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/aosp_15_r20/external/executorch/exir/dialects/edge/
H A Dedge.yaml3611 - fill_value: T1
3614 - fill_value: T1
3617 - fill_value: T1
3620 - fill_value: T1
3623 - fill_value: T1
3626 - fill_value: T1
3629 - fill_value: T1
3632 - fill_value: T1
3635 - fill_value: T1
3656 fill_value: T2
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/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/image/
H A Dimage_ops.h133 const Interpolation interpolation, const T fill_value)
137 fill_value_(fill_value) {}
180 const DenseIndex channel, const T fill_value) const {
182 DenseIndex(std::round(x)), channel, fill_value);
187 const DenseIndex channel, const T fill_value) const {
197 channel, fill_value)) +
200 channel, fill_value));
206 channel, fill_value)) +
209 channel, fill_value));
217 const DenseIndex channel, const T fill_value) const {
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H A Dimage_ops.cc88 T fill_value(0); in DoImageProjectiveTransformOp() local
93 errors::InvalidArgument("fill_value must be a scalar", in DoImageProjectiveTransformOp()
95 fill_value = static_cast<T>(*(fill_value_t.scalar<float>().data())); in DoImageProjectiveTransformOp()
110 fill_value); in DoImageProjectiveTransformOp()
205 const TYPE fill_value) const; \
253 .HostMemory("fill_value"), \
/aosp_15_r20/external/ComputeLibrary/src/cpu/kernels/pool2d/neon/nchw/
H A Dall.cpp97 …const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min :… in pooling3_fp16_neon_nchw() local
109 … x_val, y_val_0, reinterpret_cast<const float16_t *>(src_top_ptr + in.offset()), fill_value); in pooling3_fp16_neon_nchw()
111 … x_val, y_val_1, reinterpret_cast<const float16_t *>(src_middle_ptr + in.offset()), fill_value); in pooling3_fp16_neon_nchw()
113 … x_val, y_val_2, reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset()), fill_value); in pooling3_fp16_neon_nchw()
207 … const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? float_min : 0.f; in pooling2_nchw_maxpool_indices() local
215 … x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value); in pooling2_nchw_maxpool_indices()
217 … x_val, y_val_1, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value); in pooling2_nchw_maxpool_indices()
263 const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.0f; in pooling2_fp16_neon_nchw() local
277 … x_val, y_val_0, in_top_ptr, fill_value); in pooling2_fp16_neon_nchw()
279 … x_val, y_val_1, in_bottom_ptr, fill_value); in pooling2_fp16_neon_nchw()
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/aosp_15_r20/external/executorch/backends/apple/mps/operators/
H A Dconstant_ops.py54 fill_value = cast(float, node.args[1])
56 fill_value = 0
58 fill_value = cast(float, node.args[0])
60 if fill_value == float("-inf"):
61 fill_value = "-inf"
62 elif fill_value == float("inf"):
63 fill_value = "inf"
75 fill_value=fill_value,
96 raise AssertionError("Full op requires at least size & fill_value args")
100 mps_node.mpsnode_union.fill_value = cast(float, node.args[1])
/aosp_15_r20/external/pytorch/torch/csrc/jit/operator_upgraders/
H A Dupgraders_entry.cpp94 def full_names_0_4(size:List[int], fill_value:number, *, names:Optional[List[str]]=None,
97 …return torch.full(size, fill_value, names=names, dtype=dtype, layout=layout, device=device, pin_me…
100 def full_0_4(size:List[int], fill_value:number, *, dtype:Optional[int]=None,
104 fill_value = float(fill_value)
105 …return torch.full(size, fill_value, dtype=dtype, layout=layout, device=device, pin_memory=pin_memo…
108 def full_out_0_4(size:List[int], fill_value:number, *, out:Tensor) -> Tensor:
109 return torch.full(size, fill_value, out=out)
H A Dversion_map.cpp77 …"aten::full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device?…
81 …"aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layo…
85 "aten::full.out(int[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)"}}},
/aosp_15_r20/external/pytorch/torch/masked/
H A D_ops.py503 def _sparse_coo_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
508 _sparse_coo_where(mask, input, fill_value).to_dense(fill_value) ==
509 torch.where(mask.to_dense(), input.to_dense(), torch.full(input.shape, fill_value))
512 tensor, and `to_dense(fill_value)` is like `to_dense()` except
513 that the unspecified elements are mapped to `fill_value` rather
523 elements are replaced with fill_value.
580 where_mask_values, where_input_values, fill_value
821 def _sparse_csr_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
825 mask.to_sparse_coo(), input.to_sparse_coo(), fill_value
829 def _where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
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/aosp_15_r20/external/XNNPACK/test/
H A Dpad-microkernel-tester.h118 uint32_t fill_value = 0; in Test() local
119 memcpy(&fill_value, fill_pattern.data(), sizeof(fill_value)); in Test()
129 fill_value); in Test()
139 << ", fill value 0x" << std::hex << std::setw(8) << std::setfill('0') << fill_value in Test()
149 << ", fill value 0x" << std::hex << std::setw(8) << std::setfill('0') << fill_value in Test()
159 << ", fill value 0x" << std::hex << std::setw(8) << std::setfill('0') << fill_value in Test()
H A Dfill-microkernel-tester.h82 uint32_t fill_value = 0; in Test() local
83 memcpy(&fill_value, fill_pattern.data(), sizeof(fill_value)); in Test()
91 fill_value); in Test()
99 << ", fill value 0x" << std::hex << std::setw(8) << std::setfill('0') << fill_value in Test()
/aosp_15_r20/external/pytorch/test/
H A Dtest_masked.py319 @parametrize("sparse_kind,fill_value", [('coo', 0), ('hybrid_coo', 0),
322 name_fn=lambda sparse_kind, fill_value: f'{sparse_kind}_fill_value_{fill_value}')
323 def test_where(self, sparse_kind, fill_value): argument
380 F = fill_value
400 … torch.tensor(fill_value, dtype=input.dtype, device=input.device))
425 # torch.where(mask.to_dense(), input.to_dense(), fill_value)
426 # == where(mask, input, fill_value).to_dense(fill_value)
H A Dtest_matmul_cuda.py567 fill_value = 0.5
568 x = torch.full((M, K), fill_value, device=device)
569 y = torch.full((N, K), fill_value, device=device)
586 out_fp8.to(torch.float32), torch.full((M, N), K * (fill_value**2), device=device)
593 fill_value = 0.5
594 x = torch.full((M, K), fill_value, device=device)
595 y = torch.full((N, K), fill_value, device=device)
/aosp_15_r20/external/pytorch/torch/distributed/_shard/sharded_tensor/
H A D__init__.py124 fill_value=1,
178 fill_value=0,
192 fill_value, argument
203 Creates a :class:`ShardedTensor` filled with fill_value. The tensor's dtype
204 is inferred from fill_value. If dtype is specified, it will override the
205 inferred type from fill_value. Needs to be called on all ranks in an SPMD fashion.
211 fill_value (Scalar) - the value to fill the output tensor with.
241 torch.nn.init.constant_(sharded_tensor, fill_value) # type: ignore[arg-type]
/aosp_15_r20/external/pytorch/aten/src/ATen/native/
H A DTensorFactories.cpp585 const Scalar& fill_value, in infer_full_options() argument
589 if (fill_value.isBoolean()) { in infer_full_options()
591 } else if (fill_value.isIntegral(false)) { in infer_full_options()
593 } else if (fill_value.isComplex()) { in infer_full_options()
608 Tensor full(IntArrayRef size, const Scalar& fill_value, in full() argument
619 auto result = at::empty(size, infer_full_options(fill_value, options)); in full()
620 return result.fill_(fill_value); in full()
623 Tensor& full_out(IntArrayRef size, const Scalar& fill_value, Tensor& result) { in full_out() argument
628 return result.fill_(fill_value); in full_out()
633 const Scalar& fill_value, in full_like() argument
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H A DFill.cpp96 Tensor& fill_diagonal_(Tensor& self, const Scalar& fill_value, bool wrap) { in fill_diagonal_() argument
125 main_diag.fill_(fill_value); in fill_diagonal_()
137 wrap_diag.fill_(fill_value); in fill_diagonal_()
/aosp_15_r20/external/ComputeLibrary/src/cpu/kernels/pool2d/neon/
H A Dquantized.h424 …const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? std::numeric_limits<T>::min() : T… in pooling2_quantized_neon_nchw() local
433 … x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value); in pooling2_quantized_neon_nchw()
435 … x_val, y_val_1, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value); in pooling2_quantized_neon_nchw()
560 …const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0) : std::numeric_limits<T>::… in pooling3_quantized_neon_nchw() local
571 … x_val, y_val_0, reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value); in pooling3_quantized_neon_nchw()
573 … x_val, y_val_1, reinterpret_cast<const T *>(src_middle_ptr + in.offset()), fill_value); in pooling3_quantized_neon_nchw()
575 … x_val, y_val_2, reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value); in pooling3_quantized_neon_nchw()
716 …const T fill_value = (pool_info.pool_type == PoolingType::AVG) ? T(0)… in poolingMxN_quantized_neon_nchw() local
741 … const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr; in poolingMxN_quantized_neon_nchw()
758 … const T data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *in_ptr; in poolingMxN_quantized_neon_nchw()
/aosp_15_r20/external/executorch/extension/tensor/
H A Dtensor_ptr_maker.h357 * @param fill_value The value to fill the tensor with.
365 executorch::aten::Scalar fill_value,
376 * @param fill_value The value to fill the tensor with.
384 executorch::aten::Scalar fill_value,
394 fill_value,
403 * @param fill_value The value used to fill the tensor.
410 executorch::aten::Scalar fill_value,
414 return full_strided(std::move(sizes), {}, fill_value, type, dynamism);
/aosp_15_r20/external/ComputeLibrary/src/core/CPP/kernels/
H A DCPPUpsampleKernel.cpp89 const uint8_t fill_value = _output->info()->quantization_info().uniform().offset; in run() local
90 std::fill_n(_output->buffer(), _output->info()->total_size(), fill_value); in run()
95 const int8_t fill_value = _output->info()->quantization_info().uniform().offset; in run() local
96 std::fill_n(_output->buffer(), _output->info()->total_size(), fill_value); in run()
/aosp_15_r20/external/executorch/backends/cadence/reference/operators/
H A Dop_full.cpp22 const Scalar& fill_value, in full_out() argument
26 ScalarType val_type = utils::get_scalar_dtype(fill_value); in full_out()
34 ET_EXTRACT_SCALAR(fill_value, val); in full_out()
/aosp_15_r20/external/executorch/kernels/portable/cpu/
H A Dop_full.cpp22 const Scalar& fill_value, in full_out() argument
26 ScalarType val_type = utils::get_scalar_dtype(fill_value); in full_out()
41 utils::extract_scalar(fill_value, &val); in full_out()
H A Dop_full_like.cpp22 const Scalar& fill_value, in full_like_out() argument
50 ScalarType val_type = utils::get_scalar_dtype(fill_value); in full_like_out()
57 utils::extract_scalar(fill_value, &val); in full_like_out()
/aosp_15_r20/external/pytorch/torch/distributed/tensor/
H A D_api.py937 fill_value = kwargs.pop("fill_value", 0)
938 local_tensor = init_op(local_shape, fill_value, **kwargs)
1060 fill_value, argument
1069 Returns a :class:`DTensor` filled with ``fill_value`` according to ``device_mesh`` and
1076 fill_value(Scalar): the value to fill the output tensor with.
1096 fill_value=fill_value,
/aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/functional/
H A Dpadding.h30 std::optional<double> fill_value; in pad() local
32 fill_value = value; in pad()
34 return at::_pad_enum(input, pad, static_cast<int64_t>(mode_enum), fill_value); in pad()
/aosp_15_r20/external/pytorch/torch/_inductor/fx_passes/
H A Dfuse_attention.py375 fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device)
377 return torch.softmax(scores.masked_fill(attn_mask, fill_value), dim=-1) @ v
440 fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device)
444 torch.softmax(scores.masked_fill(attn_mask, fill_value), dim=-1), dropout_p
540 fill_value = torch.full((), -float("inf"), dtype=query.dtype, device=query.device)
541 attn_mask = torch.where(causal_mask, attn_mask, fill_value)
/aosp_15_r20/external/tensorflow/tensorflow/python/ops/numpy_ops/
H A Dnp_array_ops_test.py256 fill_value = fn1(f)
259 np_array_ops.full(shape, fill_value), np.full(shape, fill_value))
262 np_array_ops.full(shape, fill_value, dtype=dtype),
263 np.full(shape, fill_value, dtype=dtype))
282 fill_value = fn1(f)
285 np_array_ops.full_like(arr, fill_value),
286 np.full_like(arr, fill_value))
289 np_array_ops.full_like(arr, fill_value, dtype=dtype),
290 np.full_like(arr, fill_value, dtype=dtype))

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