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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SM = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
#trait_sampled_dense_dense = {
indexing_maps = [
affine_map<(i,j,k) -> (i,j)>, // S
affine_map<(i,j,k) -> (i,k)>, // A
affine_map<(i,j,k) -> (k,j)>, // B
affine_map<(i,j,k) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)"
}
#trait_matmul = {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d1, d0)>,
affine_map<(d0, d1, d2) -> (d0, d2)>,
affine_map<(d0, d1, d2) -> (d1, d2)>
],
iterator_types = ["reduction", "parallel", "parallel"]
}
#trait_scale = {
indexing_maps = [
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>
],
iterator_types = ["parallel", "parallel"]
}
//
// Integration test for sampled dense dense matmul fusion.
//
module {
//
// A kernel that computes a direct sampled matrix matrix multiplication
// (with dense result).
//
func.func @sampled_dd(%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,
%argb: tensor<8x8xf64>) -> tensor<8x8xf64> {
%1 = arith.constant dense<0.0> : tensor<8x8xf64>
%2 = linalg.generic #trait_sampled_dense_dense
ins(%args, %arga, %argb: tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>)
outs(%1: tensor<8x8xf64>) {
^bb(%s: f64, %a: f64, %b: f64, %x: f64):
%p = arith.mulf %a, %b : f64
%q = arith.mulf %s, %p : f64
%r = arith.addf %x, %q : f64
linalg.yield %r : f64
} -> tensor<8x8xf64>
return %2 : tensor<8x8xf64>
}
//
// A kernel that computes an unfused sampled matrix matrix multiplication
// (with dense result).
//
func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,
%argb: tensor<8x8xf64>) -> tensor<8x8xf64> {
// Perform dense-dense matrix matrix multiplication.
%1 = arith.constant dense<0.0> : tensor<8x8xf64>
%2 = linalg.generic #trait_matmul
ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)
outs(%1 : tensor<8x8xf64>) {
^bb0(%a: f64, %b: f64, %x: f64):
%p = arith.mulf %a, %b : f64
%q = arith.addf %x, %p : f64
linalg.yield %q : f64
} -> tensor<8x8xf64>
// Sample the result with elements-wise multiplication with sparse matrix.
%3 = linalg.generic #trait_scale
ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)
outs(%1 : tensor<8x8xf64>) {
^bb0(%t: f64, %s: f64, %x: f64):
%r = arith.mulf %t, %s : f64
linalg.yield %r : f64
} -> tensor<8x8xf64>
return %3 : tensor<8x8xf64>
}
//
// A kernel that computes a direct sampled matrix matrix multiplication
// (with sparse result).
//
func.func @sparse_sampled_dd(%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,
%argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {
%1 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM>
%2 = linalg.generic #trait_sampled_dense_dense
ins(%args, %arga, %argb: tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>)
outs(%1: tensor<8x8xf64, #SM>) {
^bb(%s: f64, %a: f64, %b: f64, %x: f64):
%p = arith.mulf %a, %b : f64
%q = arith.mulf %s, %p : f64
%r = arith.addf %x, %q : f64
linalg.yield %r : f64
} -> tensor<8x8xf64, #SM>
return %2 : tensor<8x8xf64, #SM>
}
//
// A kernel that computes an unfused sampled matrix matrix multiplication
// (with sparse result).
//
func.func @sparse_sampled_dd_unfused(
%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,
%argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {
// Perform dense-dense matrix matrix multiplication.
%1 = arith.constant dense<0.0> : tensor<8x8xf64>
%2 = linalg.generic #trait_matmul
ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)
outs(%1 : tensor<8x8xf64>) {
^bb0(%a: f64, %b: f64, %x: f64):
%p = arith.mulf %a, %b : f64
%q = arith.addf %x, %p : f64
linalg.yield %q : f64
} -> tensor<8x8xf64>
// Sample the result with elements-wise multiplication with sparse matrix.
%3 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM>
%4 = linalg.generic #trait_scale
ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)
outs(%3 : tensor<8x8xf64, #SM>) {
^bb0(%t: f64, %s: f64, %x: f64):
%r = arith.mulf %t, %s : f64
linalg.yield %r : f64
} -> tensor<8x8xf64, #SM>
return %4 : tensor<8x8xf64, #SM>
}
//
// Main driver.
//
func.func @entry() {
%d0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
%t = arith.constant sparse<[[0, 0], [7,7]], [1.0, 2.0]>
: tensor<8x8xf64>
%s = sparse_tensor.convert %t
: tensor<8x8xf64> to tensor<8x8xf64, #SM>
%a = arith.constant dense<3.0> : tensor<8x8xf64>
%b = arith.constant dense<4.0> : tensor<8x8xf64>
// Call the kernels.
%0 = call @sampled_dd(%s, %a, %b)
: (tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64>
%1 = call @sampled_dd_unfused(%s, %a, %b)
: (tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64>
%2 = call @sparse_sampled_dd(%s, %a, %b)
: (tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64, #SM>
%3 = call @sparse_sampled_dd_unfused(%s, %a, %b)
: (tensor<8x8xf64, #SM>,
tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64, #SM>
// Verify the outputs.
//
// CHECK: ( ( 96, 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 ),
// CHECK-SAME: ( 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, 192 ) )
//
// CHECK: ( ( 96, 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 ),
// CHECK-SAME: ( 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, 192 ) )
//
// CHECK-NEXT: ( 96, 192, 0, 0 )
//
// CHECK-NEXT: ( 96, 192, 0, 0 )
//
%m2 = sparse_tensor.values %2 : tensor<8x8xf64, #SM> to memref<?xf64>
%m3 = sparse_tensor.values %3 : tensor<8x8xf64, #SM> to memref<?xf64>
%v0 = vector.transfer_read %0[%c0, %c0], %d0
: tensor<8x8xf64>, vector<8x8xf64>
%v1 = vector.transfer_read %1[%c0, %c0], %d0
: tensor<8x8xf64>, vector<8x8xf64>
%v2 = vector.transfer_read %m2[%c0], %d0 : memref<?xf64>, vector<4xf64>
%v3 = vector.transfer_read %m3[%c0], %d0 : memref<?xf64>, vector<4xf64>
vector.print %v0 : vector<8x8xf64>
vector.print %v1 : vector<8x8xf64>
vector.print %v2 : vector<4xf64>
vector.print %v3 : vector<4xf64>
// Release the resources.
bufferization.dealloc_tensor %s : tensor<8x8xf64, #SM>
bufferization.dealloc_tensor %0 : tensor<8x8xf64>
bufferization.dealloc_tensor %1 : tensor<8x8xf64>
bufferization.dealloc_tensor %2 : tensor<8x8xf64, #SM>
bufferization.dealloc_tensor %3 : tensor<8x8xf64, #SM>
return
}
}
|
