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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/merge-fusion-with-dequantize.mlir
return %1 : tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>> } } // ----- // Merge fusion with dequantize for no activation case. module attributes {tf_saved_model.semantics} { // CHECK-LABEL: func.func private @merge_no_act_fusion func.func private @merge_no_act_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 04 23:45:53 UTC 2024 - 14K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/ir/tfr_ops.td
def TFR_TFRQuantActRangeOp : TFR_Op<"quant_act_range", [Pure]> { let description = [{ The `quant_act_range` returns the a pair of integers to indicate the fixed range for the fused activation `act` with the quantization defined by the `scale` and `zero point`. Currently, the allowed activations are `NONE`, `RELU`, `RELU6` and `RELU_N1_TO_1`. Example: ```mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 10:54:29 UTC 2024 - 17.4K bytes - Viewed (0) -
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/tensorflow/passes/quantized_function_library_tf_drq.mlir
%mul = "tf.Mul"(%cast, %scale_prod) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> func.return %mul : tensor<*xf32> } // TODO(b/263199401): Support quantization options for activation quantization for DRQ // Note: following function supports per-tensor, asymmetric, non_narrow_range. func.func private @internal_calculate_quant_params(%input : tensor<*xf32>) -> (tensor<1xf32>, tensor<1xi32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 12.2K 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/ir/tfl_op_enums.td
let cppNamespace = "::mlir::TFL"; } def TFL_DimensionTypeAttr : EnumAttr<TFL_Dialect, TFL_DimensionType, "dimension_type_attr"> { let convertFromStorage = "$_self"; } // Allowed activation function cases // These should match the ActivationFunctionType enum in TFLite schema. def TFL_AFEnum_None : I32EnumAttrCase<"NONE", 0>; def TFL_AFEnum_Relu : I32EnumAttrCase<"RELU", 1>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Oct 20 00:05:24 UTC 2022 - 6.4K 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)