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torch-mlir/test/Python/NumpyCompiler/array_basics.py

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Add value coding for ndarray. * This lets us import arrays from the outer environment, which is the first step to actually handling numpy ops.
2020-06-29 09:42:08 +08:00
# RUN: %PYTHON %s | npcomp-opt -split-input-file | FileCheck %s --dump-input=fail
import numpy as np
from npcomp.compiler import test_config
import_global = test_config.create_import_dump_decorator()
global_data = (np.zeros((2, 3)) + [1.0, 2.0, 3.0] * np.reshape([1.0, 2.0],
(2, 1)))
# CHECK-LABEL: func @global_array_to_const
@import_global
def global_array_to_const():
# CHECK: %[[CST:.*]] = constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
Fixup local ndarray<->tensor transforms to preserve shape. * Preserving shape across the copy ops makes more thing shaped by default. * Inference of ndarray types will now preserve the shape when specializing the dtype.
2020-07-06 08:45:45 +08:00
# CHECK: numpy.create_array_from_tensor %[[CST]] : (tensor<2x3xf64>) -> !numpy.ndarray<[2,3]:f64>
Add value coding for ndarray. * This lets us import arrays from the outer environment, which is the first step to actually handling numpy ops.
2020-06-29 09:42:08 +08:00
local_data = global_data
return local_data