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WIP: learning changes
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‎llvm/examples/Kaleidoscope/Chapter2/toy.cpp‎

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@@ -33,9 +33,11 @@ static int gettok() {
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static int LastChar = ' ';
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// Skip any whitespace.
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while (isspace(LastChar))
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while (isspace(LastChar)){
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LastChar = getchar();
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}
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// Get identifier
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if (isalpha(LastChar)) { // identifier: [a-zA-Z][a-zA-Z0-9]*
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IdentifierStr = LastChar;
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while (isalnum((LastChar = getchar())))

‎tmp/ToyDialect.md‎

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<!-- Autogenerated by mlir-tblgen; don't manually edit -->
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### `toy.add` (toy::AddOp)
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_Element-wise addition operation_
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The "add" operation performs element-wise addition between two tensors.
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The shapes of the tensor operands are expected to match.
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `lhs` | tensor of 64-bit float values |
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| `rhs` | tensor of 64-bit float values |
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | tensor of 64-bit float values |
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### `toy.constant` (toy::ConstantOp)
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_Constant_
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Constant operation turns a literal into an SSA value. The data is attached
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to the operation as an attribute. For example:
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```mlir
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%0 = toy.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]>
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: tensor<2x3xf64>
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```
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Traits: `AlwaysSpeculatableImplTrait`
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Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`
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Effects: `MemoryEffects::Effect{}`
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#### Attributes:
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<table>
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<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
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<tr><td><code>value</code></td><td>::mlir::DenseElementsAttr</td><td>64-bit float elements attribute</td></tr>
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</table>
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | tensor of 64-bit float values |
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### `toy.func` (toy::FuncOp)
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_User defined function operation_
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The "toy.func" operation represents a user defined function. These are
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callable SSA-region operations that contain toy computations.
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Example:
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```mlir
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toy.func @main() {
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%0 = toy.constant dense<5.500000e+00> : tensor<f64>
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%1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
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toy.print %1 : tensor<2x2xf64>
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toy.return
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}
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```
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Traits: `IsolatedFromAbove`
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Interfaces: `ArgAndResultAttrsOpInterface`, `CallableOpInterface`, `FunctionOpInterface`, `Symbol`
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#### Attributes:
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<table>
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<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
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<tr><td><code>sym_name</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr>
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<tr><td><code>function_type</code></td><td>::mlir::TypeAttr</td><td>type attribute of function type</td></tr>
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<tr><td><code>arg_attrs</code></td><td>::mlir::ArrayAttr</td><td>Array of dictionary attributes</td></tr>
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<tr><td><code>res_attrs</code></td><td>::mlir::ArrayAttr</td><td>Array of dictionary attributes</td></tr>
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</table>
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### `toy.generic_call` (toy::GenericCallOp)
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_Generic call operation_
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Syntax:
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```
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operation ::= `toy.generic_call` $callee `(` $inputs `)` attr-dict `:` functional-type($inputs, results)
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```
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Generic calls represent calls to a user defined function that needs to
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be specialized for the shape of its arguments. The callee name is attached
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as a symbol reference via an attribute. The arguments list must match the
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arguments expected by the callee. For example:
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```mlir
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%4 = toy.generic_call @my_func(%1, %3)
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: (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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```
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This is only valid if a function named "my_func" exists and takes two
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arguments.
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#### Attributes:
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<table>
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<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
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<tr><td><code>callee</code></td><td>::mlir::FlatSymbolRefAttr</td><td>flat symbol reference attribute</td></tr>
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</table>
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `inputs` | variadic of tensor of 64-bit float values |
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | tensor of 64-bit float values |
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### `toy.mul` (toy::MulOp)
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_Element-wise multiplication operation_
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The "mul" operation performs element-wise multiplication between two
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tensors. The shapes of the tensor operands are expected to match.
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `lhs` | tensor of 64-bit float values |
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| `rhs` | tensor of 64-bit float values |
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | tensor of 64-bit float values |
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### `toy.print` (toy::PrintOp)
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_Print operation_
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Syntax:
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```
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operation ::= `toy.print` $input attr-dict `:` type($input)
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```
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The "print" builtin operation prints a given input tensor, and produces
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no results.
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `input` | tensor of 64-bit float values |
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### `toy.reshape` (toy::ReshapeOp)
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_Tensor reshape operation_
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Syntax:
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```
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operation ::= `toy.reshape` `(` $input `:` type($input) `)` attr-dict `to` type(results)
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```
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Reshape operation is transforming its input tensor into a new tensor with
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the same number of elements but different shapes. For example:
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```mlir
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%0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
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```
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `input` | tensor of 64-bit float values |
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | statically shaped tensor of 64-bit float values |
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### `toy.return` (toy::ReturnOp)
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_Return operation_
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Syntax:
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```
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operation ::= `toy.return` ($input^ `:` type($input))? attr-dict
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```
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The "return" operation represents a return operation within a function.
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The operation takes an optional tensor operand and produces no results.
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The operand type must match the signature of the function that contains
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the operation. For example:
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```mlir
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toy.func @foo() -> tensor<2xf64> {
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...
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toy.return %0 : tensor<2xf64>
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}
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```
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Traits: `AlwaysSpeculatableImplTrait`, `HasParent<FuncOp>`, `Terminator`
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Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`
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Effects: `MemoryEffects::Effect{}`
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `input` | variadic of tensor of 64-bit float values |
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### `toy.transpose` (toy::TransposeOp)
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_Transpose operation_
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Syntax:
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```
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operation ::= `toy.transpose` `(` $input `:` type($input) `)` attr-dict `to` type(results)
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```
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `input` | tensor of 64-bit float values |
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| &laquo;unnamed&raquo; | tensor of 64-bit float values |
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