83f3b1cb48
1. Verify that the type of explicit/implicit values should be the same as the tensor element type. 2. Verify that implicit value could only be zero. 3. Verify that explicit/implicit values should be numeric. 4. Fix the type change issue caused by SparseTensorType(enc).
531 lines
16 KiB
MLIR
531 lines
16 KiB
MLIR
// RUN: mlir-opt %s -split-input-file -verify-diagnostics
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// expected-error@+1 {{expected '(' in dimension-specifier list}}
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#a = #sparse_tensor.encoding<{map = []}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '->'}}
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#a = #sparse_tensor.encoding<{map = ()}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected ')' in dimension-specifier list}}
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#a = #sparse_tensor.encoding<{map = (d0 -> d0)}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '(' in dimension-specifier list}}
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#a = #sparse_tensor.encoding<{map = d0 -> d0}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '(' in level-specifier list}}
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#a = #sparse_tensor.encoding<{map = (d0) -> d0}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected ':'}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0)}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected valid level format (e.g. dense, compressed or singleton)}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0:)}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+1 {{expected valid level format (e.g. dense, compressed or singleton)}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : (compressed))}>
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func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
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// -----
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// expected-error@+2 {{dimension-rank mismatch between encoding and tensor shape: 2 != 1}}
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#a = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d1 : compressed)}>
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func.func private @tensor_dimlevel_size_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{Batch lvlType can only be leading levels}}
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#a = #sparse_tensor.encoding<{map = (d0, d1, d2) -> (d0 : batch, d1 : compressed, d2: batch)}>
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func.func private @non_leading_batch(%arg0: tensor<?x?x?i32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : dense, d1 : compressed)}>
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func.func private @tensor_sizes_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{failed to infer lvlToDim from dimToLvl}}
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#a = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense)}>
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func.func private @tensor_sizes_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected bare identifier}}
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#a = #sparse_tensor.encoding<{map = (1)}>
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func.func private @tensor_type_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{unexpected key: nap}}
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#a = #sparse_tensor.encoding<{nap = (d0) -> (d0 : dense)}>
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func.func private @tensor_type_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '(' in dimension-specifier list}}
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#a = #sparse_tensor.encoding<{map = -> (d0 : dense)}>
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func.func private @tensor_type_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{unknown level format: strange}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : strange)}>
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func.func private @tensor_value_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected valid level format (e.g. dense, compressed or singleton)}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : "wrong")}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected valid level property (e.g. nonordered, nonunique or high)}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed("wrong"))}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected ')' in level-specifier list}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed[high])}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{unknown level property: wrong}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed(wrong))}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier}}
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#a = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed, dense)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<8xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{failed to infer lvlToDim from dimToLvl}}
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#a = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d0 : compressed)}>
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func.func private @tensor_no_permutation(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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// expected-error@+1 {{unexpected character}}
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#a = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed; d1 : dense)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected attribute value}}
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#a = #sparse_tensor.encoding<{map = (d0: d1) -> (d0 : compressed, d1 : dense)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected ':'}}
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#a = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 = compressed, d1 = dense)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected attribute value}}
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#a = #sparse_tensor.encoding<{map = (d0 : compressed, d1 : compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier}}
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#a = #sparse_tensor.encoding<{map = (d0 = compressed, d1 = compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier}}
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#a = #sparse_tensor.encoding<{map = (d0 = l0, d1 = l1) {l0, l1} -> (l0 = d0 : dense, l1 = d1 : compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '='}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 : d0 = dense, l1 : d1 = compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier 'd0'}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (d0 : l0 = dense, d1 : l1 = compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier 'd0'}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (d0 : dense, d1 : compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{expected '='}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 : dense, l1 : compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 = dense, l1 = compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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// expected-error@+1 {{use of undeclared identifier 'd0'}}
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#a = #sparse_tensor.encoding<{map = {l0, l1} (d0 = l0, d1 = l1) -> (d0 = l0 : dense, d1 = l1 : compressed)}>
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func.func private @tensor_dimtolvl_mismatch(%arg0: tensor<16x32xi32, #a>) -> ()
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// -----
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#a = #sparse_tensor.encoding<{posWidth = "x"}> // expected-error {{expected an integral position bitwidth}}
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func.func private @tensor_no_int_ptr(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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#a = #sparse_tensor.encoding<{posWidth = 42}> // expected-error {{unexpected position bitwidth: 42}}
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func.func private @tensor_invalid_int_ptr(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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#a = #sparse_tensor.encoding<{crdWidth = "not really"}> // expected-error {{expected an integral index bitwidth}}
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func.func private @tensor_no_int_index(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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#a = #sparse_tensor.encoding<{crdWidth = 128}> // expected-error {{unexpected coordinate bitwidth: 128}}
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func.func private @tensor_invalid_int_index(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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#a = #sparse_tensor.encoding<{key = 1}> // expected-error {{unexpected key: key}}
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func.func private @tensor_invalid_key(%arg0: tensor<16x32xf32, #a>) -> ()
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// -----
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#CSR_SLICE = #sparse_tensor.encoding<{
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map = (d0 : #sparse_tensor<slice(-1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)// expected-error{{expect positive value or ? for slice offset/size/stride}}
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}>
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func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
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// -----
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// expected-error@+2 {{Level-rank mismatch between forward-declarations and specifiers. Declared 3 level-variables; but got 2 level-specifiers.}}
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#TooManyLvlDecl = #sparse_tensor.encoding<{
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map = {l0, l1, l2} (d0, d1) -> (l0 = d0 : dense, l1 = d1 : compressed)
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}>
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func.func private @too_many_lvl_decl(%arg0: tensor<?x?xf64, #TooManyLvlDecl>) {
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return
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}
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// -----
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// expected-error@+1{{expected all singleton lvlTypes stored in the same memory layout (SoA vs AoS).}}
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#COO_SoA = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(soa, nonunique), d2 : singleton)
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}>
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func.func private @sparse_coo(tensor<?x?xf32, #COO_SoA>)
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// -----
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// expected-error@+1{{SoA is only applicable to singleton lvlTypes.}}
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#COO_SoA = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed(nonunique, soa), d1 : singleton(soa))
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}>
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func.func private @sparse_coo(tensor<?x?xf32, #COO_SoA>)
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// -----
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// expected-error@+2 {{use of undeclared identifier 'l1'}}
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#TooFewLvlDecl = #sparse_tensor.encoding<{
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map = {l0} (d0, d1) -> (l0 = d0 : dense, l1 = d1 : compressed)
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}>
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func.func private @too_few_lvl_decl(%arg0: tensor<?x?xf64, #TooFewLvlDecl>) {
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return
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}
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// -----
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// expected-error@+2 {{Level-variable ordering mismatch. The variable 'l0' was forward-declared as the 1st level; but is bound by the 0th specification.}}
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#WrongOrderLvlDecl = #sparse_tensor.encoding<{
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map = {l1, l0} (d0, d1) -> (l0 = d0 : dense, l1 = d1 : compressed)
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}>
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func.func private @wrong_order_lvl_decl(%arg0: tensor<?x?xf64, #WrongOrderLvlDecl>) {
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return
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}
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// -----
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// expected-error@+1 {{failed to infer lvlToDim from dimToLvl}}
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#BSR = #sparse_tensor.encoding<{
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map = ( i, j ) ->
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( i floordiv 2 : dense,
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j floordiv 3 : compressed,
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i : dense,
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j mod 3 : dense
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)
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}>
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func.func private @BSR(%arg0: tensor<?x?xf64, #BSR>) {
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return
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}
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// -----
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// expected-error@+1 {{failed to infer lvlToDim from dimToLvl}}
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#BSR = #sparse_tensor.encoding<{
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map = ( i, j ) ->
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( i : dense,
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j floordiv 3 : compressed,
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i floordiv 3 : dense,
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j mod 3 : dense
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)
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}>
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func.func private @BSR(%arg0: tensor<?x?xf64, #BSR>) {
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return
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}
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// -----
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// expected-error@+1 {{failed to infer lvlToDim from dimToLvl}}
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#BSR = #sparse_tensor.encoding<{
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map = ( i, j ) ->
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( i floordiv -3 : dense,
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j floordiv -3 : compressed,
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i mod 3 : dense,
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j mod 3 : dense
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)
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}>
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func.func private @BSR(%arg0: tensor<?x?xf64, #BSR>) {
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return
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}
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// -----
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// expected-error@+1 {{expected lvlToDim to be an inverse of dimToLvl}}
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#BSR_explicit = #sparse_tensor.encoding<{
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map =
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{il, jl, ii, jj}
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( i = il * 3 + ii,
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j = jl * 2 + jj
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) ->
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( il = i floordiv 2 : dense,
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jl = j floordiv 3 : compressed,
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ii = i mod 2 : dense,
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jj = j mod 3 : dense
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)
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}>
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func.func private @BSR_explicit(%arg0: tensor<?x?xf64, #BSR_explicit>) {
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return
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}
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// -----
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// expected-error@+6 {{expected structured size to be >= 0}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 4 : dense,
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j : dense,
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k mod 4 : structured[-2, 4]
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+6 {{expected n <= m in n_out_of_m}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 4 : dense,
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j : dense,
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k mod 4 : structured[5, 4]
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+1 {{expected all dense lvlTypes before a n_out_of_m level}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 4 : compressed,
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j : dense,
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k mod 4 : structured[2, 4]
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+1 {{expected n_out_of_m to be the last level type}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 4 : structured[2, 4],
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j : dense,
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k mod 4 : compressed
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+1 {{expected 1xm block structure for n_out_of_m level}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 2 : dense,
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j : dense,
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k mod 4 : structured[2, 4]
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+1 {{expected coeffiencts of Affine expressions to be equal to m of n_out_of_m level}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i : dense,
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k floordiv 2 : dense,
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j : dense,
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k mod 2 : structured[2, 4]
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)
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}>
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func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
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return
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}
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// -----
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// expected-error@+1 {{expected only one blocked level with the same coefficients}}
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#NOutOfM = #sparse_tensor.encoding<{
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map = ( i, j, k ) ->
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( i floordiv 2 : dense,
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i mod 2 : dense,
|
|
j : dense,
|
|
k floordiv 4 : dense,
|
|
k mod 4 : structured[2, 4]
|
|
)
|
|
}>
|
|
func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
#CSR_ExpType = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
explicitVal = 1 : i32,
|
|
implicitVal = 0.0 : f32
|
|
}>
|
|
|
|
// expected-error@+1 {{explicit value type mismatch between encoding and tensor element type: 'i32' != 'f32'}}
|
|
func.func private @sparse_csr(tensor<?x?xf32, #CSR_ExpType>)
|
|
|
|
// -----
|
|
|
|
#CSR_ImpType = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
explicitVal = 1 : i32,
|
|
implicitVal = 0.0 : f32
|
|
}>
|
|
|
|
// expected-error@+1 {{implicit value type mismatch between encoding and tensor element type: 'f32' != 'i32'}}
|
|
func.func private @sparse_csr(tensor<?x?xi32, #CSR_ImpType>)
|
|
|
|
// -----
|
|
|
|
// expected-error@+1 {{expected a numeric value for explicitVal}}
|
|
#CSR_ExpType = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
explicitVal = "str"
|
|
}>
|
|
func.func private @sparse_csr(tensor<?x?xi32, #CSR_ExpType>)
|
|
|
|
// -----
|
|
|
|
// expected-error@+1 {{expected a numeric value for implicitVal}}
|
|
#CSR_ImpType = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
implicitVal = "str"
|
|
}>
|
|
func.func private @sparse_csr(tensor<?x?xi32, #CSR_ImpType>)
|
|
|
|
// -----
|
|
|
|
#CSR_ImpVal = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
implicitVal = 1 : i32
|
|
}>
|
|
|
|
// expected-error@+1 {{implicit value must be zero}}
|
|
func.func private @sparse_csr(tensor<?x?xi32, #CSR_ImpVal>)
|
|
|
|
// -----
|
|
|
|
#CSR_ImpVal = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 32,
|
|
crdWidth = 32,
|
|
implicitVal = 1.0 : f32
|
|
}>
|
|
|
|
// expected-error@+1 {{implicit value must be zero}}
|
|
func.func private @sparse_csr(tensor<?x?xf32, #CSR_ImpVal>)
|
|
|
|
// -----
|
|
|
|
#CSR_OnlyOnes = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed),
|
|
posWidth = 64,
|
|
crdWidth = 64,
|
|
explicitVal = #complex.number<:f32 1.0, 0.0>,
|
|
implicitVal = #complex.number<:f32 1.0, 0.0>
|
|
}>
|
|
|
|
// expected-error@+1 {{implicit value must be zero}}
|
|
func.func private @sparse_csr(tensor<?x?xcomplex<f32>, #CSR_OnlyOnes>)
|