mirror of https://github.com/commaai/tinygrad.git
[GH-1305] Refactor test_dtypes.py to be cleaner (#1306)
Co-authored-by: waifairer <waifairer@gmail.com>
This commit is contained in:
waifairer
GitHub
parent
48c4df1263
commit
7cac5ea16c
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@ -3,29 +3,50 @@ import numpy as np
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from tinygrad.helpers import getenv, DType, DEBUG
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from tinygrad.lazy import Device
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from tinygrad.tensor import Tensor, dtypes
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from typing import List, Optional
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from extra.utils import OSX, temp
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import copy
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def _test_to_np(a:Tensor, np_dtype, target):
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print(a)
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if DEBUG >= 2: print(a)
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na = a.numpy()
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print(na, na.dtype, a.lazydata.realized)
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assert na.dtype == np_dtype
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np.testing.assert_allclose(na, target)
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if DEBUG >= 2: print(na, na.dtype, a.lazydata.realized)
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try:
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assert na.dtype == np_dtype
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np.testing.assert_allclose(na, target)
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except AssertionError as e:
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raise AssertionError(f"\ntensor {a.numpy()} does not match target {target} with np_dtype {np_dtype}") from e
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def _test_op(fxn, target_dtype:DType, target):
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c = fxn()
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if DEBUG >= 2: print(c.numpy())
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assert c.dtype == target_dtype
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np.testing.assert_allclose(c.numpy(), target)
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def _assert_eq(tensor:Tensor, target_dtype:DType, target):
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if DEBUG >= 2: print(tensor.numpy())
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try:
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assert tensor.dtype == target_dtype
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np.testing.assert_allclose(tensor.numpy(), target)
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except AssertionError as e:
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raise AssertionError(f"\ntensor {tensor.numpy()} dtype {tensor.dtype} does not match target {target} with dtype {target_dtype}") from e
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def _test_op(fxn, target_dtype:DType, target): _assert_eq(fxn(), target_dtype, target)
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def _test_cast(a:Tensor, target_dtype:DType, target): _test_op(lambda: a.cast(target_dtype), target_dtype, target)
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def _test_add(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a+b, target_dtype, target)
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def _test_mul(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a*b, target_dtype, target)
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def _test_matmul(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a@b, target_dtype, target)
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def _test_add_upcast(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a+b, target_dtype, target)
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def _test_mul_upcast(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a*b, target_dtype, target)
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def _test_matmul_upcast(a:Tensor, b:Tensor, target_dtype:DType, target): _test_op(lambda: a@b, target_dtype, target)
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# tests no-op casts from source_dtype to target_dtypes
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def _test_casts_from(tensor_contents:List, source_dtype:DType, target_dtypes:List[DType], target_contents:Optional[List]=None):
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if target_contents is None: target_contents = copy.deepcopy(tensor_contents)
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list(map(
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lambda t_dtype: _test_cast(Tensor(tensor_contents, dtype=source_dtype), t_dtype, target_contents),
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target_dtypes
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))
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# tests no-op casts from source_dtypes to target_dtype
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def _test_casts_to(tensor_contents:List, source_dtypes:List[DType], target_dtype:DType, target_contents:Optional[List]=None):
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if target_contents is None: target_contents = copy.deepcopy(tensor_contents)
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list(map(
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lambda s_dtype: _test_cast(Tensor(tensor_contents, dtype=s_dtype), target_dtype, target_contents),
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source_dtypes
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))
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def _test_ops(a_dtype:DType, b_dtype:DType, target_dtype:DType):
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_assert_eq(Tensor([1,2,3,4], dtype=a_dtype)+Tensor([1,2,3,4], dtype=b_dtype), target_dtype, [2,4,6,8])
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_assert_eq(Tensor([1,2,3,4], dtype=a_dtype)*Tensor([1,2,3,4], dtype=b_dtype), target_dtype, [1,4,9,16])
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_assert_eq(Tensor([[1,2],[3,4]], dtype=a_dtype)@Tensor.eye(2, dtype=b_dtype), target_dtype, [[1,2],[3,4]])
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class TestBFloat16DType(unittest.TestCase):
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def test_bf16_to_float(self):
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@ -63,28 +84,11 @@ class TestBFloat16DType(unittest.TestCase):
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# for LLVM, it segfaults because it can't link to the casting function
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@unittest.skipIf((getenv("CI", "") != "" and Device.DEFAULT in ["LLVM"]) or Device.DEFAULT == "WEBGPU", "float16 broken in some CI backends")
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class TestHalfDtype(unittest.TestCase):
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def test_half_to_np(self): _test_to_np(Tensor([1,2,3,4], dtype=dtypes.float16), np.float16, [1,2,3,4])
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def test_half_to_float(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.float32, [1,2,3,4])
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def test_half_to_int8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.int8, [1,2,3,4])
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def test_half_to_uint8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.uint8, [1,2,3,4])
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def test_half_to_int32(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.int32, [1,2,3,4])
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def test_half_to_int64(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.int64, [1,2,3,4])
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def test_float_to_half(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float16, [1,2,3,4])
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def test_int8_to_half(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.float16, [1,2,3,4])
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def test_uint8_to_half(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.uint8), dtypes.float16, [1,2,3,4])
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def test_half_add(self): _test_add(Tensor([1,2,3,4], dtype=dtypes.float16), Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.float16, [2,4,6,8])
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def test_half_mul(self): _test_mul(Tensor([1,2,3,4], dtype=dtypes.float16), Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.float16, [1,4,9,16])
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def test_half_matmul(self): _test_matmul(Tensor([[1,2],[3,4]], dtype=dtypes.float16), Tensor.eye(2, dtype=dtypes.float16), dtypes.float16, [[1,2],[3,4]])
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def test_half_add_upcast_float(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.float16), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [2,4,6,8])
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def test_int8_add_upcast_half(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.float16, [2,4,6,8])
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def test_int8_mul_upcast_half(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.float16), dtypes.float16, [1,4,9,16])
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def test_half_mul_upcast_float(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.float16), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [1,4,9,16])
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def test_half_matmul_upcast_float(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.float16), Tensor.eye(2, dtype=dtypes.float32), dtypes.float32, [[1,2],[3,4]])
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def test_int8_matmul_upcast_half(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.int8), Tensor.eye(2, dtype=dtypes.float16), dtypes.float16, [[1,2],[3,4]])
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def test_float16_to_np(self): _test_to_np(Tensor([1,2,3,4], dtype=dtypes.float16), np.float16, [1,2,3,4])
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def test_casts_to_half(self): _test_casts_to([1,2,3,4], source_dtypes=[dtypes.float32, dtypes.int8, dtypes.uint8], target_dtype=dtypes.float16)
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def test_casts_from_half(self): _test_casts_from([1,2,3,4], source_dtype=dtypes.float16, target_dtypes=[dtypes.int8, dtypes.uint8, dtypes.float32, dtypes.int32, dtypes.int64])
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def test_half_upcast_ops(self): _test_ops(a_dtype=dtypes.float16, b_dtype=dtypes.float32, target_dtype=dtypes.float32)
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def test_upcast_to_half_ops(self): _test_ops(a_dtype=dtypes.int8, b_dtype=dtypes.float16, target_dtype=dtypes.float16)
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@unittest.skipIf(Device.DEFAULT == "WEBGPU", "webgpu does not support int8")
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class TestInt8Dtype(unittest.TestCase):
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@ -92,35 +96,14 @@ class TestInt8Dtype(unittest.TestCase):
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def test_uint8_to_np(self): _test_to_np(Tensor([1,2,3,4], dtype=dtypes.uint8), np.uint8, [1,2,3,4])
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def test_int64_to_np(self): _test_to_np(Tensor([1,2,3,4], dtype=dtypes.int64), np.int64, [1,2,3,4])
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def test_float_to_int8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.int8, [1,2,3,4])
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def test_float_to_uint8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.uint8, [1,2,3,4])
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def test_float_to_int64(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.int64, [1,2,3,4])
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def test_casts_to_int8(self): _test_casts_from([1,2,3,4], source_dtype=dtypes.float32, target_dtypes=[dtypes.int8, dtypes.uint8, dtypes.int32, dtypes.int64])
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def test_casts_from_int8(self): _test_casts_from([1,2,3,4], source_dtype=dtypes.int8, target_dtypes=[dtypes.float32, dtypes.uint8, dtypes.int32, dtypes.int64])
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def test_casts_from_uint8(self): _test_casts_from([1,2,3,4], source_dtype=dtypes.uint8, target_dtypes=[dtypes.float32, dtypes.int8, dtypes.int32, dtypes.int64])
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def test_int8_to_float(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.float32, [1,2,3,4])
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def test_int8_to_uint8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.uint8, [1,2,3,4])
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def test_int8_to_int32(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.int32, [1,2,3,4])
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def test_int8_to_int64(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.int64, [1,2,3,4])
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def test_uint8_to_float(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.uint8), dtypes.float32, [1,2,3,4])
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def test_uint8_to_int8(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.uint8), dtypes.int8, [1,2,3,4])
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def test_uint8_to_int64(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.uint8), dtypes.int64, [1,2,3,4])
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def test_int8_add(self): _test_add(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.int8, [2,4,6,8])
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def test_int64_add(self): _test_add(Tensor([1,2,3,4], dtype=dtypes.int64),Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [2,4,6,8])
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def test_int8_mul(self): _test_mul(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.int8), dtypes.int8, [1,4,9,16])
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def test_int64_mul(self): _test_mul(Tensor([1,2,3,4], dtype=dtypes.int64), Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [1,4,9,16])
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def test_int8_matmul(self): _test_matmul(Tensor([[1,2],[3,4]], dtype=dtypes.int8), Tensor.eye(2, dtype=dtypes.int8), dtypes.int8, [[1,2],[3,4]])
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def test_int64_matmul(self): _test_matmul(Tensor([[1,2],[3,4]], dtype=dtypes.int64), Tensor.eye(2, dtype=dtypes.int64), dtypes.int64, [[1,2],[3,4]])
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def test_int8_add_upcast_float(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [2,4,6,8])
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def test_int8_mul_upcast_float(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [1,4,9,16])
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def test_int8_matmul_upcast_float(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.int8), Tensor.eye(2, dtype=dtypes.float32), dtypes.float32, [[1,2],[3,4]])
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def test_int8_add_upcast_int64(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [2,4,6,8])
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def test_int8_mul_upcast_int64(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.int8), Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [1,4,9,16])
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def test_int8_matmul_upcast_int64(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.int8), Tensor.eye(2, dtype=dtypes.int64), dtypes.int64, [[1,2],[3,4]])
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def test_int8_ops(self): _test_ops(a_dtype=dtypes.int8, b_dtype=dtypes.int8, target_dtype=dtypes.int8)
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def test_int64_ops(self): _test_ops(a_dtype=dtypes.int64, b_dtype=dtypes.int64, target_dtype=dtypes.int64)
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def test_int8_upcast_float(self): _test_ops(a_dtype=dtypes.int8, b_dtype=dtypes.float32, target_dtype=dtypes.float32)
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def test_int8_upcast_int64(self): _test_ops(a_dtype=dtypes.int8, b_dtype=dtypes.int64, target_dtype=dtypes.int64)
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@unittest.skipIf(getenv("CUDA",0)==1, "cuda saturation works differently")
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def test_int8_to_uint8_negative(self): _test_op(lambda: Tensor([-1, -2, -3, -4], dtype=dtypes.int8).cast(dtypes.uint8), dtypes.uint8, [255, 254, 253, 252])
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@ -130,23 +113,12 @@ class TestInt8Dtype(unittest.TestCase):
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class TestInt32Dtype(unittest.TestCase):
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def test_int32_to_np(self): _test_to_np(Tensor([1,2,3,4], dtype=dtypes.int32), np.int32, [1,2,3,4])
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def test_float_to_int32(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.int32, [1,2,3,4])
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def test_int64_to_int32(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int32, [1,2,3,4])
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def test_casts_to_int32(self): _test_casts_to([1,2,3,4], source_dtypes=[dtypes.float32, dtypes.int64], target_dtype=dtypes.int32)
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def test_casts_from_int32(self): _test_casts_from([1,2,3,4], source_dtype=dtypes.int32, target_dtypes=[dtypes.float32, dtypes.int64])
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def test_int32_to_float(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int32), dtypes.float32, [1,2,3,4])
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def test_int32_to_int64(self): _test_cast(Tensor([1,2,3,4], dtype=dtypes.int32), dtypes.int64, [1,2,3,4])
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def test_int32_add(self): _test_add(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.int32), dtypes.int32, [2,4,6,8])
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def test_int32_mul(self): _test_mul(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.int32), dtypes.int32, [1,4,9,16])
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def test_int32_matmul(self): _test_matmul(Tensor([[1,2],[3,4]], dtype=dtypes.int32), Tensor.eye(2, dtype=dtypes.int32), dtypes.int32, [[1,2],[3,4]])
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def test_int32_add_upcast_float(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [2,4,6,8])
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def test_int32_mul_upcast_float(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.float32), dtypes.float32, [1,4,9,16])
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def test_int32_matmul_upcast_float(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.int32), Tensor.eye(2, dtype=dtypes.float32), dtypes.float32, [[1,2],[3,4]])
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def test_int32_add_upcast_int64(self): _test_add_upcast(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [2,4,6,8])
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def test_int32_mul_upcast_int64(self): _test_mul_upcast(Tensor([1,2,3,4], dtype=dtypes.int32), Tensor([1,2,3,4], dtype=dtypes.int64), dtypes.int64, [1,4,9,16])
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def test_int32_matmul_upcast_int64(self): _test_matmul_upcast(Tensor([[1,2],[3,4]], dtype=dtypes.int32), Tensor.eye(2, dtype=dtypes.int64), dtypes.int64, [[1,2],[3,4]])
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def test_int32_ops(self): _test_ops(a_dtype=dtypes.int32, b_dtype=dtypes.int32, target_dtype=dtypes.int32)
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def test_int32_upcast_float32(self): _test_ops(a_dtype=dtypes.int32, b_dtype=dtypes.float32, target_dtype=dtypes.float32)
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def test_int32_upcast_int64(self): _test_ops(a_dtype=dtypes.int32, b_dtype=dtypes.int64, target_dtype=dtypes.int64)
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if __name__ == '__main__':
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unittest.main()
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