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Fix the op quant trait of tfl.rsqrt and the nudging for the calibration data

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PiperOrigin-RevId: 348884204
Change-Id: Iabac61239cf394fa18d491e0da1fb54d17c3b23a
This commit is contained in:
Feng Liu 2020-12-23 20:59:14 -08:00 committed by TensorFlower Gardener
parent 7494180256
commit 05303d0dce
3 changed files with 38 additions and 7 deletions
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3
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
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@ -2649,8 +2649,7 @@ slice `i`, with the first `seq_lengths[i]` slices along dimension
def TFL_RsqrtOp: TFL_Op<"rsqrt", [NoSideEffect,
TFL_SameFirstOperandAndFirstResultElementType,
SameOperandsAndResultShape,
NoQuantizableResult]> {
SameOperandsAndResultShape]> {
let summary = "Reciprocal of square root operator";
let description = [{

20
tensorflow/compiler/mlir/lite/quantization/quantization_utils.h
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@ -104,11 +104,16 @@ struct ConvertStatsToQDQs : public OpRewritePattern<quant::StatisticsOp> {
if (!stats) return failure();
for (auto it = stats.begin(), e = stats.end(); it != e; ++it) {
double min = FloatAttr::getValueAsDouble(*it++);
double max = FloatAttr::getValueAsDouble(*it);
TensorRangeSanityCheck(op, min, max);
mins.push_back(min);
maxs.push_back(max);
double rmin = FloatAttr::getValueAsDouble(*it++);
double rmax = FloatAttr::getValueAsDouble(*it);
// The default nudging implementation of mlir quant library might cause
// clamping during inference if the calibration range isn't wide enough.
// So here we adjust the range to include 0.0.
rmin = std::min(rmin, 0.0);
rmax = std::max(rmax, 0.0);
TensorRangeSanityCheck(op, rmin, rmax);
mins.push_back(rmin);
maxs.push_back(rmax);
}
quant_type =
quant::fakeQuantAttrsToType(op.getLoc(), num_bits, *op.axis(), mins,
@ -116,6 +121,11 @@ struct ConvertStatsToQDQs : public OpRewritePattern<quant::StatisticsOp> {
} else if (auto stats = op.layerStats().dyn_cast<DenseFPElementsAttr>()) {
double rmin = FloatAttr::getValueAsDouble(stats.getValue<APFloat>({0}));
double rmax = FloatAttr::getValueAsDouble(stats.getValue<APFloat>({1}));
// The default nudging implementation of mlir quant library might cause
// clamping during inference if the calibration range isn't wide enough.
// So here we adjust the range to include 0.0.
rmin = std::min(rmin, 0.0);
rmax = std::max(rmax, 0.0);
TensorRangeSanityCheck(op, rmin, rmax);
quant_type =
quant::fakeQuantAttrsToType(op.getLoc(), num_bits, rmin, rmax,

22
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
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@ -56,6 +56,28 @@ func @prepareStatistics(%arg0: tensor<8x4x3xf32>) -> tensor<8x4x3xf32> {
// CHECK: return %[[dq2]]
}
// CHECK-LABEL: prepareStatisticsNudge
func @prepareStatisticsNudge(%arg0: tensor<8x4x3xf32>) -> tensor<8x4x3xf32> {
%0 = "quant.stats"(%arg0) {
layerStats = dense<[0.1, 1.0]> : tensor<2xf32>
} : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
%1 = "quant.stats"(%0) {
layerStats = dense<[0.1, 1.0]> : tensor<2xf32>,
axisStats = dense<[
[-1.0, 1.0],
[-8.0, -1.0],
[-0.5, 0.5]
]> : tensor<3x2xf32>, axis = 2 : i64
} : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
return %1 : tensor<8x4x3xf32>
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<i8:f32, 0.0039215686274509803:-128>>}
// CHECK: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]])
// CHECK: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<i8:f32:2, {0.0078431372549019607:-1,0.031372549019607843:127,0.0039215686274509803:-1}>>}
// CHECK: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]])
// CHECK: return %[[dq2]]
}
// CHECK-LABEL: preparePrelu
func @preparePrelu(%arg0: tensor<1x10x10x3xf32>) -> tensor<1x10x10x3xf32> {
%cst = "tfl.pseudo_const"() {value = dense<[[[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>} : () -> tensor<1x1x3xf32>