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Results 1 - 3 of 3 for RequiredNarrowRangeAffineOperand (0.3 sec)
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tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
"int", "GetAffineOperandIndex", (ins), [{}], [{return 1;}]>, InterfaceMethod< [{Returns whether narrow range is required for the affine operand.}], "bool", "RequiredNarrowRangeAffineOperand", (ins), [{}], [{return true;}]>, InterfaceMethod< [{Returns quantization dim for the affine operand.}], "int", "GetQuantizationDimIndex", (ins)>, 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/transforms/prepare_quantize_dynamic_range.cc
if (!llvm::dyn_cast_or_null<CustomOp>(quantize_op)) { bool op_with_narrow_range = affine_user && affine_user.GetAffineOperandIndex() == quantize_operand_num && affine_user.RequiredNarrowRangeAffineOperand(); op_with_per_axis_support = op_with_narrow_range && affine_user.GetQuantizationDimIndex() != -1 && !quant_specs_.disable_per_channel;
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/lite/flatbuffer_import.cc
auto affine_user = llvm::dyn_cast<mlir::AffineQuantizedOpInterface>(user); if (affine_user && affine_user.GetAffineOperandIndex() == use.getOperandNumber() && affine_user.RequiredNarrowRangeAffineOperand()) continue; // Create a fully range quantized constant. if (full_range_const == value) { mlir::quant::QuantizedType new_qtype; if (auto per_axis =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 66.8K bytes - Viewed (0)