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Switch legacy quantize mode on by default for MLIR quantizer

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Also fix code to make legacy pass applied together with other patterns. It ensures when numeric_verify is set, float ops can be duplicated before quantization to happen.

PiperOrigin-RevId: 352948582
Change-Id: I6550697ee6a4508bc6518ec08cccc854b34d2321
This commit is contained in:
Taehee Jeong 2021-01-20 22:44:09 -08:00 committed by TensorFlower Gardener
parent 9c530d1204
commit 310b42a801
2 changed files with 10 additions and 5 deletions
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7
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.h
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@ -29,6 +29,11 @@ namespace lite {
// The `input_type`, `output_type` and `inference_type` can be // The `input_type`, `output_type` and `inference_type` can be
// float32/qint8/int8/int16. // float32/qint8/int8/int16.
// Return partially quantized model if `fully_quantize` is false. // Return partially quantized model if `fully_quantize` is false.
// When `verify_numeric` is true, the model will have it's original float ops
// and NumericVerify ops to compare output values from the quantized and float
// ops. When `legacy_float_scale` is true, the quantizer will use float scale
// instead of double, and call TOCO's quantization routines to maintain
// bit-exactness of the values with the TOCO quantizer.
TfLiteStatus QuantizeModel( TfLiteStatus QuantizeModel(
const tflite::ModelT& input_model, const tflite::TensorType& input_type, const tflite::ModelT& input_model, const tflite::TensorType& input_type,
const tflite::TensorType& output_type, const tflite::TensorType& output_type,
@ -37,7 +42,7 @@ TfLiteStatus QuantizeModel(
bool disable_per_channel, bool fully_quantize, bool disable_per_channel, bool fully_quantize,
flatbuffers::FlatBufferBuilder* builder, flatbuffers::FlatBufferBuilder* builder,
tflite::ErrorReporter* error_reporter, bool verify_numeric = false, tflite::ErrorReporter* error_reporter, bool verify_numeric = false,
bool legacy_float_scale = false); bool legacy_float_scale = true);
} // namespace lite } // namespace lite
} // namespace mlir } // namespace mlir

8
tensorflow/compiler/mlir/lite/transforms/quantize.cc
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@ -88,8 +88,10 @@ struct TFLFullQuantization
}; };
struct LegacyQuantizePass : public OpRewritePattern<QuantizeOp> { struct LegacyQuantizePass : public OpRewritePattern<QuantizeOp> {
// This pattern should be applied before existing quantize pattern in
// `quantize_patterns.td`, so the benefit is set to some value larger than 1.
explicit LegacyQuantizePass(MLIRContext* context) explicit LegacyQuantizePass(MLIRContext* context)
: OpRewritePattern<QuantizeOp>(context) {} : OpRewritePattern<QuantizeOp>(context, /*benefit=*/10) {}
LogicalResult matchAndRewrite(QuantizeOp op, LogicalResult matchAndRewrite(QuantizeOp op,
PatternRewriter& rewriter) const override { PatternRewriter& rewriter) const override {
DenseFPElementsAttr attr; DenseFPElementsAttr attr;
@ -127,9 +129,7 @@ void QuantizePass::runOnFunction() {
auto func = getFunction(); auto func = getFunction();
auto* ctx = func.getContext(); auto* ctx = func.getContext();
if (legacy_float_scale) { if (legacy_float_scale) {
OwningRewritePatternList legacy_patterns; patterns.insert<LegacyQuantizePass>(ctx);
legacy_patterns.insert<LegacyQuantizePass>(ctx);
applyPatternsAndFoldGreedily(func, std::move(legacy_patterns));
} }
TFL::populateWithGenerated(ctx, patterns); TFL::populateWithGenerated(ctx, patterns);
patterns.insert<TFLFullQuantization>( patterns.insert<TFLFullQuantization>(