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tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.cc
return "quant-prepare-lifting"; } StringRef getDescription() const final { // This is a brief description of the pass. return "Apply graph optimizations such as fusing and constant folding to " "prepare lifting."; } void getDependentDialects(DialectRegistry& registry) const override { registry.insert<TF::TensorFlowDialect, arith::ArithDialect>(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_tfl_passes.cc
mlir::TFL::CreatePostQuantizePass(emit_quant_adaptor_ops)); } pass_manager.addNestedPass<mlir::func::FuncOp>( mlir::TFL::CreateOptimizeOpOrderPass()); // Add optimization pass after quantization for additional fusing // opportunities. if (!pass_config.unfold_batch_matmul) { // Enable an optimization pass that transforms FC to BatchMatmul only when // `unfold_batch_matmul=false`.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 25.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantization_patterns.h
// Safeguard check to ensure that there is at least one quantizable op. if (failed(candidate_ops) || candidate_ops->empty()) return failure(); // Rewrite the floating-point ops to the quantized version, by fusing // preceding dequantize ops and succeding quantize ops. for (Operation* candidate_op : *candidate_ops) { // If it is requantize op, we shouldn't rewrite this op.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.9K bytes - Viewed (0) -
fess-crawler/src/main/resources/org/codelibs/fess/crawler/mime/tika-mimetypes.xml
</mime-type> <mime-type type="application/illustrator+ps"> <acronym>AI</acronym> <_comment>Adobe Illustrator Artwork -- the older postscript based AI files</_comment> <!-- we can do more specific versions by focusing on the integer in the regex below --> <tika:link>http://justsolve.archiveteam.org/wiki/Adobe_Illustrator_Artwork</tika:link> <magic priority="60">
Registered: Wed Jun 12 15:17:51 UTC 2024 - Last Modified: Thu Sep 21 06:46:43 UTC 2023 - 298.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h
std::unique_ptr<OperationPass<ModuleOp>> CreateLiftQuantizableSpotsAsFunctionsPass( const tensorflow::quantization::QuantizationOptions& quant_options); // Apply graph optimizations such as fusing and constant folding to prepare // lifting. std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass( tensorflow::quantization::OpSet target_opset);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 12.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/fuse_tpu_compile_and_execute_ops.mlir
// RUN: tf-tfrt-opt -verify-diagnostics -split-input-file -tfrt-fuse-tpu-compile-and-execute-ops -canonicalize %s | FileCheck %s --dump-input=fail --dump-input-filter=all module attributes {tf_saved_model.semantics} { // Test fusing _TPUCompileMlirOp and TPUExecuteOp into TPUCompileMlirAndExecuteOp. // CHECK-LABEL: func private @test_fuse_tpu_ops func.func private @test_fuse_tpu_ops(%arg0: tensor<*xi32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<*xi32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
data_format='NHWC', name='sample/conv', ) if bias_fn is not None: out = nn_ops.bias_add(out, self.bias) if has_batch_norm: # Fusing is supported for non-training case. out, _, _, _, _, _ = nn_ops.fused_batch_norm_v3( out, scale, offset, mean, variance, is_training=False ) if activation_fn is not None:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize.cc
auto users = op.getResult().getUsers(); quantizing_ops.append(users.begin(), users.end()); bool changed = false; // Rewrite the floating-point ops to the quantized version, by fusing // preceding dequantize ops and succeding quantize ops. for (Operation* quantizing_op : quantizing_ops) { // If it is requantize op, we shouldn't rewrite this op.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 05:52:39 UTC 2024 - 23.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
padding='SAME', data_format='NHWC', ) if has_bias: out = nn_ops.bias_add(out, self.bias) if has_batch_norm: # Fusing is supported for non-training case. out, _, _, _, _, _ = nn_ops.fused_batch_norm_v3( out, scale, offset, mean, variance, is_training=False ) if activation_fn is not None:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/executor_tpuv1_island_coarsening.cc
// Found an operand that isn't scheduled yet, return true. return true; } } return false; } // Sorts the operations in the provided range to enforce dominance. // This is useful after fusing / reorganizing Operations in a block and later // needing to readjust the ordering to ensure dominance. LogicalResult SortTopologically(Block::iterator begin, Block::iterator end) { Block* block = begin->getBlock();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 27.6K bytes - Viewed (0)