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Results 11 - 20 of 152 for requantize (0.19 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/passes/quantization_patterns.h
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. if (isa<QuantizeOpT, DequantizeOpT>(candidate_op)) { return failure(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/prepare_quantize.cc
auto func_op_quant_scale_spec = GetStableHloQuantConstraints; for (auto func_op : module_op.getOps<func::FuncOp>()) { // The function might contain more stats ops than required, and it will // introduce requantize if the calibration stats have conflicts. This tries // to remove all the redundant stats ops. RemoveRedundantStatsOps(func_op, func_op_quant_spec, func_op_quant_scale_spec);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 03 05:11:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver.cc
// TODO: b/323478683 - Make the attribute being part of op definition. quantize->setAttr(kVolatileOpAttrName, builder_.getUnitAttr()); // `original_result` has a use to `quantize`, so this will replace that use // by the result of `dequantize`. Remember to reset that use afterwards value.replaceAllUsesWith(dequantize); quantize.getOperation()->replaceUsesOfWith(dequantize, value); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 38.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize.cc
TFDynamicRangeQuantization>(ctx, quant_params) {} }; // Removes quantize-dequantize pairs that are not used in the quantization. // The benefit of this pattern is set to lower value than other patterns, so // that the other patterns can work on quantize/dequantize ops first. class RemoveUnusedQdqPattern : public OpRewritePattern<quantfork::DequantizeCastOp> { public:
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/stablehlo/passes/quantization_patterns.cc
} void rewrite(quantfork::DequantizeCastOp op, PatternRewriter& rewriter) const final { // Rewrite the floating-point ops to the quantized version, by fusing // preceding dequantize ops and succeding quantize ops. for (Operation* op_with_region : op.getResult().getUsers()) { // Collect all the quantized inputs and "clone" the matched op by these // inputs. SmallVector<Value, 4> inputs;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 06:04:36 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize.cc
// ranges. bool SetInputNodesQuantizationParams(func::FuncOp func); // The function might contain more stats ops than required, and it will // introduce requantize if the calibration stats have conflicts. This method // tries to remove all the redundant stats ops. bool RemoveRedundantStats(func::FuncOp func);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize.cc
// Whether the func contains Quantize ops. This is used to determine whether // to use the quantization parameters from the fixed output range property. bool ContainsQuantizeOps(func::FuncOp func); QuantizationSpecs quant_specs_; Option<bool> enable_post_training_quantize_{ *this, "post-training-quantize", llvm::cl::init(false),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.proto
// hardware performs better with integer ops. // Default value: true optional bool unpack_quantized_types = 1; // When set to True, requantize op in the quantized fusion will merge with the // subsequent dequantize op if present. // Default value: false // TODO: b/321729008 - re-consider default value after testing on prod model. bool merge_fusion_with_dequantize = 2; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 14.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
} }; // stablehlo.uniform_dequantize -> tfl.dequantize class RewriteUniformDequantizeOp : public OpRewritePattern<stablehlo::UniformDequantizeOp> { using OpRewritePattern<stablehlo::UniformDequantizeOp>::OpRewritePattern; // Determines whether the input and output types are compatible with // `tfl.dequantize`. See the definition for the `DEQUANTIZE` kernel for the // detailed limitations
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
// MixedPrecision-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0) // MixedPrecision-NEXT: %[[dq:.*]] = "tfl.dequantize"(%[[q]]) // MixedPrecision-NEXT: %[[q_0:.*]] = "tfl.quantize"(%arg1) // MixedPrecision-NEXT: %[[dq_0:.*]] = "tfl.dequantize"(%[[q_0]]) // MixedPrecision-NEXT: %[[c:.*]] = "tfl.concatenation"(%[[dq]], %[[dq_0]]) // MixedPrecision-NEXT: %[[q_1:.*]] = "tfl.quantize"(%[[c]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 67.5K bytes - Viewed (0)