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/external/tensorflow/tensorflow/compiler/tests/
Dbinary_ops_test.py48 pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
49 pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
55 rtol = 1e-15 if a.dtype == np.float64 else 1e-3
57 atol = 1e-15 if a.dtype == np.float64 else 1e-6
72 for dtype in self.float_types:
73 if dtype == dtypes.bfloat16.as_numpy_dtype:
82 np.array([[[[-1, 2.00009999], [-3, b]]]], dtype=dtype),
83 np.array([[[[a, 2], [-3.00009, 4]]]], dtype=dtype),
84 expected=np.array([[[[False, True], [True, False]]]], dtype=dtype))
88 np.array([3, 3, -1.5, -8, 44], dtype=dtype),
[all …]
Dunary_ops_test.py68 dtypes.as_dtype(inp.dtype), inp.shape, name="a")
72 self.assertEqual(output.dtype, expected.dtype)
94 for dtype in self.numeric_types - {np.int8, np.uint8}:
96 array_ops.diag, np.array([1, 2, 3, 4], dtype=dtype),
99 dtype=dtype))
102 np.arange(36).reshape([2, 3, 2, 3]).astype(dtype),
103 np.array([[0, 7, 14], [21, 28, 35]], dtype=dtype))
105 array_ops.diag, np.array([[1, 2], [3, 4]], dtype=dtype),
109 dtype=dtype))
113 np.array([[-1, 1]], dtype=dtype),
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Dternary_ops_test.py35 pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
36 pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
37 pc = array_ops.placeholder(dtypes.as_dtype(c.dtype), c.shape, name="c")
50 expected = np.linspace(start, end, num, dtype=np.float32)
68 expected=np.array([1], dtype=np.int32))
74 expected=np.array([1, 3, 5], dtype=np.int32))
77 for dtype in self.numeric_types:
81 np.array(2, dtype=dtype),
82 np.array(7, dtype=dtype),
83 expected=np.array(7, dtype=dtype))
[all …]
Dnary_ops_test.py35 array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape)
55 [np.array([[1, 2, 3]], dtype=np.float32)],
56 expected=np.array([[1, 2, 3]], dtype=np.float32))
59 [np.array([1, 2], dtype=np.float32),
60 np.array([10, 20], dtype=np.float32)],
61 expected=np.array([11, 22], dtype=np.float32))
63 [np.array([-4], dtype=np.float32),
64 np.array([10], dtype=np.float32),
65 np.array([42], dtype=np.float32)],
66 expected=np.array([48], dtype=np.float32))
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Dsegment_reduction_ops_test.py33 d = array_ops.placeholder(data.dtype, shape=data.shape)
37 i = array_ops.placeholder(indices.dtype, shape=indices.shape)
57 for dtype in self.numeric_types:
62 dtype=dtype),
64 np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
67 for dtype in self.numeric_types:
69 np.array([1, 3, 2, 9], dtype=dtype),
71 np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
72 np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4))
75 for dtype in self.numeric_types:
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/external/pytorch/torch/ao/quantization/fx/
D_decomposed.py27 # Helper to check the passed in quant min and max are valid for the dtype
28 def _quant_min_max_bounds_check(quant_min, quant_max, dtype): argument
29 if dtype not in _DTYPE_TO_QVALUE_BOUNDS:
30 raise ValueError(f"Unsupported dtype: {dtype}")
31 quant_min_lower_bound, quant_max_upper_bound = _DTYPE_TO_QVALUE_BOUNDS[dtype]
34 "quant_min out of bound for dtype, "
39 "quant_max out of bound for dtype, "
46 "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
57 dtype: torch.dtype, argument
68 dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
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/external/tensorflow/tensorflow/compiler/mlir/tfrt/jit/python_binding/
Dconversion_utils.cc21 #include "tfrt/dtype/dtype.h" // from @tf_runtime
25 using ::tfrt::DType;
29 // Returns Python buffer protocol's type string from TFRT's dtype.
30 const char* ToPythonStructFormat(DType dtype_kind) { in ToPythonStructFormat()
34 case DType::Invalid: in ToPythonStructFormat()
35 throw std::runtime_error("Invalid dtype."); in ToPythonStructFormat()
36 case DType::Unsupported: in ToPythonStructFormat()
37 throw std::runtime_error("Unsupported dtype."); in ToPythonStructFormat()
38 case DType::UI8: in ToPythonStructFormat()
40 case DType::UI16: in ToPythonStructFormat()
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/external/tensorflow/tensorflow/python/ops/
Dmath_grad_test.py68 def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None): argument
70 if dtype in (dtypes.complex64, dtypes.complex128):
79 shape, bias=bias), dtype=dtype)
91 [3, 3], dtype=dtypes.float32, max_error=2e-5, bias=0.1, sigma=1.0)
93 [3, 3], dtype=dtypes.complex64, max_error=2e-5, bias=0.1, sigma=1.0)
97 [3, 3], dtype=dtypes.float32, max_error=100.0, bias=0.0, sigma=0.1)
99 [3, 3], dtype=dtypes.complex64, max_error=100.0, bias=0.0, sigma=0.1)
106 inputs = constant_op.constant([1.0], dtype=dtypes.float32)
114 inputs = constant_op.constant([1.0], dtype=dtypes.float32)
125 inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32)
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Dinit_ops_v2.py42 def __call__(self, shape, dtype=None, **kwargs):
43 # returns a tensor of shape `shape` and dtype `dtype`
48 def __call__(self, shape, dtype=None, **kwargs): argument
53 dtype: Optional dtype of the tensor. If not provided will return tensor
95 config.pop("dtype", None)
115 the Initializer object, without knowing the shape and dtype of the variable
121 ... return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
122 ... tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
125 <tf.Variable ... shape=(3,) ... numpy=array([0., 0., 0.], dtype=float32)>
130 [0., 0., 0.]], dtype=float32)>
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/external/tensorflow/tensorflow/python/ops/signal/
Dwindow_ops.py31 def _check_params(window_length, dtype): argument
32 """Check window_length and dtype params.
36 dtype: The data type to produce. Must be a floating point type.
42 ValueError: If `dtype` is not a floating point type or window_length is not
45 if not dtype.is_floating:
46 raise ValueError('dtype must be a floating point type. Found %s' % dtype)
47 window_length = ops.convert_to_tensor(window_length, dtype=dtypes.int32)
54 def kaiser_window(window_length, beta=12., dtype=dtypes.float32, name=None): argument
60 dtype: The data type to produce. Must be a floating point type.
64 A `Tensor` of shape `[window_length]` of type `dtype`.
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/external/executorch/exir/dialects/edge/arg/
Dmodel.py29 dtype=None, argument
39 self.dtype_given = dtype is not None
44 self.dtype = torch.float if dtype is None else dtype
84 def get_random_tensor(self, size, dtype): argument
86 if dtype == torch.bool:
88 return torch.full(size, True, dtype=dtype)
90 return torch.randint(low=0, high=2, size=size, dtype=dtype)
92 if dtype in common_dtype.integral_types():
94 elif dtype in common_dtype.floating_types():
97 raise ValueError(f"Unsupported Dtype: {dtype}")
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/external/tensorflow/tensorflow/python/kernel_tests/math_ops/
Dsets_test.py38 def _values(values, dtype): argument
41 dtype=(np.str_ if (dtype == dtypes.string) else dtype.as_numpy_dtype))
44 def _constant(values, dtype): argument
45 return constant_op.constant(_values(values, dtype), dtype=dtype)
48 def _dense_to_sparse(dense, dtype): argument
60 values.append(str(cell) if dtype == dtypes.string else cell)
65 constant_op.constant(values, dtype),
73 for dtype in _DTYPES:
74 self._test_set_size_2d(dtype)
76 def _test_set_size_2d(self, dtype): argument
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/external/pytorch/test/torch_np/numpy_tests/core/
Dtest_dtype.py56 dt = np.dtype(t)
61 intp = np.dtype(np.intp)
64 right = np.dtype(np.int32)
67 right = np.dtype(np.int64)
75 assert_raises(TypeError, np.dtype, "O3")
76 assert_raises(TypeError, np.dtype, "O5")
77 assert_raises(TypeError, np.dtype, "O7")
78 assert_raises(TypeError, np.dtype, "b3")
79 assert_raises(TypeError, np.dtype, "h4")
80 assert_raises(TypeError, np.dtype, "I5")
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/external/pytorch/torch/_numpy/
D_reductions_impl.py6 Anything here only deals with torch objects, e.g. "dtype" is a torch.dtype instance etc
52 def _atleast_float(dtype, other_dtype): argument
53 """Return a dtype that is real or complex floating-point.
56 float dtype; inputs that are complex get converted to the default complex
57 dtype; real floating-point dtypes (`float*`) get passed through unchanged
59 if dtype is None:
60 dtype = other_dtype
61 if not (dtype.is_floating_point or dtype.is_complex):
63 return dtype
80 raise NotImplementedError(f"argmax with dtype={a.dtype}.")
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/external/pytorch/aten/src/ATen/cuda/
DCUDABlas.h5 gemm<Dtype>(transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c,
8 gemv<Dtype>(transa, m, n, alpha, a, lda, x, incx, beta, y, incy)
10 dot<Dtype>(n, x, incx, y, incy, result)
12 where Dtype is double, float, at::Half or at::BFloat16 (ROCm, NOT for dot).
42 #define CUDABLAS_GEMM_ARGTYPES(Dtype) \ argument
43 char transa, char transb, int64_t m, int64_t n, int64_t k, at::opmath_type<Dtype> alpha, \
44 const Dtype *a, int64_t lda, const Dtype *b, int64_t ldb, at::opmath_type<Dtype> beta,\
45 Dtype *c, int64_t ldc
47 #define CUDABLAS_GEMM_ARGS(Dtype) transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc argument
49 template <typename Dtype>
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/external/tensorflow/tensorflow/python/training/
Dftrl_test.py38 for dtype in [dtypes.half, dtypes.float32]:
42 dtype=dtype)
44 dtype=dtype)
46 var0 = variables.Variable([0.0, 0.0], dtype=dtype)
47 var1 = variables.Variable([0.0, 0.0], dtype=dtype)
48 grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
49 grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
81 for dtype in [dtypes.half, dtypes.float32]:
83 var0 = variables.Variable([1.0, 2.0], dtype=dtype)
84 var1 = variables.Variable([4.0, 3.0], dtype=dtype)
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/external/python/cpython3/Modules/_testcapi/
Dpyatomic.c34 #define IMPL_TEST_ADD(suffix, dtype) \ argument
37 dtype x = 0; \
47 assert(x == (dtype)-1); \
48 assert(_Py_atomic_add_##suffix(&x, -2) == (dtype)-1); \
49 assert(x == (dtype)-3); \
50 assert(_Py_atomic_add_##suffix(&x, 2) == (dtype)-3); \
51 assert(x == (dtype)-1); \
56 #define IMPL_TEST_COMPARE_EXCHANGE(suffix, dtype) \ argument
59 dtype x = (dtype)0; \
60 dtype y = (dtype)1; \
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/external/pytorch/test/
Dtest_unary_ufuncs.py86 def test_float_domains(self, device, dtype, op): argument
92 low_tensor = torch.tensor(low, device=device, dtype=dtype)
98 # and the dtype is imprecise (like bfloat16 is)
113 high_tensor = torch.tensor(high, device=device, dtype=dtype)
134 self, actual, expected, msg, *, dtype, exact_dtype=True, **kwargs argument
142 # Handles exact dtype comparisons between arrays and tensors
145 actual.dtype is torch.bfloat16
146 or expected.dtype != torch_to_numpy_dtype_dict[actual.dtype]
148 # Allows array dtype to be float32 when comparing with bfloat16 tensors
149 # since NumPy doesn't support the bfloat16 dtype
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Dtest_type_promotion.py29 # the default dtype being torch.float and again with the default dtype
51 int_tensor = torch.ones([4, 4, 4], dtype=torch.int32, device=device)
55 expected = torch.ones([4, 4, 4], dtype=torch.int32, device=device)
57 long_tensor = torch.ones([4, 4, 4], dtype=torch.int64, device=device)
62 self.assertEqual(int_tensor.dtype, torch.int32)
64 bool_tensor = torch.tensor([1, 1, 1], dtype=torch.bool, device=device)
65 uint8_tensor = torch.tensor([1, 1, 1], dtype=torch.uint8, device=device)
75 int16_tensor = torch.tensor([1, 1, 1], dtype=torch.int16, device=device)
80 dont_promote = torch.ones(3, dtype=torch.uint8, device=device) + 5
81 self.assertEqual(dont_promote.dtype, torch.uint8)
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Dtest_scatter_gather_ops.py16 # Protects against includes accidentally setting the default dtype
38 def test_gather(self, device, dtype): argument
43 src = make_tensor((m, n, o), device=device, dtype=dtype)
46 idx = make_tensor(idx_size, device=device, dtype=torch.long)
50 expected = torch.zeros(idx_size, device=device, dtype=dtype)
60 if not dtype.is_complex:
61 src = make_tensor((3, 4, 5), device=device, dtype=dtype)
67 def test_gather_bool(self, device, dtype): argument
68 src = torch.tensor(((False, True), (True, True)), device=device, dtype=dtype)
69 idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long)
[all …]
Dtest_binary_ufuncs.py85 self, actual, expected, msg, *, dtype, exact_dtype=True, **kwargs argument
93 # Handles exact dtype comparisons between arrays and tensors
95 # Allows array dtype to be float32 when comparing with bfloat16 tensors
96 # since NumPy doesn't support the bfloat16 dtype
99 if expected.dtype == np.float32:
100 assert actual.dtype in (
106 assert expected.dtype == torch_to_numpy_dtype_dict[actual.dtype]
110 torch.from_numpy(expected).to(actual.dtype),
120 def _test_reference_numerics(self, dtype, op, gen, equal_nan=True): argument
125 numpy_to_torch_dtype_dict[expected.dtype.type], dtype
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Dtest_complex.py19 def test_to_list(self, device, dtype): argument
23 torch.zeros((2, 2), device=device, dtype=dtype).tolist(),
28 def test_dtype_inference(self, device, dtype): argument
30 with set_default_dtype(dtype):
32 if dtype == torch.float16:
33 self.assertEqual(x.dtype, torch.chalf)
34 elif dtype == torch.float32:
35 self.assertEqual(x.dtype, torch.cfloat)
37 self.assertEqual(x.dtype, torch.cdouble)
40 def test_conj_copy(self, device, dtype): argument
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Dtest_tensor_creation_ops.py35 def _generate_input(shape, dtype, device, with_extremal): argument
37 x = torch.tensor((), dtype=dtype, device=device)
39 if dtype.is_floating_point or dtype.is_complex:
41 if dtype == torch.bfloat16:
45 x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100)
47 if with_extremal and dtype.is_floating_point:
52 elif with_extremal and dtype.is_complex:
56 elif dtype == torch.bool:
57 x = torch.zeros(shape, dtype=dtype, device=device)
60 x = torch.randint(15, 100, shape, dtype=dtype, device=device)
[all …]
/external/tensorflow/tensorflow/python/kernel_tests/array_ops/
Done_hot_op_test.py32 dtype=None, argument
37 array_ops.one_hot(dtype=dtype, **inputs)
39 ans = array_ops.one_hot(dtype=dtype, **inputs)
43 if dtype:
44 self.assertEqual(tf_ans.dtype, dtype)
54 def _testBasic(self, dtype): argument
55 indices = np.asarray([0, 2, -1, 1], dtype=np.int64)
57 on_value = np.asarray(1.0, dtype=dtype)
58 off_value = np.asarray(-1.0, dtype=dtype)
63 dtype=dtype)
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/external/pytorch/torch/masked/
D_ops.py15 from torch.types import _dtype as DType unknown
19 # The JIT doesn't understand Union, nor torch.dtype here
20 DType = int variable
87 dtype=torch.bool)``.
116 reduction, depends on input dtype. For instance, for float32, uint8,
168 sum=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
169 prod=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
170 cumsum=(("dim__as_int",), ("dtype=None", "mask=None")),
171 cumprod=(("dim__as_int",), ("dtype=None", "mask=None")),
172 amin=(("dim",), ("keepdim=False", "dtype=None", "mask=None")),
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