29b1054835
* Clarified the `inner_dim_pos` attribute in the case of high dimensionality tensors. * Added a 5D examples to show-case the use-cases that triggered this updated. * Added a reminder for linalg.unpack that number of elements are not required to be the same between input/output due to padding being dropped. I encountered some odd variations of `linalg.pack` and `linalg.unpack` while working on some TFLite models and the definition in the documentation did not match what I saw pass in IR verification. The following changes reconcile those differences. --------- Signed-off-by: Christopher McGirr <mcgirr@roofline.ai>
1929 lines
75 KiB
MLIR
1929 lines
75 KiB
MLIR
// RUN: mlir-opt %s -split-input-file -verify-diagnostics
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func.func @load_number_of_indices(%v : memref<f32>) {
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// expected-error @+2 {{incorrect number of indices for load}}
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%c0 = arith.constant 0 : index
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memref.load %v[%c0] : memref<f32>
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}
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// -----
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func.func @store_number_of_indices(%v : memref<f32>) {
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// expected-error @+3 {{store index operand count not equal to memref rank}}
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%c0 = arith.constant 0 : index
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%f0 = arith.constant 0.0 : f32
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memref.store %f0, %v[%c0] : memref<f32>
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}
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// -----
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func.func @yield_parent(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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// expected-error @+1 {{op expected parent op with LinalgOp interface}}
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linalg.yield %arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>
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}
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// -----
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func.func @index_parent() {
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// expected-error @+1 {{op expected parent op with LinalgOp interface}}
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linalg.index 0 : index
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}
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// -----
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func.func @index_dim_lower_than_number_of_loops(%arg0: memref<f32>) {
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// expected-error @+6 {{op expected dim (2) to be lower than the number of loops (0) of the enclosing LinalgOp}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0 : memref<f32>) {
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^bb(%0: f32):
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linalg.index 2 : index
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linalg.yield %0 : f32
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}
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}
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// -----
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func.func @index_dim_negative(%arg0: memref<f32>) {
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// expected-error @+6 {{op attribute 'dim' failed to satisfy constraint: 64-bit signless integer attribute whose minimum value is 0}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0 : memref<f32>) {
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^bb(%0: f32):
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linalg.index -1 : index
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linalg.yield %0 : f32
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}
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}
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// -----
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func.func @generic_no_region(%arg0: memref<f32>) {
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// expected-error @+4 {{expected '{' to begin a region}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> (0)> ],
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iterator_types = []
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} ins(%arg0 : memref<f32>)
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}
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// -----
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func.func @generic_mismatched_num_returns(%arg0: memref<f32>) {
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// expected-error @+6 {{op expected number of yield values (0) to match the number of inits / outs operands of the enclosing LinalgOp (1)}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0 : memref<f32>) {
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^bb(%0: f32):
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linalg.yield
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}
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}
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// -----
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func.func @generic_wrong_dim_in_map(%arg0: memref<1xi32>) {
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// expected-error @+1 {{op expected indexing_map #0 to have 1 dim(s) to match the number of loops}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> (0)> ],
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iterator_types = ["parallel"]}
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outs(%arg0 : memref<1xi32>) {
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^bb(%i : i32):
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linalg.yield %i : i32
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}
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}
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// -----
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func.func @generic_wrong_iterator(%arg0: memref<1xi32>) {
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// expected-error @+4 {{unexpected iterator_type (random)}}
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linalg.generic {
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indexing_maps = [ affine_map<(i) -> (i)> ],
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iterator_types = ["random"]}
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outs(%arg0 : memref<1xi32>) {
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^bb(%i : i32):
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linalg.yield %i : i32
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}
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}
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// -----
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func.func @generic_one_d_view(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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// expected-error @+1 {{expected operand rank (1) to match the result rank of indexing_map #0 (2)}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> (0, 0)> ],
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iterator_types = []}
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outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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^bb(%f : f32):
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linalg.yield %f: f32
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}
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}
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// -----
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func.func @generic_scalar_view(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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%cst = arith.constant 0.0 : f32
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// expected-error @+1 {{expected operand rank (0) to match the result rank of indexing_map #0 (1)}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> (0)>, affine_map<() -> (0, 0)> ],
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iterator_types = []}
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ins(%cst : f32)
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outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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^bb(%0 : f32, %1 : f32):
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linalg.yield %0: f32
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}
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}
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// -----
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func.func @generic_result_0_element_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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// expected-error @+7 {{'linalg.yield' op type of yield operand 1 ('i4') doesn't match the element type of the enclosing linalg.generic op ('f32')}}
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linalg.generic {
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indexing_maps = [ affine_map<(i) -> (i)> ],
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iterator_types = ["parallel"]}
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outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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^bb(%0: f32):
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%1 = arith.constant 1: i4
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linalg.yield %1: i4
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}
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}
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// -----
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func.func @generic_singular_maps(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>, %arg1: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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// expected-error @+1 {{invalid indexing maps are non-invertible: ((d0, d1) -> (d0 + d1, d0 + d1))}}
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linalg.generic {
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indexing_maps = [
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affine_map<(i, j) -> (i + j)>,
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affine_map<(i, j) -> (i + j)>
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],
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iterator_types = ["parallel","parallel"]}
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ins(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>)
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outs(%arg1 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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^bb(%0: f32, %1: f32):
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linalg.yield %1: f32
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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///////////////////////////// Region tests /////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////
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// -----
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func.func @generic_empty_region(%arg0: memref<f32>) {
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%f0 = arith.constant 0.0: f32
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// expected-error @+1 {{op expects region #0 to have 0 or 1 blocks}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
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iterator_types = []}
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ins(%arg0 : memref<f32>)
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outs(%arg0 : memref<f32>) {
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^bb1:
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linalg.yield %f0: f32
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^bb2:
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linalg.yield %f0: f32
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}
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}
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// -----
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func.func @generic_empty_region(%arg0: memref<f32>) {
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%f0 = arith.constant 0.0: f32
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// expected-error @+1 {{op expects to have 1 region with 1 block}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> , affine_map<() -> ()> ],
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iterator_types = []}
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ins(%arg0 : memref<f32>)
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outs(%arg0 : memref<f32>) {
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}
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}
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// -----
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func.func @generic_mismatched_num_arguments(%arg0: memref<f32>) {
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// expected-error @+6 {{'linalg.yield' op expected number of yield values (1) to match the number of inits / outs operands of the enclosing LinalgOp (2)}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0, %arg0 : memref<f32>, memref<f32>) {
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^bb(%f: f32):
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linalg.yield %f: f32
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}
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}
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// -----
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func.func @generic_shaped_operand_block_arg_type(%arg0: memref<f32>) {
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// expected-error @+6 {{'linalg.yield' op type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0 : memref<f32>) {
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^bb(%i: i1):
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linalg.yield %i : i1
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}
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}
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// -----
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func.func @generic_scalar_operand_block_arg_type(%arg0: tensor<f32>) {
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// expected-error @+6 {{'linalg.yield' op type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}}
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linalg.generic {
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indexing_maps = [ affine_map<() -> ()> ],
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iterator_types = []}
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outs(%arg0 : tensor<f32>) {
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^bb(%i: i1):
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linalg.yield %i : i1
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} -> tensor<f32>
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}
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// -----
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func.func @generic_result_0_element_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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// expected-error @+7 {{type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}}
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linalg.generic {
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indexing_maps = [ affine_map<(i) -> (i)> ],
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iterator_types = ["parallel"]}
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outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) {
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^bb(%i: f32):
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%0 = arith.constant 0: i1
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linalg.yield %0: i1
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}
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}
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// -----
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func.func @generic_result_tensor_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>,
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%arg1: tensor<?xf32>) {
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// expected-error @+1 {{expected type of operand #1 ('tensor<?xf32>') to match type of corresponding result ('tensor<f32>')}}
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%0 = linalg.generic {
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indexing_maps = [ affine_map<(i) -> (i)> , affine_map<(i) -> (i)> ],
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iterator_types = ["parallel"]}
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ins(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>)
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outs(%arg1 : tensor<?xf32>) {
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^bb(%i: f32, %j: f32):
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linalg.yield %i: f32
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} -> tensor<f32>
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}
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// -----
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func.func @generic(%arg0: memref<?x?xf32>) {
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// expected-error @+6 {{block with no terminator, has %0 = "arith.addf"(%arg1, %arg1) <{fastmath = #arith.fastmath<none>}> : (f32, f32) -> f32}}
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linalg.generic {
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indexing_maps = [ affine_map<(i, j) -> (i, j)> ],
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iterator_types = ["parallel", "parallel"]}
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outs(%arg0 : memref<?x?xf32>) {
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^bb(%0: f32) :
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%1 = arith.addf %0, %0: f32
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}
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return
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}
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// -----
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// This test is currently disabled: subject to verifier ordering issues.
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// Instead, when the ranks are not greater than 2, an assertion will be triggered
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// in LinalgStructuredOps.td::ConvOp::iterator_types() for now because the
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// verifier inspects the iterator_types. This is slated to become an
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// autogenerated op in the future, alleviating the issue.
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// func @conv_rank_limit(%arg0: memref<?xf32>, %arg1: memref<?xf32>, %arg2: memref<?xf32>) {
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// // DISABLED_expected -error @+1 {{expects memref ranks to be greater than 2}}
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// linalg.conv(%arg0, %arg1, %arg2) : memref<?xf32>, memref<?xf32>, memref<?xf32>
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// }
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//
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// // -----
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func.func @named_ops(%a3: memref<?x?x?xf32>, %b3: memref<?x?xf32>, %c3: memref<?x?x?xf32>) {
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// expected-error @+1 {{expected operand rank (2) to match the result rank of indexing_map #1 (3)}}
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linalg.batch_matmul ins(%a3, %b3: memref<?x?x?xf32>, memref<?x?xf32>)
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outs(%c3 : memref<?x?x?xf32>)
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return
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}
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// -----
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func.func @incorrect_region_arg_count(%m: memref<?x?xf32>) {
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// expected-error @+3 {{region expects 3 args, got 2}}
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%res = linalg.matmul ins(%m, %m : memref<?x?xf32>, memref<?x?xf32>)
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-> (tensor<?x?xf32>, tensor<?x?xf32>)
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return
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}
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// -----
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func.func @matching_inits(%m: memref<?x?xf32>, %t: tensor<?x?xf32>) {
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// expected-error @+1 {{expected type of operand #2 ('tensor<?x?xf32>') to match type of corresponding result ('tensor<?xf32>')}}
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%res = linalg.matmul ins(%m, %m : memref<?x?xf32>, memref<?x?xf32>)
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outs(%t : tensor<?x?xf32>)
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-> tensor<?xf32>
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return
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}
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// -----
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func.func @illegal_fill_tensor_no_return(%arg0 : index, %arg1 : index, %arg2 : f32)
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{
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%0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf32>
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// expected-error @+1 {{expected the number of tensor results (0) to be equal to the number of output tensors (1)}}
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linalg.fill ins(%arg2 : f32) outs(%0 : tensor<?x?xf32>)
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}
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// -----
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func.func @illegal_fill_memref_with_tensor_return
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(%arg0 : memref<?x?xf32>, %arg1 : f32) -> tensor<?x?xf32>
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{
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// expected-error @+1 {{expected the number of tensor results (1) to be equal to the number of output tensors (0)}}
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%0 = linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// -----
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func.func @illegal_fill_tensor_with_memref_return
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(%arg0 : tensor<?x?xf32>, %arg1 : f32) -> memref<?x?xf32>
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{
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// expected-error @+1 {{result #0 must be variadic of ranked tensor of any type values, but got 'memref<?x?xf32>'}}
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%0 = linalg.fill ins(%arg1 : f32) outs(%arg0 : tensor<?x?xf32>) -> memref<?x?xf32>
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return %0 : memref<?x?xf32>
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}
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// -----
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func.func @illegal_fill_value_type(%arg0 : tensor<2x2xf32>, %arg1 : tensor<2xf32>) -> tensor<2x2xf32>
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{
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// expected-error @+1 {{expected op with scalar input}}
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%0 = linalg.fill ins(%arg1 : tensor<2xf32>) outs(%arg0 : tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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func.func @invalid_static_matmul(%arg0: memref<2x4xf32>, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) {
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// expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 4, but found 3}}
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linalg.matmul ins(%arg0, %arg1 : memref<2x4xf32>, memref<3x4xf32>)
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outs(%arg2 :memref<2x4xf32>)
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return
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}
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// -----
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func.func @invalid_scalar_input_matmul(%arg0: f32, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) {
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// expected-error @+1 {{'linalg.matmul' op expected operand rank (0) to match the result rank of indexing_map #0 (2)}}
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linalg.matmul ins(%arg0, %arg1 : f32, memref<3x4xf32>)
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outs(%arg2 : memref<2x4xf32>)
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return
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}
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// -----
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func.func @invalid_scalar_output_matmul(%arg0: memref<2x3xf32>, %arg1: memref<3x4xf32>, %arg2: f32) {
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// expected-error @+1 {{'linalg.matmul' op operand #2 must be variadic of shaped of any type values, but got 'f32'}}
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linalg.matmul ins(%arg0, %arg1 : memref<2x3xf32>, memref<3x4xf32>)
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outs(%arg2 : f32)
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return
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}
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// -----
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func.func @invalid_indexing_maps_matmul(%arg0: memref<2x4xf32>, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) {
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// expected-error @+1 {{expected attribute value}}
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linalg.matmul indexing_maps = [
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,
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affine_map<(d0, d1, d2) -> (d2, d1)>,
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affine_map<(d0, d1, d2) -> (d0, d1)>
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]
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ins(%arg0, %arg1 : memref<2x4xf32>, memref<3x4xf32>)
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outs(%arg2 :memref<2x4xf32>)
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return
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}
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// -----
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func.func @invalid_matmul_dim_a(%arg0: memref<5x5xf32>, %arg1: memref<5x5xf32>, %arg2: memref<5x5xf32>) {
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// expected-error @+1 {{Unexpected dim expression in map result}}
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linalg.matmul indexing_maps = [
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affine_map<(d0, d1, d2) -> (d1, d2)>,
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affine_map<(d0, d1, d2) -> (d2, d1)>,
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affine_map<(d0, d1, d2) -> (d0, d1)>
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]
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ins(%arg0, %arg1 : memref<5x5xf32>, memref<5x5xf32>) outs(%arg2: memref<5x5xf32>)
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return
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}
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// -----
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func.func @invalid_matmul_dim_b(%arg0: memref<5x5xf32>, %arg1: memref<5x5xf32>, %arg2: memref<5x5xf32>) {
|
|
// expected-error @+1 {{Unexpected dim expression in map result}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d0)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<5x5xf32>, memref<5x5xf32>) outs(%arg2: memref<5x5xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_transpose_a_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) -> tensor<4x64xf32> {
|
|
// expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 4, but found 1}}
|
|
%0 = linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d2, d0)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return %0: tensor<4x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_transpose_b_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) -> tensor<4x64xf32> {
|
|
// expected-error @+1 {{inferred input/output operand #1 has shape's dimension #1 to be 1, but found 64}}
|
|
%0 = linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d1, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return %0: tensor<4x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_a(%arg0: memref<3xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{'linalg.matmul' op Invalid broadcast requested, should be (d2)}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0)>,
|
|
affine_map<(d0, d1, d2) -> (d1, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<3xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_b(%arg0: memref<3x5xf32>, %arg1: memref<7xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{'linalg.matmul' op Invalid broadcast requested, should be (d2)}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<3x5xf32>, memref<7xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_a_rank_mismatch(%arg0: memref<3x5xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{'linalg.matmul' op expected operand rank (2) to match the result rank of indexing_map #0 (1)}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<3x5xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_b_rank_mismatch(%arg0: memref<3x5xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{'linalg.matmul' op expected operand rank (2) to match the result rank of indexing_map #1 (1)}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<3x5xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_matmul_bcast_b_transpose_a(%arg0: memref<5x3xf32>, %arg1: memref<7xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 5, but found 7}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d2, d0)>,
|
|
affine_map<(d0, d1, d2) -> (d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<5x3xf32>, memref<7xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_matmul_bcast_b_transpose_a_wrong_dim(%arg0: memref<3x5xf32>, %arg1: memref<5xf32>, %arg2: memref<3x7xf32>) {
|
|
// expected-error @+1 {{'linalg.matmul' op Unexpected dim expression in map result.}}
|
|
linalg.matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d1, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<3x5xf32>, memref<5xf32>) outs(%arg2: memref<3x7xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_indexing_maps_placement_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+2 {{custom op 'indexing_maps' is unknown (tried 'func.indexing_maps' as well)}}
|
|
linalg.matmul ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) outs(%init : tensor<4x64xf32>)
|
|
indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
]
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_indexing_maps_placement_contraction(
|
|
%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+3 {{custom op 'linalg.contract' expected 'indexing_maps' attribute}}
|
|
// NB: indexing_maps should be provided before ins and outs
|
|
linalg.contract
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>)
|
|
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>]
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_affine_map_in_indexing_maps_contraction(
|
|
%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{provided affine_map is not a projected permutation}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1, d2) -> (d0 + d2, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @differing_iteration_space_of_affine_maps_contraction(
|
|
%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{iteration spaces of provided affine_maps differ}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @mismatched_ranks_affine_map_and_operand_contraction(
|
|
%lhs: tensor<4x1x2xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{ranks of shaped operand and results of corresponding affine_map differ}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : tensor<4x1x2xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
// -----
|
|
|
|
func.func @mismatch_type_affine_map_and_operand_contraction(
|
|
%lhs: f32, %rhs: tensor<4x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{affine_map specifies shaped access while operand has non-shaped type}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1) -> (d0)>,
|
|
affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : f32, tensor<4x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unused_iteration_space_dim_contraction(
|
|
%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{iteration space dim at index 3 not used to access any operand}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unused_iteration_space_dim_contraction(
|
|
%lhs: tensor<8x4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) {
|
|
// expected-error @+1 {{iteration space dim at index 3 is neither a contracting dim nor of parallel iteration type}}
|
|
linalg.contract
|
|
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1)>]
|
|
ins(%lhs, %rhs : tensor<8x4x1xf32>, tensor<1x64xf32>)
|
|
outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32>
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_static_2d_conv(%input : memref<1x3x4x2xf32>, %filter: memref<3x2x2x1xf32>, %output: memref<1x2x3x1xf32>) {
|
|
// expected-error @+1 {{inferred input/output operand #0 has shape's dimension #1 to be greater than or equal to 4, but found 3}}
|
|
linalg.conv_2d_nhwc_hwcf
|
|
{ dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
|
|
ins(%input, %filter : memref<1x3x4x2xf32>, memref<3x2x2x1xf32>)
|
|
outs(%output : memref<1x2x3x1xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
#attrs = {
|
|
indexing_maps = [
|
|
affine_map<(i) -> (3 - i)>,
|
|
affine_map<(i) -> (i)>
|
|
],
|
|
iterator_types = ["parallel"]
|
|
}
|
|
|
|
func.func @invalid_reverse(%A: memref<5xf32>, %B: memref<5xf32>) {
|
|
// expected-error @+1 {{unexpected result less than 0 at expression #0 in}}
|
|
linalg.generic #attrs ins(%A: memref<5xf32>) outs(%B: memref<5xf32>) {
|
|
^bb0(%a: f32, %b: f32):
|
|
linalg.yield %a : f32
|
|
}
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_binary_wrong_yield_operands(
|
|
%lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>)
|
|
-> tensor<64xf32> {
|
|
%add = linalg.map
|
|
ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>)
|
|
outs(%init:tensor<64xf32>)
|
|
(%lhs_elem: f32, %rhs_elem: f32) {
|
|
%0 = arith.addf %lhs_elem, %rhs_elem: f32
|
|
// expected-error @+1{{'linalg.yield' op expected number of yield values (2) to match the number of inits / outs operands of the enclosing LinalgOp (1)}}
|
|
linalg.yield %0, %0: f32, f32
|
|
}
|
|
func.return %add : tensor<64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_input_mapper_arity_mismatch(
|
|
%lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>)
|
|
-> tensor<64xf32> {
|
|
// expected-error@+1{{'linalg.map' op expects number of operands to match the arity of mapper, but got: 2 and 3}}
|
|
%add = linalg.map
|
|
ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>)
|
|
outs(%init:tensor<64xf32>)
|
|
(%lhs_elem: f32, %rhs_elem: f32, %extra_elem: f32) {
|
|
%0 = arith.addf %lhs_elem, %rhs_elem: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %add : tensor<64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_input_mapper_type_mismatch(
|
|
%lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>)
|
|
-> tensor<64xf32> {
|
|
// expected-error@+1{{'linalg.map' op expected element type of input 'f32' to match bbArg type 'f64'}}
|
|
%add = linalg.map
|
|
ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>)
|
|
outs(%init:tensor<64xf32>)
|
|
(%lhs_elem: f64, %rhs_elem: f64) {
|
|
%0 = arith.addf %lhs_elem, %rhs_elem: f64
|
|
linalg.yield %0: f64
|
|
}
|
|
func.return %add : tensor<64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_input_output_shape_mismatch(
|
|
%lhs: tensor<64x64xf32>, %rhs: tensor<64x64xf32>, %init: tensor<32xf32>)
|
|
-> tensor<32xf32> {
|
|
// expected-error@+1{{'linalg.map' op expected shape of input (64, 64) to match shape of output (32)}}
|
|
%add = linalg.map
|
|
ins(%lhs, %rhs : tensor<64x64xf32>, tensor<64x64xf32>)
|
|
outs(%init:tensor<32xf32>)
|
|
(%lhs_elem: f32, %rhs_elem: f32) {
|
|
%0 = arith.addf %lhs_elem, %rhs_elem: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %add : tensor<32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_no_operands1() {
|
|
// expected-error @+1 {{'linalg.map' op expected 1 or more operands, but found 0}}
|
|
linalg.map { arith.addf }
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_no_operands2() {
|
|
// expected-error @+1 {{'linalg.map' op expected 1 or more operands, but found 0}}
|
|
"linalg.map"() ({
|
|
^bb0:
|
|
}) : () -> ()
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @map_no_operands3(
|
|
%lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>)
|
|
-> tensor<64xf32> {
|
|
// expected-error @+1 {{cannot name an operation with no results}}
|
|
%add = linalg.map { arith.addf }
|
|
func.return %add : tensor<64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_input_vs_init_dimension_mismatch(
|
|
%input: tensor<16x32x64xf32>,
|
|
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
|
|
// expected-error @+1 {{'linalg.reduce' op init dimensions [16, 64] doesn't match input dimensions after reduction [16, 32]}}
|
|
%reduce = linalg.reduce
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<16x64xf32>)
|
|
dimensions = [2]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce : tensor<16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_dimensions_out_of_range(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
|
|
// expected-error @+1 {{'linalg.reduce' op dimensions for reduction should be in the range [0, 2].}}
|
|
%reduce = linalg.reduce
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<16x64xf32>)
|
|
dimensions = [3]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce : tensor<16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_duplicate_dimensions(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<16xf32>) -> tensor<16xf32> {
|
|
// expected-error @+1 {{'linalg.reduce' op attribute 'dimensions' failed to satisfy constraint: i64 dense array attribute should be in increasing order}}
|
|
%reduce = linalg.reduce
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<16xf32>)
|
|
dimensions = [1, 1]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce : tensor<16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_non_increasing_dimensions(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<16xf32>) -> tensor<16xf32> {
|
|
// expected-error @+1 {{'linalg.reduce' op attribute 'dimensions' failed to satisfy constraint: i64 dense array attribute should be in increasing order}}
|
|
%reduce = linalg.reduce
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<16xf32>)
|
|
dimensions = [2, 1]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce : tensor<16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_reduced_input_init_rank_mismatch(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
|
|
// expected-error @+1 {{'linalg.reduce' op number of dimensions after reduction 1 doesn't match the init rank 2}}
|
|
%reduce = linalg.reduce
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<16x64xf32>)
|
|
dimensions = [1, 2]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce : tensor<16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_wrong_number_of_block_arguments(
|
|
%input1: tensor<16x32x64xf32>,
|
|
%init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>,
|
|
%init2: tensor<16x64xf32>) -> (tensor<16x64xf32>, tensor<16x64xf32>) {
|
|
// expected-error @+1{{'linalg.reduce' op mismatching number of operands and block arguments}}
|
|
%reduce, %reduce2 = linalg.reduce
|
|
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>)
|
|
outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf32>)
|
|
dimensions = [1]
|
|
(%in: f32, %out: f32) {
|
|
%0 = arith.addf %in, %out: f32
|
|
linalg.yield %0: f32
|
|
}
|
|
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_wrong_block_argument_input_type(
|
|
%input1: tensor<16x32x64xf32>,
|
|
%init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>,
|
|
%init2: tensor<16x64xf32>) -> (tensor<16x64xf32>, tensor<16x64xf32>) {
|
|
// expected-error @+1{{'linalg.reduce' op input element type 'f32' does not match corresponding block argument type 'f64'}}
|
|
%reduce, %reduce2 = linalg.reduce
|
|
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>)
|
|
outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf32>)
|
|
dimensions = [1]
|
|
(%in1: f32, %in2: f64, %out1: f32, %out2: f64) {
|
|
%0 = arith.addf %in1, %out1: f32
|
|
%1 = arith.addf %in2, %out2: f64
|
|
linalg.yield %0, %1: f32, f64
|
|
}
|
|
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_wrong_block_argument_output_type(
|
|
%input1: tensor<16x32x64xf32>,
|
|
%init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>,
|
|
%init2: tensor<16x64xf64>) -> (tensor<16x64xf32>, tensor<16x64xf32>) {
|
|
// expected-error @+1{{'linalg.reduce' op output element type 'f64' does not match corresponding block argument type 'f32'}}
|
|
%reduce, %reduce2 = linalg.reduce
|
|
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>)
|
|
outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf64>)
|
|
dimensions = [1]
|
|
(%in1: f32, %in2: f32, %out1: f32, %out2: f32) {
|
|
%0 = arith.addf %in1, %out1: f32
|
|
linalg.yield %0, %out2: f32, f32
|
|
}
|
|
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf64>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_different_input_shapes(%input1: tensor<16x32x64xf32>,
|
|
%init1: tensor<16x64xf32>, %input2: tensor<17x32x64xf32>,
|
|
%init2: tensor<17x64xf32>) -> (tensor<16x64xf32>, tensor<17x64xf32>) {
|
|
// expected-error @+1{{'linalg.reduce' op expects all inputs to have the same shapes. Shape at input-index 1 is not equal to the shape at input-index 0.}}
|
|
%reduce, %reduce2 = linalg.reduce
|
|
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<17x32x64xf32>)
|
|
outs(%init1, %init2 : tensor<16x64xf32>, tensor<17x64xf32>)
|
|
dimensions = [1]
|
|
(%in1: f32, %in2: f32, %out1: f32, %out2: f32) {
|
|
%0 = arith.addf %in1, %out1: f32
|
|
%1 = arith.addf %in2, %out2: f32
|
|
linalg.yield %0, %1: f32, f32
|
|
}
|
|
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<17x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @reduce_different_output_shapes(%input1: tensor<16x32x64xf32>,
|
|
%init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>,
|
|
%init2: tensor<17x64xf32>) -> (tensor<16x64xf32>, tensor<17x64xf32>) {
|
|
// expected-error @+1{{'linalg.reduce' op expects all outputs to have the same shapes. Shape at output-index 1 is not equal to the shape at output-index 0.}}
|
|
%reduce, %reduce2 = linalg.reduce
|
|
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>)
|
|
outs(%init1, %init2 : tensor<16x64xf32>, tensor<17x64xf32>)
|
|
dimensions = [1]
|
|
(%in1: f32, %in2: f32, %out1: f32, %out2: f32) {
|
|
%0 = arith.addf %in1, %out1: f32
|
|
%1 = arith.addf %in2, %out2: f32
|
|
linalg.yield %0, %1: f32, f32
|
|
}
|
|
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<17x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_invalid_permutation(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op permutation is not valid}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [1, 1, 2]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_out_of_range_permutation(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op permutation is not valid}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [1, 2, 3]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_negative_permutation(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op permutation is not valid}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [1, 2, -1]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
// -----
|
|
func.func @transpose_permutated_dims_mismatch(%input: tensor<16x32x64xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op dim(result, 0) = 32 doesn't match dim(input, permutation[0]) = 16}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [0, 1, 2]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_rank_permutation_size_mismatch(
|
|
%input: tensor<16x32x64xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op size of permutation 2 does not match the argument rank 3}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32x64xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [1, 0]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_input_init_rank_mismatch(%input: tensor<16x32xf32>,
|
|
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{'linalg.transpose' op input rank 2 does not match init rank 3}}
|
|
%transpose = linalg.transpose
|
|
ins(%input:tensor<16x32xf32>)
|
|
outs(%init:tensor<32x64x16xf32>)
|
|
permutation = [1, 0, 2]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_no_operands1() {
|
|
// expected-error @+1 {{'linalg.transpose' op expected 2 operands, but found 0}}
|
|
linalg.transpose permutation = [1, 0, 2]
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_no_operands2() {
|
|
// expected-error @+1 {{'linalg.transpose' op expected 2 operands, but found 0}}
|
|
"linalg.transpose"() <{permutation = array<i64: 1, 0, 2>}> ({
|
|
^bb0:
|
|
}) : () -> ()
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @transpose_no_operands3() -> tensor<32x64x16xf32> {
|
|
// expected-error @+1 {{cannot name an operation with no results}}
|
|
%transpose = linalg.transpose permutation = [1, 0, 2]
|
|
func.return %transpose : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_input_dims_rank_mismatch(
|
|
%input: tensor<4x16xf32>, %init: tensor<4x8x16xf32>)
|
|
-> tensor<4x8x16xf32> {
|
|
// expected-error @+1 {{'linalg.broadcast' op input rank plus added dimensions does not match init rank. }}
|
|
%bcast = linalg.broadcast
|
|
ins(%input:tensor<4x16xf32>)
|
|
outs(%init:tensor<4x8x16xf32>)
|
|
dimensions = [1, 2]
|
|
func.return %bcast : tensor<4x8x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_unsorted_dims(
|
|
%input: tensor<4x16xf32>, %init: tensor<4x8x16xf32>)
|
|
-> tensor<4x8x16xf32> {
|
|
// expected-error @+1 {{'linalg.broadcast' op dimension 0 is out of range. expected range: [0, 2], got: 5}}
|
|
%bcast = linalg.broadcast
|
|
ins(%input:tensor<4x16xf32>)
|
|
outs(%init:tensor<4x8x16xf32>)
|
|
dimensions = [5]
|
|
func.return %bcast : tensor<4x8x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_mapped_dim_mismatch(
|
|
%input: tensor<4x16xf32>, %init: tensor<5x8x16xf32>)
|
|
-> tensor<5x8x16xf32> {
|
|
// expected-error @+1 {{'linalg.broadcast' op input dim 0 should match init dim 0. input: 4, init: 5}}
|
|
%bcast = linalg.broadcast
|
|
ins(%input:tensor<4x16xf32>)
|
|
outs(%init:tensor<5x8x16xf32>)
|
|
dimensions = [1]
|
|
func.return %bcast : tensor<5x8x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_size_1_extension_not_supported(
|
|
%input: tensor<1x16xf32>, %init: tensor<4x?x16xf32>)
|
|
-> tensor<4x?x16xf32> {
|
|
// expected-error @+1 {{'linalg.broadcast' op input dim 0 should match init dim 0. input: 1, init: 4}}
|
|
%bcast = linalg.broadcast
|
|
ins(%input:tensor<1x16xf32>)
|
|
outs(%init:tensor<4x?x16xf32>)
|
|
dimensions = [1]
|
|
func.return %bcast : tensor<4x?x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_no_operands1() {
|
|
// expected-error @+1 {{'linalg.broadcast' op expected 2 operands, but found 0}}
|
|
linalg.broadcast dimensions = [1]
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_no_operands2() {
|
|
// expected-error @+1 {{'linalg.broadcast' op expected 2 operands, but found 0}}
|
|
"linalg.broadcast"() <{dimensions = array<i64: 1>}> ({
|
|
^bb0:
|
|
}) : () -> ()
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @broadcast_no_operands3()
|
|
-> tensor<4x?x16xf32> {
|
|
// expected-error @+1 {{cannot name an operation with no results}}
|
|
%broadcast = linalg.broadcast dimensions = [1]
|
|
func.return %broadcast : tensor<32x64x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @missing_iterator_types() {
|
|
// expected-error @below {{expected "iterator_types" array attribute}}
|
|
linalg.generic {} ins() outs()
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @illegal_softmax_output_shape(%arg0: tensor<2x16x32xf32>) -> tensor<2x16xf32> {
|
|
%0 = tensor.empty() : tensor<2x16xf32>
|
|
// expected-error @+1 {{incompatible output shape}}
|
|
%1 = linalg.softmax dimension(2) ins(%arg0 : tensor<2x16x32xf32>)
|
|
outs(%0: tensor<2x16xf32>)
|
|
-> tensor<2x16xf32>
|
|
return %1 : tensor<2x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @mmt4d_dims_mismatch(%A: tensor<16x16x8x1xf32>,
|
|
%B: tensor<16x16x8x1xf32>,
|
|
%C_in: tensor<16x16x8x1xf32>) -> tensor<16x16x8x1xf32> {
|
|
// expected-error @+1 {{inferred input/output operand #2 has shape's dimension #3 to be 8, but found 1}}
|
|
%res = linalg.mmt4d
|
|
ins(%A, %B: tensor<16x16x8x1xf32>, tensor<16x16x8x1xf32>)
|
|
outs(%C_in: tensor<16x16x8x1xf32>)
|
|
-> tensor<16x16x8x1xf32>
|
|
return %res : tensor<16x16x8x1xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @mmt4d_rank_mismatch(%A: tensor<16x16x8x1xf32>,
|
|
%B: tensor<16x16x8x1xf32>,
|
|
%C_in: tensor<8x8xf32>) -> tensor<8x8xf32> {
|
|
// expected-error @+1 {{expected operand rank (2) to match the result rank of indexing_map #2 (4)}}
|
|
%res = linalg.mmt4d
|
|
ins(%A, %B: tensor<16x16x8x1xf32>, tensor<16x16x8x1xf32>)
|
|
outs(%C_in: tensor<8x8xf32>)
|
|
-> tensor<8x8xf32>
|
|
return %res : tensor<8x8xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @mixed_semantics(%a: tensor<?x?xf32>, %b: tensor<?x?xf32>, %c: memref<?x?xf32>) {
|
|
// expected-error @+1 {{expected to have pure tensor or buffer semantics}}
|
|
linalg.matmul ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>)
|
|
outs(%c: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_filter_transform_height(%arg0: tensor<2x4x3x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> {
|
|
// expected-error @+1 {{expect filter height either equals to r or 1}}
|
|
%0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x4x3x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>
|
|
return %0 : tensor<6x6x5x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_filter_transform_width(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> {
|
|
// expected-error @+1 {{expect filter width either equals to r or 1}}
|
|
%0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x3x4x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>
|
|
return %0 : tensor<6x6x5x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_filter_transform(%arg0: tensor<2x1x1x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> {
|
|
// expected-error @+1 {{expect either filter height or width equals to r}}
|
|
%0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x1x1x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>
|
|
return %0 : tensor<6x6x5x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_filter_dyn(%arg0: tensor<?x3x3x?xf32>, %arg1: tensor<6x5x?x?xf32>) -> tensor<6x5x?x?xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<?x3x3x?xf32>) outs(%arg1 : tensor<6x5x?x?xf32>) -> tensor<6x5x?x?xf32>
|
|
return %0 : tensor<6x5x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_height(%arg0: tensor<2x13x14x5xf32>, %arg1: tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x13x14x5xf32>) outs(%arg1 : tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32>
|
|
return %0 : tensor<6x6x3x3x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_width(%arg0: tensor<2x14x13x5xf32>, %arg1: tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x13x5xf32>) outs(%arg1 : tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32>
|
|
return %0 : tensor<6x6x3x3x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_output_tileH(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x6x2x3x2x5xf32>) -> tensor<6x6x2x3x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x6x2x3x2x5xf32>) -> tensor<6x6x2x3x2x5xf32>
|
|
return %0 : tensor<6x6x2x3x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_output_tileW(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x6x3x2x2x5xf32>) -> tensor<6x6x3x2x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x6x3x2x2x5xf32>) -> tensor<6x6x3x2x2x5xf32>
|
|
return %0 : tensor<6x6x3x2x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_output_height(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<5x6x3x3x2x5xf32>) -> tensor<5x6x3x3x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<5x6x3x3x2x5xf32>) -> tensor<5x6x3x3x2x5xf32>
|
|
return %0 : tensor<5x6x3x3x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_transform_output_width(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x5x3x3x2x5xf32>) -> tensor<6x5x3x3x2x5xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x5x3x3x2x5xf32>) -> tensor<6x5x3x3x2x5xf32>
|
|
return %0 : tensor<6x5x3x3x2x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_input_dyn(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<6x5x?x?x?x?xf32>) -> tensor<6x5x?x?x?x?xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<?x?x?x?xf32>) outs(%arg1 : tensor<6x5x?x?x?x?xf32>) -> tensor<6x5x?x?x?x?xf32>
|
|
return %0 : tensor<6x5x?x?x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_output_transform_input_height(%arg0: tensor<5x6x3x3x2x2xf32>, %arg1: tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> {
|
|
// expected-error @+1 {{expect input height equals to input tile size}}
|
|
%0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<5x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32>
|
|
return %0 : tensor<2x12x12x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_output_transform_input_width(%arg0: tensor<6x5x3x3x2x2xf32>, %arg1: tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> {
|
|
// expected-error @+1 {{expect input width equals to input tile size}}
|
|
%0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x5x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32>
|
|
return %0 : tensor<2x12x12x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_output_transform_output_height(%arg0: tensor<6x6x3x3x2x2xf32>, %arg1: tensor<2x11x12x2xf32>) -> tensor<2x11x12x2xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x11x12x2xf32>) -> tensor<2x11x12x2xf32>
|
|
return %0 : tensor<2x11x12x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @winograd_output_transform_output_width(%arg0: tensor<6x6x3x3x2x2xf32>, %arg1: tensor<2x12x11x2xf32>) -> tensor<2x12x11x2xf32> {
|
|
// expected-error @+1 {{the output shape is not expected}}
|
|
%0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x11x2xf32>) -> tensor<2x12x11x2xf32>
|
|
return %0 : tensor<2x12x11x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @indexing_map_size_mismatch_batch_matmul(%arg0: memref<?x?x?xf32>,
|
|
%arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{Indexing_map attribute must have 3 affine maps}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @indexing_map_size_one_batch_matmul(%arg0: memref<?x?x?xf32>,
|
|
%arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{Indexing_map attribute must have 3 affine maps}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @missing_indexing_map_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{expected attribute value}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2 :memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_dim_expr_batch_matmul_a(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_dim_expr_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d1)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_batch_matmul_a(%arg0: memref<?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_single_dim_bcast_expr_batch_matmul_a(%arg0: memref<?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d3, d0)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_single_dim_bcast_expr_batch_matmul_B(%A: memref<?x?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d3, d0)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?xf32>) outs(%C: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?xf32>) outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_batch_dim_batch_matmul_a(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid batch dimension expression}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d1, d0, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_batch_dim_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid batch dimension expression}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d2, d3, d0)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_A_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op no. of result dim expressions exceeds 3.}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_B_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op no. of result dim expressions exceeds 3.}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_C_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op expects 3 dims, but got (2).}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d1, d2)>
|
|
]
|
|
ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_C_map_result_dim_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
|
|
// expected-error @+1 {{'linalg.batch_matmul' op Invalid output map result dimension.}}
|
|
linalg.batch_matmul indexing_maps = [
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
|
|
affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
|
|
]
|
|
ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?x?xf32>)
|
|
return
|
|
}
|
|
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// linalg.batch_reduce_matmul
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func.func @missing_one_indexing_map(%arg0: memref<?x?x?xf32>,
|
|
%arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Indexing_map attribute must have 3 affine maps}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, n, k)>]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @missing_two_indexing_map(%arg0: memref<?x?x?xf32>,
|
|
%arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Indexing_map attribute must have 3 affine maps}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2: memref<?x?xf32>)
|
|
return
|
|
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @missing_indexing_map(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) {
|
|
// expected-error @+1 {{expected attribute value}}
|
|
linalg.batch_reduce_matmul indexing_maps = [
|
|
,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%arg2 :memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_dim_expr_A(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, n, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C :memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_dim_expr_B(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, m)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C :memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_A(%A: memref<?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid broadcast requested}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_multi_dim_bcast_expr_A(%A: memref<?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid broadcast requested}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (k, batch)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_multi_dim_bcast_expr_B(%A: memref<?x?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid broadcast requested}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (k, batch)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_bcast_B(%A: memref<?x?x?xf32>, %B: memref<?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid broadcast requested}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (n)>,
|
|
affine_map<(batch, m, n, k) -> (batch, m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_batch_dim_A(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid batch dimension expression}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (m, batch, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C :memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_batch_dim_B(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid batch dimension expression}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (n, k, batch)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C :memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_A_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{no. of result dim expressions exceeds 3.}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_B_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{no. of result dim expressions exceeds 3.}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n, k)>,
|
|
affine_map<(batch, m, n, k) -> (m, n)>]
|
|
ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_C_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{expects 2 dims, but got (1).}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m)>]
|
|
ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @invalid_C_map_result_dim(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) {
|
|
// expected-error @+1 {{Invalid output map result dimension.}}
|
|
linalg.batch_reduce_matmul
|
|
indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>,
|
|
affine_map<(batch, m, n, k) -> (batch, k, n)>,
|
|
affine_map<(batch, m, n, k) -> (m, k)>]
|
|
ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
|
outs(%C: memref<?x?xf32>)
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// linalg.pack
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func.func @pack_invalid_no_padding_no_full_tiles(%input: tensor<256x128xf32>, %output: tensor<8x8x16x33xf32>) -> tensor<8x8x16x33xf32> {
|
|
// expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 33] into %output : tensor<256x128xf32> -> tensor<8x8x16x33xf32>
|
|
return %0 : tensor<8x8x16x33xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles(%input: tensor<256x128xf32>, %output: tensor<10x8x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<10x8x?x?xf32> {
|
|
// expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32> -> tensor<10x8x?x?xf32>
|
|
return %0 : tensor<10x8x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles_outperm(%input: tensor<256x128xf32>, %output: tensor<8x10x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<8x10x?x?xf32> {
|
|
// expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
|
|
%0 = linalg.pack %input outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32> -> tensor<8x10x?x?xf32>
|
|
return %0 : tensor<8x10x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pad_and_pack_invalid_type(%input: tensor<13x15xf32>, %output: tensor<2x8x8x2xf32>, %pad: i32) -> tensor<2x8x8x2xf32> {
|
|
// expected-error@+1 {{expected padding_value has 'f32' but got: 'i32'}}
|
|
%0 = linalg.pack %input padding_value(%pad: i32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<13x15xf32> -> tensor<2x8x8x2xf32>
|
|
return %0 : tensor<2x8x8x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_inner_dims_pos_vector(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{invalid inner_dims_pos vector}}
|
|
%0 = linalg.pack %input inner_dims_pos = [2, 0] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
|
|
return %0 : tensor<8x8x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_duplicate_element_in_inner_dims(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{invalid inner_dims_pos vector}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 1] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
|
|
return %0 : tensor<8x8x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_duplicate_element_in_outer_perm(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{invalid outer_dims_perm vector}}
|
|
%0 = linalg.pack %input outer_dims_perm = [1, 1] inner_dims_pos = [0, 1] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
|
|
return %0 : tensor<8x8x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_output_rank(%input: tensor<256x128xf32>, %output: tensor<64x32x16xf32>) -> tensor<64x32x16xf32> {
|
|
// expected-error@+1 {{packed rank != (unpacked rank + num tiling factors), got 3 != 4}}
|
|
%0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %output : tensor<256x128xf32> -> tensor<64x32x16xf32>
|
|
return %0 : tensor<64x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{invalid zero tile factor}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [0, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
|
|
return %0 : tensor<8x8x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
func.func @pack_mismatch_inner_tile_size_and_output_shape(
|
|
%input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> {
|
|
// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
|
|
%0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32>
|
|
return %0 : tensor<?x?x8x8xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_dynamic_inner_tile_size_and_static_output_shape(
|
|
%input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> {
|
|
%c8 = arith.constant 8 : index
|
|
// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
|
|
%0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, %c8] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32>
|
|
return %0 : tensor<?x?x8x8xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_static_inner_tile_size_and_dynamic_output_shape(
|
|
%input : tensor<?x?xf32>, %output : tensor<?x?x8x?xf32>) -> tensor<?x?x8x?xf32> {
|
|
// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
|
|
%0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %output : tensor<?x?xf32> -> tensor<?x?x8x?xf32>
|
|
return %0 : tensor<?x?x8x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid_outer_dims_perm(%source: tensor<128x256xf32>, %dest: tensor<16x4x32x16xf32>) -> tensor<16x4x32x16xf32> {
|
|
// expected-error@+1 {{outer_dims_perm must be a permutation or empty}}
|
|
%0 = linalg.pack %source outer_dims_perm = [0] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<128x256xf32> -> tensor<16x4x32x16xf32>
|
|
return %0 : tensor<16x4x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// linalg.unpack
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func.func @unpack_invalid_output_rank(%input: tensor<256x128xf32>, %output: tensor<64x32x16xf32>) -> tensor<256x128xf32> {
|
|
// expected-error@+1 {{packed rank != (unpacked rank + num tiling factors), got 3 != 4}}
|
|
%0 = linalg.unpack %output inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %input : tensor<64x32x16xf32> -> tensor<256x128xf32>
|
|
return %0 : tensor<256x128xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unpack_invalid_out_of_bound_outer_perm(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{invalid outer_dims_perm vector}}
|
|
%0 = linalg.unpack %output outer_dims_perm = [2, 1] inner_dims_pos = [0, 1] inner_tiles = [2, 2] into %input : tensor<8x8x32x16xf32> -> tensor<256x128xf32>
|
|
return %0 : tensor<256x128xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unpack_invalid_outer_dims_perm(%source: tensor<128x256xf32>, %dest: tensor<16x4x32x16xf32>) -> tensor<128x256xf32> {
|
|
// expected-error@+1 {{outer_dims_perm must be a permutation or empty}}
|
|
%0 = linalg.unpack %dest outer_dims_perm = [1] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %source : tensor<16x4x32x16xf32> -> tensor<128x256xf32>
|
|
return %0 : tensor<128x256xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// The outer dims in the output tensor are incorrectly/unexpectedly transposed.
|
|
// This could be fixed by adding `outer_dims_perm = [1, 0]` (the default value assumes no transpose).
|
|
func.func @pack_invalid_result_shape(%input: tensor<256x128xf32>, %output: tensor<4x16x32x16xf32>) -> tensor<4x16x32x16xf32> {
|
|
// expected-error@+1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<16x4x32x16xf32>', got 'tensor<4x16x32x16xf32>'}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [32, 16] into %output : tensor<256x128xf32> -> tensor<4x16x32x16xf32>
|
|
return %0 : tensor<4x16x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
|
|
// expected-error@+1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x8x16x32xf32>', got 'tensor<8x8x32x16xf32>'}}
|
|
%0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 32] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
|
|
return %0 : tensor<8x8x32x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unpack_invalid(%output: tensor<256x128xf32>, %input: tensor<8x8x32x16xf32>) -> tensor<256x128xf32> {
|
|
// expected-error@+1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x32x4x32xf32>', got 'tensor<8x8x32x16xf32>'}}
|
|
%0 = linalg.unpack %input inner_dims_pos = [1, 0] inner_tiles = [4, 32] into %output : tensor<8x8x32x16xf32> -> tensor<256x128xf32>
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return %0 : tensor<256x128xf32>
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}
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|
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// -----
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|
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func.func @unpack_mismatch_inner_tile_size_and_output_shape(
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%input : tensor<?x?x8x8xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
|
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// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
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%0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x8x8xf32> -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
|
|
|
|
// -----
|
|
|
|
func.func @unpack_dynamic_inner_tile_size_and_static_output_shape(
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%input : tensor<?x?x8x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
|
|
%c8 = arith.constant 8 : index
|
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// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
|
|
%0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%c8, 4] into %output : tensor<?x?x8x4xf32> -> tensor<?x?xf32>
|
|
return %0 : tensor<?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @unpack_static_inner_tile_size_and_dynamic_output_shape(
|
|
%input : tensor<?x?x?x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
|
|
// expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
|
|
%0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x?x4xf32> -> tensor<?x?xf32>
|
|
return %0 : tensor<?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// linalg.reduce
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
|
|
func.func @reduce_non_operation_name(%arg0: tensor<4xf32>, %arg1: tensor<f32>) -> tensor<f32> {
|
|
// expected-error @below {{expected bare identifier or keyword}}
|
|
%0 = linalg.reduce {@reduce_fusion_elementwise} ins(
|
|
%arg0: tensor<4xf32>) outs(%arg1: tensor<f32>) dimensions = [0]
|
|
return %0 : tensor<f32>
|
|
}
|
|
|
|
// -----
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Tests for generic infrastructure for named Ops. The actual Ops used are
|
|
// secondary - we merely want to ensure that the diagnostic infra triggers
|
|
// correctly.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
module {
|
|
func.func @add_invalid_mixed_types(%in_f32: memref<3xf32>, %in_i32 : memref< 3xi32>, %out_f32: memref<3xf32>, %arg3: memref<3xf32>) {
|
|
// expected-error @below {{Cannot build binary Linalg operation: expects allComplex, allFloatingPoint, or allInteger, got 'f32' and 'i32'}}
|
|
linalg.add ins(%in_f32, %in_i32 : memref<3xf32>, memref< 3xi32>) outs(%out_f32 : memref<3xf32>)
|
|
return
|
|
}
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @elemwise_unary_invalid_mixed_types(%arg0 : tensor<?xi32>) -> tensor<?xi32> {
|
|
// expected-error @below {{unsupported non numeric type}}
|
|
%0 = linalg.elemwise_unary ins(%arg0 : tensor<?xi32>) outs(%arg0 : tensor<?xi32>) -> tensor<?xi32>
|
|
return %0 : tensor<?xi32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @matmul_invalid_mixed_types(%t: tensor<?xf16>, %f: vector<4xf16>)
|
|
-> (tensor<?xf16>, vector<4xf16>)
|
|
{
|
|
// expected-warning @unknown {{could not cast operand of type 'f16' to 'vector<4xf16>'}}
|
|
// expected-error @below {{Cannot build binary Linalg operation: expects allComplex, allFloatingPoint, or allInteger, got 'vector<4xf16>' and 'f16'}}
|
|
%0 = linalg.matmul ins(%t, %t : tensor<?xf16>, tensor<?xf16>)
|
|
outs(%f : vector<4xf16>) -> tensor<?xf16>
|
|
func.return %0, %f : tensor<?xf16>, vector<4xf16>
|
|
}
|