diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc --- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc +++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc @@ -124,3 +124,114 @@ O(n, oh, ow, c) = std_addf(std_mulf( I(n, oh * strides[0] + kh, ow * strides[1] + kw, c), K(kh, kw, c))); } + +ods_def: +def conv_1d_input_nwc_filter_wcf(I: f32(N, W, C), K: f32(KW, C, F)) -> (O: f32(N, W, F)) + attr(strides: 1xi64, dilations: 1xi64) +""" A 1-D convolution given NWC layout input and WCF layout filter. + +Computes a 1-D convolution given 3-D input and filter. The data layout +of input is NWC and the data layout of filter is WCF. + +The indexing maps for these three tensors contain 5 dimensions, following the +order of (`N`, `W`, `F`, `KW`, `C`). +""" +{ + O(n, w, f) = std_addf( + std_mulf(I(n, w * strides[0] + kw * dilations[0], c), K(kw, c, f))); +} + +ods_def: +def conv_1d_input_ncw_filter_wcf(I: f32(N, C, W), K: f32(KW, C, F)) -> (O: f32(N, F, W)) + attr(strides: 1xi64, dilations: 1xi64) +""" A 1-D convolution given NCW layout input and WCF layout filter. + +Computes a 1-D convolution given 3-D input and filter. The data layout +of input is NCW and the data layout of filter is WCF. + +The indexing maps for these three tensors contain 5 dimensions, following the +order of (`N`, `F`, `W`, `KW`, `C`). +""" +{ + O(n, f, w) = std_addf( + std_mulf(I(n, c, w * strides[0] + kw * dilations[0]), K(kw, c, f))); +} + +ods_def: +def conv_2d_input_nhwc_filter_hwcf(I: f32(N, H, W, C), K: f32(KH, KW, C, F)) -> (O: f32(N, H, W, F)) + attr(strides: 2xi64, dilations: 2xi64) +""" A 2-D convolution given NHWC layout input and HWCF layout filter. + +Computes a 2-D convolution given 4-D input and filter. The data layout +of input is NHWC and the data layout of filter is HWCF. + +The indexing maps for these three tensors contain 7 dimensions, following the +order of (`N`, `H`, `W`, `F`, `KH`, `KW`, `C`). +""" +{ + O(n, h, w, f) = + std_addf(std_mulf(I(n, h * strides[0] + kh * dilations[0], + w * strides[1] + kw * dilations[1], c), + K(kh, kw, c, f))); +} + +ods_def: +def conv_2d_input_nchw_filter_hwcf + (I: f32(N, C, H, W), K: f32(KH, KW, C, F)) + -> (O: f32(N, F, H, W)) + attr(strides: 2xi64, dilations: 2xi64) +""" A 2-D convolution given NCHW layout input and HWCF layout filter. + +Computes a 2-D convolution given 4-D input and filter. The data layout +of input is NCHW and the data layout of filter is HWCF. + +The indexing maps for these three tensors contain 7 dimensions, following the +order of (`N`, `F`, `H`, `W`, `KH`, `KW`, `C`). +""" +{ + O(n, f, h, w) = + std_addf(std_mulf(I(n, c, h * strides[0] + kh * dilations[0], + w * strides[1] + kw * dilations[1]), + K(kh, kw, c, f))); +} + +ods_def: +def conv_3d_input_ndhwc_filter_dhwcf + (I: f32(N, D, H, W, C), K: f32(KD, KH, KW, C, F)) + -> (O: f32(N, D, H, W, F)) + attr(strides: 3xi64, dilations: 3xi64) +""" A 3-D convolution given NDHWC layout input and DHWCF layout filter. + +Computes a 3-D convolution given 5-D input and filter. The data layout +of input is NDHWC and the data layout of filter is DHWCF. + +The indexing maps for these three tensors contain 9 dimensions, following the +order of (`N`, `D`, `H`, `W`, `F`, `KD`, `KH`, `KW`, `C`). +""" +{ + O(n, d, h, w, f) = + std_addf(std_mulf(I(n, d * strides[0] + kd * dilations[0], + h * strides[1] + kh * dilations[1], + w * strides[2] + kw * dilations[2], c), + K(kd, kh, kw, c, f))); +} + +ods_def: +def conv_3d_input_ncdhw_filter_dhwcf + (I: f32(N, C, D, H, W), K: f32(KD, KH, KW, C, F)) + -> (O: f32(N, F, D, H, W)) + attr(strides: 3xi64, dilations: 3xi64) +""" A 3-D convolution given NCDHW layout input and DHWCF layout filter. + +Computes a 3-D convolution given 5-D input and filter. The data layout +of input is NCDHW and the data layout of filter is DHWCF. + +The indexing maps for these three tensors contain 9 dimensions, following the +order of (`N`, `F`, `D`, `H`, `W`, `KD`, `KH`, `KW`, `C`). +""" +{ + O(n, f, d, h, w) = std_addf(std_mulf( + I(n, c, d * strides[0] + kd * dilations[0], + h * strides[1] + kh * dilations[1], w * strides[2] + kw * dilations[2]), + K(kd, kh, kw, c, f))); +} diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-ncw-filter-wcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-ncw-filter-wcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-ncw-filter-wcf-call.mlir @@ -0,0 +1,70 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,4" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,4" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f +func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_1d_input_ncw_filter_wcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter1D_ncw = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (memref) + %in1D_ncw = call @alloc_3d_filled_f32(%c1, %c1, %c8, %val) : (index, index, index, f32) -> (memref) + %out1D_ncw = call @alloc_3d_filled_f32(%c1, %c1, %c6, %zero) : (index, index, index, f32) -> (memref) + + store %f10, %in1D_ncw[%c0, %c0, %c3] : memref + call @conv_1d_input_ncw_filter_wcf(%in1D_ncw, %filter1D_ncw, %out1D_ncw) : (memref, memref, memref) -> () + %out1D_ncw_ = memref_cast %out1D_ncw : memref to memref<*xf32> + call @print_memref_f32(%out1D_ncw_): (memref<*xf32>) -> () + + dealloc %filter1D_ncw : memref + dealloc %in1D_ncw : memref + dealloc %out1D_ncw : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [12, 28, 28, 28, 12, 12] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-1d-input-nwc-filter-wcf-call.mlir @@ -0,0 +1,81 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,4" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,4" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f +func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_1d_input_nwc_filter_wcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter1D_nwc = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (memref) + %in1D_nwc = call @alloc_3d_filled_f32(%c3, %c8, %c1, %val) : (index, index, index, f32) -> (memref) + %out1D_nwc = call @alloc_3d_filled_f32(%c3, %c6, %c1, %zero) : (index, index, index, f32) -> (memref) + + store %f10, %in1D_nwc[%c0, %c3, %c0] : memref + call @conv_1d_input_nwc_filter_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (memref, memref, memref) -> () + %out1D_nwc_ = memref_cast %out1D_nwc : memref to memref<*xf32> + call @print_memref_f32(%out1D_nwc_): (memref<*xf32>) -> () + + dealloc %filter1D_nwc : memref + dealloc %in1D_nwc : memref + dealloc %out1D_nwc : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [12], +// CHECK-COUNT-3: [28], +// CHECK-NEXT: [12], +// CHECK-NEXT: [12] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-5: [12], +// CHECK-NEXT: [12] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-5: [12], +// CHECK-NEXT: [12] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nchw-filter-hwcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nchw-filter-hwcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nchw-filter-hwcf-call.mlir @@ -0,0 +1,83 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,0,4,4" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,1,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,0,4,4" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,1,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f +func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3, %s4) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_2d_input_nchw_filter_hwcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter2D_nchw = call @alloc_4d_filled_f32(%c3, %c3, %c1, %c1, %val) : (index, index, index, index, f32) -> (memref) + %in2D_nchw = call @alloc_4d_filled_f32(%c3, %c1, %c8, %c8, %val) : (index, index, index, index, f32) -> (memref) + %out2D_nchw = call @alloc_4d_filled_f32(%c3, %c1, %c6, %c6, %zero) : (index, index, index, index, f32) -> (memref) + + store %f10, %in2D_nchw[%c0, %c0, %c0, %c3] : memref + call @conv_2d_input_nchw_filter_hwcf(%in2D_nchw, %filter2D_nchw, %out2D_nchw) : (memref, memref, memref) -> () + %out2D_nchw_ = memref_cast %out2D_nchw : memref to memref<*xf32> + call @print_memref_f32(%out2D_nchw_): (memref<*xf32>) -> () + + dealloc %filter2D_nchw : memref + dealloc %in2D_nchw : memref + dealloc %out2D_nchw : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [36, 52, 52, 52, 36, 36], +// CHECK-COUNT-5: [36, 36, 36, 36, 36, 36] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [36, 36, 36, 36, 36, 36] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [36, 36, 36, 36, 36, 36] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-2d-input-nhwc-filter-hwcf-call.mlir @@ -0,0 +1,129 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,3,3,2" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,1,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,3,3,2" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,1,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f +func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3, %s4) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_2d_input_nhwc_filter_hwcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter2D_nhwc = call @alloc_4d_filled_f32(%c3, %c3, %c3, %c1, %val) :(index, index, index, index, f32) -> (memref) + %in2D_nhwc = call @alloc_4d_filled_f32(%c3, %c8, %c8, %c3, %val) : (index, index, index, index, f32) -> (memref) + %out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (memref) + + store %f10, %in2D_nhwc[%c0, %c0, %c3, %c0] : memref + call @conv_2d_input_nhwc_filter_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (memref, memref, memref) -> () + %out2D_nhwc_ = memref_cast %out2D_nhwc : memref to memref<*xf32> + call @print_memref_f32(%out2D_nhwc_): (memref<*xf32>) -> () + + dealloc %filter2D_nhwc : memref + dealloc %in2D_nhwc : memref + dealloc %out2D_nhwc : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [108], +// CHECK-COUNT-3: [124], +// CHECK-COUNT-2: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ncdhw-filter-dhwcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ncdhw-filter-dhwcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ncdhw-filter-dhwcf-call.mlir @@ -0,0 +1,90 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,5,5,5" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,1,1,3,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,5,5,5" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,1,1,3,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 5-D buffer of size (%s1, %s2, %s3, %s4, %s5) filled with the value %f +func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3, %s4, %s5) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_3d_input_ncdhw_filter_dhwcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter3D_ncdhw = call @alloc_5d_filled_f32(%c3, %c3, %c3, %c1, %c1, %val) : (index, index, index, index, index, f32) -> (memref) + %in3D_ncdhw = call @alloc_5d_filled_f32(%c1, %c1, %c8, %c8, %c8, %val) : (index, index, index, index, index, f32) -> (memref) + %out3D_ncdhw = call @alloc_5d_filled_f32(%c1, %c1, %c6, %c6, %c6, %zero) : (index, index, index, index, index, f32) -> (memref) + + store %f10, %in3D_ncdhw[%c0, %c0, %c0, %c0, %c3] : memref + call @conv_3d_input_ncdhw_filter_dhwcf(%in3D_ncdhw, %filter3D_ncdhw, %out3D_ncdhw) : (memref, memref, memref) -> () + %out3D_ncdhw_ = memref_cast %out3D_ncdhw : memref to memref<*xf32> + call @print_memref_f32(%out3D_ncdhw_): (memref<*xf32>) -> () + + dealloc %filter3D_ncdhw : memref + dealloc %in3D_ncdhw : memref + dealloc %out3D_ncdhw : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [108, 124, 124, 124, 108, 108], +// CHECK-COUNT-5: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108, 108, 108, 108, 108, 108] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir new file mode 100644 --- /dev/null +++ b/mlir/integration_test/Dialect/Linalg/CPU/test-conv-3d-input-ndhwc-filter-dhwcf-call.mlir @@ -0,0 +1,192 @@ +// RUN: mlir-opt %s -convert-linalg-to-loops -convert-scf-to-std -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,5,5,5" -convert-linalg-to-loops -convert-scf-to-std \ +// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -test-conv-vectorization="tile-sizes=1,1,1,1,1,3,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,5,5,5" \ +// RUN: -test-conv-vectorization="tile-sizes=1,1,1,1,1,3,3,3,3" -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \ +// RUN: mlir-cpu-runner -e main -entry-point-result=void \ +// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \ +// RUN: | FileCheck %s + +func private @print_memref_f32(memref<*xf32>) + +// Creates and returns 5-D buffer of size (%s1, %s2, %s3, %s4, %s5) filled with the value %f +func @alloc_5d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %s5 : index, %f : f32) -> memref { + %buf = alloc(%s1, %s2, %s3, %s4, %s5) : memref + linalg.fill(%buf, %f) : memref, f32 + return %buf : memref +} + +func @conv_3d_input_ndhwc_filter_dhwcf(%arg0: memref, %arg1: memref, %arg2: memref) { + linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%arg0, %arg1: memref, memref) + outs (%arg2: memref) + return +} + + +func @main() { + %c0 = constant 0 : index + %c1 = constant 1 : index + %c3 = constant 3 : index + %c6 = constant 6 : index + %c8 = constant 8 : index + %f10 = constant 10.00000e+00 : f32 + %val = constant 2.00000e+00 : f32 + %zero = constant 0.00000e+00 : f32 + + %filter3D_ndhwc = call @alloc_5d_filled_f32(%c3, %c3, %c3, %c1, %c1, %val) : (index, index, index, index, index, f32) -> (memref) + %in3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c8, %c8, %c8, %c1, %val) : (index, index, index, index, index, f32) -> (memref) + %out3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c6, %c6, %c6, %c1, %zero) : (index, index, index, index, index, f32) -> (memref) + + store %f10, %in3D_ndhwc[%c0, %c0, %c0, %c3, %c0] : memref + call @conv_3d_input_ndhwc_filter_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc) : (memref, memref, memref) -> () + %out3D_ndhwc_ = memref_cast %out3D_ndhwc : memref to memref<*xf32> + call @print_memref_f32(%out3D_ndhwc_): (memref<*xf32>) -> () + + dealloc %filter3D_ndhwc : memref + dealloc %in3D_ndhwc : memref + dealloc %out3D_ndhwc : memref + return +} + +// CHECK: Unranked Memref {{.*}} +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [ +// CHECK-SAME: [108], +// CHECK-COUNT-3: [124], +// CHECK-COUNT-2: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-SAME: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ], +// CHECK-NEXT: [ +// CHECK-COUNT-6: [108] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] +// CHECK-SAME: ] diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp --- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp +++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp @@ -622,27 +622,39 @@ populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns(tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns( tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); populateVectorizationPatterns( tiling, promotion, vectorization, tileSizes, context); + populateVectorizationPatterns( + tiling, promotion, vectorization, tileSizes, context); patterns.push_back(std::move(tiling)); patterns.push_back(std::move(promotion)); diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir --- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir +++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir @@ -99,3 +99,163 @@ // CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 // CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 // CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_1d_input_nwc_filter_wcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1 + d3, d4)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d3, d4, d2)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> + +// CHECK: func @conv_1d_input_nwc_filter_wcf + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_1d_input_ncw_filter_wcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d4, d2 + d3)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d3, d4, d1)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> + +// CHECK: func @conv_1d_input_ncw_filter_wcf +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_2d_input_nhwc_filter_hwcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<2> : tensor<2xi64>, + strides = dense<3> : tensor<2xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 * 3 + d4 * 2, d2 * 3 + d5 * 2, d6)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> + +// CHECK: func @conv_2d_input_nhwc_filter_hwcf + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_2d_input_nchw_filter_hwcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d6, d2 + d4, d3 + d5)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d1)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> + +// CHECK: func @conv_2d_input_nchw_filter_hwcf + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_3d_input_ndhwc_filter_dhwcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d0, d1 + d5, d2 + d6, d3 + d7, d8)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d5, d6, d7, d8, d4)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d0, d1, d2, d3, d4)> + +// CHECK: func @conv_3d_input_ndhwc_filter_dhwcf + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 + +// ----- + +func @conv_3d_input_ncdhw_filter_dhwcf(%input: memref, %filter: memref, %output: memref) { + linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d0, d8, d2 + d5, d3 + d6, d4 + d7)> +// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d5, d6, d7, d8, d1)> +// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8) -> (d0, d1, d2, d3, d4)> + +// CHECK: func @conv_3d_input_ncdhw_filter_dhwcf + +// CHECK: linalg.generic +// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]] +// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction", "parallel"]} +// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) +// CHECK-SAME: outs(%{{.+}} : memref) + +// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) +// CHECK-NEXT: %[[MUL:.+]] = mulf %[[BBARG0]], %[[BBARG1]] : f32 +// CHECK-NEXT: %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]] : f32 +// CHECK-NEXT: linalg.yield %[[ADD]] : f32 diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir --- a/mlir/test/Dialect/Linalg/named-ops.mlir +++ b/mlir/test/Dialect/Linalg/named-ops.mlir @@ -54,3 +54,195 @@ outs(%output: memref<1x56x56x96xf32>) return } + +// ----- + +// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf +func @conv_1d_input_nwc_filter_wcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_1d_input_nwc_filter_wcf + // CHECK-SAME: dilations = dense<1> : tensor<1xi64> + // CHECK-SAME: strides = dense<1> : tensor<1xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf +func @conv_1d_input_nwc_filter_wcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_1d_input_nwc_filter_wcf + // CHECK-SAME: dilations = dense<1> : tensor<1xi64> + // CHECK-SAME: strides = dense<1> : tensor<1xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// ----- + +// CHECK-LABEL: func @conv_1d_input_ncw_filter_wcf +func @conv_1d_input_ncw_filter_wcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_1d_input_ncw_filter_wcf + // CHECK-SAME: dilations = dense<1> : tensor<1xi64> + // CHECK-SAME: strides = dense<1> : tensor<1xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_1d_input_ncw_filter_wcf +func @conv_1d_input_ncw_filter_wcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_1d_input_ncw_filter_wcf + // CHECK-SAME: dilations = dense<1> : tensor<1xi64> + // CHECK-SAME: strides = dense<1> : tensor<1xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>, + strides = dense<1> : tensor<1xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// ----- + +// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf +func @conv_2d_input_nhwc_filter_hwcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_2d_input_nhwc_filter_hwcf + // CHECK-SAME: dilations = dense<1> : tensor<2xi64> + // CHECK-SAME: strides = dense<1> : tensor<2xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf +func @conv_2d_input_nhwc_filter_hwcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_2d_input_nhwc_filter_hwcf + // CHECK-SAME: dilations = dense<1> : tensor<2xi64> + // CHECK-SAME: strides = dense<1> : tensor<2xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// ----- + +// CHECK-LABEL: func @conv_2d_input_nchw_filter_hwcf +func @conv_2d_input_nchw_filter_hwcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_2d_input_nchw_filter_hwcf + // CHECK-SAME: dilations = dense<1> : tensor<2xi64> + // CHECK-SAME: strides = dense<1> : tensor<2xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_2d_input_nchw_filter_hwcf +func @conv_2d_input_nchw_filter_hwcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_2d_input_nchw_filter_hwcf + // CHECK-SAME: dilations = dense<1> : tensor<2xi64> + // CHECK-SAME: strides = dense<1> : tensor<2xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>, + strides = dense<1> : tensor<2xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// ----- + +// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf +func @conv_3d_input_ndhwc_filter_dhwcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_3d_input_ndhwc_filter_dhwcf + // CHECK-SAME: dilations = dense<1> : tensor<3xi64> + // CHECK-SAME: strides = dense<1> : tensor<3xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf +func @conv_3d_input_ndhwc_filter_dhwcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_3d_input_ndhwc_filter_dhwcf + // CHECK-SAME: dilations = dense<1> : tensor<3xi64> + // CHECK-SAME: strides = dense<1> : tensor<3xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +} + +// ----- + +// CHECK-LABEL: func @conv_3d_input_ncdhw_filter_dhwcf +func @conv_3d_input_ncdhw_filter_dhwcf(%input: tensor, %filter: tensor, %init: tensor) -> tensor { + // CHECK: %{{.+}} = linalg.conv_3d_input_ncdhw_filter_dhwcf + // CHECK-SAME: dilations = dense<1> : tensor<3xi64> + // CHECK-SAME: strides = dense<1> : tensor<3xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor, tensor) + // CHECK-SAME: outs(%{{.+}} : tensor) -> tensor + %0 = linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: tensor, tensor) + outs (%init: tensor) -> tensor + return %0 : tensor +} + +// ----- + +// CHECK-LABEL: func @conv_3d_input_ncdhw_filter_dhwcf +func @conv_3d_input_ncdhw_filter_dhwcf(%input: memref, %filter: memref, %output: memref) { + // CHECK: linalg.conv_3d_input_ncdhw_filter_dhwcf + // CHECK-SAME: dilations = dense<1> : tensor<3xi64> + // CHECK-SAME: strides = dense<1> : tensor<3xi64> + // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref, memref) + // CHECK-SAME: outs(%{{.+}} : memref) + linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>, + strides = dense<1> : tensor<3xi64>} + ins (%input, %filter: memref, memref) + outs (%output: memref) + return +}