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tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h
// storage type. The available values use the full range of the storage value, // i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point // value can be a non-zero value. // If `narrow_range` is set true (ex: for weights), a restricted range of // integers will be used for symmetric mapping, i.e. [-127, 127]. UniformQuantizedType CreateI8F32UniformQuantizedType(Location loc, MLIRContext& context,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq_min_elements.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions-drq="min-num-elements-for-weights=2500000" | FileCheck %s // CHECK-LABEL: lift_float_matmul func.func @lift_float_matmul(%arg0: tensor<1x12x12x512xf32>) -> (tensor<*xf32>, tensor<*xf32>) { %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32> %out_1 = "tf.MatMul"(%arg0, %cst) { device = "", transpose_a = false, transpose_b = false
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 2.1K bytes - Viewed (0) -
pilot/pkg/networking/core/loadbalancer/loadbalancer_test.go
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Tue Apr 23 05:38:57 UTC 2024 - 39.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_patterns.td
// point constant. def : Pat<(TFL_DequantizeOp (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)), (TFL_ConstOp $cst)>; // Transpose conv supports hybrid computation with quantized weights. def FoldQuantWeightsIntoTposeConv : Pat< (TFL_TransposeConvOp $output_shape, (TFL_DequantizeOp $quant_weights), $quant_input, $bias, $padding, $stride_h, $stride_w, $faf),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 2.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
(binaryOp (TFL_TransposeConvOp:$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)