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Results 1 - 10 of 43 for Scales (0.45 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/legalize_tf_quant_test.cc
func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> { %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32> %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32> %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) { quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 29 18:43:55 UTC 2024 - 7.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference_with_shape_specialization.mlir
// CHECK-NEXT: return %[[UDQ]] : tensor<1xf32> func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> { %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32> %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32> %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) { quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 2.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow_to_stablehlo/tests/test_tf_to_stablehlo.mlir
func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> { %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32> %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32> %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) { quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 22:58:42 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/FakeQuantSupport.cc
} double scale; int64_t nudgedZeroPoint; getNudgedScaleAndZeroPoint(qmin, qmax, rmin, rmax, scale, nudgedZeroPoint); scales.push_back(scale); zeroPoints.push_back(nudgedZeroPoint); } unsigned flags = isSigned ? quant::QuantizationFlags::Signed : 0; return quant::UniformQuantizedPerAxisType::getChecked( loc, flags, storageType, expressedType, scales, zeroPoints,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 11:52:27 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h
UniformQuantizedPerAxisType CreateI8F32UniformQuantizedPerAxisType( Location loc, MLIRContext& context, ArrayRef<double> scales, ArrayRef<int64_t> zero_points, int quantization_dimension, bool narrow_range = false); // Creates a `UniformQuantizedPerAxisType` with the given `scales` and // `zero_points` values. The produced type has f32 as its expressed type and
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/common/uniform_quantized_types.cc
SmallVector<double>(scales), SmallVector<int64_t>(zero_points), quantization_dimension, /*storageTypeMin=*/llvm::minIntN(8) + (narrow_range ? 1 : 0), /*storageTypeMax=*/llvm::maxIntN(8)); } UniformQuantizedPerAxisType CreateI32F32UniformQuantizedPerAxisType( const Location loc, MLIRContext& context, const ArrayRef<double> scales,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/device_target.h
OutputInputSameScale, OutputInputFreeScale, CustomScale, }; // Each kernel signature has its own specification for scales. struct KernelSpec { // Scale constraint ScaleConstraintType type; // Custom function to derive the scales. Only available when the scale // constraint is `CustomScale`. ScaleFn scale_fn; }; class KernelSpecs { public:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 08 10:41:08 UTC 2024 - 7.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
class FixedResultScale<QuantizedType qt> : NativeOpTrait<!strconcat( "quant::FixedResult", qt.name, "Scale<", qt.asTraitArgsStr, ">::Impl")>; // Specify this trait if the bias-th input of the op is a bias input, which // needs a scale based on the scales of op1 and op2. class AccumulatorUniformScale<int bias, int op1, int op2> : NativeOpTrait< !strconcat("quant::AccumulatorUniformScale<",
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/quantize_patterns.td
CPred<"$_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">, "float constant tensor">; // Squash tfl.dequantize and tfl.quantize pairs. // TODO(fengliuai): Compare the scale of input and output. This can also be // squashed to a requantize op if the scales are different. def : Pat<(TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in)>; // If the tfl.dequantize op wasn't fused, we shouldn't quantize the floating
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 2.3K bytes - Viewed (0) -
src/cmd/vendor/github.com/google/pprof/internal/measurement/measurement.go
} } return false } // Scale a measurement from a unit to a different unit and returns // the scaled value and the target unit. The returned target unit // will be empty if uninteresting (could be skipped). func Scale(value int64, fromUnit, toUnit string) (float64, string) { // Avoid infinite recursion on overflow. if value < 0 && -value > 0 { v, u := Scale(-value, fromUnit, toUnit) return -v, u }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri May 31 19:48:28 UTC 2024 - 8.8K bytes - Viewed (0)