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Results 21 - 30 of 371 for weights (0.14 sec)
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tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
(binaryOp (TFL_TransposeConvOp:$output $output_shape, $weights, $input, (Arith_ConstantOp FloatElementsAttr:$bias), $padding, $stride_h, $stride_w, TFL_AF_None), (Arith_ConstantOp FloatElementsAttr:$value), $act_fn), (TFL_TransposeConvOp $output_shape, $weights, $input, (binaryOp (Arith_ConstantOp $bias), (Arith_ConstantOp $value), TFL_AF_None),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
pilot/pkg/xds/endpoints/ep_filters.go
return scaleFactor } weight := uint32(math.MaxUint32) if ep.GetLoadBalancingWeight().Value < math.MaxUint32/scaleFactor { weight = ep.GetLoadBalancingWeight().Value * scaleFactor } return weight } // Apply the weight for this endpoint to the network gateways.
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Wed May 29 01:17:58 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h
// Whether to allow weight-only quantization. This scheme quantizes // weights but will dequantize them back at runtime which is useful for // memory bound case without kernel support available in lower precisions. // Used in MLIR dynamic range quantizer. bool weight_only_quantization = false; // The minimum number of elements in a weights array required to apply
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 13 10:16:19 UTC 2024 - 10.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
TFL_TensorOf<[F32, I8]>:$fw_recurrent_to_output_weights, // Forward Cell weights TFL_TensorOfOrNone<[F32, I8]>:$fw_cell_to_input_weights, // Optional Forward cell weights TFL_TensorOfOrNone<[F32, I8]>:$fw_cell_to_forget_weights, // Optional Forward cell weights TFL_TensorOfOrNone<[F32, I8]>:$fw_cell_to_output_weights, // Forward Bias
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
src/cmd/internal/pgo/pprof.go
} return postProcessNamedEdgeMap(weight, totalWeight) } func sortByWeight(edges []NamedCallEdge, weight map[NamedCallEdge]int64) { sort.Slice(edges, func(i, j int) bool { ei, ej := edges[i], edges[j] if wi, wj := weight[ei], weight[ej]; wi != wj { return wi > wj // want larger weight first } // same weight, order by name/line number if ei.CallerName != ej.CallerName {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Mar 27 20:20:01 UTC 2024 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto
// determined. The activation and weight are quantized to INT8 while bias is // quantized to INT32. METHOD_STATIC_RANGE_INT8 = 2; // Dynamic range quantization. Quantized tensor values' ranges are // determined in the graph executions. The weights are quantized during // conversion. METHOD_DYNAMIC_RANGE_INT8 = 3; // Weight-only quantization. Only weights are quantized during conversion.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 19 06:31:19 UTC 2024 - 9.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
input_ = fused_func_op_.getArgument(0); bias_ = fused_func_op_.getArgument(2); weight_ = fused_func_op_.getArgument(1); weight_type_ = mlir::cast<RankedTensorType>(weight_.getType()); if (weight_type_.getRank() != 2) { return fused_func_op_.emitError() << "The weight tensor was not of rank 2"; } if (weight_type_.getDimSize(1) % num_gates_ != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 36.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/preprocess_op.cc
METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8, "weight_only", "Post-training weight-only quantizaiton"))}; Option<bool> enable_per_channel_quantization_{ *this, "enable-per-channel-quantization", llvm::cl::init(false), llvm::cl::desc("Whether enable per-channel quantized weights.")}; }; // Apply constant transformations for the op_set.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.4K bytes - Viewed (0) -
src/internal/profile/graph.go
ret += edge.Weight } return ret } type edgeList []*Edge func (el edgeList) Len() int { return len(el) } func (el edgeList) Less(i, j int) bool { if el[i].Weight != el[j].Weight { return abs64(el[i].Weight) > abs64(el[j].Weight) } from1 := el[i].Src.Info.PrintableName() from2 := el[j].Src.Info.PrintableName() if from1 != from2 { return from1 < from2 }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Feb 05 20:59:15 UTC 2024 - 13.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantize_composite_functions.cc
enable_per_channel_quantized_weight_; // Change this to user-given bit width once we have custom configuration. options.bit_width_ = 8; // Insert quantization parameters for weights for ops with `weight_only_ptq` // attribute. pm.addNestedPass<func::FuncOp>(createInsertWeightParamPass()); // PrepareQuantizePass uses SymbolTable to fetch relevant GEMM ops for
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 02:59:01 UTC 2024 - 4.6K bytes - Viewed (0)