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Results 1 - 10 of 13 for dequantize (0.52 sec)
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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) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true" -cse | FileCheck %s // RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true legacy-float-scale=true" -cse| FileCheck --check-prefix=Legacy %s // CHECK-LABEL: QuantizeLstmCellInput func.func @QuantizeLstmCellInput(%arg0: tensor<1x28x28xf32>) -> tensor<1x28x20xf32> { %cst_2 = "tfl.no_value"() {value = unit} : () -> none
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
^bb0(%arg0: tensor<1x2xf32>): %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32> %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>) %2 = "tf.Transpose"(%1, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/compose_uniform_quantized_type_pass.cc
if (!combined_scale_constant_op) { LLVM_DEBUG(llvm::dbgs() << "Failed to match combined_scale_constant_op.\n"); return failure(); } // Quantize -> Dequantize following r3. auto output_uniform_quantize_call_op = dyn_cast_or_null<func::CallOp>( *combined_scale_multiply_op.getResult().user_begin()); if (!output_uniform_quantize_call_op->hasOneUse()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_composite_functions.cc
call_op, result_types, args, FlatSymbolRefAttr::get(new_quant_func_name)); return success(); } // For composite functions followed by Dequantize ops, merges the Dequantize // op into the functions by creating quantized functions with float output. LogicalResult mergeDequantizeOpFollowingQuantizedFunction( TF::PartitionedCallOp call_op, const SmallVector<Value, 4>& args,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 54.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
%2 = "tfl.dequantize"(%1) : (tensor<1x384x384x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x384x384xf32> %3 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x1x384xf32>} : () -> tensor<1x384x384xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
// The original model reshape->custom->custom->squeeze. ASSERT_THAT(*float_graph->operators(), SizeIs(4)); // The resulting model should be: // reshape->dequantize->custom->custom->quantize->squeeze. ASSERT_THAT(subgraph->operators, SizeIs(6)); const std::vector<BuiltinOperator> op_codes = { BuiltinOperator_RESHAPE, BuiltinOperator_DEQUANTIZE,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// before converting TF_Conv to TFL_Conv (void)applyPatternsAndFoldGreedily(func, std::move(patterns)); // Remove the wrapper of the tf.FakeQuant* ops and also insert the // tfl.quantize and tfl.dequantize to preserve the quantization parameters. // This is done after the first round of optimization to make sure all the // min/max operands of the tf.FakeQuant* are constants to be matched. The
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>; // This pattern applies when the same quantize/dequantize have been used twice // with the same scale. We want to remove the redundancy. // TODO(fengliuai): move this to the sanity check of pre-quantize pass. def eliminate_dq_q_pairs : Pat< (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
return false; }); } // Return the half_range value that is used by DequantizeOp. half_range is used // to offset the quantized representation before it gets scaled. In the case // of negative quantize types, this offset is half the type's range. static DenseElementsAttr DequantizeHalfRange(OpBuilder *builder, Value input) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0)