6021ed572f
#174156 made all gettors return `Py*` but skipped downcasting where possible. So restore it by calling `.maybeDowncast`.
192 lines
7.0 KiB
Python
192 lines
7.0 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
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import numpy as np
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from mlir.ir import *
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from mlir.dialects import quant
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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: test_type_hierarchy
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@run
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def test_type_hierarchy():
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with Context():
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i8 = IntegerType.get_signless(8)
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any = Type.parse("!quant.any<i8<-8:7>:f32>")
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uniform = Type.parse("!quant.uniform<i8<-8:7>:f32, 0.99872:127>")
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per_axis = Type.parse("!quant.uniform<i8:f32:1, {2.0e+2,0.99872:120}>")
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sub_channel = Type.parse(
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"!quant.uniform<i8:f32:{0:1, 1:2}, {{2.0:10, 3.0:20}, {4.0:30, 5.0:40}}>"
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)
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calibrated = Type.parse("!quant.calibrated<f32<-0.998:1.2321>>")
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assert not isinstance(i8, quant.QuantizedType)
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assert isinstance(any, quant.QuantizedType)
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assert isinstance(uniform, quant.QuantizedType)
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assert isinstance(per_axis, quant.QuantizedType)
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assert isinstance(sub_channel, quant.QuantizedType)
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assert isinstance(calibrated, quant.QuantizedType)
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assert isinstance(any, quant.AnyQuantizedType)
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assert isinstance(uniform, quant.UniformQuantizedType)
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assert isinstance(per_axis, quant.UniformQuantizedPerAxisType)
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assert isinstance(sub_channel, quant.UniformQuantizedSubChannelType)
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assert isinstance(calibrated, quant.CalibratedQuantizedType)
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assert not isinstance(uniform, quant.AnyQuantizedType)
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assert not isinstance(per_axis, quant.UniformQuantizedType)
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assert not isinstance(sub_channel, quant.UniformQuantizedType)
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assert not isinstance(sub_channel, quant.UniformQuantizedPerAxisType)
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generic_t = Type(uniform)
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assert type(generic_t) is Type
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r = quant.QuantizedType.cast_to_storage_type(generic_t)
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assert isinstance(r, IntegerType)
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# CHECK-LABEL: TEST: test_any_quantized_type
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@run
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def test_any_quantized_type():
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with Context():
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i8 = IntegerType.get_signless(8)
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f32 = F32Type.get()
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any = quant.AnyQuantizedType.get(
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quant.QuantizedType.FLAG_SIGNED, i8, f32, -8, 7
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)
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# CHECK: flags: 1
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print(f"flags: {any.flags}")
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# CHECK: signed: True
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print(f"signed: {any.is_signed}")
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# CHECK: storage type: i8
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print(f"storage type: {any.storage_type}")
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# CHECK: expressed type: f32
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print(f"expressed type: {any.expressed_type}")
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# CHECK: storage min: -8
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print(f"storage min: {any.storage_type_min}")
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# CHECK: storage max: 7
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print(f"storage max: {any.storage_type_max}")
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# CHECK: storage width: 8
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print(f"storage width: {any.storage_type_integral_width}")
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# CHECK: quantized element type: !quant.any<i8<-8:7>:f32>
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print(f"quantized element type: {any.quantized_element_type}")
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# CHECK: !quant.any<i8<-8:7>:f32>
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print(any)
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assert any == Type.parse("!quant.any<i8<-8:7>:f32>")
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# CHECK-LABEL: TEST: test_uniform_type
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@run
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def test_uniform_type():
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with Context():
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i8 = IntegerType.get_signless(8)
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f32 = F32Type.get()
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uniform = quant.UniformQuantizedType.get(
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quant.UniformQuantizedType.FLAG_SIGNED, i8, f32, 0.99872, 127, -8, 7
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)
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# CHECK: scale: 0.99872
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print(f"scale: {uniform.scale}")
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# CHECK: zero point: 127
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print(f"zero point: {uniform.zero_point}")
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# CHECK: fixed point: False
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print(f"fixed point: {uniform.is_fixed_point}")
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# CHECK: !quant.uniform<i8<-8:7>:f32, 9.987200e-01:127>
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print(uniform)
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assert uniform == Type.parse("!quant.uniform<i8<-8:7>:f32, 0.99872:127>")
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# CHECK-LABEL: TEST: test_uniform_per_axis_type
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@run
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def test_uniform_per_axis_type():
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with Context():
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i8 = IntegerType.get_signless(8)
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f32 = F32Type.get()
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per_axis = quant.UniformQuantizedPerAxisType.get(
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quant.QuantizedType.FLAG_SIGNED,
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i8,
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f32,
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[200, 0.99872],
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[0, 120],
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quantized_dimension=1,
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storage_type_min=quant.QuantizedType.default_minimum_for_integer(
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is_signed=True, integral_width=8
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),
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storage_type_max=quant.QuantizedType.default_maximum_for_integer(
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is_signed=True, integral_width=8
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),
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)
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# CHECK: scales: [200.0, 0.99872]
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print(f"scales: {per_axis.scales}")
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# CHECK: zero_points: [0, 120]
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print(f"zero_points: {per_axis.zero_points}")
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# CHECK: quantized dim: 1
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print(f"quantized dim: {per_axis.quantized_dimension}")
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# CHECK: fixed point: False
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print(f"fixed point: {per_axis.is_fixed_point}")
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# CHECK: !quant.uniform<i8:f32:1, {2.000000e+02,9.987200e-01:120}>
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print(per_axis)
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assert per_axis == Type.parse("!quant.uniform<i8:f32:1, {2.0e+2,0.99872:120}>")
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# CHECK-LABEL: TEST: test_uniform_sub_channel_type
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@run
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def test_uniform_sub_channel_type():
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with Context():
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i8 = IntegerType.get_signless(8)
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f32 = F32Type.get()
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sub_channel = quant.UniformQuantizedSubChannelType.get(
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quant.QuantizedType.FLAG_SIGNED,
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i8,
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f32,
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DenseElementsAttr.get(
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np.asarray([2.0, 3.0, 4.0, 5.0], np.float32).reshape(2, 2)
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),
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DenseElementsAttr.get(np.asarray([10, 20, 30, 40], np.int8).reshape(2, 2)),
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[0, 1],
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[1, 2],
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storage_type_min=quant.QuantizedType.default_minimum_for_integer(
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is_signed=True, integral_width=8
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),
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storage_type_max=quant.QuantizedType.default_maximum_for_integer(
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is_signed=True, integral_width=8
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),
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)
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# CHECK: quantized dimensions: [0, 1]
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print(f"quantized dimensions: {sub_channel.quantized_dimensions}")
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# CHECK: block sizes: [1, 2]
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print(f"block sizes: {sub_channel.block_sizes}")
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# CHECK: scales: {{\[}}[2. 3.]
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# CHECK: [4. 5.]]
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print(f"scales: {np.asarray(sub_channel.scales)}")
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# CHECK: zero-points: {{\[}}[10 20]
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# CHECK: [30 40]]
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print(f"zero-points: {np.asarray(sub_channel.zero_points)}")
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# CHECK: !quant.uniform<i8:f32:{0:1, 1:2}, {{\{}}{2.000000e+00:10, 3.000000e+00:20}, {4.000000e+00:30, 5.000000e+00:40}}>
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print(sub_channel)
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assert sub_channel == Type.parse(
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"!quant.uniform<i8:f32:{0:1, 1:2},{{2.0:10, 3.0:20}, {4.0:30, 5.0:40}}>"
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)
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# CHECK-LABEL: TEST: test_calibrated_type
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@run
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def test_calibrated_type():
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with Context():
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f32 = F32Type.get()
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calibrated = quant.CalibratedQuantizedType.get(f32, -0.998, 1.2321)
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# CHECK: min: -0.998
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print(f"min: {calibrated.min}")
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# CHECK: max: 1.2321
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print(f"max: {calibrated.max}")
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# CHECK: !quant.calibrated<f32<-0.998:1.232100e+00>>
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print(calibrated)
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assert calibrated == Type.parse("!quant.calibrated<f32<-0.998:1.2321>>")
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