diff --git a/mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h b/mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h --- a/mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h +++ b/mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h @@ -230,7 +230,6 @@ Value v1); private: - bool isZero(unsigned e) const; bool maybeZero(unsigned e) const; bool isInvariant(unsigned e) const; Type inferType(unsigned e, Value src); diff --git a/mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp b/mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp --- a/mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp +++ b/mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp @@ -489,11 +489,6 @@ // ---+---+---+ ---+---+---+ // !x | 0 | y | !x | 0 |-y | // x | x |x+y| x | x |x-y| - // - // TODO: remove this zero "folding" in favor of external pass into linalg - // - if (isZero(tensorExps[e].children.e1)) - return buildLattices(tensorExps[e].children.e0, i); return takeDisj(kind, // take binary disjunction buildLattices(tensorExps[e].children.e0, i), buildLattices(tensorExps[e].children.e1, i)); @@ -516,17 +511,6 @@ return buildTensorExp(op, yield->getOperand(0)); } -/// Only returns true if we are certain this is a zero. -bool Merger::isZero(unsigned e) const { - if (tensorExps[e].kind == kInvariant) { - if (auto c = tensorExps[e].val.getDefiningOp()) - return c.getValue() == 0; - if (auto c = tensorExps[e].val.getDefiningOp()) - return c.getValue().isZero(); - } - return false; -} - /// Only returns false if we are certain this is a nonzero. bool Merger::maybeZero(unsigned e) const { if (tensorExps[e].kind == kInvariant) { diff --git a/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir new file mode 100644 --- /dev/null +++ b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir @@ -0,0 +1,157 @@ +// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py +// RUN: mlir-opt %s \ +// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ +// RUN: --sparsification | FileCheck %s + +#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> + +// CHECK-LABEL: func @matmul( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = constant 30 : index +// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_11:.*]] = memref.buffer_cast %[[VAL_1]] : memref<20x30xf32> +// CHECK: %[[VAL_12:.*]] = memref.buffer_cast %[[VAL_2]] : memref<10x30xf32> +// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32> +// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32> +// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref +// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref +// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] { +// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_19:.*]] = addi %[[VAL_16]], %[[VAL_4]] : index +// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref +// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] { +// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]]] : memref +// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref +// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] { +// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32> +// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]], %[[VAL_24]]] : memref<20x30xf32> +// CHECK: %[[VAL_27:.*]] = mulf %[[VAL_23]], %[[VAL_26]] : f32 +// CHECK: %[[VAL_28:.*]] = addf %[[VAL_25]], %[[VAL_27]] : f32 +// CHECK: memref.store %[[VAL_28]], %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32> +// CHECK: } +// CHECK: } +// CHECK: } +// CHECK: %[[VAL_29:.*]] = memref.tensor_load %[[VAL_13]] : memref<10x30xf32> +// CHECK: return %[[VAL_29]] : tensor<10x30xf32> +// CHECK: } +func @matmul(%a: tensor<10x20xf32, #DCSR>, + %b: tensor<20x30xf32>, + %c: tensor<10x30xf32>) -> tensor<10x30xf32> { + %0 = linalg.matmul + ins(%a, %b: tensor<10x20xf32, #DCSR>, tensor<20x30xf32>) + outs(%c: tensor<10x30xf32>) -> tensor<10x30xf32> + return %0 : tensor<10x30xf32> +} + +// CHECK-LABEL: func @conv2d( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> { +// CHECK-DAG: %[[VAL_3:.*]] = constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = constant 6 : index +// CHECK: %[[VAL_6:.*]] = memref.buffer_cast %[[VAL_0]] : memref<8x8xi32> +// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_12:.*]] = memref.buffer_cast %[[VAL_2]] : memref<6x6xi32> +// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32> +// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32> +// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref +// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref +// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] { +// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref +// CHECK: %[[VAL_19:.*]] = addi %[[VAL_16]], %[[VAL_4]] : index +// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref +// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] { +// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref +// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]]] : memref +// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] { +// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] { +// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32> +// CHECK: %[[VAL_27:.*]] = addi %[[VAL_25]], %[[VAL_17]] : index +// CHECK: %[[VAL_28:.*]] = addi %[[VAL_24]], %[[VAL_22]] : index +// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_27]], %[[VAL_28]]] : memref<8x8xi32> +// CHECK: %[[VAL_30:.*]] = muli %[[VAL_29]], %[[VAL_23]] : i32 +// CHECK: %[[VAL_31:.*]] = addi %[[VAL_26]], %[[VAL_30]] : i32 +// CHECK: memref.store %[[VAL_31]], %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32> +// CHECK: } +// CHECK: } +// CHECK: } +// CHECK: } +// CHECK: %[[VAL_32:.*]] = memref.tensor_load %[[VAL_13]] : memref<6x6xi32> +// CHECK: return %[[VAL_32]] : tensor<6x6xi32> +// CHECK: } +func @conv2d(%input: tensor<8x8xi32>, + %filter: tensor<3x3xi32, #DCSR>, + %output: tensor<6x6xi32>) -> tensor<6x6xi32> { + %0 = linalg.conv_2d + ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>) + outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32> + return %0 : tensor<6x6xi32> +} + +// CHECK-LABEL: func @quantized_matmul( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> { +// CHECK-DAG: %[[VAL_3:.*]] = constant 2 : i64 +// CHECK-DAG: %[[VAL_4:.*]] = constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = constant 5 : index +// CHECK: %[[VAL_7:.*]] = memref.buffer_cast %[[VAL_0]] : memref<5x3xi8> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_13:.*]] = memref.buffer_cast %[[VAL_2]] : memref<5x6xi64> +// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64> +// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64> +// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref +// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref +// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] { +// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref +// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref +// CHECK: %[[VAL_20:.*]] = addi %[[VAL_17]], %[[VAL_5]] : index +// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref +// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_5]] { +// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref +// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_22]]] : memref +// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_5]] { +// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64> +// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]], %[[VAL_18]]] : memref<5x3xi8> +// CHECK: %[[VAL_28:.*]] = sexti %[[VAL_27]] : i8 to i64 +// CHECK: %[[VAL_29:.*]] = subi %[[VAL_28]], %[[VAL_3]] : i64 +// CHECK: %[[VAL_30:.*]] = sexti %[[VAL_24]] : i8 to i64 +// CHECK: %[[VAL_31:.*]] = muli %[[VAL_29]], %[[VAL_30]] : i64 +// CHECK: %[[VAL_32:.*]] = addi %[[VAL_26]], %[[VAL_31]] : i64 +// CHECK: memref.store %[[VAL_32]], %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64> +// CHECK: } +// CHECK: } +// CHECK: } +// CHECK: %[[VAL_33:.*]] = memref.tensor_load %[[VAL_14]] : memref<5x6xi64> +// CHECK: return %[[VAL_33]] : tensor<5x6xi64> +// CHECK: } +func @quantized_matmul(%input1: tensor<5x3xi8>, + %input2: tensor<3x6xi8, #DCSR>, + %output: tensor<5x6xi64>) -> tensor<5x6xi64> { + %c0 = constant 0 : i32 + %c2 = constant 2 : i32 + %0 = linalg.quantized_matmul + ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32) + outs(%output : tensor<5x6xi64>) -> tensor<5x6xi64> + return %0: tensor<5x6xi64> +} diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir --- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir +++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir @@ -1,5 +1,5 @@ // RUN: mlir-opt %s \ -// RUN: --linalg-generalize-named-ops \ +// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ // RUN: --sparsification --sparse-tensor-conversion \ // RUN: --convert-vector-to-scf --convert-scf-to-std \ // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ @@ -14,7 +14,7 @@ // Do the same run, but now with SIMDization as well. This should not change the outcome. // // RUN: mlir-opt %s \ -// RUN: --linalg-generalize-named-ops \ +// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ // RUN: --sparsification="vectorization-strategy=2 vl=2" --sparse-tensor-conversion \ // RUN: --convert-vector-to-scf --convert-scf-to-std \ // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_quantized_matmul.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_quantized_matmul.mlir --- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_quantized_matmul.mlir +++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_quantized_matmul.mlir @@ -1,5 +1,5 @@ // RUN: mlir-opt %s \ -// RUN: --linalg-generalize-named-ops \ +// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ // RUN: --sparsification --sparse-tensor-conversion \ // RUN: --convert-vector-to-scf --convert-scf-to-std \ // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \