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Migrate/jit/adaptive avg pool backward #1530

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merged 17 commits into from
Mar 26, 2024
Merged

Migrate/jit/adaptive avg pool backward #1530

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c54c86f
separate forward backward
louisfd Mar 21, 2024
45d0806
refactor with pool strategy
louisfd Mar 21, 2024
053673f
refactor further
louisfd Mar 21, 2024
3a28bb5
pooling refactored
louisfd Mar 21, 2024
64beeb8
refactoring for adaptive wip
louisfd Mar 22, 2024
aa9de1a
wip adaptive
louisfd Mar 22, 2024
71b04ca
adaptive
louisfd Mar 22, 2024
86dbd33
merge main
louisfd Mar 22, 2024
ad7d41f
delete some wgsl
louisfd Mar 22, 2024
22fb624
avg pool backward
louisfd Mar 22, 2024
27d5587
clippy
louisfd Mar 22, 2024
1c344dc
Merge branch 'main' into migrate/jit/pool_backward
louisfd Mar 22, 2024
9eef2c9
Merge branch 'main' into migrate/jit/pool_backward
louisfd Mar 25, 2024
f878042
minor refactor
louisfd Mar 25, 2024
9a5ef46
works
louisfd Mar 25, 2024
ef26711
delete wgsl
louisfd Mar 25, 2024
63053ad
merge main
louisfd Mar 25, 2024
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298 changes: 252 additions & 46 deletions crates/burn-jit/src/kernel/pool/adaptive_avg_pool2d_backward.rs
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Original file line number Diff line number Diff line change
@@ -1,17 1,244 @@
use std::marker::PhantomData;

use crate::{
compute::StaticKernel,
codegen::{
execute_dynamic, Compilation, CompilationInfo, CompilationSettings, EagerHandle, InputInfo,
OutputInfo, WorkgroupLaunch,
},
element::JitElement,
kernel::{elemwise_workgroup, KernelSettings, WORKGROUP_DEFAULT},
kernel_wgsl,
gpu::{gpu, Elem, Scope, Variable, Visibility},
kernel::{DynamicKernelSource, SourceTemplate},
tensor::JitTensor,
Runtime,
Compiler, Runtime, RuntimeInt,
};
use burn_compute::server::Handle;

kernel_wgsl!(
AdaptiveAvgPool2dBackward,
"../../template/pool/adaptive_avg_pool2d_backward.wgsl"
);
#[derive(new)]
struct AdaptiveAvgPool2dBackwardEagerKernel<R, E> {
_runtime: PhantomData<R>,
_elem: PhantomData<E>,
}

struct AdaptiveAvgPool2dBackwardComputeShader {
grad: Variable,
output: Variable,
}

impl AdaptiveAvgPool2dBackwardComputeShader {
fn expand(self, scope: &mut Scope) {
let grad = self.grad;
let output = self.output;
let id = Variable::Id;

let grad_stride_0 = scope.create_local(Elem::UInt);
let grad_stride_1 = scope.create_local(Elem::UInt);
let grad_stride_2 = scope.create_local(Elem::UInt);
let grad_stride_3 = scope.create_local(Elem::UInt);

let grad_shape_2 = scope.create_local(Elem::UInt);
let grad_shape_3 = scope.create_local(Elem::UInt);

let output_stride_0 = scope.create_local(Elem::UInt);
let output_stride_1 = scope.create_local(Elem::UInt);
let output_stride_2 = scope.create_local(Elem::UInt);
let output_stride_3 = scope.create_local(Elem::UInt);

let output_shape_0 = scope.create_local(Elem::UInt);
let output_shape_1 = scope.create_local(Elem::UInt);
let output_shape_2 = scope.create_local(Elem::UInt);
let output_shape_3 = scope.create_local(Elem::UInt);

gpu!(scope, grad_stride_0 = stride(grad, 0u32));
gpu!(scope, grad_stride_1 = stride(grad, 1u32));
gpu!(scope, grad_stride_2 = stride(grad, 2u32));
gpu!(scope, grad_stride_3 = stride(grad, 3u32));

gpu!(scope, grad_shape_2 = shape(grad, 2u32));
gpu!(scope, grad_shape_3 = shape(grad, 3u32));

gpu!(scope, output_stride_0 = stride(output, 0u32));
gpu!(scope, output_stride_1 = stride(output, 1u32));
gpu!(scope, output_stride_2 = stride(output, 2u32));
gpu!(scope, output_stride_3 = stride(output, 3u32));

gpu!(scope, output_shape_0 = shape(output, 0u32));
gpu!(scope, output_shape_1 = shape(output, 1u32));
gpu!(scope, output_shape_2 = shape(output, 2u32));
gpu!(scope, output_shape_3 = shape(output, 3u32));

let b = scope.create_local(Elem::UInt);
let c = scope.create_local(Elem::UInt);
let ih = scope.create_local(Elem::UInt);
let iw = scope.create_local(Elem::UInt);

gpu!(scope, b = id / output_stride_0);
gpu!(scope, b = b % output_shape_0);

gpu!(scope, c = id / output_stride_1);
gpu!(scope, c = c % output_shape_1);

gpu!(scope, ih = id / output_stride_2);
gpu!(scope, ih = ih % output_shape_2);

gpu!(scope, iw = id / output_stride_3);
gpu!(scope, iw = iw % output_shape_3);

let oh_start = Self::start_index(scope, ih, output_shape_2, grad_shape_2);
let oh_end = Self::end_index(scope, ih, output_shape_2, grad_shape_2);

let ow_start = Self::start_index(scope, iw, output_shape_3, grad_shape_3);
let ow_end = Self::end_index(scope, iw, output_shape_3, grad_shape_3);

let grad_acc = scope.create_local(output.item());
let contributed_h = scope.create_local(Elem::Bool);
let contributed_w = scope.create_local(Elem::Bool);
let contributed_tmp = scope.create_local(Elem::Bool);

let count = scope.create_local(Elem::UInt);
let count_tmp = scope.create_local(Elem::UInt);
let count_float = scope.create_local(output.item());
let the_grad = scope.create_local(output.item());
let avg = scope.create_local(output.item());

let index_base = scope.create_local(Elem::UInt);
let index_tmp = scope.create_local(Elem::UInt);
let index = scope.create_local(Elem::UInt);
gpu!(scope, index_base = b * grad_stride_0);
gpu!(scope, index_tmp = c * grad_stride_1);
gpu!(scope, index_base = index_tmp);

gpu!(
scope,
range(oh_start, oh_end).for_each(|oh, scope| {
let ih_start = Self::start_index(scope, oh, grad_shape_2, output_shape_2);
let ih_end = Self::end_index(scope, oh, grad_shape_2, output_shape_2);
gpu!(scope, contributed_h = ih >= ih_start);
gpu!(scope, contributed_tmp = ih < ih_end);
gpu!(scope, contributed_h = contributed_h && contributed_tmp);

gpu!(scope, if(contributed_h).then(|scope|{
gpu!(
scope,
range(ow_start, ow_end).for_each(|ow, scope| {
let iw_start = Self::start_index(scope, ow, grad_shape_3, output_shape_3);
let iw_end = Self::end_index(scope, ow, grad_shape_3, output_shape_3);

gpu!(scope, contributed_w = iw >= iw_start);
gpu!(scope, contributed_tmp = iw < iw_end);
gpu!(scope, contributed_w = contributed_w && contributed_tmp);


gpu!(scope, if(contributed_w).then(|scope|{
gpu!(scope, count = ih_end - ih_start);
gpu!(scope, count_tmp = iw_end - iw_start);
gpu!(scope, count *= count_tmp);
gpu!(scope, count_float = cast(count));

gpu!(scope, index = index_base);
gpu!(scope, index_tmp = oh * grad_stride_2);
gpu!(scope, index = index_tmp);
gpu!(scope, index_tmp = ow * grad_stride_3);
gpu!(scope, index = index_tmp);

gpu!(scope, the_grad = grad[index]);
gpu!(scope, avg = the_grad / count_float);
gpu!(scope, grad_acc = avg);
}));
})
);
}));
})
);

gpu!(scope, output[id] = grad_acc);
}

fn start_index(
scope: &mut Scope,
output_size_index: Variable,
output_size: Variable,
input_size: Variable,
) -> Variable {
let numerator_float = scope.create_local(Elem::Float);
let div = scope.create_local(Elem::Float);
let index = scope.create_local(Elem::UInt);

gpu!(scope, index = output_size_index * input_size);
gpu!(scope, numerator_float = cast(index));
gpu!(scope, div = cast(output_size));
gpu!(scope, div = numerator_float / div);
gpu!(scope, div = floor(div));
gpu!(scope, index = cast(div));
index
}

fn end_index(
scope: &mut Scope,
output_size_index: Variable,
output_size: Variable,
input_size: Variable,
) -> Variable {
let numerator_float = scope.create_local(Elem::Float);
let div = scope.create_local(Elem::Float);
let index = scope.create_local(Elem::UInt);
let min = scope.create_local(Elem::Bool);
let end_index = scope.create_local(Elem::UInt);

gpu!(scope, index = output_size_index 1u32);
gpu!(scope, index *= input_size);
gpu!(scope, numerator_float = cast(index));
gpu!(scope, div = cast(output_size));
gpu!(scope, div = numerator_float / div);
gpu!(scope, div = ceil(div));
gpu!(scope, index = cast(div));

gpu!(scope, min = input_size < index);
gpu!(scope, if(min).then(|scope|{
gpu!(scope, end_index = input_size);
}).else(|scope|{
gpu!(scope, end_index = index);
}));
end_index
}
}

impl<R: Runtime, E: JitElement> DynamicKernelSource for AdaptiveAvgPool2dBackwardEagerKernel<R, E> {
fn source(&self) -> SourceTemplate {
let mut scope = Scope::root();
let item = E::gpu_elem().into();

let grad = Variable::GlobalInputArray(0, item);
let output = Variable::GlobalOutputArray(0, item);

scope.write_global_custom(output);

AdaptiveAvgPool2dBackwardComputeShader { grad, output }.expand(&mut scope);

let grad = InputInfo::Array {
item,
visibility: Visibility::Read,
};
let scalars = InputInfo::Scalar {
elem: Elem::UInt,
size: 6,
};
let output = OutputInfo::Array { item };

let info = CompilationInfo {
inputs: vec![grad, scalars],
outputs: vec![output],
scope,
};

let settings = CompilationSettings::default();
let shader = Compilation::new(info).compile(settings);
let shader = <R::Compiler as Compiler>::compile(shader);
SourceTemplate::new(shader.to_string())
}

fn id(&self) -> String {
format!("{:?}", core::any::TypeId::of::<Self>(),)
}
}

pub(crate) fn adaptive_avg_pool2d_backward<R: Runtime, E: JitElement>(
x: JitTensor<R, E, 4>,
Expand All @@ -27,45 254,24 @@ pub(crate) fn adaptive_avg_pool2d_backward<R: Runtime, E: JitElement>(
output_buffer,
);

let kernel = StaticKernel::<
KernelSettings<AdaptiveAvgPool2dBackward, E, i32, WORKGROUP_DEFAULT, WORKGROUP_DEFAULT, 1>,
>::new(elemwise_workgroup(
output.shape.num_elements(),
WORKGROUP_DEFAULT,
));
let kernel = AdaptiveAvgPool2dBackwardEagerKernel::new();

let info_handle = build_info(&x, &out_grad);

x.client.execute(
Box::new(kernel),
&[&out_grad.handle, &output.handle, &info_handle],
execute_dynamic::<R, AdaptiveAvgPool2dBackwardEagerKernel<R, E>, RuntimeInt<R>>(
&[EagerHandle::new(
&out_grad.handle,
&out_grad.strides,
&out_grad.shape.dims,
)],
&[EagerHandle::new(
&output.handle,
&output.strides,
&output.shape.dims,
)],
None,
kernel,
WorkgroupLaunch::Output { pos: 0 },
x.client,
);

output
}

fn build_info<R: Runtime, E: JitElement>(
x: &JitTensor<R, E, 4>,
output: &JitTensor<R, E, 4>,
) -> Handle<R::Server> {
let mut info: [u32; 16] = [0; 16];
info[0] = x.strides[0] as u32;
info[1] = x.strides[1] as u32;
info[2] = x.strides[2] as u32;
info[3] = x.strides[3] as u32;
info[4] = x.shape.dims[0] as u32;
info[5] = x.shape.dims[1] as u32;
info[6] = x.shape.dims[2] as u32;
info[7] = x.shape.dims[3] as u32;

info[8] = output.strides[0] as u32;
info[9] = output.strides[1] as u32;
info[10] = output.strides[2] as u32;
info[11] = output.strides[3] as u32;
info[12] = output.shape.dims[0] as u32;
info[13] = output.shape.dims[1] as u32;
info[14] = output.shape.dims[2] as u32;
info[15] = output.shape.dims[3] as u32;

output.client.create(bytemuck::cast_slice(&info))
}
2 changes: 1 addition & 1 deletion crates/burn-jit/src/kernel/pool/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 11,7 @@ pub(crate) use adaptive_pool2d_shader::*;
pub(crate) use pool2d_shader::*;

pub(crate) use adaptive_avg_pool2d::*;
pub use adaptive_avg_pool2d_backward::*;
pub(crate) use adaptive_avg_pool2d_backward::*;
pub(crate) use avg_pool2d::*;
pub(crate) use avg_pool2d_backward::*;
pub(super) use base::*;
Expand Down
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