- Sort Score
- Result 10 results
- Languages All
Results 11 - 20 of 20 for Motivation (0.19 sec)
-
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
// Ternary ops patterns. //===----------------------------------------------------------------------===// // Multi-pattern consisting of matching stand-alone convolution op followed by // activation op. multiclass FuseActFnIntoConvOpPat<Op ActFnOp, ConstantStrAttr ActFnAttr> { def FuseActivationFuncWithConv#ActFnOp#ActFnAttr : Pat< (ActFnOp (TFL_Conv2DOp:$conv_out $input, $filter, $bias, $h_factor,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions.mlir
// dynamic batch dimension is properly quantized. // Note that this checks for identical condition as // quantize_conv_with_bias_dynamic_fn, omitting stablehlo.maximum. // This is because activation clipping which includes 0.0f can be simply // omitted from the graph as the lifted function's out_scale and out_zp are // already calculated based on the clipped distribution.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 91.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto
// optimizations in the pipeline. METHOD_NO_QUANTIZE = 1; // Static range quantization. Quantized tensor values' ranges are statically // 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
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/quantization/stablehlo/quantization_config.proto
QuantizationSpecs specs = 4; // Configures the quantization debugger. DebuggerConfig debugger_config = 5; // Defines calibration options for quantization. This option is only used for // activation of static range quantization (SRQ). Quantization calibration // method is set to MIN_MAX by default. CalibrationOptions calibration_options = 6; // Path to file to save the quantization report, which is essentially a
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 14.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/uniform-quantized-stablehlo-to-tfl.mlir
// CHECK: %[[QCONST_0:.+]] = "tfl.pseudo_qconst"() // CHECK: "tfl.batch_matmul"(%[[ARG]], %[[QCONST_0]]) <{adj_x = false, adj_y = false}> // ----- // Tests static range quantized dot_general with activation as RHS
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 106.2K bytes - Viewed (0) -
RELEASE.md
* Add `UnifiedGRU` as the new GRU implementation for tf2.0. Change the default recurrent activation function for GRU from `hard_sigmoid` to `sigmoid`, and `reset_after` to True in 2.0. Historically recurrent activation is `hard_sigmoid` since it is fast than 'sigmoid'. With new unified backend between CPU and GPU mode, since the CuDNN kernel is using sigmoid, we change
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
QuantizableResult, PredOpTrait<"input and output must have same element type", TFL_TCresVTEtIsSameAsOp<0, 0>>]> { let summary = "Hardswish activation function."; let description = [{ Computes hard-swish activation function f(x) -> (x * relu6(x+3))/6 element-wise. }]; let arguments = (ins TFL_TensorOf<[F32, QUI8, QI8]>:$input);
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/tensorflow/tests/fused_kernel_matcher.mlir
// RUN: tf-opt %s -tf-fused-kernel-matcher | FileCheck %s //===----------------------------------------------------------------------===// // Conv2D + BiasAdd + <Activation> fusions. //===----------------------------------------------------------------------===// // CHECK-LABEL: conv2DBiasAdd_noActivation func.func @conv2DBiasAdd_noActivation(%arg0: tensor<128xf32>, %arg1: tensor<1x1x3x128xf32>, %arg2: tensor<8x32x32x3xf32>) -> (tensor<*xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/passes.td
Option<"quantize_signed_", "quantize-signed", "bool", "false", "signed inference type. Only used in tests">, Option<"activation_number_of_bits_", "activation-number-of-bits", "int", "8", "number of bits for inference type. Only used in tests">, Option<"post_training_quantize_", "post-training-quantize", "bool", "false",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 22.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true activation-number-of-bits=16" -cse | FileCheck %s // CHECK-LABEL: QuantizeUnidirectionalLstmFullPerTensor func.func @QuantizeUnidirectionalLstmFullPerTensor(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) { %input = "quantfork.stats"(%arg0) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0)