99dee31ef4
Right now the bindings assume that all DenseElementsAttrs correspond to tensor values, making it impossible to create vector-typed constants. I didn't want to change the API significantly, so I opted for reusing the current signature of `.get`. Its `type` argument now accepts both element types (in which case `shape` and `signless` can be specified too), or a shaped type, which specifies the full type of the created attr (`shape` cannot be specified in that case). Reviewed By: ftynse Differential Revision: https://reviews.llvm.org/D145053
239 lines
7.7 KiB
Python
239 lines
7.7 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
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from mlir.ir import *
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import mlir.dialects.builtin as builtin
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import mlir.dialects.func as func
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import numpy as np
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def run(f):
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print("\nTEST:", f.__name__)
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f()
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return f
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# CHECK-LABEL: TEST: testFromPyFunc
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@run
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def testFromPyFunc():
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with Context() as ctx, Location.unknown() as loc:
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ctx.allow_unregistered_dialects = True
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m = builtin.ModuleOp()
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f32 = F32Type.get()
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f64 = F64Type.get()
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with InsertionPoint(m.body):
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# CHECK-LABEL: func @unary_return(%arg0: f64) -> f64
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# CHECK: return %arg0 : f64
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@func.FuncOp.from_py_func(f64)
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def unary_return(a):
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return a
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# CHECK-LABEL: func @binary_return(%arg0: f32, %arg1: f64) -> (f32, f64)
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# CHECK: return %arg0, %arg1 : f32, f64
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@func.FuncOp.from_py_func(f32, f64)
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def binary_return(a, b):
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return a, b
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# CHECK-LABEL: func @none_return(%arg0: f32, %arg1: f64)
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# CHECK: return
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@func.FuncOp.from_py_func(f32, f64)
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def none_return(a, b):
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pass
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# CHECK-LABEL: func @call_unary
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# CHECK: %0 = call @unary_return(%arg0) : (f64) -> f64
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# CHECK: return %0 : f64
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@func.FuncOp.from_py_func(f64)
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def call_unary(a):
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return unary_return(a)
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# CHECK-LABEL: func @call_binary
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# CHECK: %0:2 = call @binary_return(%arg0, %arg1) : (f32, f64) -> (f32, f64)
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# CHECK: return %0#0, %0#1 : f32, f64
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@func.FuncOp.from_py_func(f32, f64)
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def call_binary(a, b):
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return binary_return(a, b)
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# We expect coercion of a single result operation to a returned value.
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# CHECK-LABEL: func @single_result_op
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# CHECK: %0 = "custom.op1"() : () -> f32
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# CHECK: return %0 : f32
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@func.FuncOp.from_py_func()
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def single_result_op():
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return Operation.create("custom.op1", results=[f32])
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# CHECK-LABEL: func @call_none
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# CHECK: call @none_return(%arg0, %arg1) : (f32, f64) -> ()
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# CHECK: return
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@func.FuncOp.from_py_func(f32, f64)
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def call_none(a, b):
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return none_return(a, b)
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## Variants and optional feature tests.
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# CHECK-LABEL: func @from_name_arg
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@func.FuncOp.from_py_func(f32, f64, name="from_name_arg")
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def explicit_name(a, b):
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return b
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@func.FuncOp.from_py_func(f32, f64)
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def positional_func_op(a, b, func_op):
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assert isinstance(func_op, func.FuncOp)
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return b
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@func.FuncOp.from_py_func(f32, f64)
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def kw_func_op(a, b=None, func_op=None):
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assert isinstance(func_op, func.FuncOp)
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return b
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@func.FuncOp.from_py_func(f32, f64)
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def kwargs_func_op(a, b=None, **kwargs):
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assert isinstance(kwargs["func_op"], func.FuncOp)
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return b
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# CHECK-LABEL: func @explicit_results(%arg0: f32, %arg1: f64) -> f64
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# CHECK: return %arg1 : f64
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@func.FuncOp.from_py_func(f32, f64, results=[f64])
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def explicit_results(a, b):
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func.ReturnOp([b])
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print(m)
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# CHECK-LABEL: TEST: testFromPyFuncErrors
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@run
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def testFromPyFuncErrors():
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with Context() as ctx, Location.unknown() as loc:
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m = builtin.ModuleOp()
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f32 = F32Type.get()
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f64 = F64Type.get()
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with InsertionPoint(m.body):
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try:
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@func.FuncOp.from_py_func(f64, results=[f64])
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def unary_return(a):
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return a
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except AssertionError as e:
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# CHECK: Capturing a python function with explicit `results=` requires that the wrapped function returns None.
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print(e)
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# CHECK-LABEL: TEST: testBuildFuncOp
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@run
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def testBuildFuncOp():
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ctx = Context()
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with Location.unknown(ctx) as loc:
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m = builtin.ModuleOp()
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f32 = F32Type.get()
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tensor_type = RankedTensorType.get((2, 3, 4), f32)
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with InsertionPoint.at_block_begin(m.body):
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f = func.FuncOp(name="some_func",
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type=FunctionType.get(
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inputs=[tensor_type, tensor_type],
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results=[tensor_type]),
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visibility="nested")
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# CHECK: Name is: "some_func"
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print("Name is: ", f.name)
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# CHECK: Type is: (tensor<2x3x4xf32>, tensor<2x3x4xf32>) -> tensor<2x3x4xf32>
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print("Type is: ", f.type)
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# CHECK: Visibility is: "nested"
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print("Visibility is: ", f.visibility)
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try:
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entry_block = f.entry_block
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except IndexError as e:
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# CHECK: External function does not have a body
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print(e)
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with InsertionPoint(f.add_entry_block()):
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func.ReturnOp([f.entry_block.arguments[0]])
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pass
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try:
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f.add_entry_block()
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except IndexError as e:
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# CHECK: The function already has an entry block!
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print(e)
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# Try the callback builder and passing type as tuple.
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f = func.FuncOp(name="some_other_func",
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type=([tensor_type, tensor_type], [tensor_type]),
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visibility="nested",
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body_builder=lambda f: func.ReturnOp(
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[f.entry_block.arguments[0]]))
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# CHECK: module {
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# CHECK: func nested @some_func(%arg0: tensor<2x3x4xf32>, %arg1: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
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# CHECK: return %arg0 : tensor<2x3x4xf32>
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# CHECK: }
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# CHECK: func nested @some_other_func(%arg0: tensor<2x3x4xf32>, %arg1: tensor<2x3x4xf32>) -> tensor<2x3x4xf32> {
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# CHECK: return %arg0 : tensor<2x3x4xf32>
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# CHECK: }
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print(m)
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# CHECK-LABEL: TEST: testFuncArgumentAccess
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@run
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def testFuncArgumentAccess():
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with Context() as ctx, Location.unknown():
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ctx.allow_unregistered_dialects = True
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module = Module.create()
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f32 = F32Type.get()
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f64 = F64Type.get()
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with InsertionPoint(module.body):
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f = func.FuncOp("some_func", ([f32, f32], [f32, f32]))
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with InsertionPoint(f.add_entry_block()):
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func.ReturnOp(f.arguments)
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f.arg_attrs = ArrayAttr.get([
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DictAttr.get({
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"custom_dialect.foo": StringAttr.get("bar"),
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"custom_dialect.baz": UnitAttr.get()
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}),
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DictAttr.get({"custom_dialect.qux": ArrayAttr.get([])})
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])
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f.result_attrs = ArrayAttr.get([
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DictAttr.get({"custom_dialect.res1": FloatAttr.get(f32, 42.0)}),
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DictAttr.get({"custom_dialect.res2": FloatAttr.get(f64, 256.0)})
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])
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other = func.FuncOp("other_func", ([f32, f32], []))
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with InsertionPoint(other.add_entry_block()):
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func.ReturnOp([])
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other.arg_attrs = [
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DictAttr.get({"custom_dialect.foo": StringAttr.get("qux")}),
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DictAttr.get()
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]
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# CHECK: [{custom_dialect.baz, custom_dialect.foo = "bar"}, {custom_dialect.qux = []}]
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print(f.arg_attrs)
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# CHECK: [{custom_dialect.res1 = 4.200000e+01 : f32}, {custom_dialect.res2 = 2.560000e+02 : f64}]
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print(f.result_attrs)
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# CHECK: func @some_func(
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# CHECK: %[[ARG0:.*]]: f32 {custom_dialect.baz, custom_dialect.foo = "bar"},
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# CHECK: %[[ARG1:.*]]: f32 {custom_dialect.qux = []}) ->
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# CHECK: f32 {custom_dialect.res1 = 4.200000e+01 : f32},
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# CHECK: f32 {custom_dialect.res2 = 2.560000e+02 : f64})
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# CHECK: return %[[ARG0]], %[[ARG1]] : f32, f32
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#
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# CHECK: func @other_func(
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# CHECK: %{{.*}}: f32 {custom_dialect.foo = "qux"},
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# CHECK: %{{.*}}: f32)
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print(module)
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# CHECK-LABEL: testDenseElementsAttr
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@run
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def testDenseElementsAttr():
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with Context(), Location.unknown():
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values = np.arange(4, dtype=np.int32)
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i32 = IntegerType.get_signless(32)
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print(DenseElementsAttr.get(values, type=i32))
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# CHECK{LITERAL}: dense<[0, 1, 2, 3]> : tensor<4xi32>
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print(DenseElementsAttr.get(values, type=i32, shape=(2, 2)))
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# CHECK{LITERAL}: dense<[[0, 1], [2, 3]]> : tensor<2x2xi32>
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print(DenseElementsAttr.get(values, type=VectorType.get((2, 2), i32)))
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# CHECK{LITERAL}: dense<[[0, 1], [2, 3]]> : vector<2x2xi32>
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