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|
# RUN: %PYTHON %s | FileCheck %s
# Note that this is separate from ir_attributes.py since it depends on numpy,
# and we may want to disable if not available.
import gc
from mlir.ir import *
import numpy as np
def run(f):
print("\nTEST:", f.__name__)
f()
gc.collect()
assert Context._get_live_count() == 0
return f
################################################################################
# Tests of the array/buffer .get() factory method on unsupported dtype.
################################################################################
@run
def testGetDenseElementsUnsupported():
with Context():
array = np.array([["hello", "goodbye"]])
try:
attr = DenseElementsAttr.get(array)
except ValueError as e:
# CHECK: unimplemented array format conversion from format:
print(e)
# CHECK-LABEL: TEST: testGetDenseElementsUnSupportedTypeOkIfExplicitTypeProvided
@run
def testGetDenseElementsUnSupportedTypeOkIfExplicitTypeProvided():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
# datetime64 specifically isn't important: it's just a 64-bit type that
# doesn't have a format under the Python buffer protocol. A more
# realistic example would be a NumPy extension type like the bfloat16
# type from the ml_dtypes package, which isn't a dependency of this
# test.
attr = DenseElementsAttr.get(array.view(np.datetime64),
type=IntegerType.get_signless(64))
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi64>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
################################################################################
# Splats.
################################################################################
# CHECK-LABEL: TEST: testGetDenseElementsSplatInt
@run
def testGetDenseElementsSplatInt():
with Context(), Location.unknown():
t = IntegerType.get_signless(32)
element = IntegerAttr.get(t, 555)
shaped_type = RankedTensorType.get((2, 3, 4), t)
attr = DenseElementsAttr.get_splat(shaped_type, element)
# CHECK: dense<555> : tensor<2x3x4xi32>
print(attr)
# CHECK: is_splat: True
print("is_splat:", attr.is_splat)
# CHECK: splat_value: IntegerAttr(555 : i32)
splat_value = attr.get_splat_value()
print("splat_value:", repr(splat_value))
assert splat_value == element
# CHECK-LABEL: TEST: testGetDenseElementsSplatFloat
@run
def testGetDenseElementsSplatFloat():
with Context(), Location.unknown():
t = F32Type.get()
element = FloatAttr.get(t, 1.2)
shaped_type = RankedTensorType.get((2, 3, 4), t)
attr = DenseElementsAttr.get_splat(shaped_type, element)
# CHECK: dense<1.200000e+00> : tensor<2x3x4xf32>
print(attr)
assert attr.get_splat_value() == element
# CHECK-LABEL: TEST: testGetDenseElementsSplatErrors
@run
def testGetDenseElementsSplatErrors():
with Context(), Location.unknown():
t = F32Type.get()
other_t = F64Type.get()
element = FloatAttr.get(t, 1.2)
other_element = FloatAttr.get(other_t, 1.2)
shaped_type = RankedTensorType.get((2, 3, 4), t)
dynamic_shaped_type = UnrankedTensorType.get(t)
non_shaped_type = t
try:
attr = DenseElementsAttr.get_splat(non_shaped_type, element)
except ValueError as e:
# CHECK: Expected a static ShapedType for the shaped_type parameter: Type(f32)
print(e)
try:
attr = DenseElementsAttr.get_splat(dynamic_shaped_type, element)
except ValueError as e:
# CHECK: Expected a static ShapedType for the shaped_type parameter: Type(tensor<*xf32>)
print(e)
try:
attr = DenseElementsAttr.get_splat(shaped_type, other_element)
except ValueError as e:
# CHECK: Shaped element type and attribute type must be equal: shaped=Type(tensor<2x3x4xf32>), element=Attribute(1.200000e+00 : f64)
print(e)
# CHECK-LABEL: TEST: testRepeatedValuesSplat
@run
def testRepeatedValuesSplat():
with Context():
array = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], dtype=np.float32)
attr = DenseElementsAttr.get(array)
# CHECK: dense<1.000000e+00> : tensor<2x3xf32>
print(attr)
# CHECK: is_splat: True
print("is_splat:", attr.is_splat)
# CHECK{LITERAL}: [[1. 1. 1.]
# CHECK{LITERAL}: [1. 1. 1.]]
print(np.array(attr))
# CHECK-LABEL: TEST: testNonSplat
@run
def testNonSplat():
with Context():
array = np.array([2.0, 1.0, 1.0], dtype=np.float32)
attr = DenseElementsAttr.get(array)
# CHECK: is_splat: False
print("is_splat:", attr.is_splat)
################################################################################
# Tests of the array/buffer .get() factory method, in all of its permutations.
################################################################################
### explicitly provided types
@run
def testGetDenseElementsBF16():
with Context():
array = np.array([[2, 4, 8], [16, 32, 64]], dtype=np.uint16)
attr = DenseElementsAttr.get(array, type=BF16Type.get())
# Note: These values don't mean much since just bit-casting. But they
# shouldn't change.
# CHECK: dense<{{\[}}[1.836710e-40, 3.673420e-40, 7.346840e-40], [1.469370e-39, 2.938740e-39, 5.877470e-39]]> : tensor<2x3xbf16>
print(attr)
@run
def testGetDenseElementsInteger4():
with Context():
array = np.array([[2, 4, 7], [-2, -4, -8]], dtype=np.uint8)
attr = DenseElementsAttr.get(array, type=IntegerType.get_signless(4))
# Note: These values don't mean much since just bit-casting. But they
# shouldn't change.
# CHECK: dense<{{\[}}[2, 4, 7], [-2, -4, -8]]> : tensor<2x3xi4>
print(attr)
@run
def testGetDenseElementsBool():
with Context():
bool_array = np.array([[1, 0, 1], [0, 1, 0]], dtype=np.bool_)
array = np.packbits(bool_array, axis=None, bitorder="little")
attr = DenseElementsAttr.get(
array, type=IntegerType.get_signless(1), shape=bool_array.shape
)
# CHECK: dense<{{\[}}[true, false, true], [false, true, false]]> : tensor<2x3xi1>
print(attr)
@run
def testGetDenseElementsBoolSplat():
with Context():
zero = np.array(0, dtype=np.uint8)
one = np.array(255, dtype=np.uint8)
print(one)
# CHECK: dense<false> : tensor<4x2x5xi1>
print(
DenseElementsAttr.get(
zero, type=IntegerType.get_signless(1), shape=(4, 2, 5)
)
)
# CHECK: dense<true> : tensor<4x2x5xi1>
print(
DenseElementsAttr.get(
one, type=IntegerType.get_signless(1), shape=(4, 2, 5)
)
)
### float and double arrays.
# CHECK-LABEL: TEST: testGetDenseElementsF16
@run
def testGetDenseElementsF16():
with Context():
array = np.array([[2.0, 4.0, 8.0], [16.0, 32.0, 64.0]], dtype=np.float16)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[2.000000e+00, 4.000000e+00, 8.000000e+00], [1.600000e+01, 3.200000e+01, 6.400000e+01]]> : tensor<2x3xf16>
print(attr)
# CHECK: {{\[}}[ 2. 4. 8.]
# CHECK: {{\[}}16. 32. 64.]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsF32
@run
def testGetDenseElementsF32():
with Context():
array = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1.100000e+00, 2.200000e+00, 3.300000e+00], [4.400000e+00, 5.500000e+00, 6.600000e+00]]> : tensor<2x3xf32>
print(attr)
# CHECK: {{\[}}[1.1 2.2 3.3]
# CHECK: {{\[}}4.4 5.5 6.6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsF64
@run
def testGetDenseElementsF64():
with Context():
array = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float64)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1.100000e+00, 2.200000e+00, 3.300000e+00], [4.400000e+00, 5.500000e+00, 6.600000e+00]]> : tensor<2x3xf64>
print(attr)
# CHECK: {{\[}}[1.1 2.2 3.3]
# CHECK: {{\[}}4.4 5.5 6.6]]
print(np.array(attr))
### 16 bit integer arrays
# CHECK-LABEL: TEST: testGetDenseElementsI16Signless
@run
def testGetDenseElementsI16Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi16>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI16Signless
@run
def testGetDenseElementsUI16Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint16)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi16>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsI16
@run
def testGetDenseElementsI16():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xsi16>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI16
@run
def testGetDenseElementsUI16():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint16)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xui16>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
### 32 bit integer arrays
# CHECK-LABEL: TEST: testGetDenseElementsI32Signless
@run
def testGetDenseElementsI32Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI32Signless
@run
def testGetDenseElementsUI32Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint32)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsI32
@run
def testGetDenseElementsI32():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xsi32>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI32
@run
def testGetDenseElementsUI32():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint32)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xui32>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
## 64bit integer arrays
# CHECK-LABEL: TEST: testGetDenseElementsI64Signless
@run
def testGetDenseElementsI64Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi64>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI64Signless
@run
def testGetDenseElementsUI64Signless():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint64)
attr = DenseElementsAttr.get(array)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi64>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsI64
@run
def testGetDenseElementsI64():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xsi64>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsUI64
@run
def testGetDenseElementsUI64():
with Context():
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint64)
attr = DenseElementsAttr.get(array, signless=False)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xui64>
print(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(np.array(attr))
# CHECK-LABEL: TEST: testGetDenseElementsIndex
@run
def testGetDenseElementsIndex():
with Context(), Location.unknown():
idx_type = IndexType.get()
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
attr = DenseElementsAttr.get(array, type=idx_type)
# CHECK: dense<{{\[}}[1, 2, 3], [4, 5, 6]]> : tensor<2x3xindex>
print(attr)
arr = np.array(attr)
# CHECK: {{\[}}[1 2 3]
# CHECK: {{\[}}4 5 6]]
print(arr)
# CHECK: True
print(arr.dtype == np.int64)
|
