| /external/XNNPACK/test/ |
| D | tanh-nc.cc | 45 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
49 .input_scale(input_scale) in TEST()
125 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
129 .input_scale(input_scale) in TEST()
189 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
195 .input_scale(input_scale) in TEST()
251 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
255 .input_scale(input_scale) in TEST()
331 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
335 .input_scale(input_scale) in TEST()
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| D | sigmoid-nc.cc | 158 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
162 .input_scale(input_scale) in TEST()
238 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
242 .input_scale(input_scale) in TEST()
302 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
308 .input_scale(input_scale) in TEST()
365 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
369 .input_scale(input_scale) in TEST()
445 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
449 .input_scale(input_scale) in TEST()
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| D | elu-nc.cc | 185 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
189 .input_scale(input_scale) in TEST()
265 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
269 .input_scale(input_scale) in TEST()
342 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 10.0f) { in TEST() local
348 .input_scale(input_scale) in TEST()
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| D | clamp.cc | 29 const float input_scale = scale_dist(rng); in TEST_F() local
31 const float output_scale = input_scale; in TEST_F()
34 const float output_min = (quantized_output_min - input_zero_point) * input_scale; in TEST_F()
35 const float output_max = (quantized_output_max - input_zero_point) * input_scale; in TEST_F()
46 … subgraph, xnn_datatype_qint8, input_zero_point, input_scale, dims.size(), dims.data(), in TEST_F()
73 const float input_scale = scale_dist(rng); in TEST_F() local
75 const float output_scale = input_scale; in TEST_F()
78 const float output_min = (quantized_output_min - input_zero_point) * input_scale; in TEST_F()
79 const float output_max = (quantized_output_max - input_zero_point) * input_scale; in TEST_F()
90 … subgraph, xnn_datatype_quint8, input_zero_point, input_scale, dims.size(), dims.data(), in TEST_F()
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| D | tanh-operator-tester.h | 75 inline TanhOperatorTester& input_scale(float input_scale) { in input_scale() function
76 assert(input_scale > 0.0f); in input_scale()
77 assert(std::isnormal(input_scale)); in input_scale()
78 this->input_scale_ = input_scale; in input_scale()
82 inline float input_scale() const { in input_scale() function
147 const float x = input_scale() * in TestQS8()
165 int8_t(input_zero_point() - 0x80), input_scale(), in TestQS8()
208 const float x = input_scale() * in TestQU8()
226 input_zero_point(), input_scale(), in TestQU8()
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| D | leaky-relu-nc.cc | 179 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 3.14159265f) { in TEST() local
183 .input_scale(input_scale) in TEST()
299 for (float input_scale = 1.0e-2f; input_scale < 1.0e+2f; input_scale *= 3.14159265f) { in TEST() local
303 .input_scale(input_scale) in TEST()
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| D | leaky-relu-operator-tester.h | 93 inline LeakyReLUOperatorTester& input_scale(float input_scale) { in input_scale() argument
94 assert(input_scale > 0.0f); in input_scale()
95 assert(std::isnormal(input_scale)); in input_scale()
96 this->input_scale_ = input_scale; in input_scale()
100 inline float input_scale() const { in input_scale() function
284 … const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point()); in TestQS8()
300 input_zero_point(), input_scale(), in TestQS8()
325 << ", positive input-to-output ratio " << (input_scale() / output_scale()) in TestQS8()
326 … << ", negative input-to-output ratio " << (input_scale() / output_scale() * negative_slope()); in TestQS8()
353 … const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point()); in TestQU8()
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| D | softmax-operator-tester.h | 79 inline SoftMaxOperatorTester& input_scale(float input_scale) { in input_scale() argument
80 assert(input_scale > 0.0f); in input_scale()
81 assert(std::isnormal(input_scale)); in input_scale()
82 this->input_scale_ = input_scale; in input_scale()
86 inline float input_scale() const { in input_scale() function
263 input_scale()); in TestQU8()
268 input_scale()) / in TestQU8()
281 input_scale(), in TestQU8()
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| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | logsoftmax_quantized_test.cc | 39 int32 input_offset, const double input_scale, in RunLogSoftmaxFloatReference() argument
50 dq_params.scale = input_scale; in RunLogSoftmaxFloatReference()
72 int32 input_offset, const double input_scale, in RunLogSoftmaxFloatReference() argument
83 dq_params.scale = input_scale; in RunLogSoftmaxFloatReference()
148 const double input_scale, int stride, float beta) { in RunOneLogSoftmaxTest() argument
155 input_scale, stride, beta, in RunOneLogSoftmaxTest()
164 beta, input_scale, kScaledDiffIntegerBits, &input_beta_multiplier, in RunOneLogSoftmaxTest()
184 optimized_ops::PopulateSoftmaxLookupTable(¶ms, input_scale, beta); in RunOneLogSoftmaxTest()
185 optimized_ops::LogSoftmax(params, input_scale, shape_common, input_data, in RunOneLogSoftmaxTest()
206 const double input_scale, int stride, float beta) { in RunOneLogSoftmaxTest() argument
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| D | softmax_quantized_test.cc | 37 int32 input_offset, const double input_scale, in RunSoftmaxFloatReference() argument
48 dq_params.scale = input_scale; in RunSoftmaxFloatReference()
108 const double input_scale, int stride, float beta) { in RunOneSoftmaxTest() argument
114 RunSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, in RunOneSoftmaxTest()
120 tflite::PreprocessSoftmaxScaling(beta, input_scale, kScaledDiffIntegerBits, in RunOneSoftmaxTest()
137 optimized_ops::PopulateSoftmaxLookupTable(¶ms, input_scale, beta); in RunOneSoftmaxTest()
158 optimized_ops::PopulateSoftmaxUInt8LookupTable(¶ms, input_scale, beta); in RunOneSoftmaxTest()
182 const double input_scale = std::pow(10.0, UniformRandomFloat(-2.0, 1.0)); in TryOneUniformSoftmax() local
192 RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, in TryOneUniformSoftmax()
215 const double input_scale = std::pow(10.0, UniformRandomFloat(-2.0, 1.0)); in TryOneSkyscraperSoftmax() local
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/src/ |
| D | global-average-pooling.c | 24 float input_scale, in pytorch_qnnp_create_global_average_pooling_nwc_q8() argument
49 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_global_average_pooling_nwc_q8()
52 input_scale); in pytorch_qnnp_create_global_average_pooling_nwc_q8()
65 const float input_output_scale = input_scale / output_scale; in pytorch_qnnp_create_global_average_pooling_nwc_q8()
97 global_average_pooling_op->input_scale = input_scale; in pytorch_qnnp_create_global_average_pooling_nwc_q8()
151 global_average_pooling_op->input_scale / in pytorch_qnnp_setup_global_average_pooling_nwc_q8()
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| D | softargmax.c | 21 float input_scale, in pytorch_qnnp_create_softargmax_nc_q8() argument
44 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_softargmax_nc_q8()
47 input_scale); in pytorch_qnnp_create_softargmax_nc_q8()
97 qscale * exp((double)(i - 255) * (double)input_scale); in pytorch_qnnp_create_softargmax_nc_q8()
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| D | tanh.c | 22 float input_scale, in pytorch_qnnp_create_tanh_nc_q8() argument
47 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_tanh_nc_q8()
50 input_scale); in pytorch_qnnp_create_tanh_nc_q8()
109 input_scale * (float)(i - (int32_t)(uint32_t)input_zero_point); in pytorch_qnnp_create_tanh_nc_q8()
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| D | sigmoid.c | 22 float input_scale, in pytorch_qnnp_create_sigmoid_nc_q8() argument
47 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_sigmoid_nc_q8()
50 input_scale); in pytorch_qnnp_create_sigmoid_nc_q8()
109 input_scale * (float)(i - (int32_t)(uint32_t)input_zero_point); in pytorch_qnnp_create_sigmoid_nc_q8()
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| D | hardswish.c | 22 float input_scale, in pytorch_qnnp_create_hardswish_nc_q8() argument
47 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_hardswish_nc_q8()
50 input_scale); in pytorch_qnnp_create_hardswish_nc_q8()
93 input_scale * (float)(i - (int32_t)(uint32_t)input_zero_point); in pytorch_qnnp_create_hardswish_nc_q8()
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| D | leaky-relu.c | 23 float input_scale, in pytorch_qnnp_create_leaky_relu_nc_q8() argument
62 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_leaky_relu_nc_q8()
65 input_scale); in pytorch_qnnp_create_leaky_relu_nc_q8()
87 const float input_output_scale = input_scale / output_scale; in pytorch_qnnp_create_leaky_relu_nc_q8()
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| D | hardsigmoid.c | 22 float input_scale, in pytorch_qnnp_create_hardsigmoid_nc_q8() argument
47 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_hardsigmoid_nc_q8()
50 input_scale); in pytorch_qnnp_create_hardsigmoid_nc_q8()
110 input_scale * (float)(i - (int32_t)(uint32_t)input_zero_point); in pytorch_qnnp_create_hardsigmoid_nc_q8()
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| D | average-pooling.c | 41 float input_scale, in pytorch_qnnp_create_average_pooling2d_nhwc_q8() argument
95 if (input_scale <= 0.0f || !isnormal(input_scale)) { in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
99 input_scale); in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
113 const float input_output_scale = input_scale / output_scale; in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
118 input_scale, in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
188 input_scale / (output_scale * (float)pooling_size), in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
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| /external/tensorflow/tensorflow/lite/kernels/internal/optimized/integer_ops/ |
| D | depthwise_conv_hybrid_3x3_filter.h | 129 static inline void Run(const float* input_scale,
1028 [input_scale] "r"(input_scale),
1064 static inline void Run(const float* input_scale, const int8* input_ptr,
2034 [input_scale] "r"(input_scale),
2072 static inline void Run(const float* input_scale, const int8* input_ptr,
2171 [params_ptr] "r"(params_ptr), [input_scale] "r"(input_scale)
2188 static inline void Run(const float* input_scale, const int8* input_ptr,
2340 [input_scale] "r"(input_scale),
2358 static inline void Run(const float* input_scale, const int8* input_ptr,
2545 [input_scale] "r"(input_scale), [params_ptr] "r"(params_ptr)
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| /external/XNNPACK/src/operators/ |
| D | lut-elementwise-nc.c | 25 float input_scale, in create_lut_elementwise_nc() argument
71 if (input_scale <= 0.0f || !isnormal(input_scale)) { in create_lut_elementwise_nc()
74 xnn_operator_type_to_string(operator_type), input_scale); in create_lut_elementwise_nc()
113 const float dequantized_input = (i - input_zero_point) * input_scale; in create_lut_elementwise_nc()
149 float input_scale, in xnn_create_elu_nc_qs8() argument
166 (int32_t) input_zero_point, input_scale, INT8_MIN, in xnn_create_elu_nc_qs8()
183 float input_scale, in xnn_create_sigmoid_nc_qs8() argument
207 (int32_t) input_zero_point, input_scale, INT8_MIN, in xnn_create_sigmoid_nc_qs8()
220 float input_scale, in xnn_create_sigmoid_nc_qu8() argument
244 (int32_t) (uint32_t) input_zero_point, input_scale, 0 /* input min */, in xnn_create_sigmoid_nc_qu8()
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| D | global-average-pooling-nwc.c | 206 float input_scale, in xnn_create_global_average_pooling_nwc_qu8() argument
214 if (input_scale <= 0.0f || !isnormal(input_scale)) { in xnn_create_global_average_pooling_nwc_qu8()
217 xnn_operator_type_to_string(xnn_operator_type_global_average_pooling_nwc_qu8), input_scale); in xnn_create_global_average_pooling_nwc_qu8()
235 const float input_output_scale = input_scale / output_scale; in xnn_create_global_average_pooling_nwc_qu8()
259 global_average_pooling_op->input_scale = input_scale; in xnn_create_global_average_pooling_nwc_qu8()
270 float input_scale, in xnn_create_global_average_pooling_nwc_qs8() argument
278 if (input_scale <= 0.0f || !isnormal(input_scale)) { in xnn_create_global_average_pooling_nwc_qs8()
281 xnn_operator_type_to_string(xnn_operator_type_global_average_pooling_nwc_qs8), input_scale); in xnn_create_global_average_pooling_nwc_qs8()
299 const float input_output_scale = input_scale / output_scale; in xnn_create_global_average_pooling_nwc_qs8()
323 global_average_pooling_op->input_scale = input_scale; in xnn_create_global_average_pooling_nwc_qs8()
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| /external/tensorflow/tensorflow/compiler/mlir/quantization/tensorflow/passes/ |
| D | quantized_function_library.mlir | 36 %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
39 …%scale_prod = "tf.Mul"(%input_scale, %filter_scale) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf…
57 %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
60 … %rescale = "tf.PartitionedCall"(%accumulation, %input_scale, %input_zp, %filter_scale, %filter_zp,
74 %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
77 … %rescale = "tf.PartitionedCall"(%accumulation, %input_scale, %input_zp, %filter_scale, %filter_zp,
93 %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
96 … %rescale = "tf.PartitionedCall"(%accumulation, %input_scale, %input_zp, %filter_scale, %filter_zp,
118 %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
121 …%accumulation_scale = "tf.Mul"(%input_scale, %filter_scale) : (tensor<*xf32>, tensor<*xf32>) -> te…
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| /external/executorch/backends/cadence/hifi/operators/ |
| D | quantized_layer_norm.cpp | 32 float input_scale, in quantized_layer_norm_per_tensor_() argument
71 float mean = XT_DIV_S(XT_MUL_S(input_scale, sum), last_dim); in quantized_layer_norm_per_tensor_()
74 XT_MUL_S(sq_sum, XT_MUL_S(input_scale, input_scale)), last_dim) - in quantized_layer_norm_per_tensor_()
84 x[j], input_scale, input_zero_point); in quantized_layer_norm_per_tensor_()
106 float input_scale = in_scale.const_data_ptr<float>()[0]; in quantized_layer_norm_() local
112 input_scale, in quantized_layer_norm_()
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| /external/executorch/backends/cadence/reference/operators/ |
| D | quantized_layer_norm.cpp | 29 double input_scale, in quantized_layer_norm_per_tensor_() argument
67 float mean = (input_scale * sum) / last_dim; in quantized_layer_norm_per_tensor_()
69 (sq_sum * input_scale * input_scale) / last_dim - mean * mean; in quantized_layer_norm_per_tensor_()
77 float val = kernels::dequantize<T>(x[j], input_scale, input_zero_point); in quantized_layer_norm_per_tensor_()
99 float input_scale = in_scale.const_data_ptr<float>()[0]; in quantized_layer_norm_() local
105 input_scale, in quantized_layer_norm_()
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/ |
| D | qlinear.cpp | 291 double input_scale, in apply_with_input_q_dq_qweight_dq_output_fp32() argument
296 …return apply_with_input_q_dq_qweight_dq_output_fp32_impl<false>(input, input_scale, input_zero_poi… in apply_with_input_q_dq_qweight_dq_output_fp32()
301 double input_scale, in apply_with_input_q_dq_qweight_dq_relu_output_fp32() argument
306 …return apply_with_input_q_dq_qweight_dq_output_fp32_impl<true>(input, input_scale, input_zero_poin… in apply_with_input_q_dq_qweight_dq_relu_output_fp32()
313 double input_scale, in apply_with_input_q_dq_qweight_dq_output_fp32_impl() argument
336 float input_scale_float = input_scale; in apply_with_input_q_dq_qweight_dq_output_fp32_impl()
472 const auto input_scale = input_contig.q_scale(); in apply_impl_xnnp() local
481 (!this->input_scale.has_value() || in apply_impl_xnnp()
482 this->input_scale.value() != input_scale)) { in apply_impl_xnnp()
484 this->input_scale = input_scale; in apply_impl_xnnp()
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