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Searched refs:inference_type (Results 1 – 25 of 60) sorted by relevance

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/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/common/
Dtargets.h63 inline std::string GetInferenceString(InferenceType inference_type) { in GetInferenceString() argument
64 if (inference_type == FLOAT) { in GetInferenceString()
66 } else if (inference_type == QUANTIZED_INT8) { in GetInferenceString()
68 } else if (inference_type == QUANTIZED_UINT8) { in GetInferenceString()
70 } else if (inference_type == HYBRID) { in GetInferenceString()
98 auto inference_type = op->getAttrOfType<StringAttr>(kInferenceType); in GetInferenceTypeAnnotation() local
99 if (inference_type == nullptr) return llvm::None; in GetInferenceTypeAnnotation()
101 llvm::StringRef device_name_str = inference_type.getValue(); in GetInferenceTypeAnnotation()
108 InferenceType inference_type; member
112 (inference_type == other.inference_type);
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/tests/
Dget-alternative-subgraph.mlir11 …ensor<1xf32>) -> tensor<2x1xf32> attributes {tac.device = "CPU", tac.inference_type = "FLOAT", tac…
12 …%0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", tac.inference_type = "FLOAT", v…
16 … tensor<1xf32>) -> tensor<1xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
17 … %arg1 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
18 … %arg2 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
22 … tensor<1xf32>) -> tensor<1xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
23 … %arg1 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
34 …ensor<1xf32>) -> tensor<2x1xf32> attributes {tac.device = "CPU", tac.inference_type = "FLOAT", tac…
35 …ck"(%[[VAL_0]], %[[VAL_1]]) {axis = 0 : i32, tac.device = "CPU", tac.inference_type = "FLOAT", val…
39 … tensor<1xf32>) -> tensor<1xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
[all …]
Dpick-subgraphs.mlir5 …%0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "F…
6 …%1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.device = "GPU", tac.inference_type = "FLOAT", …
7 …%2 = func.call @func_2_CPU_FLOAT(%0, %1) {tac.device = "CPU", tac.inference_type = "FLOAT", tac.in…
10 …> attributes {tac.cost = 2.000000e+01 : f32, tac.device = "CPU", tac.inference_type = "FLOAT", tac…
14 …> attributes {tac.cost = 4.000000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac…
19 …> attributes {tac.cost = 2.000000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac…
23 …> attributes {tac.cost = 8.040000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac…
29 …> attributes {tac.cost = 2.000000e+02 : f32, tac.device = "CPU", tac.inference_type = "FLOAT", tac…
34 …> attributes {tac.cost = 1.000000e+02 : f32, tac.device = "CPU", tac.inference_type = "FLOAT", tac…
40 …_GPU_FLOAT([[VAL_0]], [[VAL_1]], [[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
[all …]
Draise-target-subgraphs.mlir5 …%arg1) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
6 …%arg2) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
7 …%arg3) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
8 …%3 = "tfl.pack"(%1, %2) {tac.device = "CPU", tac.inference_type = "FLOAT", axis = 0 : i32, values_…
14 …AL_1]], %[[VAL_2]], %[[VAL_0]], %[[VAL_3]]) {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
15 …unc_1_CPU_FLOAT(%[[VAL_4]]#0, %[[VAL_4]]#1) {tac.device = "CPU", tac.inference_type = "FLOAT", tac…
19 …> (tensor<1xf32>, tensor<1xf32>) attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
20 …AL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
21 …AL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
22 …AL_4]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
[all …]
Dtarget-annotation.mlir4 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
12 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
20 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
28 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
36 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
38 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
40 // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
42 // CHECK: tac.device = "CPU", tac.inference_type = "FLOAT"
48 // CHECK-NOT: tac.device tac.inference_type
50 // CHECK-NOT: tac.device tac.inference_type
[all …]
Dcompute-cost.mlir43 …!quant.uniform<i8:f32, 0.3:-3>> attributes {tac.device = "CPU", tac.inference_type = "QUANTIZED_I…
45 …%1 = "tfl.fully_connected"(%arg0, %arg1, %0) {tac.device = "CPU", tac.inference_type = "QUANTIZED_…
47 …%3 = "tfl.reshape"(%1, %2) {tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8"} : (tensor<3…
48 …%4 = "tfl.mul"(%3, %arg2) {tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8", fused_activa…
49 …%5 = "tfl.add"(%4, %arg3) {tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8", fused_activa…
/external/tensorflow/tensorflow/compiler/mlir/lite/quantization/lite/
Dquantize_weights.cc80 const tflite::TensorType& inference_type, const StringSet& denylisted_ops, in QuantizeWeights() argument
111 quant_specs.inference_type = tflite::TflTypeToTfType(inference_type); in QuantizeWeights()
127 if (quant_specs.inference_type == tensorflow::DT_INT8) in QuantizeWeights()
128 quant_specs.inference_type = tensorflow::DT_QINT8; in QuantizeWeights()
130 if (!(quant_specs.inference_type == tensorflow::DT_HALF || in QuantizeWeights()
131 quant_specs.inference_type == tensorflow::DT_QINT8)) { in QuantizeWeights()
142 << ", inference_type: " << quant_specs.inference_type << "\n"; in QuantizeWeights()
192 tflite::TensorType inference_type; in QuantizeWeights() local
195 inference_type = tflite::TensorType_FLOAT16; in QuantizeWeights()
198 inference_type = tflite::TensorType_INT8; in QuantizeWeights()
[all …]
Dquantize_model.cc53 const tflite::TensorType& inference_type, in QuantizeModel() argument
92 quant_specs.inference_type = tflite::TflTypeToTfType(inference_type); in QuantizeModel()
102 << ", inference_type: " << quant_specs.inference_type in QuantizeModel()
114 input_mlir_type = tflite::ConvertElementType(inference_type, mlir_builder); in QuantizeModel()
/external/tensorflow/tensorflow/compiler/mlir/lite/quantization/
Dquantization_config.h99 tensorflow::DataType inference_type = tensorflow::DT_FLOAT; member
134 return inference_type != tensorflow::DT_FLOAT && !weight_quantization; in RunPropagationAndRewriteQuantizationPasses()
143 (inference_type != tensorflow::DT_FLOAT) && weight_quantization && in RunAndRewriteDynamicRangeQuantizationPasses()
151 switch (inference_type) { in IsSignedInferenceType()
163 switch (inference_type) { in GetQuantizationTypeWidth()
214 absl::string_view inference_type,
224 tensorflow::DataType inference_type, QuantizationSpecs* quant_specs);
Dquantization_config.cc93 absl::string_view inference_type, in ParseInputNodeQuantSpecs() argument
122 if (!inference_type.empty() && in ParseInputNodeQuantSpecs()
123 !DataType_Parse(std::string(inference_type), &final_type)) { in ParseInputNodeQuantSpecs()
134 tensorflow::DataType inference_type, QuantizationSpecs* quant_specs) { in GetInputNodeQuantSpecs() argument
135 quant_specs->inference_type = inference_type; in GetInputNodeQuantSpecs()
141 if (IsQuantizationType(inference_type)) { in GetInputNodeQuantSpecs()
/external/tensorflow/tensorflow/lite/testing/
Dtflite_model_test.bzl28 inference_type = "float",
43 inference_type: The data type for inference and output.
61 inference_type = inference_type,
101 inference_type):
108 inference_type: The data type for inference and output.
113 if inference_type == "float":
115 "--inference_type=FLOAT",
118 elif inference_type == "quantized":
120 "--inference_type=QUANTIZED_UINT8",
124 fail("Invalid inference type (%s). Expected 'float' or 'quantized'" % inference_type)
/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/transforms/
Dget_alternative_subgraph.cc61 GetInferenceString(device_inference_type.inference_type)); in GetFunctionImplName()
68 InferenceType inference_type, ArrayRef<std::string> devices) { in GetAllAlternativeInferenceDeviceType() argument
71 if (inference_type == QUANTIZED_INT8) { in GetAllAlternativeInferenceDeviceType()
73 } else if (inference_type == QUANTIZED_UINT8) { in GetAllAlternativeInferenceDeviceType()
127 const InferenceDeviceType& inference_type);
239 target_device_inference_type.inference_type))); in GetAlternativeViewForSpec()
246 if ((current_device_inference_type.inference_type == QUANTIZED_UINT8 || in GetAlternativeViewForSpec()
247 current_device_inference_type.inference_type == QUANTIZED_INT8) && in GetAlternativeViewForSpec()
248 target_device_inference_type.inference_type == FLOAT) { in GetAlternativeViewForSpec()
264 target_device_inference_type.inference_type))); in GetAlternativeViewForSpec()
/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/
DREADME.md181 …%arg1) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
182 …%arg2) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
183 …%arg3) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (…
184 …%3 = "tfl.pack"(%1, %2) {tac.device = "CPU", tac.inference_type = "FLOAT", axis = 0 : i32, values_…
193 … tensor<1xf32>) -> tensor<1xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
194 … %arg1 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
202 …ensor<1xf32>) -> tensor<2x1xf32> attributes {tac.device = "CPU", tac.inference_type = "FLOAT", tac…
203 …%0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", tac.inference_type = "FLOAT", v…
207 … tensor<1xf32>) -> tensor<1xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac…
208 … %arg1 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : t…
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/hardwares/
Dgpu_hardware.cc86 InferenceType inference_type = GetInferenceType(op); in IsOpSupported() local
87 if (inference_type != FLOAT) { in IsOpSupported()
108 InferenceType inference_type = GetInferenceType(op); in IsOpSupported() local
109 if (inference_type != FLOAT) { in IsOpSupported()
130 InferenceType inference_type = GetInferenceType(op); in IsOpSupported() local
131 if (inference_type != FLOAT) { in IsOpSupported()
154 InferenceType inference_type = GetInferenceType(op); in IsOpSupported() local
155 if (inference_type != FLOAT) { in IsOpSupported()
Dcpu_hardware.cc40 inline float InferenceTypeEfficiency(InferenceType inference_type) { in InferenceTypeEfficiency() argument
41 if (inference_type == QUANTIZED_INT8 || inference_type == QUANTIZED_UINT8) { in InferenceTypeEfficiency()
/external/tensorflow/tensorflow/compiler/mlir/lite/python/
Dtf_tfl_flatbuffer_helpers.cc216 tensorflow::DataType inference_type = in PopulateQuantizationSpecs() local
217 ConvertIODataTypeToDataType(toco_flags.inference_type()); in PopulateQuantizationSpecs()
221 inference_type = quant_specs->inference_input_type; in PopulateQuantizationSpecs()
243 if (inference_type == DT_QINT8 || inference_type == DT_QUINT8) { in PopulateQuantizationSpecs()
247 flag.std_value(), inference_type)); in PopulateQuantizationSpecs()
258 inference_type, quant_specs)) { in PopulateQuantizationSpecs()
270 quant_specs->inference_type = tensorflow::DT_HALF; in PopulateQuantizationSpecs()
273 quant_specs->inference_type = tensorflow::DT_QINT8; in PopulateQuantizationSpecs()
/external/tensorflow/tensorflow/compiler/mlir/lite/transforms/
Dprepare_quantize_dynamic_range.cc64 quant_specs_.inference_type = tensorflow::DT_QINT8; in PrepareDynamicRangeQuantizePass()
119 if (quant_specs_.inference_type == tensorflow::DT_QINT8 && in matchAndRewrite()
123 if (quant_specs_.inference_type == tensorflow::DT_HALF && in matchAndRewrite()
296 if (quant_specs_.inference_type == tensorflow::DT_QINT8 && in getQuantizableOps()
299 } else if (quant_specs_.inference_type == tensorflow::DT_HALF) { in getQuantizableOps()
313 if (quant_specs_.inference_type == tensorflow::DT_QINT8) { in quantizeOps()
315 } else if (quant_specs_.inference_type == tensorflow::DT_HALF) { in quantizeOps()
413 quant_specs_.inference_type = tensorflow::DT_HALF; in runOnOperation()
/external/tensorflow/tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/
Dsimple-graph.mlir13 // CHECK: [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU", tac.inference_type = "…
14 // CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "…
15 …3 : i32, fused_activation_function = "NONE", tac.device = "GPU", tac.inference_type = "FLOAT"} : (…
/external/tensorflow/tensorflow/lite/python/
Dwrap_toco.py37 fully_quantize, inference_type, argument
44 input_data_str, disable_per_channel, fully_quantize, inference_type,
Dtflite_convert.py166 if flags.inference_type:
167 converter.inference_type = _parse_inference_type(flags.inference_type,
182 if converter.inference_type == dtypes.float32:
233 if converter.inference_type != dtypes.float32:
236 converter.inference_type = dtypes.float32
Dconvert.py58 return ((conversion_flags.inference_type in quantized_inference_types or
202 inference_type=_types_pb2.QUANTIZED_INT8, argument
236 input_data_str, disable_per_channel, fully_quantize, inference_type,
471 def build_conversion_flags(inference_type=dtypes.float32, argument
594 conversion_flags.inference_type = convert_inference_tf_type_to_tflite_type(
595 inference_type, usage="inference_type flag")
601 conversion_flags.inference_input_type = conversion_flags.inference_type
/external/tensorflow/tensorflow/compiler/mlir/lite/
Dtf_tfl_translate.cc237 input_arrays, min_values, max_values, inference_type, &quant_specs)) { in main()
244 quant_specs.inference_type = tensorflow::DT_QINT8; in main()
246 quant_specs.inference_type = tensorflow::DT_HALF; in main()
253 quant_specs.inference_input_type = quant_specs.inference_type; in main()
/external/tensorflow/tensorflow/lite/toco/
Dtoco_cmdline_flags.cc81 Flag("inference_type", parsed_flags.inference_type.bind(), in ParseTocoFlagsFromCommandLineFlags()
82 parsed_flags.inference_type.default_value(), in ParseTocoFlagsFromCommandLineFlags()
280 PARSE_TOCO_FLAG(IODataType, inference_type, FlagRequirement::kNone); in ReadTocoFlagsFromCommandLineFlags()
353 if (toco_flags->inference_type() == IODataType::QUANTIZED_UINT8) { in ReadTocoFlagsFromCommandLineFlags()
Dtoco_tooling.cc169 type = ConvertIODataTypeToArrayDataType(toco_flags.inference_type()); in SetFinalDataTypeOnInputs()
248 const IODataType inference_type = toco_flags.inference_type(); in TransformWithStatus() local
252 (inference_type == QUANTIZED_UINT8 || inference_type == QUANTIZED_INT16); in TransformWithStatus()
/external/tensorflow/tensorflow/compiler/mlir/quantization/tensorflow/passes/
Dprepare_quantize_drq.cc62 quant_specs_.inference_type = tensorflow::DT_QINT8; in PrepareQuantizeDRQPass()
135 if (quant_specs_.inference_type == tensorflow::DT_QINT8 && in getQuantizableOps()
203 if (quant_specs_.inference_type == tensorflow::DT_QINT8) { in quantizeOps()

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