779dcd2ecc
Makes individual testing and debugging easier. Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D135319
207 lines
14 KiB
MLIR
Executable File
207 lines
14 KiB
MLIR
Executable File
// RUN: mlir-opt %s --tensor-copy-insertion --sparse-tensor-rewrite --sparsification --cse | FileCheck %s
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#SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
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#trait_matmul = {
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indexing_maps = [
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affine_map<(d0, d1, d2) -> (d1, d0)>,
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affine_map<(d0, d1, d2) -> (d0, d2)>,
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affine_map<(d0, d1, d2) -> (d1, d2)>
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],
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iterator_types = ["reduction", "parallel", "parallel"]
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}
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#trait_scale = {
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indexing_maps = [
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affine_map<(d0, d1) -> (d0, d1)>,
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affine_map<(d0, d1) -> (d0, d1)>,
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affine_map<(d0, d1) -> (d0, d1)>
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],
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iterator_types = ["parallel", "parallel"]
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}
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// CHECK-LABEL: func.func @fold_yield_arg_zero() -> tensor<1024x1024xf64> {
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// CHECK: %[[VAL_0:.*]] = arith.constant dense<0.000000e+00> : tensor<1024x1024xf64>
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// CHECK: %[[VAL_1:.*]] = bufferization.alloc_tensor() copy(%[[VAL_0]]) {bufferization.escape = [false], memory_space = 0 : ui64} : tensor<1024x1024xf64>
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// CHECK: return %[[VAL_1]] : tensor<1024x1024xf64>
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// CHECK: }
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func.func @fold_yield_arg_zero() -> tensor<1024x1024xf64> {
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%cst = arith.constant 0.000000e+00 : f64
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%0 = tensor.empty() : tensor<1024x1024xf64>
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%1 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> ()>,
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affine_map<(d0, d1) -> (d0, d1)>],
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iterator_types = ["parallel", "parallel"]}
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ins(%cst : f64)
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outs(%0 : tensor<1024x1024xf64>) {
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^bb0(%a: f64, %x: f64):
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linalg.yield %a : f64
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} -> tensor<1024x1024xf64>
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return %1 : tensor<1024x1024xf64>
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}
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// CHECK-LABEL: func.func @fold_yield_direct_zero() -> tensor<32xf64> {
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// CHECK: %[[VAL_0:.*]] = arith.constant dense<0.000000e+00> : tensor<32xf64>
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// CHECK: %[[VAL_1:.*]] = bufferization.alloc_tensor() copy(%[[VAL_0]]) {bufferization.escape = [false], memory_space = 0 : ui64} : tensor<32xf64>
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// CHECK: return %[[VAL_1]] : tensor<32xf64>
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// CHECK: }
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func.func @fold_yield_direct_zero() -> tensor<32xf64> {
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%cst = arith.constant 0.000000e+00 : f64
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%0 = tensor.empty() : tensor<32xf64>
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%1 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>],
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iterator_types = ["parallel"]}
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outs(%0 : tensor<32xf64>) {
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^bb0(%x: f64):
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linalg.yield %cst : f64
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} -> tensor<32xf64>
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return %1 : tensor<32xf64>
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}
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// CHECK-LABEL: func.func @sampled_dd_unfused(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
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// CHECK: %[[VAL_7:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) {bufferization.escape = [false]} : tensor<8x8xf64>
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// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) {bufferization.escape = [false], memory_space = 0 : ui64} : tensor<8x8xf64>
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// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
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// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xf64>
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// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_8]] : memref<8x8xf64>
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_5]] {
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_19]], %[[VAL_5]] : index
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_22]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_21]] to %[[VAL_23]] step %[[VAL_5]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_24]]] : memref<?xindex>
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_20]], %[[VAL_25]]] : memref<8x8xf64>
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// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_24]]] : memref<?xf64>
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// CHECK: %[[VAL_28:.*]] = scf.for %[[VAL_29:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_30:.*]] = %[[VAL_26]]) -> (f64) {
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// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]], %[[VAL_29]]] : memref<8x8xf64>
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// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_29]], %[[VAL_25]]] : memref<8x8xf64>
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// CHECK: %[[VAL_33:.*]] = arith.mulf %[[VAL_31]], %[[VAL_32]] : f64
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// CHECK: %[[VAL_34:.*]] = arith.mulf %[[VAL_33]], %[[VAL_27]] : f64
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// CHECK: %[[VAL_35:.*]] = arith.addf %[[VAL_30]], %[[VAL_34]] : f64
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// CHECK: scf.yield %[[VAL_35]] : f64
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// CHECK: }
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// CHECK: memref.store %[[VAL_24:.*]], %[[VAL_16]]{{\[}}%[[VAL_20]], %[[VAL_25]]] : memref<8x8xf64>
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_37:.*]] = bufferization.to_tensor %[[VAL_16]] : memref<8x8xf64>
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// CHECK: return %[[VAL_37]] : tensor<8x8xf64>
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// CHECK: }
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func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
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%arga: tensor<8x8xf64>,
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%argb: tensor<8x8xf64>) -> tensor<8x8xf64> {
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// Perform dense-dense matrix matrix multiplication.
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%1 = arith.constant dense<0.0> : tensor<8x8xf64>
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%2 = linalg.generic #trait_matmul
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ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)
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outs(%1 : tensor<8x8xf64>) {
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^bb0(%a: f64, %b: f64, %x: f64):
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%p = arith.mulf %a, %b : f64
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%q = arith.addf %x, %p : f64
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linalg.yield %q : f64
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} -> tensor<8x8xf64>
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// Sample the result with elements-wise multiplication with sparse matrix.
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%3 = linalg.generic #trait_scale
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ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)
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outs(%1 : tensor<8x8xf64>) {
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^bb0(%t: f64, %s: f64, %x: f64):
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%r = arith.mulf %t, %s : f64
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linalg.yield %r : f64
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} -> tensor<8x8xf64>
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return %3 : tensor<8x8xf64>
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}
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// CHECK-LABEL: func.func @sparse_sampled_dd_unfused(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant false
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// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
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// CHECK-DAG: %[[VAL_8:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
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// CHECK: %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) {bufferization.escape = [false]} : tensor<8x8xf64>
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// CHECK: %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
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// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
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// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] {
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
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// CHECK: %[[VAL_26:.*]] = scf.for %[[VAL_27:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_28:.*]] = %[[VAL_25]]) -> (index) {
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// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64>
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// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_20]], %[[VAL_5]] : index
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// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_31]]] : memref<?xindex>
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// CHECK: %[[VAL_33:.*]] = scf.for %[[VAL_34:.*]] = %[[VAL_30]] to %[[VAL_32]] step %[[VAL_5]] iter_args(%[[VAL_35:.*]] = %[[VAL_28]]) -> (index) {
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// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref<?xindex>
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// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref<?xf64>
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// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]], %[[VAL_36]]] : memref<8x8xf64>
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// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_29]], %[[VAL_38]] : f64
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// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_34]]] : memref<?xf64>
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// CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_39]], %[[VAL_40]] : f64
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// CHECK: %[[VAL_42:.*]] = arith.addf %[[VAL_37]], %[[VAL_41]] : f64
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// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref<?xi1>
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// CHECK: %[[VAL_44:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_6]] : i1
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// CHECK: %[[VAL_45:.*]] = scf.if %[[VAL_44]] -> (index) {
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// CHECK: memref.store %[[VAL_7]], %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref<?xi1>
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// CHECK: memref.store %[[VAL_36]], %[[VAL_24]]{{\[}}%[[VAL_35]]] : memref<?xindex>
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// CHECK: %[[VAL_46:.*]] = arith.addi %[[VAL_35]], %[[VAL_5]] : index
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// CHECK: scf.yield %[[VAL_46]] : index
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// CHECK: } else {
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// CHECK: scf.yield %[[VAL_35]] : index
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// CHECK: }
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// CHECK: memref.store %[[VAL_42]], %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref<?xf64>
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// CHECK: scf.yield %[[VAL_47:.*]] : index
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// CHECK: }
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// CHECK: scf.yield %[[VAL_48:.*]] : index
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// CHECK: }
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// CHECK: sparse_tensor.compress %[[VAL_22]], %[[VAL_23]], %[[VAL_24]], %[[VAL_49:.*]] into %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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// CHECK: %[[VAL_50:.*]] = sparse_tensor.load %[[VAL_10]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: return %[[VAL_50]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
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%arga: tensor<8x8xf64>,
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%argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {
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// Perform dense-dense matrix matrix multiplication.
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%1 = arith.constant dense<0.0> : tensor<8x8xf64>
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%2 = linalg.generic #trait_matmul
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ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)
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outs(%1 : tensor<8x8xf64>) {
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^bb0(%a: f64, %b: f64, %x: f64):
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%p = arith.mulf %a, %b : f64
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%q = arith.addf %x, %p : f64
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linalg.yield %q : f64
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} -> tensor<8x8xf64>
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// Sample the result with elements-wise multiplication with sparse matrix.
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%3 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM>
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%4 = linalg.generic #trait_scale
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ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)
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outs(%3 : tensor<8x8xf64, #SM>) {
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^bb0(%t: f64, %s: f64, %x: f64):
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%r = arith.mulf %t, %s : f64
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linalg.yield %r : f64
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} -> tensor<8x8xf64, #SM>
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return %4 : tensor<8x8xf64, #SM>
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}
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