//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#CCCC = #sparse_tensor.encoding<{
map = (d0, d1, d2, d3) -> (d0 : compressed, d1 : compressed, d2 : compressed, d3 : compressed)
}>
#CDCD = #sparse_tensor.encoding<{
map = (d0, d1, d2, d3) -> (d0 : compressed, d1 : dense, d2 : compressed, d3 : dense)
}>
#DCCD = #sparse_tensor.encoding<{
map = (d0, d1, d2, d3) -> (d0 : dense, d1 : compressed, d2 : compressed, d3 : dense)
}>
// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f
func.func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> tensor<?x?x?x?xf32> {
%buf = tensor.empty(%s1, %s2, %s3, %s4) : tensor<?x?x?x?xf32>
%ret = linalg.fill ins(%f : f32) outs(%buf : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %ret : tensor<?x?x?x?xf32>
}
func.func @conv_2d_nhwc_hwcf(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
outs (%arg2: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %ret : tensor<?x?x?x?xf32>
}
func.func @conv_2d_nhwc_hwcf_CCCC(%arg0: tensor<?x?x?x?xf32, #CCCC>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32, #CCCC> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = tensor.empty(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CCCC>
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>)
outs (%s: tensor<?x?x?x?xf32, #CCCC>) -> tensor<?x?x?x?xf32, #CCCC>
return %ret : tensor<?x?x?x?xf32, #CCCC>
}
func.func @conv_2d_nhwc_hwcf_CDCD(%arg0: tensor<?x?x?x?xf32, #CDCD>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32, #CDCD> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = tensor.empty(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #CDCD>
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32>)
outs (%s: tensor<?x?x?x?xf32, #CDCD>) -> tensor<?x?x?x?xf32, #CDCD>
return %ret : tensor<?x?x?x?xf32, #CDCD>
}
func.func @conv_2d_nhwc_hwcf_DCCD(%arg0: tensor<?x?x?x?xf32, #DCCD>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32, #DCCD> {
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%s = tensor.empty(%c3, %c6, %c6, %c1) : tensor<?x?x?x?xf32, #DCCD>
%ret = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%arg0, %arg1: tensor<?x?x?x?xf32, #DCCD>, tensor<?x?x?x?xf32>)
outs (%s: tensor<?x?x?x?xf32, #DCCD>) -> tensor<?x?x?x?xf32, #DCCD>
return %ret : tensor<?x?x?x?xf32, #DCCD>
}
func.func @main() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c6 = arith.constant 6 : index
%c8 = arith.constant 8 : index
%f10 = arith.constant 10.00000e+00 : f32
%val = arith.constant 2.00000e+00 : f32
%zero = arith.constant 0.00000e+00 : f32
%filter2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %val) :(index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_tmp = call @alloc_4d_filled_f32(%c3, %c8, %c8, %c3, %val) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_nhwc = tensor.insert %f10 into %in2D_tmp[%c0, %c0, %c3, %c0] : tensor<?x?x?x?xf32>
%out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (tensor<?x?x?x?xf32>)
%in2D_nhwc_CCCC = sparse_tensor.convert %in2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CCCC>
%in2D_nhwc_CDCD = sparse_tensor.convert %in2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #CDCD>
%in2D_nhwc_DCCD = sparse_tensor.convert %in2D_nhwc
: tensor<?x?x?x?xf32> to tensor<?x?x?x?xf32, #DCCD>
%dense_ret = call @conv_2d_nhwc_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32>)
%CCCC_ret = call @conv_2d_nhwc_hwcf_CCCC(%in2D_nhwc_CCCC, %filter2D_nhwc) : (tensor<?x?x?x?xf32, #CCCC>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32, #CCCC>)
%CDCD_ret = call @conv_2d_nhwc_hwcf_CDCD(%in2D_nhwc_CDCD, %filter2D_nhwc) : (tensor<?x?x?x?xf32, #CDCD>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32, #CDCD>)
%DCCD_ret = call @conv_2d_nhwc_hwcf_DCCD(%in2D_nhwc_DCCD, %filter2D_nhwc) : (tensor<?x?x?x?xf32, #DCCD>, tensor<?x?x?x?xf32>) -> (tensor<?x?x?x?xf32, #DCCD>)
// CHECK: ( ( ( ( 108 ), ( 124 ), ( 124 ), ( 124 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ),
// CHECK-SAME: ( ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ),
// CHECK-SAME: ( ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ), ( 108 ) ) ) )
%dense_v = vector.transfer_read %dense_ret[%c0, %c0, %c0, %c0], %zero
: tensor<?x?x?x?xf32>, vector<3x6x6x1xf32>
vector.print %dense_v : vector<3x6x6x1xf32>
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 108
// CHECK-NEXT: dim = ( 3, 6, 6, 1 )
// CHECK-NEXT: lvl = ( 3, 6, 6, 1 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 2 )
// CHECK-NEXT: pos[1] : ( 0, 6, 12, 18 )
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )
// CHECK-NEXT: pos[2] : ( 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108 )
// CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0,
// CHECK-SAME: 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1,
// CHECK-SAME: 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2,
// CHECK-SAME: 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3,
// CHECK-SAME: 4, 5, 0, 1, 2, 3, 4, 5 )
// CHECK-NEXT: pos[3] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
// CHECK-SAME: 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
// CHECK-SAME: 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
// CHECK-SAME: 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
// CHECK-SAME: 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
// CHECK-SAME: 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108 )
// CHECK-NEXT: crd[3] : ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
// CHECK-SAME: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
// CHECK-SAME: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
// CHECK-SAME: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
// CHECK-SAME: 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: values : ( 108, 124, 124, 124, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108 )
// CHECK-NEXT: ----
//
sparse_tensor.print %CCCC_ret : tensor<?x?x?x?xf32, #CCCC>
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 108
// CHECK-NEXT: dim = ( 3, 6, 6, 1 )
// CHECK-NEXT: lvl = ( 3, 6, 6, 1 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 2 )
// CHECK-NEXT: pos[2] : ( 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108 )
// CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0,
// CHECK-SAME: 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1,
// CHECK-SAME: 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2,
// CHECK-SAME: 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3,
// CHECK-SAME: 4, 5, 0, 1, 2, 3, 4, 5 )
// CHECK-NEXT: values : ( 108, 124, 124, 124, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108 )
// CHECK-NEXT: ----
//
sparse_tensor.print %CDCD_ret : tensor<?x?x?x?xf32, #CDCD>
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 108
// CHECK-NEXT: dim = ( 3, 6, 6, 1 )
// CHECK-NEXT: lvl = ( 3, 6, 6, 1 )
// CHECK-NEXT: pos[1] : ( 0, 6, 12, 18 )
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )
// CHECK-NEXT: pos[2] : ( 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108 )
// CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0,
// CHECK-SAME: 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1,
// CHECK-SAME: 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2,
// CHECK-SAME: 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3,
// CHECK-SAME: 4, 5, 0, 1, 2, 3, 4, 5 )
// CHECK-NEXT: values : ( 108, 124, 124, 124, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
// CHECK-SAME: 108, 108, 108 )
// CHECK-NEXT: ----
//
sparse_tensor.print %DCCD_ret : tensor<?x?x?x?xf32, #DCCD>
// Free the resources
bufferization.dealloc_tensor %in2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %filter2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %out2D_nhwc : tensor<?x?x?x?xf32>
bufferization.dealloc_tensor %in2D_nhwc_CDCD : tensor<?x?x?x?xf32, #CDCD>
bufferization.dealloc_tensor %in2D_nhwc_CCCC : tensor<?x?x?x?xf32, #CCCC>
bufferization.dealloc_tensor %in2D_nhwc_DCCD : tensor<?x?x?x?xf32, #DCCD>
bufferization.dealloc_tensor %CCCC_ret : tensor<?x?x?x?xf32, #CCCC>
bufferization.dealloc_tensor %CDCD_ret : tensor<?x?x?x?xf32, #CDCD>
bufferization.dealloc_tensor %DCCD_ret : tensor<?x?x?x?xf32, #DCCD>
return
}
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