tract_cuda/kernels/array/
cast.rs1use cudarc::driver::{CudaStream, LaunchConfig, PushKernelArg};
2use derive_new::new;
3use std::fmt;
4use tract_core::internal::*;
5use tract_gpu::tensor::DeviceTensor;
6
7use crate::context::{TractCudaStream, cuda_context};
8use crate::kernels::launch_args::TractLaunchArgs;
9use crate::kernels::{LibraryName, get_cuda_view, launch_args};
10
11#[derive(Debug, Clone, new, PartialEq, Eq, Hash)]
12pub struct Cast;
13
14impl fmt::Display for Cast {
15 fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
16 write!(f, "{self:?}")
17 }
18}
19
20impl Cast {
21 pub fn is_supported_dt(dt: DatumType) -> bool {
22 matches!(
23 dt,
24 DatumType::F32
25 | DatumType::F16
26 | DatumType::U8
27 | DatumType::U16
28 | DatumType::U32
29 | DatumType::U64
30 | DatumType::I8
31 | DatumType::I16
32 | DatumType::I32
33 | DatumType::I64
34 | DatumType::Bool
35 )
36 }
37
38 pub fn kernel_name(&self, from_dt: DatumType, to_dt: DatumType) -> TractResult<String> {
39 ensure!(
40 Self::is_supported_dt(from_dt),
41 "Unsupported from_dt {:?} for cuda castop",
42 from_dt
43 );
44 ensure!(Self::is_supported_dt(to_dt), "Unsupported to_dt {:?} for cuda castop", to_dt);
45 let from_tname = DeviceTensor::tname(from_dt)?;
46 let to_tname = DeviceTensor::tname(to_dt)?;
47 Ok(format!("cast_{from_tname}_{to_tname}"))
48 }
49
50 pub fn eval(
51 &self,
52 stream: &TractCudaStream,
53 input: &DeviceTensor,
54 to_dt: DatumType,
55 ) -> TractResult<DeviceTensor> {
56 let output = unsafe { DeviceTensor::uninitialized_dt(to_dt, input.shape())? };
57 self.dispatch_eval(stream, input, &output)?;
58 stream.synchronize()?;
59 Ok(output)
60 }
61
62 pub fn dispatch_eval(
63 &self,
64 stream: &TractCudaStream,
65 input: &DeviceTensor,
66 output: &DeviceTensor,
67 ) -> TractResult<()> {
68 ensure!(
69 input.shape() == output.shape(),
70 "Cast I/O don't have the same shape in: {:?}, out: {:?}",
71 input.shape(),
72 output.shape()
73 );
74
75 let kernel_name = self.kernel_name(input.datum_type(), output.datum_type())?;
76
77 let i_view = get_cuda_view(input);
78 let o_view = get_cuda_view(output);
79 let len = output.len();
80 let func = cuda_context().load_pipeline(LibraryName::Array, kernel_name)?;
81
82 let mut launch_args = TractLaunchArgs::new(stream, &func);
83 launch_args.push_view(&i_view);
84 launch_args.push_view(&o_view);
85 launch_args.push_i32(len);
86 let cfg = LaunchConfig::for_num_elems(len as _);
87
88 launch_args.launch(cfg)
89 }
90}
91
92pub fn cuda_cast_dispatch(input: &DeviceTensor, output: &DeviceTensor) -> TractResult<()> {
93 crate::with_cuda_stream(|stream| Cast.dispatch_eval(stream, input, output))
94}
95
96crate::register_cuda_op!(tract_core::ops::cast::Cast, |_source, _node, op| {
97 Ok(crate::transform::cuda_cast_new(op.to).map(|c| Box::new(c) as _))
98});
99
100#[cfg(test)]
101mod tests {
102
103 use super::*;
104 use tract_gpu::tensor::IntoDevice;
105 use tract_itertools::Itertools;
106
107 use num_traits::{FromPrimitive, Zero};
108
109 use tract_core::internal::Tensor;
110
111 fn run_test_case<T0: Datum + Copy + FromPrimitive, T1: Datum>(
112 shape: &[usize],
113 ) -> TractResult<()> {
114 crate::with_cuda_stream(|stream| {
115 let len = shape.iter().product::<usize>();
116 let data = (0..len).map(|f| T0::from_f32(f as f32 / 2.).unwrap()).collect::<Vec<_>>();
117 let input = Tensor::from_shape(shape, &data)?;
118
119 let output = Cast {}.eval(stream, &input.clone().into_device()?, T1::datum_type())?;
120
121 assert_eq!(
122 output.to_host()?.into_tensor(),
123 input.cast_to_dt(T1::datum_type())?.into_owned()
124 );
125 Ok(())
126 })
127 }
128
129 #[test]
130 fn test_cast() -> TractResult<()> {
131 run_test_case::<f16, f32>(&[3, 4])?;
132 run_test_case::<u8, f32>(&[2, 5])?;
133 run_test_case::<f16, u32>(&[3, 2, 2])?;
134 Ok(())
135 }
136}