0b33890f45
Add an interface that allows grouping together all covolution and pooling ops within Linalg named ops. The interface currently - the indexing map used for input/image access is valid - the filter and output are accessed using projected permutations - that all loops are charecterizable as one iterating over - batch dimension, - output image dimensions, - filter convolved dimensions, - output channel dimensions, - input channel dimensions, - depth multiplier (for depthwise convolutions) Differential Revision: https://reviews.llvm.org/D109793
195 lines
7.6 KiB
MLIR
195 lines
7.6 KiB
MLIR
// RUN: mlir-opt -split-input-file -verify-diagnostics %s
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func @test_conv_op_not_linalg_op(%arg0 : tensor<?xf32>, %arg1 : tensor<?xf32>,
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%arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{expected a LinalgOp}}
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%0 = "test.conv_op_not_linalg_op"(%arg0, %arg1, %arg2)
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: (tensor<?xf32>, tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// Check for number of operands being >= 2.
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#map = affine_map<(d0) -> (d0)>
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func @test_conv_op_wrong_num_operands(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{expected op with 2 inputs and 1 output}}
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%0 = test.linalg_conv_op {
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indexing_maps = [#map, #map],
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iterator_types = ["parallel"]}
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ins(%arg0 : tensor<?xf32>) outs(%arg1 : tensor<?xf32>) {
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^bb0(%arg2 : f32, %arg3 : f32):
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linalg.yield %arg3 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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func @test_conv_op_wrong_input_indexing_map1(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{unexpected input index map for convolution}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d0 * 2)>,
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affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d0)>],
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iterator_types = ["parallel", "reduction"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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func @test_conv_op_wrong_input_indexing_map2(%arg0 : tensor<?x?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{unexpected input index map for convolution}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d0 + d1, d0)>,
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affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d0)>],
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iterator_types = ["parallel", "reduction"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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func @test_conv_op_filter_index_map_not_projection(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{expected output/filter indexing maps to be projected permutations}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d1 + d0)>,
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affine_map<(d0, d1) -> (d0)>],
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iterator_types = ["parallel", "reduction"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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func @test_conv_op_output_index_map_not_projection(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{expected output/filter indexing maps to be projected permutations}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d0)>,
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affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d0 + d1)>],
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iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// Convolution op illegal if a loop dimension is used to access
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// output, filter and is convolved.
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func @test_conv_op_output_filter_convolved(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
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// expected-error @+1 {{unexpected loop dimension for convolution op}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d0 + d1)>,
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affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d0, d1)>],
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iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// -----
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// Convolution op illegal if a loop dimension is used only in the output.
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func @test_conv_op_output_only_dim(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
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// expected-error @+1 {{unexpected loop dimension for convolution op}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0 + d1)>,
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affine_map<(d0, d1, d2) -> (d1)>,
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affine_map<(d0, d1, d2) -> (d0, d2)>],
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iterator_types = ["parallel", "reduction", "parallel"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// -----
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// Convolution op illegal if a loop dimension is used only in the filter.
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func @test_conv_op_filter_only_dim(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?x?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{unexpected loop dimension for convolution op}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0 + d1)>,
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affine_map<(d0, d1, d2) -> (d1, d2)>,
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affine_map<(d0, d1, d2) -> (d0)>],
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iterator_types = ["parallel", "reduction", "reduction"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?x?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// Convolution op illegal if a loop dimension is used only in the input.
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func @test_conv_op_input_only_dim(%arg0 : tensor<?x?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{unexpected loop dimension for convolution op}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1, d2) -> (d0 + d1, d2)>,
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affine_map<(d0, d1, d2) -> (d1)>,
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affine_map<(d0, d1, d2) -> (d0)>],
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iterator_types = ["parallel", "reduction", "reduction"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// Convolution op illegal if a loop dimension accessing output is not parallel.
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func @test_conv_op_non_output_access_loop_parallel(%arg0 : tensor<?xf32>,
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%arg1 : tensor<?xf32>, %arg2 : tensor<?xf32>) -> tensor<?xf32> {
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// expected-error @+1 {{expected all iterators not used to access outputs to be reduction}}
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%0 = test.linalg_conv_op {
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indexing_maps = [affine_map<(d0, d1) -> (d0 + d1)>,
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affine_map<(d0, d1) -> (d1)>,
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affine_map<(d0, d1) -> (d0)>],
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iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%arg2 : tensor<?xf32>) {
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^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32):
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linalg.yield %arg5 : f32
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} -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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