torch-mlir/test/Python/Tracing/function_trace.py

42 lines
1.7 KiB
Python

# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
import os
os.environ["NUMPY_EXPERIMENTAL_ARRAY_FUNCTION"] = "1"
from npcomp.types import *
from npcomp.exporter import *
from npcomp.tracing.mlir_trace import *
def simple_mul(a: np.ndarray, b: np.ndarray) -> np.ndarray:
return a * b + a + b
# TODO: Implement subclassing and deriving constraints by run
exp = Exporter()
exp.simple_mul = simple_mul
exp.simple_mul.sig.args["a"] += Shape(1, 4)
exp.simple_mul.sig.args["a"] += DynamicDim(0)
exp.simple_mul.sig.args["a"] += DType(np.float32)
exp.simple_mul.sig.args["b"] += Shape(1)
exp.simple_mul.sig.args["b"] += DType(np.float32)
exp.simple_mul.sig.result += Shape(1, 4)
exp.simple_mul.sig.result += DynamicDim(0)
exp.simple_mul.sig.result += DType(np.float32)
mb = ModuleBuilder()
mb.trace(exp.simple_mul)
# This test exercises the full tracing path and incidentally checks the ops.
# CHECK: func @simple_mul(%arg0: tensor<?x4xf32>, %arg1: tensor<1xf32>) -> tensor<?x4xf32> {
# CHECK: %0 = numpy.builtin_ufunc_call<"numpy.multiply"> (%arg0, %arg1) : (tensor<?x4xf32>, tensor<1xf32>) -> tensor<*x!basicpy.UnknownType>
# CHECK: %1 = numpy.builtin_ufunc_call<"numpy.add"> (%0, %arg0) : (tensor<*x!basicpy.UnknownType>, tensor<?x4xf32>) -> tensor<*x!basicpy.UnknownType>
# CHECK: %2 = numpy.builtin_ufunc_call<"numpy.add"> (%1, %arg1) : (tensor<*x!basicpy.UnknownType>, tensor<1xf32>) -> tensor<*x!basicpy.UnknownType>
# CHECK: %3 = numpy.narrow %2 : (tensor<*x!basicpy.UnknownType>) -> tensor<?x4xf32>
# CHECK: return %3 : tensor<?x4xf32>
# CHECK: }
print(str(mb.module))