forked from OSchip/llvm-project
[MLIR] Update tutorial to add missing tests and bring directory paths and code snippets up to date.
Summary: The tests referred to in Chapter 3 of the tutorial were missing from the tutorial test directory; this adds those missing tests. This also cleans up some stale directory paths and code snippets used throughout the tutorial. Subscribers: mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, aartbik, liufengdb, Joonsoo, llvm-commits Tags: #llvm Differential Revision: https://reviews.llvm.org/D76809
This commit is contained in:
Lucy Fox
parent
41ad8b7388
commit
495cf27291
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@ -109,48 +109,48 @@ The AST from the above code is fairly straightforward; here is a dump of it:
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```
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Module:
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Function
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Proto 'multiply_transpose' @test/ast.toy:5:1'
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Args: [a, b]
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Function
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Proto 'multiply_transpose' @test/Examples/Toy/Ch1/ast.toy:4:1'
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Params: [a, b]
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Block {
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Return
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BinOp: * @test/ast.toy:6:25
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Call 'transpose' [ @test/ast.toy:6:10
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var: a @test/ast.toy:6:20
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BinOp: * @test/Examples/Toy/Ch1/ast.toy:5:25
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Call 'transpose' [ @test/Examples/Toy/Ch1/ast.toy:5:10
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var: a @test/Examples/Toy/Ch1/ast.toy:5:20
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]
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Call 'transpose' [ @test/ast.toy:6:25
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var: b @test/ast.toy:6:35
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Call 'transpose' [ @test/Examples/Toy/Ch1/ast.toy:5:25
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var: b @test/Examples/Toy/Ch1/ast.toy:5:35
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]
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} // Block
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Function
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Proto 'main' @test/ast.toy:9:1'
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Args: []
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Function
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Proto 'main' @test/Examples/Toy/Ch1/ast.toy:8:1'
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Params: []
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Block {
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VarDecl a<> @test/ast.toy:11:3
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Literal: <2, 3>[<3>[1.000000e+00, 2.000000e+00, 3.000000e+00], <3>[4.000000e+00, 5.000000e+00, 6.000000e+00]] @test/ast.toy:11:17
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VarDecl b<2, 3> @test/ast.toy:12:3
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Literal: <6>[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00] @test/ast.toy:12:17
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VarDecl c<> @test/ast.toy:15:3
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Call 'multiply_transpose' [ @test/ast.toy:15:11
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var: a @test/ast.toy:15:30
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var: b @test/ast.toy:15:33
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VarDecl a<> @test/Examples/Toy/Ch1/ast.toy:11:3
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Literal: <2, 3>[ <3>[ 1.000000e+00, 2.000000e+00, 3.000000e+00], <3>[ 4.000000e+00, 5.000000e+00, 6.000000e+00]] @test/Examples/Toy/Ch1/ast.toy:11:11
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VarDecl b<2, 3> @test/Examples/Toy/Ch1/ast.toy:15:3
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Literal: <6>[ 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00] @test/Examples/Toy/Ch1/ast.toy:15:17
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VarDecl c<> @test/Examples/Toy/Ch1/ast.toy:19:3
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Call 'multiply_transpose' [ @test/Examples/Toy/Ch1/ast.toy:19:11
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var: a @test/Examples/Toy/Ch1/ast.toy:19:30
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var: b @test/Examples/Toy/Ch1/ast.toy:19:33
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]
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VarDecl d<> @test/ast.toy:18:3
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Call 'multiply_transpose' [ @test/ast.toy:18:11
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var: b @test/ast.toy:18:30
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var: a @test/ast.toy:18:33
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VarDecl d<> @test/Examples/Toy/Ch1/ast.toy:22:3
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Call 'multiply_transpose' [ @test/Examples/Toy/Ch1/ast.toy:22:11
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var: b @test/Examples/Toy/Ch1/ast.toy:22:30
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var: a @test/Examples/Toy/Ch1/ast.toy:22:33
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]
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VarDecl e<> @test/ast.toy:21:3
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Call 'multiply_transpose' [ @test/ast.toy:21:11
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var: b @test/ast.toy:21:30
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var: c @test/ast.toy:21:33
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VarDecl e<> @test/Examples/Toy/Ch1/ast.toy:25:3
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Call 'multiply_transpose' [ @test/Examples/Toy/Ch1/ast.toy:25:11
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var: b @test/Examples/Toy/Ch1/ast.toy:25:30
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var: c @test/Examples/Toy/Ch1/ast.toy:25:33
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]
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VarDecl f<> @test/ast.toy:24:3
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Call 'multiply_transpose' [ @test/ast.toy:24:11
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Call 'transpose' [ @test/ast.toy:24:30
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var: a @test/ast.toy:24:40
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VarDecl f<> @test/Examples/Toy/Ch1/ast.toy:28:3
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Call 'multiply_transpose' [ @test/Examples/Toy/Ch1/ast.toy:28:11
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Call 'transpose' [ @test/Examples/Toy/Ch1/ast.toy:28:30
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var: a @test/Examples/Toy/Ch1/ast.toy:28:40
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]
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var: c @test/ast.toy:24:44
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var: c @test/Examples/Toy/Ch1/ast.toy:28:44
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]
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} // Block
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```
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@ -491,8 +491,7 @@ def ConstantOp : Toy_Op<"constant"> {
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#### Specifying a Custom Assembly Format
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At this point we can generate our "Toy IR". A simplified version of the previous
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example:
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At this point we can generate our "Toy IR". For example, the following:
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```toy
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# User defined generic function that operates on unknown shaped arguments.
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@ -514,22 +513,22 @@ Results in the following IR:
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```mlir
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module {
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
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%0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64> loc("test/codegen.toy":5:10)
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%1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64> loc("test/codegen.toy":5:25)
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%2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64> loc("test/codegen.toy":5:25)
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"toy.return"(%2) : (tensor<*xf64>) -> () loc("test/codegen.toy":5:3)
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} loc("test/codegen.toy":4:1)
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%0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10)
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%1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
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%2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
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"toy.return"(%2) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":5:3)
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} loc("test/Examples/Toy/Ch2/codegen.toy":4:1)
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func @main() {
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%0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> loc("test/codegen.toy":9:17)
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%1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64> loc("test/codegen.toy":9:3)
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%2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64> loc("test/codegen.toy":10:17)
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%3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64> loc("test/codegen.toy":10:3)
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%4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":11:11)
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%5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":12:11)
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"toy.print"(%5) : (tensor<*xf64>) -> () loc("test/codegen.toy":13:3)
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"toy.return"() : () -> () loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":0:0)
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%0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17)
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%1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3)
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%2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17)
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%3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3)
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%4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11)
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%5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11)
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"toy.print"(%5) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":13:3)
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"toy.return"() : () -> () loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
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} loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
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} loc(unknown)
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```
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One thing to notice here is that all of our Toy operations are printed using the
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@ -645,22 +644,22 @@ now get a much more readable:
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```mlir
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module {
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
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%0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("test/codegen.toy":5:10)
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%1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("test/codegen.toy":5:25)
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%2 = toy.mul %0, %1 : tensor<*xf64> loc("test/codegen.toy":5:25)
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toy.return %2 : tensor<*xf64> loc("test/codegen.toy":5:3)
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} loc("test/codegen.toy":4:1)
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%0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10)
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%1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
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%2 = toy.mul %0, %1 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
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toy.return %2 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:3)
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} loc("test/Examples/Toy/Ch2/codegen.toy":4:1)
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func @main() {
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%0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("test/codegen.toy":9:17)
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%1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("test/codegen.toy":9:3)
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%2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("test/codegen.toy":10:17)
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%3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("test/codegen.toy":10:3)
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%4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":11:11)
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%5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":12:11)
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toy.print %5 : tensor<*xf64> loc("test/codegen.toy":13:3)
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toy.return loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":0:0)
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%0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17)
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%1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3)
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%2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17)
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%3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3)
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%4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11)
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%5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11)
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toy.print %5 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":13:3)
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toy.return loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
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} loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
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} loc(unknown)
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```
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Above we introduce several of the concepts for defining operations in the ODS
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@ -670,53 +669,12 @@ variadic operands, etc. Check out the
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## Complete Toy Example
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At this point we can generate our "Toy IR". A simplified version of the previous
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example:
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```toy
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# User defined generic function that operates on unknown shaped arguments.
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def multiply_transpose(a, b) {
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return transpose(a) * transpose(b);
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}
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def main() {
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var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
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var b<2, 3> = [1, 2, 3, 4, 5, 6];
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var c = multiply_transpose(a, b);
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var d = multiply_transpose(b, a);
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print(d);
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}
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```
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Results in the following IR:
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```mlir
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module {
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
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%0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("test/codegen.toy":5:10)
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%1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("test/codegen.toy":5:25)
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%2 = toy.mul %0, %1 : tensor<*xf64> loc("test/codegen.toy":5:25)
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toy.return %2 : tensor<*xf64> loc("test/codegen.toy":5:3)
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} loc("test/codegen.toy":4:1)
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func @main() {
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%0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("test/codegen.toy":9:17)
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%1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("test/codegen.toy":9:3)
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%2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("test/codegen.toy":10:17)
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%3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("test/codegen.toy":10:3)
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%4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":11:11)
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%5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/codegen.toy":12:11)
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toy.print %5 : tensor<*xf64> loc("test/codegen.toy":13:3)
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toy.return loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":8:1)
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} loc("test/codegen.toy":0:0)
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```
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You can build `toyc-ch2` and try yourself: `toyc-ch2
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test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo`. We can also
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check our RoundTrip: `toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir
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-mlir-print-debuginfo 2> codegen.mlir` followed by `toyc-ch2 codegen.mlir
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-emit=mlir`. You should also use `mlir-tblgen` on the final definition file and
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study the generated C++ code.
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We can now generate our "Toy IR". You can build `toyc-ch2` and try yourself on
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the above example: `toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir
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-mlir-print-debuginfo`. We can also check our RoundTrip: `toyc-ch2
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test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo 2>
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codegen.mlir` followed by `toyc-ch2 codegen.mlir -emit=mlir`. You should also
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use `mlir-tblgen` on the final definition file and study the generated C++ code.
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At this point, MLIR knows about our Toy dialect and operations. In the
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[next chapter](Ch-3.md), we will leverage our new dialect to implement some
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@ -129,8 +129,8 @@ similar way to LLVM:
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pm.addNestedPass<mlir::FuncOp>(mlir::createCanonicalizerPass());
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```
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Finally, we can run `toyc-ch3 test/transpose_transpose.toy -emit=mlir -opt` and
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observe our pattern in action:
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Finally, we can run `toyc-ch3 test/Examples/Toy/Ch3/transpose_transpose.toy
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-emit=mlir -opt` and observe our pattern in action:
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```mlir
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func @transpose_transpose(%arg0: tensor<*xf64>) -> tensor<*xf64> {
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@ -216,7 +216,7 @@ def FoldConstantReshapeOptPattern : Pat<
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```
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We demonstrate these reshape optimizations using the following
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trivialReshape.toy program:
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trivial_reshape.toy program:
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```c++
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def main() {
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@ -240,8 +240,8 @@ module {
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}
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```
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|
||||
We can try to run `toyc-ch3 test/trivialReshape.toy -emit=mlir -opt` and observe
|
||||
our pattern in action:
|
||||
We can try to run `toyc-ch3 test/Examples/Toy/Ch3/trivial_reshape.toy -emit=mlir
|
||||
-opt` and observe our pattern in action:
|
||||
|
||||
```mlir
|
||||
module {
|
||||
|
|
|
|||
|
|
@ -222,7 +222,7 @@ def PrintOp : Toy_Op<"print"> {
|
|||
|
||||
## Complete Toy Example
|
||||
|
||||
Looking back at our current working example:
|
||||
Let's take a concrete example:
|
||||
|
||||
```mlir
|
||||
func @main() {
|
||||
|
|
@ -336,8 +336,8 @@ func @main() {
|
|||
|
||||
Here, we can see that a redundant allocation was removed, the two loop nests
|
||||
were fused, and some unnecessary `load`s were removed. You can build `toyc-ch5`
|
||||
and try yourself: `toyc-ch5 test/lowering.toy -emit=mlir-affine`. We can also
|
||||
check our optimizations by adding `-opt`.
|
||||
and try yourself: `toyc-ch5 test/Examples/Toy/Ch5/affine-lowering.mlir
|
||||
-emit=mlir-affine`. We can also check our optimizations by adding `-opt`.
|
||||
|
||||
In this chapter we explored some aspects of partial lowering, with the intent to
|
||||
optimize. In the [next chapter](Ch-6.md) we will continue the discussion about
|
||||
|
|
|
|||
|
|
@ -239,8 +239,8 @@ define void @main()
|
|||
}
|
||||
```
|
||||
|
||||
The full code listing for dumping LLVM IR can be found in `Ch6/toy.cpp` in the
|
||||
`dumpLLVMIR()` function:
|
||||
The full code listing for dumping LLVM IR can be found in
|
||||
`examples/toy/Ch6/toy.cpp` in the `dumpLLVMIR()` function:
|
||||
|
||||
```c++
|
||||
|
||||
|
|
@ -318,5 +318,8 @@ You can also play with `-emit=mlir`, `-emit=mlir-affine`, `-emit=mlir-llvm`, and
|
|||
[`--print-ir-after-all`](../../WritingAPass.md#ir-printing) to track the
|
||||
evolution of the IR throughout the pipeline.
|
||||
|
||||
The example code used throughout this section can be found in
|
||||
test/Examples/Toy/Ch6/llvm-lowering.mlir.
|
||||
|
||||
So far, we have worked with primitive data types. In the
|
||||
[next chapter](Ch-7.md), we will add a composite `struct` type.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,25 @@
|
|||
# RUN: toyc-ch3 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
# User defined generic function that operates on unknown shaped arguments
|
||||
def transpose_transpose(x) {
|
||||
return transpose(transpose(x));
|
||||
}
|
||||
|
||||
def main() {
|
||||
var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
|
||||
var b = transpose_transpose(a);
|
||||
print(b);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @transpose_transpose(
|
||||
# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>) -> tensor<*xf64>
|
||||
# CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return [[VAL_2]] : tensor<*xf64>
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_4:%.*]] = toy.reshape([[VAL_3]] : tensor<2x3xf64>) to tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_5:%.*]] = toy.generic_call @transpose_transpose([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<*xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_5]] : tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# RUN: toyc-ch3 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
def main() {
|
||||
var a<2,1> = [1, 2];
|
||||
var b<2,1> = a;
|
||||
var c<2,1> = b;
|
||||
print(c);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_0:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
|
||||
# CHECK-NEXT: [[VAL_1:%.*]] = toy.reshape([[VAL_0]] : tensor<2xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.reshape([[VAL_1]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.reshape([[VAL_2]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_3]] : tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# RUN: toyc-ch4 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
# User defined generic function that operates on unknown shaped arguments
|
||||
def transpose_transpose(x) {
|
||||
return transpose(transpose(x));
|
||||
}
|
||||
|
||||
def main() {
|
||||
var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
|
||||
var b = transpose_transpose(a);
|
||||
print(b);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @transpose_transpose(
|
||||
# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>) -> tensor<*xf64>
|
||||
# CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return [[VAL_2]] : tensor<*xf64>
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_4:%.*]] = toy.reshape([[VAL_3]] : tensor<2x3xf64>) to tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_5:%.*]] = toy.generic_call @transpose_transpose([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<*xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_5]] : tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# RUN: toyc-ch4 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
def main() {
|
||||
var a<2,1> = [1, 2];
|
||||
var b<2,1> = a;
|
||||
var c<2,1> = b;
|
||||
print(c);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_0:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
|
||||
# CHECK-NEXT: [[VAL_1:%.*]] = toy.reshape([[VAL_0]] : tensor<2xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.reshape([[VAL_1]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.reshape([[VAL_2]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_3]] : tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# RUN: toyc-ch5 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
# User defined generic function that operates on unknown shaped arguments
|
||||
def transpose_transpose(x) {
|
||||
return transpose(transpose(x));
|
||||
}
|
||||
|
||||
def main() {
|
||||
var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
|
||||
var b = transpose_transpose(a);
|
||||
print(b);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @transpose_transpose(
|
||||
# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>) -> tensor<*xf64>
|
||||
# CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return [[VAL_2]] : tensor<*xf64>
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_4:%.*]] = toy.reshape([[VAL_3]] : tensor<2x3xf64>) to tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_5:%.*]] = toy.generic_call @transpose_transpose([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<*xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_5]] : tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# RUN: toyc-ch5 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
def main() {
|
||||
var a<2,1> = [1, 2];
|
||||
var b<2,1> = a;
|
||||
var c<2,1> = b;
|
||||
print(c);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_0:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
|
||||
# CHECK-NEXT: [[VAL_1:%.*]] = toy.reshape([[VAL_0]] : tensor<2xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.reshape([[VAL_1]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.reshape([[VAL_2]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_3]] : tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# RUN: toyc-ch6 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
# User defined generic function that operates on unknown shaped arguments
|
||||
def transpose_transpose(x) {
|
||||
return transpose(transpose(x));
|
||||
}
|
||||
|
||||
def main() {
|
||||
var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
|
||||
var b = transpose_transpose(a);
|
||||
print(b);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @transpose_transpose(
|
||||
# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>) -> tensor<*xf64>
|
||||
# CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return [[VAL_2]] : tensor<*xf64>
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_4:%.*]] = toy.reshape([[VAL_3]] : tensor<2x3xf64>) to tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_5:%.*]] = toy.generic_call @transpose_transpose([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<*xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_5]] : tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# RUN: toyc-ch6 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
def main() {
|
||||
var a<2,1> = [1, 2];
|
||||
var b<2,1> = a;
|
||||
var c<2,1> = b;
|
||||
print(c);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_0:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
|
||||
# CHECK-NEXT: [[VAL_1:%.*]] = toy.reshape([[VAL_0]] : tensor<2xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.reshape([[VAL_1]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.reshape([[VAL_2]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_3]] : tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# RUN: toyc-ch7 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
# User defined generic function that operates on unknown shaped arguments
|
||||
def transpose_transpose(x) {
|
||||
return transpose(transpose(x));
|
||||
}
|
||||
|
||||
def main() {
|
||||
var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
|
||||
var b = transpose_transpose(a);
|
||||
print(b);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @transpose_transpose(
|
||||
# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>) -> tensor<*xf64>
|
||||
# CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return [[VAL_2]] : tensor<*xf64>
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_4:%.*]] = toy.reshape([[VAL_3]] : tensor<2x3xf64>) to tensor<2x3xf64>
|
||||
# CHECK-NEXT: [[VAL_5:%.*]] = toy.generic_call @transpose_transpose([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<*xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_5]] : tensor<*xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# RUN: toyc-ch7 %s -emit=mlir 2>&1 | FileCheck %s
|
||||
|
||||
def main() {
|
||||
var a<2,1> = [1, 2];
|
||||
var b<2,1> = a;
|
||||
var c<2,1> = b;
|
||||
print(c);
|
||||
}
|
||||
|
||||
# CHECK-LABEL: func @main()
|
||||
# CHECK-NEXT: [[VAL_0:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
|
||||
# CHECK-NEXT: [[VAL_1:%.*]] = toy.reshape([[VAL_0]] : tensor<2xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_2:%.*]] = toy.reshape([[VAL_1]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: [[VAL_3:%.*]] = toy.reshape([[VAL_2]] : tensor<2x1xf64>) to tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.print [[VAL_3]] : tensor<2x1xf64>
|
||||
# CHECK-NEXT: toy.return
|
||||
Loading…
Reference in New Issue