0d0371f58f
Update the shapes of the convolution / pooling tests that where detected after enabling verification during printing (https://reviews.llvm.org/D114680). Also split the emit_structured_generic.py file that previously contained all tests into multiple separate files to simplify debugging. Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D114731
94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
|
|
|
|
from mlir.ir import *
|
|
from mlir.dialects import builtin
|
|
from mlir.dialects import linalg
|
|
from mlir.dialects import std
|
|
|
|
from mlir.dialects.linalg.opdsl.lang import *
|
|
|
|
# This tests miscellaneous features of the emitter that are not tested by the
|
|
# matmul, convolution, or, pooling tests. The features include:
|
|
# - constant defined in the body
|
|
# - fix/predefined types
|
|
# - exponential functions
|
|
# - custom op names.
|
|
|
|
@linalg_structured_op
|
|
def fill_rng_poly(
|
|
min=ScalarDef(F64),
|
|
max=ScalarDef(F64),
|
|
seed=ScalarDef(I32),
|
|
O=TensorDef(T, S.M, S.N, output=True)):
|
|
multiplier = cast(I32, const(1103515245))
|
|
increment = cast(I32, const(12345))
|
|
rand1 = (cast(I32, index(D.m)) + seed) * multiplier + increment
|
|
rand2 = (cast(I32, index(D.n)) + rand1) * multiplier + increment
|
|
inv_range = cast(F64, const(2.3283064e-10))
|
|
offset = cast(F64, const(2147483647))
|
|
scaling = (max - min) * inv_range
|
|
O[D.m, D.n] = cast(T, (offset + cast(F64, rand2)) * scaling + min)
|
|
|
|
|
|
@linalg_structured_op
|
|
def soft_plus_poly(
|
|
I=TensorDef(T, S.M, S.N), O=TensorDef(U, S.M, S.N, output=True)):
|
|
O[D.m, D.n] = \
|
|
PrimFn.log(cast(U, const(1.0)) + cast(U, PrimFn.exp(I[D.m, D.n])))
|
|
|
|
|
|
@linalg_structured_op(op_name="custom_op_name")
|
|
def non_default_op_name(I=TensorDef(T, S.N), O=TensorDef(T, S.N, output=True)):
|
|
O[D.n] = I[D.n]
|
|
|
|
|
|
with Context() as ctx, Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
f64 = F64Type.get()
|
|
i32 = IntegerType.get_signless(32)
|
|
with InsertionPoint(module.body):
|
|
|
|
# CHECK-LABEL: @test_i32_fill_rng
|
|
# CHECK: ^{{.*}}(%[[MIN:.+]]: f64, %[[MAX:.+]]: f64, %[[SEED:.+]]: i32, %{{.*}}
|
|
# CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
|
|
# CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32
|
|
# CHECK-DAG: %[[RND0:.+]] = arith.addi %[[IDX0_CAST]], %[[SEED]] : i32
|
|
# CHECK-DAG: %[[CST0:.+]] = arith.constant 1103515245 : i64
|
|
# CHECK-DAG: %[[CST0_CAST:.+]] = arith.trunci %[[CST0]] : i64 to i32
|
|
# Skip the remaining random number computation and match the scaling logic.
|
|
# CHECK-DAG: %[[DIFF:.+]] = arith.subf %[[MAX]], %[[MIN]] : f64
|
|
# CHECK-DAG: %[[CST3:.+]] = arith.constant 2.3283063999999999E-10 : f64
|
|
# CHECK-DAG: %[[FACT:.+]] = arith.mulf %[[DIFF]], %[[CST3]] : f64
|
|
# CHECK-DAG: %[[RND4:.+]] = arith.mulf %{{.+}}, %[[FACT]] : f64
|
|
# CHECK-DAG: %[[RND5:.+]] = arith.addf %[[RND4]], %[[MIN]] : f64
|
|
# CHECK-DAG: %{{.*}} = arith.fptosi %[[RND5]] : f64 to i32
|
|
@builtin.FuncOp.from_py_func(f64, f64, i32,
|
|
RankedTensorType.get((4, 16), i32))
|
|
def test_i32_fill_rng(min, max, seed, init_result):
|
|
return fill_rng_poly(min, max, seed, outs=[init_result])
|
|
|
|
# CHECK-LABEL: @test_f32_soft_plus
|
|
# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
|
|
# CHECK-NEXT: %[[C1:.+]] = arith.constant 1.000000e+00 : f64
|
|
# CHECK-NEXT: %[[C1_CAST:.+]] = arith.truncf %[[C1]] : f64 to f32
|
|
# CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32
|
|
# CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[C1_CAST]], %[[EXP]] : f32
|
|
# CHECK-NEXT: %[[LOG:.+]] = math.log %[[SUM]] : f32
|
|
# CHECK-NEXT: linalg.yield %[[LOG]] : f32
|
|
# CHECK-NEXT: -> tensor<4x16xf32>
|
|
@builtin.FuncOp.from_py_func(
|
|
RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
|
|
def test_f32_soft_plus(input, init_result):
|
|
return soft_plus_poly(input, outs=[init_result])
|
|
|
|
# Just check that we don't assert out on name mismatch.
|
|
# CHECK-LABEL: @test_non_default_op_name
|
|
@builtin.FuncOp.from_py_func(
|
|
RankedTensorType.get((42,), f32), RankedTensorType.get((42,), f32))
|
|
def test_non_default_op_name(input, init_result):
|
|
return non_default_op_name(input, outs=[init_result])
|
|
|
|
|
|
print(module)
|