86f91e45a2
This change cleans up the conversion pass re the "dim"-vs-"lvl" and "sizes"-vs-"shape" distinctions of the runtime. A quick synopsis includes:
* Adds new `SparseTensorStorageBase::getDimSize` method, with `sparseDimSize` wrapper in SparseTensorRuntime.h, and `genDimSizeCall` generator in SparseTensorConversion.cpp
* Changes `genLvlSizeCall` to perform no logic, just generate the function call.
* Adds `createOrFold{Dim,Lvl}Call` functions to handle the logic of replacing `gen{Dim,Lvl}SizeCall` with constants whenever possible. The `createOrFoldDimCall` function replaces the old `sizeFromPtrAtDim`.
* Adds `{get,fill}DimSizes` functions for iterating `createOrFoldDimCall` across the whole type. These functions replace the old `sizesFromPtr`.
* Adds `{get,fill}DimShape` functions for lowering a `ShapedType` into constants. These functions replace the old `sizesFromType`.
* Changes the `DimOp` rewrite to do the right thing.
* Changes the `ExpandOp` rewrite to compute the proper expansion size.
Depends On D138365
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D139165
93 lines
5.8 KiB
MLIR
93 lines
5.8 KiB
MLIR
// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR
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//
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// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion --canonicalize | \
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// RUN: FileCheck %s --check-prefix=CHECK-MIR
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#X = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "dense", "dense" ],
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dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
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}>
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#trait = {
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indexing_maps = [
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affine_map<(i,j,k) -> (k,i,j)>, // A (in)
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affine_map<(i,j,k) -> ()> // X (out)
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],
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iterator_types = ["reduction", "reduction", "reduction"]
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}
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// CHECK-HIR-LABEL: func @sparse_dynamic_dims(
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// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
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// CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
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// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
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// CHECK-HIR-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
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// CHECK-HIR: %[[VAL_11:.*]] = tensor.extract %[[VAL_1]][] : tensor<f32>
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// CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
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// CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
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// CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index
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// CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index
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// CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
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// CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index
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// CHECK-HIR: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index
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// CHECK-HIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
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// CHECK-HIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
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// CHECK-HIR: scf.yield %[[VAL_26]] : f32
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// CHECK-HIR: }
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// CHECK-HIR: scf.yield %[[VAL_20]] : f32
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// CHECK-HIR: }
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// CHECK-HIR: scf.yield %[[VAL_15]] : f32
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// CHECK-HIR: }
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// CHECK-HIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
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// CHECK-HIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
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// CHECK-HIR: return %[[VAL_30]] : tensor<f32>
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// CHECK-HIR: }
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//
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// CHECK-MIR-LABEL: func @sparse_dynamic_dims(
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// CHECK-MIR-SAME: %[[ARGA:.*]]: !llvm.ptr<i8>,
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// CHECK-MIR-SAME: %[[ARGX:.*]]: tensor<f32>) -> tensor<f32> {
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// CHECK-MIR-DAG: %[[I0:.*]] = arith.constant 0 : index
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// CHECK-MIR-DAG: %[[I1:.*]] = arith.constant 1 : index
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// CHECK-MIR-DAG: %[[I2:.*]] = arith.constant 2 : index
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// CHECK-MIR-DAG: %[[DimSize0:.*]] = call @sparseDimSize(%[[ARGA]], %[[I0]])
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// CHECK-MIR-DAG: %[[DimSize1:.*]] = call @sparseDimSize(%[[ARGA]], %[[I1]])
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// CHECK-MIR-DAG: %[[DimSize2:.*]] = call @sparseDimSize(%[[ARGA]], %[[I2]])
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// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF32(%[[ARGA]]) : (!llvm.ptr<i8>) -> memref<?xf32>
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// CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[ARGX]] : memref<f32>
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// CHECK-MIR: %[[VAL_11:.*]] = tensor.extract %[[ARGX]][] : tensor<f32>
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// CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[D2:.*]] = %[[I0]] to %[[DimSize2]] step %[[I1]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
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// CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[D0:.*]] = %[[I0]] to %[[DimSize0]] step %[[I1]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
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// CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[DimSize0]], %[[D2]] : index
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// CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[D0]] : index
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// CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[D1:.*]] = %[[I0]] to %[[DimSize1]] step %[[I1]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
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// CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[DimSize1]], %[[VAL_19]] : index
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// CHECK-MIR: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[D1]] : index
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// CHECK-MIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
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// CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
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// CHECK-MIR: scf.yield %[[VAL_26]] : f32
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// CHECK-MIR: }
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// CHECK-MIR: scf.yield %[[VAL_20]] : f32
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// CHECK-MIR: }
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// CHECK-MIR: scf.yield %[[VAL_15]] : f32
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// CHECK-MIR: }
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// CHECK-MIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
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// CHECK-MIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
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// CHECK-MIR: return %[[VAL_30]] : tensor<f32>
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// CHECK-MIR: }
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func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
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%argx: tensor<f32>) -> tensor<f32> {
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%0 = linalg.generic #trait
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ins(%arga: tensor<?x?x?xf32, #X>)
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outs(%argx: tensor<f32>) {
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^bb(%a : f32, %x: f32):
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%0 = arith.addf %x, %a : f32
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linalg.yield %0 : f32
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} -> tensor<f32>
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return %0 : tensor<f32>
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}
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