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tensorflow/compiler/mlir/lite/tests/end2end/quant_stats.pbtxt
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \ # RUN: -tf-input-shapes=4:4 \ # RUN: -tf-input-data-types=DT_FLOAT,DT_FLOAT \ # RUN: -tf-output-arrays=Add \ # RUN: -tf-inference-type=DT_QUINT8 \ # RUN: -tf-input-min-values='-2,-3' \ # RUN: -tf-input-max-values='2,3' \ # RUN: --quant-stats=%s.stats \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4K bytes - Viewed (0) -
platforms/core-runtime/serialization/src/test/groovy/org/gradle/internal/serialize/kryo/KryoBackedDecoderTest.groovy
def "can read from stream and then restart to use another stream"() { def input1 = encoded("string 1") def input2 = encoded("string 2") given: def decoder = new KryoBackedDecoder(input1) decoder.readString() decoder.restart(input2) expect: decoder.readPosition == 0 decoder.readString() == "string 2"
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Mon Apr 15 16:06:56 UTC 2024 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s module { func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.json
// CHECK-DAG: %[[input_18:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-8.000000e-01, 1.600000e+00]> : tensor<2xf32>}> : (tensor<1x4xf32>) -> tensor<1x4xf32> // CHECK-DAG: %[[input_19:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-2.000000e+00, 4.000000e+00]> : tensor<2xf32>}> : (tensor<1x2xf32>) -> tensor<1x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 06:25:50 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/flatbuffer_to_mlir.cc
const llvm::MemoryBuffer* input = source_mgr->getMemoryBuffer(source_mgr->getMainFileID()); std::string error; auto loc = mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0); std::vector<std::string> inputs; std::vector<std::string> outputs; return tflite::FlatBufferToMlir( absl::string_view(input->getBufferStart(), input->getBufferSize()),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun May 12 12:39:37 UTC 2024 - 3.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/insert_call_once_op.mlir
} func.func @serving_default(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<*x!tf_type.string> {tf_saved_model.index_path = ["r"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "hash_table_Lookup/LookupTableFindV2:0"}, tf_saved_model.exported_names = ["serving_default"]} { %cst = arith.constant dense<"f"> : tensor<!tf_type.string>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/tf_mlir_translate.h
ABSL_DEPRECATED( "Please use the other overload of this function which accepts structured " "inputs instead of strings") // Converts a TensorFlow GraphDef contained in `input` param into a MLIR module. // Creates MLIR entities into the given MLIR `context`. absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> GraphdefToMlirTranslateFunction( llvm::StringRef input, absl::string_view input_arrays,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_saver_op.cc
absl::AbortedError("The input `min` must have float type.")); OP_REQUIRES(context, context->input_type(i * 3 + 1) == DT_FLOAT, absl::AbortedError("The input `max` must have float type.")); OP_REQUIRES( context, context->input_type(i * 3 + 2) == DT_INT64, absl::AbortedError("The input `histogram` must have int64 type.")); } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 13 01:31:23 UTC 2024 - 8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/analysis/cost_analysis.cc
cost += GetRankedTensorSize(type); } else { // For unranked tensors, use the max size among the input tensors. This is // because the only dynamic information of the function should be the // input, so the size of dynamic tensors should be usually capped by // inputs' sizes. cost += max_arg_size_; } } cost_map_[op] = cost; } } // namespace tfrt_compiler
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.6K bytes - Viewed (0)