use crate::tensor::{DeviceTensor, DeviceTensorExt};
use derive_new::new;
use tract_core::internal::*;
pub type DispatchLeakyReluFn = fn(f32, &DeviceTensor, &DeviceTensor) -> TractResult<()>;
#[derive(Clone, new)]
pub struct GpuLeakyRelu {
pub alpha: f32,
pub backend_name: &'static str,
pub dispatch: DispatchLeakyReluFn,
}
impl std::fmt::Debug for GpuLeakyRelu {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
write!(f, "{}LeakyRelu(alpha: {})", self.backend_name, self.alpha)
}
}
impl PartialEq for GpuLeakyRelu {
fn eq(&self, other: &Self) -> bool {
self.backend_name == other.backend_name && self.alpha == other.alpha
}
}
impl Eq for GpuLeakyRelu {}
impl std::hash::Hash for GpuLeakyRelu {
fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
self.backend_name.hash(state);
self.alpha.to_bits().hash(state);
}
}
impl Op for GpuLeakyRelu {
fn name(&self) -> StaticName {
format!("{}LeakyRelu", self.backend_name).into()
}
op_as_typed_op!();
}
impl EvalOp for GpuLeakyRelu {
fn is_stateless(&self) -> bool {
true
}
fn eval_with_session(
&self,
node_id: usize,
session: &TurnState,
inputs: TVec<TValue>,
) -> TractResult<TVec<TValue>> {
let input_value = args_1!(inputs);
let input = input_value.to_device_tensor()?;
let output = crate::session_handler::make_tensor_for_node(
session,
node_id,
input.datum_type(),
input.shape(),
)?;
(self.dispatch)(self.alpha, input, &output)?;
Ok(tvec!(output.into_tensor().into_tvalue()))
}
}
impl TypedOp for GpuLeakyRelu {
fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
crate::utils::facts_to_device_facts(inputs, |facts| {
let dt = facts[0].datum_type;
let fact = dt.fact(facts[0].shape.clone());
Ok(tvec!(fact))
})
.with_context(|| format!("Error while computing facts for {:?}", self.name()))
}
as_op!();
}