Support MLIR #1

tucan9389 · 2023-04-29T02:33:09Z

TODO

Runnable ratio with `mlir.Parser` and `mlir.Tokenizer`

Update at 23.05.10

repo name	tensorflow	mlir-hlo	openxla/iree	llvm-project	llvm/circt
# of mlir files	1.5k	4.4k	1.1k	1.3k	500
success ratio	0.523	0.049	0.088	0.106	0.044

netron term vs. MLIR term

netron term	MLIR term	Graph term
`mlir.Graph`	Region or Function	Graph or Model
`mlir.Parameter`	Value (Operation's Operand or Result)	Edge
`mlir.Argument`	Value (Operation's Operand or Result)	Edge
`mlir.Node`	Operation	Node
`mlir.Attributes`	Operation's Attribute	Node's metadata
`mlir.Tensor`	Constant Operation	Kind of node
`mlir.TensorType`		Edge's metadata
`mlir.TensorShape`		Edge's metadata

MLIR graph structure

netron graph structure

Test the `mlir.Parser` and `mlir.Tokenizer`

Add following code bottom of the source/mlir.js and run node source/mlir.js.

const input = `
func.func @main(%arg0: f32) -> f32 {
    %result:2 = "foo_div"() : () -> (f32, i32)

    // Pretty form that defines a unique name for each result.
    %foo, %bar = "foo_div"() : () -> (f32, i32)

    // Invoke a TensorFlow function called tf.scramble with two inputs
    // and an attribute "fruit" stored in properties.
    %2 = "tf.scramble"(%result#0, %bar) <{fruit = "banana"}> : (f32, i32) -> f32

    // Invoke an operation with some discardable attributes
    %foo2, %bar2 = "foo_div"() {some_attr = "value", other_attr = 42 : i64} : () -> (f32, i32)

    return %bar2: f32
}
`

const decoder = text.Decoder.open(input);
const parser = new mlir.Parser(decoder);
const obj = parser.read();
console.log(JSON.stringify(obj, null, 2));

cc. @chococigar

The text was updated successfully, but these errors were encountered:

tucan9389 · 2023-04-29T04:28:52Z

Example MLIR (stablehlo_sample.mlir)

https://github.com/openxla/stablehlo/blob/main/docs/spec.md

stablehlo.func @main(
  %image: tensor<28x28xf32>,
  %weights: tensor<784x10xf32>,
  %bias: tensor<1x10xf32>
) -> tensor<1x10xf32> {
  %0 = "stablehlo.reshape"(%image) : (tensor<28x28xf32>) -> tensor<1x784xf32>
  %1 = "stablehlo.dot"(%0, %weights) : (tensor<1x784xf32>, tensor<784x10xf32>) -> tensor<1x10xf32>
  %2 = "stablehlo.add"(%1, %bias) : (tensor<1x10xf32>, tensor<1x10xf32>) -> tensor<1x10xf32>
  %3 = "stablehlo.constant"() { value = dense<0.0> : tensor<1x10xf32> } : () -> tensor<1x10xf32>
  %4 = "stablehlo.maximum"(%2, %3) : (tensor<1x10xf32>, tensor<1x10xf32>) -> tensor<1x10xf32>
  "stablehlo.return"(%4): (tensor<1x10xf32>) -> ()
}

tucan9389 · 2023-05-01T03:58:08Z

🎊🎊 First MLIR graph

tucan9389 · 2023-05-01T04:58:50Z

Examples

input: it's the content of the input file having .mlir extension

intermediate output: with the intermediate output, we can make the mlir.Graph and mlir.Node, etc. to visualize the graph

tucan9389 · 2023-05-01T07:37:35Z

Test Inputs for mlir.Tokenizer

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 440 : i32}, tf_saved_model.semantics} {
"tf_saved_model.global_tensor"() {is_mutable, sym_name = "__sm_node4__optimizer.iter", tf_saved_model.exported_names = [], type = tensor<i64>, value = dense<0> : tensor<i64>} : () -> ()
    "tf_saved_model.global_tensor"() {sym_name = "__sm_node6__optimizer.learning_rate", tf_saved_model.exported_names = [], type = tensor<f32>, value = dense<0.00999999977> : tensor<f32>} : () -> ()
    func @__inference_predict_3320(%arg0: tensor<32x28x28x1xf32> {tf._user_specified_name = "inputs", tf_saved_model.index_path = [0]}, %arg1: tensor<32x1xf32> {tf._user_specified_name = "targets", tf_saved_model.index_path = [1]}, %arg2: tensor<!tf.resource<tensor<5x5x1x32xf32>>> {tf_saved_model.bound_input = @__sm_node17__model.conv1.kernel}, %arg3: tensor<!tf.resource<tensor<5x5x32x32xf32>>> {tf_saved_model.bound_input = @__sm_node26__model.conv2.kernel}, %arg4: tensor<!tf.resource<tensor<1568x1024xf32>>> {tf_saved_model.bound_input = @__sm_node39__model.dense1.kernel}, %arg5: tensor<!tf.resource<tensor<1024xf32>>> {tf_saved_model.bound_input = @__sm_node40__model.dense1.bias}, %arg6: tensor<!tf.resource<tensor<1024x10xf32>>> {tf_saved_model.bound_input = @__sm_node49__model.dense2.kernel}, %arg7: tensor<!tf.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @__sm_node50__model.dense2.bias}, %arg8: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @__sm_node6__optimizer.learning_rate}, %arg9: tensor<!tf.resource<tensor<i64>>> {tf_saved_model.bound_input = @__sm_node4__optimizer.iter}) -> (tensor<f32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [#tf.shape<32x28x28x1>, #tf.shape<32x1>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>], tf.signature.is_stateful, tf_saved_model.exported_names = ["predict"]} {
        %0 = mhlo.constant dense<3.125000e-02> : tensor<32x10xf32>
        %0 = mhlo.constant dense<3.125000e-02> : tensor<32x10xf32>
      %1 = mhlo.constant dense<3.200000e+01> : tensor<f32>
      %2 = mhlo.constant dense<1> : tensor<i64>
      %14 = "tf.Cast"(%arg2) {Truncate = false} : (tensor<!tf.resource<tensor<5x5x1x32xf32>>>) -> tensor<!tf.resource>
    %27 = "mhlo.convolution"(%arg0, %22) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<32x28x28x1xf32>, tensor<5x5x1x32xf32>) -> tensor<32x28x28x32xf32>
    %28 = mhlo.maximum %27, %11 : tensor<32x28x28x32xf32>
    %29 = "mhlo.reduce_window"(%28, %8) ( {
    ^bb0(%arg10: tensor<f32>, %arg11: tensor<f32>):  // no predecessors
      %130 = mhlo.maximum %arg10, %arg11 : tensor<f32>
      "mhlo.return"(%130) : (tensor<f32>) -> ()
    }) {padding = dense<0> : tensor<4x2xi64>, window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>, window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<32x28x28x32xf32>, tensor<f32>) -> tensor<32x14x14x32xf32>
    %30 = "mhlo.convolution"(%29, %21) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<32x14x14x32xf32>, tensor<5x5x32x32xf32>) -> tensor<32x14x14x32xf32>
    "tf.AssignVariableOp"(%20, %126) : (tensor<!tf.resource>, tensor<*xi64>) -> ()
    %127 = "mhlo.reduce"(%68, %12) ( {
    ^bb0(%arg10: tensor<f32>, %arg11: tensor<f32>):  // no predecessors
      %130 = mhlo.add %arg10, %arg11 : tensor<f32>
      "mhlo.return"(%130) : (tensor<f32>) -> ()
    }) {dimensions = dense<0> : tensor<1xi64>} : (tensor<32xf32>, tensor<f32>) -> tensor<f32>
    %128 = mhlo.divide %127, %1 : tensor<f32>
    %129 = "mhlo.select"(%13, %12, %128) : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
    return %129 : tensor<f32>
  }
}

// Compute A*B using an implementation of multiply kernel and print the
// result using a TensorFlow op. The dimensions of A and B are partially
// known. The shapes are assumed to match.
func.func @mul(%A: tensor<100x?xf32>, %B: tensor<?x50xf32>) -> (tensor<100x50xf32>) {
  // Compute the inner dimension of %A using the dim operation.
  %n = memref.dim %A, 1 : tensor<100x?xf32>

  // Allocate addressable "buffers" and copy tensors %A and %B into them.
  %A_m = memref.alloc(%n) : memref<100x?xf32>
  memref.tensor_store %A to %A_m : memref<100x?xf32>

  %B_m = memref.alloc(%n) : memref<?x50xf32>
  memref.tensor_store %B to %B_m : memref<?x50xf32:<>>

  // Call function @multiply passing memrefs as arguments,
  // and getting returned the result of the multiplication.
  %C_m = call @multiply(%A_m, %B_m)
          : (memref<100x?xf32>, memref<?x50xf32>) -> (memref<100x50xf32>)

  memref.dealloc %A_m : memref<100x?xf32>
  memref.dealloc %B_m : memref<?x50xf32>

  // Load the buffer data into a higher level "tensor" value.
  %C = memref.tensor_load %C_m : memref<100x50xf32>
  memref.dealloc %C_m : memref<100x50xf32>

  // Call TensorFlow built-in function to print the result tensor.
  "tf.Print"(%C){message: "mul result"} : (tensor<100x50xf32>) -> (tensor<100x50xf32>)

  return %C : tensor<100x50xf32>
}

module {
    
      func @my_function(%arg0: memref<32x32xf32>) attributes {affine_map_attr = affine_map<(d0, d1) -> (d0 + d1, d0 - d1)>} {
        // function body
      }
      func @my_function() attributes {array_attr = [1, 2, 3]} {
        // function body
      }
      func @my_function() attributes {nested_attr = {inner_attr1 = true, inner_attr2 = 3.14 : f64}} {
        // function body
      }
      func @my_function(%arg0: f32, %arg1: f32) -> f32 attributes {attr1 = "value", attr2 = 42 : i32, attr3 = dense<[1, 2, 3]> : tensor<3xi32>} {
        // function body
      }

      stablehlo.func @main(%image: tensor<28x28xf32>, %weights: tensor<784x10xf32>,%bias: tensor<1x10xf32>) -> tensor<1x10xf32> {
        %99 = "mhlo.broadcast_in_dim"(%41) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>) -> tensor<5x5x32x32xf32>
        %98 = "mhlo.broadcast_in_dim"(%41) : (tensor<f32>) -> tensor<5x5x32x32xf32> {broadcast_dimensions = dense<> : tensor<0xi64>}
        "stablehlo.return"(%1): (tensor<1x10xf32>) -> ()
    }


      func @example(%arg1: i32, %arg2: i32) -> (i32) { }
    func @example(%arg1: i32, %arg2: i32) -> (i32, i32) { }
    func @example(%arg1: i32, %arg2: i32) -> i32 { }

    }

 module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 440 : i32}, tf_saved_model.semantics} {
        "tf_saved_model.global_tensor"() {is_mutable, sym_name = "__sm_node4__optimizer.iter", tf_saved_model.exported_names = [], type = tensor<i64>, value = dense<0> : tensor<i64>} : () -> ()
        "tf_saved_model.global_tensor"() {sym_name = "__sm_node6__optimizer.learning_rate", tf_saved_model.exported_names = [], type = tensor<f32>, value = dense<0.00999999977> : tensor<f32>} : () -> ()
            func @__inference_predict_3320(%arg0: tensor<32x28x28x1xf32> {tf._user_specified_name = "inputs", tf_saved_model.index_path = [0]}, %arg1: tensor<32x1xf32> {tf._user_specified_name = "targets", tf_saved_model.index_path = [1]}, %arg2: tensor<!tf.resource<tensor<5x5x1x32xf32>>> {tf_saved_model.bound_input = @__sm_node17__model.conv1.kernel}, %arg3: tensor<!tf.resource<tensor<5x5x32x32xf32>>> {tf_saved_model.bound_input = @__sm_node26__model.conv2.kernel}, %arg4: tensor<!tf.resource<tensor<1568x1024xf32>>> {tf_saved_model.bound_input = @__sm_node39__model.dense1.kernel}, %arg5: tensor<!tf.resource<tensor<1024xf32>>> {tf_saved_model.bound_input = @__sm_node40__model.dense1.bias}, %arg6: tensor<!tf.resource<tensor<1024x10xf32>>> {tf_saved_model.bound_input = @__sm_node49__model.dense2.kernel}, %arg7: tensor<!tf.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @__sm_node50__model.dense2.bias}, %arg8: tensor<!tf.resource<tensor<f32>>> {tf_saved_model.bound_input = @__sm_node6__optimizer.learning_rate}, %arg9: tensor<!tf.resource<tensor<i64>>> {tf_saved_model.bound_input = @__sm_node4__optimizer.iter}) -> (tensor<f32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = [#tf.shape<32x28x28x1>, #tf.shape<32x1>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>], tf.signature.is_stateful, tf_saved_model.exported_names = ["predict"]} {
                %0 = mhlo.constant dense<3.125000e-02> : tensor<32x10xf32>
                %0 = mhlo.constant dense<3.125000e-02> : tensor<32x10xf32>
              %1 = mhlo.constant dense<3.200000e+01> : tensor<f32>
              %2 = mhlo.constant dense<1> : tensor<i64>
              %14 = "tf.Cast"(%arg2) {Truncate = false} : (tensor<!tf.resource<tensor<5x5x1x32xf32>>>) -> tensor<!tf.resource>
            %27 = "mhlo.convolution"(%arg0, %22) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<32x28x28x1xf32>, tensor<5x5x1x32xf32>) -> tensor<32x28x28x32xf32>
            %28 = mhlo.maximum %27, %11 : tensor<32x28x28x32xf32>
            %29 = "mhlo.reduce_window"(%28, %8) ( {
            ^bb0(%arg10: tensor<f32>, %arg11: tensor<f32>):  // no predecessors
              %130 = mhlo.maximum %arg10, %arg11 : tensor<f32>
              "mhlo.return"(%130) : (tensor<f32>) -> ()
            }) {padding = dense<0> : tensor<4x2xi64>, window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>, window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<32x28x28x32xf32>, tensor<f32>) -> tensor<32x14x14x32xf32>
            %30 = "mhlo.convolution"(%29, %21) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<2> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<32x14x14x32xf32>, tensor<5x5x32x32xf32>) -> tensor<32x14x14x32xf32>
            "tf.AssignVariableOp"(%20, %126) : (tensor<!tf.resource>, tensor<*xi64>) -> ()
            %127 = "mhlo.reduce"(%68, %12) ( {
            ^bb0(%arg10: tensor<f32>, %arg11: tensor<f32>):  // no predecessors
              %130 = mhlo.add %arg10, %arg11 : tensor<f32>
              "mhlo.return"(%130) : (tensor<f32>) -> ()
            }) {dimensions = dense<0> : tensor<1xi64>} : (tensor<32xf32>, tensor<f32>) -> tensor<f32>
            %128 = mhlo.divide %127, %1 : tensor<f32>
            %129 = "mhlo.select"(%13, %12, %128) : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
            return %129 : tensor<f32>
          }
        }

tucan9389 · 2023-05-04T15:41:46Z

no args, no parens operation

Done: be9a3df

// CHECK: use_two_sep_ops_empty_tensor_list
func.func @use_two_sep_ops_empty_tensor_list() {
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %elem_shape = "tf.Const"() {value = dense<[-1, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
  %size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  // CHECK: EmptyTensorList
  // CHECK: EmptyTensorList
  // CHECK: EmptyTensorList
  %tl = "tf.EmptyTensorList"(%elem_shape, %size) : (tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  %elem_1 = "tf._FirstOp"() : () -> tensor<8x1xf32>
  %tl_set_item = "tf.TensorListSetItem"(%tl, %one, %elem_1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<8x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  %elem_2 = "tf._SecondOp"() : () -> tensor<16x1xf32>
  %tl_set_item2 = "tf.TensorListSetItem"(%tl, %one, %elem_2) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  func.return
}

tucan9389 · 2023-05-04T15:42:35Z

while

Done: 1e06958

:integer-literal of op-result:
region-list: op_name ( args ) ({...}, {...}) : function-type

func.func @while_region_op_two_sep_args_empty_tensor_list() {
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %elem_shape = "tf.Const"() {value = dense<[-1, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
  %size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  %tl = "tf.EmptyTensorList"(%elem_shape, %size) : (tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  %while:2 = "tf.WhileRegion"(%tl, %tl) ({
    ^bb0(%barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>, %barg2: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
      %cond = "tf.false"():()-> tensor<i1>
      "tf.Yield"(%cond) : (tensor<i1>) -> ()
  }, {
    ^bb0(%barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>, %barg2: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
    "tf.Yield"(%barg1, %barg2) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<!tf_type.variant<tensor<?x1xf32>>>) -> ()
  }) {is_stateless = false} : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<!tf_type.variant<tensor<?x1xf32>>>) -> (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<!tf_type.variant<tensor<?x1xf32>>>)
  func.return
}

Done: 1e06958

// https://github.com/tensorflow/tensorflow/blob/2d9d9c053c51ae417c9abfd4e0ea903e447d6952/tensorflow/compiler/mlir/tensorflow/tests/hoist_loop_invariant.mlir#L19
func.func @hoist_loop_invariant(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %cst_0 = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %cst_1 = "tf.Const"() { value = dense<0> : tensor<i32> } : () -> tensor<i32>
    %1 = "tf.Add"(%cst_1, %cst_0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %2 = "tf.Mul"(%1, %cst_1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %3 = "tf.AddV2"(%arg2, %1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %4 = "tf.Div"(%arg3, %2) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    "tf.Yield"(%3, %4) : (tensor<i32>, tensor<i32>) -> ()
  }) {is_stateless = true, parallel_iterations = 10 : i64} : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
  return %0#0, %0#1 : tensor<i32>, tensor<i32>
}

tucan9389 · 2023-05-05T00:27:19Z

sharp keyword

access for a variable having multiple variables (%result#0)
Attribute Value Aliases
Dialect Attribute Values

https://mlir.llvm.org/docs/LangRef/

access for a variable having multiple variables (%result#0)

%2 = "tf.scramble"(%result#0, %bar) <{fruit = "banana"}> : (f32, i32) -> f32

https://mlir.llvm.org/docs/LangRef/

Attribute Value Aliases

#map = affine_map<(d0) -> (d0 + 10)>

// Using the original attribute.
%b = affine.apply affine_map<(d0) -> (d0 + 10)> (%a)

// Using the attribute alias.
%b = affine.apply #map(%a)

https://mlir.llvm.org/docs/LangRef/

Dialect Attribute Values

// A string attribute.
#foo<string<"">>

// A complex attribute.
#foo<"a123^^^" + bar>

// A string attribute.
#foo.string<"">

https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/compiler/mlir/tensorflow/tests/decompose_reduce_dataset.mlir

// CHECK-LABEL: func @skip_noncompiled_reduce_dataset
func.func @skip_noncompiled_reduce_dataset(
      %arg0 : tensor<!tf_type.variant>,
      %arg1: tensor<i64>
    ) {
    // CHECK: tf.ReduceDataset
    %1 = "tf.ReduceDataset"(%arg0, %arg1) {
      Targuments = [],
      Tstate = [i64], device = "",
      f = @__reduce_func_0, f._tf_data_function = true,
      output_shapes = [#tf_type.shape<>],
      output_types = [i64], use_inter_op_parallelism = true } : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>)
    func.return
}

tucan9389 · 2023-05-05T00:28:24Z

various op syntax in MLIR Ref doc

https://mlir.llvm.org/docs/LangRef/

Done: cc5daab

// An operation that produces two results.
// The results of %result can be accessed via the <name> `#` <opNo> syntax.
%result:2 = "foo_div"() : () -> (f32, i32)

// Pretty form that defines a unique name for each result.
%foo, %bar = "foo_div"() : () -> (f32, i32)

// Invoke a TensorFlow function called tf.scramble with two inputs
// and an attribute "fruit" stored in properties.
%2 = "tf.scramble"(%result#0, %bar) <{fruit = "banana"}> : (f32, i32) -> f32

// Invoke an operation with some discardable attributes
%foo, %bar = "foo_div"() {some_attr = "value", other_attr = 42 : i64} : () -> (f32, i32)

tucan9389 · 2023-05-05T00:29:33Z

block syntax in MLIR Ref doc

https://mlir.llvm.org/docs/LangRef/

func.func @simple(i64, i1) -> i64 {
^bb0(%a: i64, %cond: i1): // Code dominated by ^bb0 may refer to %a
  cf.cond_br %cond, ^bb1, ^bb2

^bb1:
  cf.br ^bb3(%a: i64)    // Branch passes %a as the argument

^bb2:
  %b = arith.addi %a, %a : i64
  cf.br ^bb3(%b: i64)    // Branch passes %b as the argument

// ^bb3 receives an argument, named %c, from predecessors
// and passes it on to bb4 along with %a. %a is referenced
// directly from its defining operation and is not passed through
// an argument of ^bb3.
^bb3(%c: i64):
  cf.br ^bb4(%c, %a : i64, i64)

^bb4(%d : i64, %e : i64):
  %0 = arith.addi %d, %e : i64
  return %0 : i64   // Return is also a terminator.
}

tucan9389 · 2023-05-05T00:31:03Z

graph region in MLIR Ref doc

Done: cc5daab

"test.graph_region"() ({ // A Graph region
  %1 = "op1"(%1, %3) : (i32, i32) -> (i32)  // OK: %1, %3 allowed here
  %2 = "test.ssacfg_region"() ({
     %5 = "op2"(%1, %2, %3, %4) : (i32, i32, i32, i32) -> (i32) // OK: %1, %2, %3, %4 all defined in the containing region
  }) : () -> (i32)
  %3 = "op2"(%1, %4) : (i32, i32) -> (i32)  // OK: %4 allowed here
  %4 = "op3"(%1) : (i32) -> (i32)
}) : () -> ()

tucan9389 · 2023-05-05T00:33:02Z

exclamation mark(`!`)

https://mlir.llvm.org/docs/LangRef/#type-aliases

Type Aliases

!avx_m128 = vector<4 x f32>

// Using the original type.
"foo"(%x) : vector<4 x f32> -> ()

// Using the type alias.
"foo"(%x) : !avx_m128 -> ()

https://mlir.llvm.org/docs/LangRef/#dialect-types

Dialect Aliases

// A tensorflow string type.
!tf<string>

// A type with complex components.
!foo<something<abcd>>

// An even more complex type.
!foo<"a123^^^" + bar>

https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/compiler/mlir/tensorflow/tests/decompose_reduce_dataset.mlir

func.func @single_state_single_dataset_type_no_arguments(
      %arg0: tensor<!tf_type.variant>,
      %arg1: tensor<i64>
    ) {
    %1 = "tf.ReduceDataset"(%arg0, %arg1) {
      Targuments = [],
      Tstate = [i64], device = "/job:localhost/replica:0/task:0/device:TPU:1",
      f = @__reduce_func_1, f._tf_data_function = true,
      output_shapes = [#tf_type.shape<>],
      output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>)
    func.return
}

tucan9389 self-assigned this Apr 29, 2023

tucan9389 mentioned this issue May 7, 2023

MLIR support lutzroeder/netron#1044

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Support MLIR #1

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Support MLIR #1

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tucan9389 commented Apr 29, 2023 • edited Loading

TODO

Runnable ratio with mlir.Parser and mlir.Tokenizer

netron term vs. MLIR term

MLIR graph structure

netron graph structure

Test the mlir.Parser and mlir.Tokenizer

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Example MLIR (stablehlo_sample.mlir)

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Examples

issue_1043.mlir (onnx dialect, 7 lines)

stablehlo_sample.mlir (stablehlo dialect, 14 lines)

examples.mnist_xla.mlir (tf dialect, mhlo dialect, 208 lines)

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Test Inputs for mlir.Tokenizer

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no args, no parens operation

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while

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sharp keyword

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various op syntax in MLIR Ref doc

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block syntax in MLIR Ref doc

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graph region in MLIR Ref doc

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exclamation mark(!)

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Runnable ratio with `mlir.Parser` and `mlir.Tokenizer`

Test the `mlir.Parser` and `mlir.Tokenizer`

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exclamation mark(`!`)