23.4

Tiny, no-nonsense, self contained, TensorFlow and ONNX inference
Documentation
use crate::kernels::flash_attn::CudaFlashAttn;
use derive_new::new;
use tract_core::internal::*;
use tract_gpu::tensor::DeviceTensorExt;

#[derive(Clone, Debug, new, PartialEq)]
pub struct CudaFlashAttention {
    scale: f32,
    is_causal: bool,
}
impl Eq for CudaFlashAttention {}

impl Op for CudaFlashAttention {
    fn name(&self) -> StaticName {
        "CudaFlashAttention".into()
    }

    op_as_typed_op!();
}

impl EvalOp for CudaFlashAttention {
    fn is_stateless(&self) -> bool {
        true
    }

    fn eval_with_session(
        &self,
        node_id: usize,
        session: &TurnState,
        inputs: TVec<TValue>,
    ) -> TractResult<TVec<TValue>> {
        crate::with_cuda_stream(|stream| {
            ensure!(inputs.len() >= 3, "flash-attn expects [q, k, v, (mask)]");

            let q = inputs[0].to_device_tensor()?;
            let k = inputs[1].to_device_tensor()?;
            let v = inputs[2].to_device_tensor()?;
            let mask = inputs.get(3).map(|m| m.to_device_tensor()).transpose()?;
            let output = tract_gpu::session_handler::make_tensor_for_node(
                session,
                node_id,
                q.datum_type(),
                &CudaFlashAttn.output_shape(q.shape(), k.shape(), v.shape())?,
            )?;
            CudaFlashAttn.dispatch_eval(
                stream,
                q,
                k,
                v,
                mask,
                self.scale,
                &output,
                self.is_causal,
            )?;
            Ok(tvec!(output.into_tensor().into_tvalue()))
        })
    }
}

impl TypedOp for CudaFlashAttention {
    fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
        tract_gpu::utils::facts_to_device_facts(inputs, |facts| {
            ensure!(facts.len() >= 3);
            let dt = facts[0].datum_type;

            ensure!(facts.iter().all(|f| f.rank() == 4));
            let shape =
                CudaFlashAttn.output_shape(&facts[0].shape, &facts[1].shape, &facts[2].shape)?;
            let fact = dt.fact(shape);
            Ok(tvec!(fact))
        })
        .with_context(|| format!("Error while computing facts for {:?}", self.name()))
    }

    as_op!();
}