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Results 21 - 30 of 81 for dequantize (0.3 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.td
"MLIR dump file name.">, Option<"merge_fusion_with_dequantize_", "merge-fusion-with-dequantize", "bool", /*default=*/"false", "Whether to merge quantized conv/dot_general fusion with subsequent dequantize.">, ]; let dependentDialects = [ "mlir::arith::ArithDialect", "mlir::stablehlo::StablehloDialect", "mlir::quant::QuantizationDialect",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 10.3K 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/tf2xla/tests/legalize-tf-quant.mlir
// CHECK: %[[DEQUANTIZE:.*]] = mhlo.uniform_dequantize %[[CONVERT_2]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xf32> // CHECK: return %[[DEQUANTIZE]] : tensor<2xf32> %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) { quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 01:25:29 UTC 2024 - 37.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir
} : (tensor<i8>, tensor<*xf32>, tensor<*xi32>) -> tensor<*xf32> %clamp_max = "tf.Maximum"(%dequantize, %clip_min) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> %clamp_min = "tf.Minimum"(%clamp_max, %clip_max) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> func.return %clamp_min : tensor<*xf32> } // Dequantizes and applies quantized Relu by clipping.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 30.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h
// correct float op should be the user of the last DequantizeOp. if (llvm::isa<QuantizeOpT>(user)) { user = *user->getResult(0).getUsers().begin(); } if (auto dequantize = llvm::dyn_cast<DequantizeOpT>(user)) { // Replace all uses, except not quantizable ops that are being used in // the float backbone.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/modify_io_nodes.mlir
%6 = "tfl.dequantize"(%5) : (tensor<1x401408x!quant.uniform<i8:f32, 3.906250e-03>>) -> tensor<1x401408xf32> func.return %6 : tensor<1x401408xf32> // CHECK-LABEL: func @modified(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> // CHECK-NEXT: %[[shape:.*]] = arith.constant dense<[1, 401408]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K 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/quantized_function_library_uniform_quantized.mlir
} : (tensor<*xf32>, tensor<*xf32>, tensor<*xi32>) -> tensor<*x!tf_type.qint32> func.return %quantize : tensor<*x!tf_type.qint32> } // Dequantize final graph output back to f32. Input is qint8. func.func @dequantize_i8(%input : tensor<*x!tf_type.qint8>, %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>) -> tensor<*xf32> { %dequantize = "tf.UniformDequantize"(%input, %input_scale, %input_zp) { Tin = "tfdtype$DT_QINT8",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 19.3K bytes - Viewed (0) -
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/lite/transforms/prepare_quantize_dynamic_range.cc
quant::QuantizationSpecs quant_specs_; }; #include "tensorflow/compiler/mlir/lite/utils/generated_op_quant_spec_getters.inc" // If the weight is applicable to dynamic range quantization, insert Quantize // and Dequantize ops with either per-axis or per-tensor scale. class PrepareDynamicRangeQuantizableOp : public OpRewritePattern<arith::ConstantOp> { public: explicit PrepareDynamicRangeQuantizableOp(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0)