Beaver.MLIR.Type (beaver v0.4.2)

This module provides functions to work with MLIR's type system, allowing creation of MLIR type.

Type Categories

Basic Types

Composite Types

Summary

Types

t()

Functions

any_quantized_type?(t)
bf16?(t)
calibrated_quantized_type?(t)
complex(element_type)
complex?(t)
emit_c_array_type?(t)
emit_c_opaque_type?(t)
emit_c_pointer_type?(t)
emit_c_ptr_diff_t_type?(t)
emit_c_signed_size_t_type?(t)
emit_c_size_t_type?(t)
emit_cl_value_type?(t)
f16(opts \\ [])
f16?(t)
f32(opts \\ [])
f32?(t)
f64(opts \\ [])
f64?(t)
f(bitwidth, opts \\ [])

See Beaver.MLIR.Type.float/2.

float4_e2_m1_fn?(t)
float6_e2_m3_fn?(t)
float6_e3_m2_fn?(t)
float8_e3_m4?(t)
float8_e4_m3?(t)
float8_e4_m3_b11_fnuz?(t)
float8_e4_m3_fn?(t)
float8_e4_m3_fnuz?(t)
float8_e5_m2?(t)
float8_e5_m2_fnuz?(t)
float8_e8_m0_fnu?(t)
float(bitwidth, opts \\ [])
float?(t)
function(inputs, results, opts \\ [])
function?(t)
get(string, opts \\ [])
gpu_async_token_type?(t)
i1(opts \\ [])
i8(opts \\ [])
i16(opts \\ [])
i32(opts \\ [])
i64(opts \\ [])
i128(opts \\ [])
i(bitwidth, opts \\ [])

See Beaver.MLIR.Type.integer/2.

index(opts \\ [])
index?(t)
integer(bitwidth, opts \\ [signed: false])
integer?(t)
llvm_pointer_type?(t)
llvm_struct_type?(t)
make(value)
memref(shape, element_type, opts \\ [layout: nil, memory_space: nil])
memref!(shape, element_type, opts \\ [])
memref?(t)
none(opts \\ [])
none?(t)
nvgpu_tensor_map_descriptor_type?(t)
opaque?(t)
pdl_attribute_type?(t)
pdl_operation_type?(t)
pdl_range_type?(t)
pdl_type?(t)
pdl_type_type?(t)
pdl_value_type?(t)
quantized_type?(t)
ranked_tensor(shape, element_type, opts \\ [])
ranked_tensor!(shape, element_type, opts \\ [])
ranked_tensor?(t)
shaped?(t)
tensor?(t)
tf32?(t)
transform_any_op_type?(t)
transform_any_param_type?(t)
transform_any_value_type?(t)
transform_operation_type?(t)
transform_param_type?(t)
tuple(elements, opts \\ [])

Get a tuple type.

tuple?(t)
uniform_quantized_per_axis_type?(t)
uniform_quantized_type?(t)
unranked_memref(element_type, opts \\ [])
unranked_memref!(element_type, opts \\ [])
unranked_memref?(t)
unranked_tensor(element_type, opts \\ [])
unranked_tensor!(element_type, opts \\ [])
unranked_tensor?(t)
vector(shape, element_type, opts \\ [])

Get a vector type creator.

vector!(shape, element_type, opts \\ [])
vector?(t)

Types

t()

@type t() :: %Beaver.MLIR.Type{ref: term()}

Functions

any_quantized_type?(t)

bf16?(t)

calibrated_quantized_type?(t)

complex(element_type)

complex?(t)

emit_c_array_type?(t)

emit_c_opaque_type?(t)

emit_c_pointer_type?(t)

emit_c_ptr_diff_t_type?(t)

emit_c_signed_size_t_type?(t)

emit_c_size_t_type?(t)

emit_cl_value_type?(t)

f16(opts \\ [])

f16?(t)

f32(opts \\ [])

f32?(t)

f64(opts \\ [])

f64?(t)

f(bitwidth, opts \\ [])

See Beaver.MLIR.Type.float/2.

float4_e2_m1_fn?(t)

float6_e2_m3_fn?(t)

float6_e3_m2_fn?(t)

float8_e3_m4?(t)

float8_e4_m3?(t)

float8_e4_m3_b11_fnuz?(t)

float8_e4_m3_fn?(t)

float8_e4_m3_fnuz?(t)

float8_e5_m2?(t)

float8_e5_m2_fnuz?(t)

float8_e8_m0_fnu?(t)

float(bitwidth, opts \\ [])

float?(t)

function(inputs, results, opts \\ [])

function?(t)

get(string, opts \\ [])

gpu_async_token_type?(t)

i1(opts \\ [])

i8(opts \\ [])

i16(opts \\ [])

i32(opts \\ [])

i64(opts \\ [])

i128(opts \\ [])

i(bitwidth, opts \\ [])

See Beaver.MLIR.Type.integer/2.

index(opts \\ [])

index?(t)

integer(bitwidth, opts \\ [signed: false])

integer?(t)

llvm_pointer_type?(t)

llvm_struct_type?(t)

make(value)

memref(shape, element_type, opts \\ [layout: nil, memory_space: nil])

memref!(shape, element_type, opts \\ [])

memref?(t)

none(opts \\ [])

none?(t)

nvgpu_tensor_map_descriptor_type?(t)

opaque?(t)

pdl_attribute_type?(t)

pdl_operation_type?(t)

pdl_range_type?(t)

pdl_type?(t)

pdl_type_type?(t)

pdl_value_type?(t)

quantized_type?(t)

ranked_tensor(shape, element_type, opts \\ [])

ranked_tensor!(shape, element_type, opts \\ [])

ranked_tensor?(t)

shaped?(t)

tensor?(t)

tf32?(t)

transform_any_op_type?(t)

transform_any_param_type?(t)

transform_any_value_type?(t)

transform_operation_type?(t)

transform_param_type?(t)

tuple(elements, opts \\ [])

Get a tuple type.

Examples 

iex> ctx = MLIR.Context.create()
iex> MLIR.Type.tuple([MLIR.Type.i32, MLIR.Type.i32], ctx: ctx) |> MLIR.to_string()
"tuple<i32, i32>"
iex> MLIR.Context.destroy(ctx)

tuple?(t)

uniform_quantized_per_axis_type?(t)

uniform_quantized_type?(t)

unranked_memref(element_type, opts \\ [])

unranked_memref!(element_type, opts \\ [])

unranked_memref?(t)

unranked_tensor(element_type, opts \\ [])

unranked_tensor!(element_type, opts \\ [])

unranked_tensor?(t)

vector(shape, element_type, opts \\ [])

Get a vector type creator.

Examples 

iex> ctx = MLIR.Context.create()
iex> MLIR.Type.vector!([1, 2, 3], MLIR.Type.i32(ctx: ctx)) |> MLIR.to_string()
"vector<1x2x3xi32>"
iex> MLIR.Context.destroy(ctx)

vector!(shape, element_type, opts \\ [])

vector?(t)