7294be2b8e
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
157 lines
7.0 KiB
MLIR
157 lines
7.0 KiB
MLIR
// RUN: mlir-opt %s \
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// RUN: -async-parallel-for \
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// RUN: -async-to-async-runtime \
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// RUN: -async-runtime-ref-counting \
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// RUN: -async-runtime-ref-counting-opt \
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// RUN: -convert-async-to-llvm \
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// RUN: -convert-linalg-to-loops \
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// RUN: -convert-scf-to-cf \
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// RUN: -arith-expand \
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// RUN: -memref-expand \
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// RUN: -convert-vector-to-llvm \
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// RUN: -convert-memref-to-llvm \
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// RUN: -convert-func-to-llvm \
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// RUN: -reconcile-unrealized-casts \
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// RUN: | mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void -O3 \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \
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// RUN: | FileCheck %s --dump-input=always
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// RUN: mlir-opt %s \
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// RUN: -async-parallel-for=async-dispatch=false \
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// RUN: -async-to-async-runtime \
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// RUN: -async-runtime-ref-counting \
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// RUN: -async-runtime-ref-counting-opt \
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// RUN: -convert-async-to-llvm \
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// RUN: -convert-linalg-to-loops \
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// RUN: -convert-scf-to-cf \
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// RUN: -arith-expand \
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// RUN: -memref-expand \
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// RUN: -convert-vector-to-llvm \
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// RUN: -convert-memref-to-llvm \
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// RUN: -convert-func-to-llvm \
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// RUN: -reconcile-unrealized-casts \
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// RUN: | mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void -O3 \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \
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// RUN: | FileCheck %s --dump-input=always
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// RUN: mlir-opt %s \
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// RUN: -convert-linalg-to-loops \
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// RUN: -convert-scf-to-cf \
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// RUN: -convert-vector-to-llvm \
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// RUN: -convert-memref-to-llvm \
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// RUN: -convert-func-to-llvm \
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// RUN: -reconcile-unrealized-casts \
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// RUN: | mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void -O3 \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext\
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_async_runtime%shlibext \
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// RUN: | FileCheck %s --dump-input=always
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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func @scf_parallel(%lhs: memref<?x?xf32>,
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%rhs: memref<?x?xf32>,
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%sum: memref<?x?xf32>) {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%d0 = memref.dim %lhs, %c0 : memref<?x?xf32>
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%d1 = memref.dim %lhs, %c1 : memref<?x?xf32>
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scf.parallel (%i, %j) = (%c0, %c0) to (%d0, %d1) step (%c1, %c1) {
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%lv = memref.load %lhs[%i, %j] : memref<?x?xf32>
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%rv = memref.load %lhs[%i, %j] : memref<?x?xf32>
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%r = arith.addf %lv, %rv : f32
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memref.store %r, %sum[%i, %j] : memref<?x?xf32>
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}
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return
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}
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func @entry() {
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%f1 = arith.constant 1.0 : f32
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%f4 = arith.constant 4.0 : f32
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%cN = arith.constant 50 : index
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//
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// Sanity check for the function under test.
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//
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%LHS10 = memref.alloc() {alignment = 64} : memref<1x10xf32>
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%RHS10 = memref.alloc() {alignment = 64} : memref<1x10xf32>
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%DST10 = memref.alloc() {alignment = 64} : memref<1x10xf32>
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linalg.fill ins(%f1 : f32) outs(%LHS10 : memref<1x10xf32>)
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linalg.fill ins(%f1 : f32) outs(%RHS10 : memref<1x10xf32>)
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%LHS = memref.cast %LHS10 : memref<1x10xf32> to memref<?x?xf32>
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%RHS = memref.cast %RHS10 : memref<1x10xf32> to memref<?x?xf32>
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%DST = memref.cast %DST10 : memref<1x10xf32> to memref<?x?xf32>
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call @scf_parallel(%LHS, %RHS, %DST)
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: (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>) -> ()
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// CHECK: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
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%U = memref.cast %DST10 : memref<1x10xf32> to memref<*xf32>
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call @print_memref_f32(%U): (memref<*xf32>) -> ()
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memref.dealloc %LHS10: memref<1x10xf32>
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memref.dealloc %RHS10: memref<1x10xf32>
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memref.dealloc %DST10: memref<1x10xf32>
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//
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// Allocate data for microbenchmarks.
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//
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%LHS1024 = memref.alloc() {alignment = 64} : memref<1024x1024xf32>
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%RHS1024 = memref.alloc() {alignment = 64} : memref<1024x1024xf32>
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%DST1024 = memref.alloc() {alignment = 64} : memref<1024x1024xf32>
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%LHS0 = memref.cast %LHS1024 : memref<1024x1024xf32> to memref<?x?xf32>
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%RHS0 = memref.cast %RHS1024 : memref<1024x1024xf32> to memref<?x?xf32>
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%DST0 = memref.cast %DST1024 : memref<1024x1024xf32> to memref<?x?xf32>
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//
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// Warm up.
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//
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call @scf_parallel(%LHS0, %RHS0, %DST0)
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: (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>) -> ()
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//
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// Measure execution time.
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//
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%t0 = call @rtclock() : () -> f64
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scf.for %i = %c0 to %cN step %c1 {
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call @scf_parallel(%LHS0, %RHS0, %DST0)
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: (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>) -> ()
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}
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%t1 = call @rtclock() : () -> f64
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%t1024 = arith.subf %t1, %t0 : f64
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// Print timings.
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vector.print %t1024 : f64
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// Free.
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memref.dealloc %LHS1024: memref<1024x1024xf32>
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memref.dealloc %RHS1024: memref<1024x1024xf32>
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memref.dealloc %DST1024: memref<1024x1024xf32>
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return
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}
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func private @rtclock() -> f64
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func private @print_memref_f32(memref<*xf32>)
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attributes { llvm.emit_c_interface }
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