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Results 1 - 5 of 5 for AffineQuantizedOpInterface (0.3 sec)
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tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
InterfaceMethod< [{Returns the fixed output range.}], "UniformQuantizedType", "GetFixedOutputRange", (ins "bool":$sign, "int":$bit_width) >, ]; } def AffineQuantizedOpInterface : OpInterface< "AffineQuantizedOpInterface"> { let description = [{ Interface for affine quantized ops (conv2d, fully_connected, etc.) }]; let methods = [ InterfaceMethod<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
class TFL_ConvOp<string mnemonic, string opSummary, int index, list<Trait> additional_traits = []> : TFL_Op<mnemonic,[Pure, AccumulatorUniformScale<2, 0, 1>, AffineQuantizedOpInterface, AffineOpCoefficient<index, 1>, QuantizableResult, TFL_SparseOp] # additional_traits> { let summary = opSummary # " operator";
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc
int bit_width = quant_specs_.GetQuantizationTypeWidth(); Operation* quantize_op = quant_op.first; int quantize_operand_num = quant_op.second; auto affine_user = dyn_cast<AffineQuantizedOpInterface>(quantize_op); bool op_with_per_axis_support = false; if (!llvm::dyn_cast_or_null<CustomOp>(quantize_op)) { bool op_with_narrow_range = affine_user &&
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver.cc
// FC and Conv* ops. Restriction for the weight can be relaxed if there are // needs for adjusting scale of variable weights. auto affine_op = dyn_cast<AffineQuantizedOpInterface>(op); auto bias_op = op->getOperand(bias_index).getDefiningOp<arith::ConstantOp>(); if (!affine_op || !bias_op || input_indices.size() != 2) return false;
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/lite/flatbuffer_import.cc
} for (auto& use : value.getUses()) { Operation* user = use.getOwner(); if (user->hasTrait<mlir::OpTrait::IsTerminator>()) continue; auto affine_user = llvm::dyn_cast<mlir::AffineQuantizedOpInterface>(user); if (affine_user && affine_user.GetAffineOperandIndex() == use.getOperandNumber() && affine_user.RequiredNarrowRangeAffineOperand()) continue;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 66.8K bytes - Viewed (0)