chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# coding: utf-8
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import logging
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import os
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import sys
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import pytest
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import torch
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from ray.experimental.channel.serialization_context import _SerializationContext
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from ray.experimental.util.types import Device
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logger = logging.getLogger(__name__)
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@pytest.mark.parametrize(
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"scalar_and_dtype",
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[
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# Basic tests
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(1.23456, torch.float16),
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(1.23456, torch.bfloat16),
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(1.23456, torch.float32),
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(1.23456, torch.float64),
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(123, torch.int8),
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(123, torch.int16),
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(123456, torch.int32),
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(123456, torch.int64),
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(123, torch.uint8),
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(123, torch.uint16),
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(123456, torch.uint32),
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(123456, torch.uint64),
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(True, torch.bool),
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# Boundary values tests - integers
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(127, torch.int8), # INT8_MAX
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(-128, torch.int8), # INT8_MIN
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(32767, torch.int16), # INT16_MAX
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(-32768, torch.int16), # INT16_MIN
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(2147483647, torch.int32), # INT32_MAX
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(-2147483648, torch.int32), # INT32_MIN
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(9223372036854775807, torch.int64), # INT64_MAX
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(-9223372036854775808, torch.int64), # INT64_MIN
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# Boundary values tests - unsigned integers
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(255, torch.uint8), # UINT8_MAX
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(0, torch.uint8), # UINT8_MIN
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(65535, torch.uint16), # UINT16_MAX
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(0, torch.uint16), # UINT16_MIN
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(4294967295, torch.uint32), # UINT32_MAX
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(0, torch.uint32), # UINT32_MIN
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(18446744073709551615, torch.uint64), # UINT64_MAX
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(0, torch.uint64), # UINT64_MIN
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# Floating point special values
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(float("inf"), torch.float32),
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(float("-inf"), torch.float32),
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(float("nan"), torch.float32),
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(float("inf"), torch.float64),
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(float("-inf"), torch.float64),
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(float("nan"), torch.float64),
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# Float precision tests
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(1.2345678901234567, torch.float32), # Beyond float32 precision
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(1.2345678901234567, torch.float64), # Within float64 precision
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(1e-45, torch.float32), # Near float32 smallest positive normal
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(
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2.2250738585072014e-308,
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torch.float64,
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), # Near float64 smallest positive normal
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],
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)
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def test_scalar_tensor(scalar_and_dtype):
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scalar, dtype = scalar_and_dtype
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context = _SerializationContext()
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scalar_tensor = torch.tensor(scalar, dtype=dtype)
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np_array, tensor_dtype, tensor_device_type = context.serialize_to_numpy_or_scalar(
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scalar_tensor
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)
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assert tensor_dtype == dtype
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deserialized_tensor = context.deserialize_from_numpy_or_scalar(
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np_array, dtype, tensor_device_type, Device.CPU
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)
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# Special handling for NaN values
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if torch.is_floating_point(scalar_tensor) and torch.isnan(scalar_tensor):
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assert torch.isnan(deserialized_tensor)
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else:
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assert (deserialized_tensor == scalar_tensor).all()
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@pytest.mark.parametrize(
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"tensor_shape_and_dtype",
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[
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((10, 10), torch.float16),
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((10, 10), torch.bfloat16),
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((10, 10, 10), torch.float32),
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((10, 10, 10, 10), torch.float64),
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((10, 10), torch.int8),
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((10, 10), torch.int16),
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((10, 10), torch.int32),
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((10, 10), torch.int64),
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((10, 10), torch.uint8),
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((10, 10), torch.uint16),
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((10, 10), torch.uint32),
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((10, 10), torch.uint64),
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],
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)
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def test_non_scalar_tensor(tensor_shape_and_dtype):
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tensor_shape, dtype = tensor_shape_and_dtype
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context = _SerializationContext()
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# Create tensor based on dtype with varying values
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if dtype in [torch.float16, torch.bfloat16, torch.float32, torch.float64]:
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# For floating point types, use randn
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tensor = torch.randn(*tensor_shape).to(dtype)
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else:
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# For integer types, create varying values within appropriate ranges
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total_elements = torch.prod(torch.tensor(tensor_shape)).item()
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if dtype == torch.uint8:
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# Range: 0 to 255
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values = torch.arange(0, min(total_elements, 256), dtype=torch.int32) % 256
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elif dtype == torch.uint16:
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# Range: 0 to 65535
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values = (
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torch.arange(0, min(total_elements, 65536), dtype=torch.int32) % 65536
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)
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elif dtype == torch.int8:
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# Range: -128 to 127
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values = (
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torch.arange(0, min(total_elements, 256), dtype=torch.int32) % 256
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) - 128
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elif dtype == torch.int16:
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# Range: -32768 to 32767
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values = (
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torch.arange(0, min(total_elements, 65536), dtype=torch.int32) % 65536
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) - 32768
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elif dtype == torch.int32:
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# Use a smaller range to avoid overflow
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values = torch.arange(0, total_elements, dtype=torch.int32) % 10000 - 5000
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else: # int64
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# Use a smaller range to avoid overflow
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values = torch.arange(0, total_elements, dtype=torch.int64) % 10000 - 5000
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# Reshape the values to match the target shape
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tensor = values.reshape(tensor_shape).to(dtype)
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np_array, tensor_dtype, tensor_device_type = context.serialize_to_numpy_or_scalar(
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tensor
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)
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deserialized_tensor = context.deserialize_from_numpy_or_scalar(
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np_array, tensor_dtype, tensor_device_type, Device.CPU
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)
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assert (tensor == deserialized_tensor).all()
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if __name__ == "__main__":
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if os.environ.get("PARALLEL_CI"):
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sys.exit(pytest.main(["-n", "auto", "--boxed", "-vs", __file__]))
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else:
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sys.exit(pytest.main(["-sv", __file__]))
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