forked from OSchip/llvm-project
[mlir][Linalg] Add conv ops with TF definition.
The dimension order of a filter in tensorflow is [filter_height, filter_width, in_channels, out_channels], which is different from current definition. The current definition follows TOSA spec. Add TF version conv ops to .tc, so we do not have to insert a transpose op around a conv op. Reviewed By: antiagainst Differential Revision: https://reviews.llvm.org/D96038
This commit is contained in:
Hanhan Wang
parent
8334cdde2e
commit
9325b8da17
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@ -124,3 +124,114 @@ Note: this op only supports channel multiplier == 1.
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O(n, oh, ow, c) = std_addf<kh, kw>(std_mulf(
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I(n, oh * strides[0] + kh, ow * strides[1] + kw, c), K(kh, kw, c)));
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}
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ods_def<ConvInputNWCFilterWCFOp>:
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def conv_1d_input_nwc_filter_wcf(I: f32(N, W, C), K: f32(KW, C, F)) -> (O: f32(N, W, F))
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attr(strides: 1xi64, dilations: 1xi64)
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""" A 1-D convolution given NWC layout input and WCF layout filter.
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Computes a 1-D convolution given 3-D input and filter. The data layout
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of input is NWC and the data layout of filter is WCF.
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The indexing maps for these three tensors contain 5 dimensions, following the
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order of (`N`, `W`, `F`, `KW`, `C`).
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"""
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{
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O(n, w, f) = std_addf<kw>(
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std_mulf(I(n, w * strides[0] + kw * dilations[0], c), K(kw, c, f)));
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}
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ods_def<ConvInputNCWFilterWCFOp>:
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def conv_1d_input_ncw_filter_wcf(I: f32(N, C, W), K: f32(KW, C, F)) -> (O: f32(N, F, W))
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attr(strides: 1xi64, dilations: 1xi64)
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""" A 1-D convolution given NCW layout input and WCF layout filter.
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Computes a 1-D convolution given 3-D input and filter. The data layout
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of input is NCW and the data layout of filter is WCF.
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The indexing maps for these three tensors contain 5 dimensions, following the
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order of (`N`, `F`, `W`, `KW`, `C`).
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"""
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{
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O(n, f, w) = std_addf<kw>(
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std_mulf(I(n, c, w * strides[0] + kw * dilations[0]), K(kw, c, f)));
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}
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ods_def<ConvInputNHWCFilterHWCFOp>:
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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))
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attr(strides: 2xi64, dilations: 2xi64)
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""" A 2-D convolution given NHWC layout input and HWCF layout filter.
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Computes a 2-D convolution given 4-D input and filter. The data layout
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of input is NHWC and the data layout of filter is HWCF.
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The indexing maps for these three tensors contain 7 dimensions, following the
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order of (`N`, `H`, `W`, `F`, `KH`, `KW`, `C`).
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"""
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{
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O(n, h, w, f) =
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std_addf<kh, kw>(std_mulf(I(n, h * strides[0] + kh * dilations[0],
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w * strides[1] + kw * dilations[1], c),
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K(kh, kw, c, f)));
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}
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ods_def<ConvInputNCHWFilterHWCFOp>:
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def conv_2d_input_nchw_filter_hwcf
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(I: f32(N, C, H, W), K: f32(KH, KW, C, F))
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-> (O: f32(N, F, H, W))
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attr(strides: 2xi64, dilations: 2xi64)
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""" A 2-D convolution given NCHW layout input and HWCF layout filter.
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Computes a 2-D convolution given 4-D input and filter. The data layout
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of input is NCHW and the data layout of filter is HWCF.
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The indexing maps for these three tensors contain 7 dimensions, following the
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order of (`N`, `F`, `H`, `W`, `KH`, `KW`, `C`).
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"""
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{
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O(n, f, h, w) =
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std_addf<kh, kw>(std_mulf(I(n, c, h * strides[0] + kh * dilations[0],
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w * strides[1] + kw * dilations[1]),
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K(kh, kw, c, f)));
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}
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ods_def<ConvInputNDHWCFilterDHWCFOp>:
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def conv_3d_input_ndhwc_filter_dhwcf
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(I: f32(N, D, H, W, C), K: f32(KD, KH, KW, C, F))
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-> (O: f32(N, D, H, W, F))
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attr(strides: 3xi64, dilations: 3xi64)
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""" A 3-D convolution given NDHWC layout input and DHWCF layout filter.
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Computes a 3-D convolution given 5-D input and filter. The data layout
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of input is NDHWC and the data layout of filter is DHWCF.
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The indexing maps for these three tensors contain 9 dimensions, following the
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order of (`N`, `D`, `H`, `W`, `F`, `KD`, `KH`, `KW`, `C`).
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"""
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{
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O(n, d, h, w, f) =
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std_addf<kd, kh, kw>(std_mulf(I(n, d * strides[0] + kd * dilations[0],
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h * strides[1] + kh * dilations[1],
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w * strides[2] + kw * dilations[2], c),
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K(kd, kh, kw, c, f)));
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}
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ods_def<ConvInputNCDHWFilterDHWCFOp>:
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def conv_3d_input_ncdhw_filter_dhwcf
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(I: f32(N, C, D, H, W), K: f32(KD, KH, KW, C, F))
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-> (O: f32(N, F, D, H, W))
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attr(strides: 3xi64, dilations: 3xi64)
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""" A 3-D convolution given NCDHW layout input and DHWCF layout filter.
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Computes a 3-D convolution given 5-D input and filter. The data layout
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of input is NCDHW and the data layout of filter is DHWCF.
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The indexing maps for these three tensors contain 9 dimensions, following the
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order of (`N`, `F`, `D`, `H`, `W`, `KD`, `KH`, `KW`, `C`).
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"""
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{
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O(n, f, d, h, w) = std_addf<kd, kh, kw>(std_mulf(
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I(n, c, d * strides[0] + kd * dilations[0],
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h * strides[1] + kh * dilations[1], w * strides[2] + kw * dilations[2]),
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K(kd, kh, kw, c, f)));
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}
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@ -0,0 +1,70 @@
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,4" -convert-linalg-to-loops -convert-scf-to-std \
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// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=0,0,4" \
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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func private @print_memref_f32(memref<*xf32>)
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// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
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func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> {
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%buf = alloc(%s1, %s2, %s3) : memref<?x?x?xf32>
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linalg.fill(%buf, %f) : memref<?x?x?xf32>, f32
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return %buf : memref<?x?x?xf32>
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}
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func @conv_1d_input_ncw_filter_wcf(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
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linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>,
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strides = dense<1> : tensor<1xi64>}
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ins (%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
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outs (%arg2: memref<?x?x?xf32>)
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return
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}
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func @main() {
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%c0 = constant 0 : index
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%c1 = constant 1 : index
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%c3 = constant 3 : index
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%c6 = constant 6 : index
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%c8 = constant 8 : index
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%f10 = constant 10.00000e+00 : f32
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%val = constant 2.00000e+00 : f32
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%zero = constant 0.00000e+00 : f32
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%filter1D_ncw = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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%in1D_ncw = call @alloc_3d_filled_f32(%c1, %c1, %c8, %val) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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%out1D_ncw = call @alloc_3d_filled_f32(%c1, %c1, %c6, %zero) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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store %f10, %in1D_ncw[%c0, %c0, %c3] : memref<?x?x?xf32>
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call @conv_1d_input_ncw_filter_wcf(%in1D_ncw, %filter1D_ncw, %out1D_ncw) : (memref<?x?x?xf32>, memref<?x?x?xf32>, memref<?x?x?xf32>) -> ()
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%out1D_ncw_ = memref_cast %out1D_ncw : memref<?x?x?xf32> to memref<*xf32>
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call @print_memref_f32(%out1D_ncw_): (memref<*xf32>) -> ()
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dealloc %filter1D_ncw : memref<?x?x?xf32>
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dealloc %in1D_ncw : memref<?x?x?xf32>
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dealloc %out1D_ncw : memref<?x?x?xf32>
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return
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}
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// CHECK: Unranked Memref {{.*}}
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// CHECK-NEXT: [
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// CHECK-SAME: [
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// CHECK-SAME: [12, 28, 28, 28, 12, 12]
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// CHECK-SAME: ]
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// CHECK-SAME: ]
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@ -0,0 +1,81 @@
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,4" -convert-linalg-to-loops -convert-scf-to-std \
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// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,4" \
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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func private @print_memref_f32(memref<*xf32>)
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// Creates and returns 3-D buffer of size (%s1, %s2, %s3) filled with the value %f
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func @alloc_3d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %f : f32) -> memref<?x?x?xf32> {
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%buf = alloc(%s1, %s2, %s3) : memref<?x?x?xf32>
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linalg.fill(%buf, %f) : memref<?x?x?xf32>, f32
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return %buf : memref<?x?x?xf32>
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}
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func @conv_1d_input_nwc_filter_wcf(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
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linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
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strides = dense<1> : tensor<1xi64>}
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ins (%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
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outs (%arg2: memref<?x?x?xf32>)
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return
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}
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func @main() {
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%c0 = constant 0 : index
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%c1 = constant 1 : index
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%c3 = constant 3 : index
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%c6 = constant 6 : index
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%c8 = constant 8 : index
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%f10 = constant 10.00000e+00 : f32
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%val = constant 2.00000e+00 : f32
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%zero = constant 0.00000e+00 : f32
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%filter1D_nwc = call @alloc_3d_filled_f32(%c3, %c1, %c1, %val) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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%in1D_nwc = call @alloc_3d_filled_f32(%c3, %c8, %c1, %val) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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%out1D_nwc = call @alloc_3d_filled_f32(%c3, %c6, %c1, %zero) : (index, index, index, f32) -> (memref<?x?x?xf32>)
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store %f10, %in1D_nwc[%c0, %c3, %c0] : memref<?x?x?xf32>
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call @conv_1d_input_nwc_filter_wcf(%in1D_nwc, %filter1D_nwc, %out1D_nwc) : (memref<?x?x?xf32>, memref<?x?x?xf32>, memref<?x?x?xf32>) -> ()
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%out1D_nwc_ = memref_cast %out1D_nwc : memref<?x?x?xf32> to memref<*xf32>
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call @print_memref_f32(%out1D_nwc_): (memref<*xf32>) -> ()
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dealloc %filter1D_nwc : memref<?x?x?xf32>
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dealloc %in1D_nwc : memref<?x?x?xf32>
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dealloc %out1D_nwc : memref<?x?x?xf32>
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return
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}
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// CHECK: Unranked Memref {{.*}}
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// CHECK-NEXT: [
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// CHECK-SAME: [
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// CHECK-SAME: [12],
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// CHECK-COUNT-3: [28],
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// CHECK-NEXT: [12],
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// CHECK-NEXT: [12]
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// CHECK-SAME: ],
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// CHECK-NEXT: [
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// CHECK-COUNT-5: [12],
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// CHECK-NEXT: [12]
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// CHECK-SAME: ],
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// CHECK-NEXT: [
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// CHECK-COUNT-5: [12],
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// CHECK-NEXT: [12]
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// CHECK-SAME: ]
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// CHECK-SAME: ]
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@ -0,0 +1,83 @@
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,0,4,4" -convert-linalg-to-loops -convert-scf-to-std \
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// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-std -convert-std-to-llvm | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,0,4,4" \
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// 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 | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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func private @print_memref_f32(memref<*xf32>)
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// Creates and returns 4-D buffer of size (%s1, %s2, %s3, %s4) filled with the value %f
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func @alloc_4d_filled_f32(%s1 : index, %s2 : index, %s3 : index, %s4 : index, %f : f32) -> memref<?x?x?x?xf32> {
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%buf = alloc(%s1, %s2, %s3, %s4) : memref<?x?x?x?xf32>
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linalg.fill(%buf, %f) : memref<?x?x?x?xf32>, f32
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return %buf : memref<?x?x?x?xf32>
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}
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func @conv_2d_input_nchw_filter_hwcf(%arg0: memref<?x?x?x?xf32>, %arg1: memref<?x?x?x?xf32>, %arg2: memref<?x?x?x?xf32>) {
|
||||
linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%arg0, %arg1: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%arg2: memref<?x?x?x?xf32>)
|
||||
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<?x?x?x?xf32>)
|
||||
%in2D_nchw = call @alloc_4d_filled_f32(%c3, %c1, %c8, %c8, %val) : (index, index, index, index, f32) -> (memref<?x?x?x?xf32>)
|
||||
%out2D_nchw = call @alloc_4d_filled_f32(%c3, %c1, %c6, %c6, %zero) : (index, index, index, index, f32) -> (memref<?x?x?x?xf32>)
|
||||
|
||||
store %f10, %in2D_nchw[%c0, %c0, %c0, %c3] : memref<?x?x?x?xf32>
|
||||
call @conv_2d_input_nchw_filter_hwcf(%in2D_nchw, %filter2D_nchw, %out2D_nchw) : (memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) -> ()
|
||||
%out2D_nchw_ = memref_cast %out2D_nchw : memref<?x?x?x?xf32> to memref<*xf32>
|
||||
call @print_memref_f32(%out2D_nchw_): (memref<*xf32>) -> ()
|
||||
|
||||
dealloc %filter2D_nchw : memref<?x?x?x?xf32>
|
||||
dealloc %in2D_nchw : memref<?x?x?x?xf32>
|
||||
dealloc %out2D_nchw : memref<?x?x?x?xf32>
|
||||
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: ]
|
||||
|
|
@ -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<?x?x?x?xf32> {
|
||||
%buf = alloc(%s1, %s2, %s3, %s4) : memref<?x?x?x?xf32>
|
||||
linalg.fill(%buf, %f) : memref<?x?x?x?xf32>, f32
|
||||
return %buf : memref<?x?x?x?xf32>
|
||||
}
|
||||
|
||||
func @conv_2d_input_nhwc_filter_hwcf(%arg0: memref<?x?x?x?xf32>, %arg1: memref<?x?x?x?xf32>, %arg2: memref<?x?x?x?xf32>) {
|
||||
linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%arg0, %arg1: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%arg2: memref<?x?x?x?xf32>)
|
||||
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<?x?x?x?xf32>)
|
||||
%in2D_nhwc = call @alloc_4d_filled_f32(%c3, %c8, %c8, %c3, %val) : (index, index, index, index, f32) -> (memref<?x?x?x?xf32>)
|
||||
%out2D_nhwc = call @alloc_4d_filled_f32(%c3, %c6, %c6, %c1, %zero) : (index, index, index, index, f32) -> (memref<?x?x?x?xf32>)
|
||||
|
||||
store %f10, %in2D_nhwc[%c0, %c0, %c3, %c0] : memref<?x?x?x?xf32>
|
||||
call @conv_2d_input_nhwc_filter_hwcf(%in2D_nhwc, %filter2D_nhwc, %out2D_nhwc) : (memref<?x?x?x?xf32>, memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) -> ()
|
||||
%out2D_nhwc_ = memref_cast %out2D_nhwc : memref<?x?x?x?xf32> to memref<*xf32>
|
||||
call @print_memref_f32(%out2D_nhwc_): (memref<*xf32>) -> ()
|
||||
|
||||
dealloc %filter2D_nhwc : memref<?x?x?x?xf32>
|
||||
dealloc %in2D_nhwc : memref<?x?x?x?xf32>
|
||||
dealloc %out2D_nhwc : memref<?x?x?x?xf32>
|
||||
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: ]
|
||||
|
|
@ -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<?x?x?x?x?xf32> {
|
||||
%buf = alloc(%s1, %s2, %s3, %s4, %s5) : memref<?x?x?x?x?xf32>
|
||||
linalg.fill(%buf, %f) : memref<?x?x?x?x?xf32>, f32
|
||||
return %buf : memref<?x?x?x?x?xf32>
|
||||
}
|
||||
|
||||
func @conv_3d_input_ncdhw_filter_dhwcf(%arg0: memref<?x?x?x?x?xf32>, %arg1: memref<?x?x?x?x?xf32>, %arg2: memref<?x?x?x?x?xf32>) {
|
||||
linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%arg0, %arg1: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%arg2: memref<?x?x?x?x?xf32>)
|
||||
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<?x?x?x?x?xf32>)
|
||||
%in3D_ncdhw = call @alloc_5d_filled_f32(%c1, %c1, %c8, %c8, %c8, %val) : (index, index, index, index, index, f32) -> (memref<?x?x?x?x?xf32>)
|
||||
%out3D_ncdhw = call @alloc_5d_filled_f32(%c1, %c1, %c6, %c6, %c6, %zero) : (index, index, index, index, index, f32) -> (memref<?x?x?x?x?xf32>)
|
||||
|
||||
store %f10, %in3D_ncdhw[%c0, %c0, %c0, %c0, %c3] : memref<?x?x?x?x?xf32>
|
||||
call @conv_3d_input_ncdhw_filter_dhwcf(%in3D_ncdhw, %filter3D_ncdhw, %out3D_ncdhw) : (memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) -> ()
|
||||
%out3D_ncdhw_ = memref_cast %out3D_ncdhw : memref<?x?x?x?x?xf32> to memref<*xf32>
|
||||
call @print_memref_f32(%out3D_ncdhw_): (memref<*xf32>) -> ()
|
||||
|
||||
dealloc %filter3D_ncdhw : memref<?x?x?x?x?xf32>
|
||||
dealloc %in3D_ncdhw : memref<?x?x?x?x?xf32>
|
||||
dealloc %out3D_ncdhw : memref<?x?x?x?x?xf32>
|
||||
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: ]
|
||||
|
|
@ -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<?x?x?x?x?xf32> {
|
||||
%buf = alloc(%s1, %s2, %s3, %s4, %s5) : memref<?x?x?x?x?xf32>
|
||||
linalg.fill(%buf, %f) : memref<?x?x?x?x?xf32>, f32
|
||||
return %buf : memref<?x?x?x?x?xf32>
|
||||
}
|
||||
|
||||
func @conv_3d_input_ndhwc_filter_dhwcf(%arg0: memref<?x?x?x?x?xf32>, %arg1: memref<?x?x?x?x?xf32>, %arg2: memref<?x?x?x?x?xf32>) {
|
||||
linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%arg0, %arg1: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%arg2: memref<?x?x?x?x?xf32>)
|
||||
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<?x?x?x?x?xf32>)
|
||||
%in3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c8, %c8, %c8, %c1, %val) : (index, index, index, index, index, f32) -> (memref<?x?x?x?x?xf32>)
|
||||
%out3D_ndhwc = call @alloc_5d_filled_f32(%c1, %c6, %c6, %c6, %c1, %zero) : (index, index, index, index, index, f32) -> (memref<?x?x?x?x?xf32>)
|
||||
|
||||
store %f10, %in3D_ndhwc[%c0, %c0, %c0, %c3, %c0] : memref<?x?x?x?x?xf32>
|
||||
call @conv_3d_input_ndhwc_filter_dhwcf(%in3D_ndhwc, %filter3D_ndhwc, %out3D_ndhwc) : (memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) -> ()
|
||||
%out3D_ndhwc_ = memref_cast %out3D_ndhwc : memref<?x?x?x?x?xf32> to memref<*xf32>
|
||||
call @print_memref_f32(%out3D_ndhwc_): (memref<*xf32>) -> ()
|
||||
|
||||
dealloc %filter3D_ndhwc : memref<?x?x?x?x?xf32>
|
||||
dealloc %in3D_ndhwc : memref<?x?x?x?x?xf32>
|
||||
dealloc %out3D_ndhwc : memref<?x?x?x?x?xf32>
|
||||
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: ]
|
||||
|
|
@ -622,27 +622,39 @@ void mlir::linalg::populateConvVectorizationPatterns(
|
|||
|
||||
populateVectorizationPatterns<ConvNWCOp, 3>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNWCFilterWCFOp, 3>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvNCWOp, 3>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNCWFilterWCFOp, 3>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvHWOp, 2>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvNHWCOp, 4>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNHWCFilterHWCFOp, 4>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvNCHWOp, 4>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNCHWFilterHWCFOp, 4>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvDHWOp, 3>(tiling, promotion, vectorization,
|
||||
tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvNDHWCOp, 5>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNDHWCFilterDHWCFOp, 5>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
populateVectorizationPatterns<ConvNCDHWOp, 5>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
populateVectorizationPatterns<ConvInputNCDHWFilterDHWCFOp, 5>(
|
||||
tiling, promotion, vectorization, tileSizes, context);
|
||||
|
||||
patterns.push_back(std::move(tiling));
|
||||
patterns.push_back(std::move(promotion));
|
||||
|
|
|
|||
|
|
@ -99,3 +99,163 @@ func @depthwise_conv_2d_input_nhwc_filter_hwc(%input: memref<1x113x113x96xf32>,
|
|||
// 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<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
|
||||
linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?xf32>)
|
||||
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<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
|
||||
|
||||
// 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<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
|
||||
linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?xf32>)
|
||||
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<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
|
||||
|
||||
// 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<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
|
||||
linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<2> : tensor<2xi64>,
|
||||
strides = dense<3> : tensor<2xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?xf32>)
|
||||
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<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
|
||||
|
||||
// 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<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
|
||||
linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?xf32>)
|
||||
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<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
|
||||
|
||||
// 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<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
|
||||
linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?x?xf32>)
|
||||
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<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>)
|
||||
|
||||
// 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<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
|
||||
linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?x?xf32>)
|
||||
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<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>)
|
||||
|
||||
// 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
|
||||
|
|
|
|||
|
|
@ -54,3 +54,195 @@ func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<1x113
|
|||
outs(%output: memref<1x56x56x96xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf
|
||||
func @conv_1d_input_nwc_filter_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
|
||||
// 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<?x?x?xf32>, tensor<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
|
||||
%0 = linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
|
||||
return %0 : tensor<?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_1d_input_nwc_filter_wcf
|
||||
func @conv_1d_input_nwc_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
|
||||
// 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<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
|
||||
linalg.conv_1d_input_nwc_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_1d_input_ncw_filter_wcf
|
||||
func @conv_1d_input_ncw_filter_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
|
||||
// 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<?x?x?xf32>, tensor<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
|
||||
%0 = linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
|
||||
return %0 : tensor<?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_1d_input_ncw_filter_wcf
|
||||
func @conv_1d_input_ncw_filter_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) {
|
||||
// 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<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
|
||||
linalg.conv_1d_input_ncw_filter_wcf {dilations = dense<1> : tensor<1xi64>,
|
||||
strides = dense<1> : tensor<1xi64>}
|
||||
ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf
|
||||
func @conv_2d_input_nhwc_filter_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
|
||||
// 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<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
|
||||
%0 = linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
|
||||
return %0 : tensor<?x?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf
|
||||
func @conv_2d_input_nhwc_filter_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
|
||||
// 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<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
|
||||
linalg.conv_2d_input_nhwc_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_2d_input_nchw_filter_hwcf
|
||||
func @conv_2d_input_nchw_filter_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
|
||||
// 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<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
|
||||
%0 = linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
|
||||
return %0 : tensor<?x?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_2d_input_nchw_filter_hwcf
|
||||
func @conv_2d_input_nchw_filter_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
|
||||
// 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<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
|
||||
linalg.conv_2d_input_nchw_filter_hwcf {dilations = dense<1> : tensor<2xi64>,
|
||||
strides = dense<1> : tensor<2xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf
|
||||
func @conv_3d_input_ndhwc_filter_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
|
||||
// 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<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
|
||||
%0 = linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
|
||||
return %0 : tensor<?x?x?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf
|
||||
func @conv_3d_input_ndhwc_filter_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
|
||||
// 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<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>)
|
||||
linalg.conv_3d_input_ndhwc_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_3d_input_ncdhw_filter_dhwcf
|
||||
func @conv_3d_input_ncdhw_filter_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
|
||||
// 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<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
|
||||
%0 = linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
|
||||
outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
|
||||
return %0 : tensor<?x?x?x?x?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv_3d_input_ncdhw_filter_dhwcf
|
||||
func @conv_3d_input_ncdhw_filter_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
|
||||
// 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<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>)
|
||||
linalg.conv_3d_input_ncdhw_filter_dhwcf {dilations = dense<1> : tensor<3xi64>,
|
||||
strides = dense<1> : tensor<3xi64>}
|
||||
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
|
||||
outs (%output: memref<?x?x?x?x?xf32>)
|
||||
return
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue