experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Set the calibrated quantization parameters to be volatile

Browse Source 
The QDQ will be removed if there are not fused after quantization.

PiperOrigin-RevId: 354638972
Change-Id: I1a0cd1514f302ec47d1f64fbba4232a354fc9fb6
This commit is contained in:
Feng Liu 2021-01-29 17:02:48 -08:00 committed by TensorFlower Gardener
parent 23d9a2b49d
commit c6de267c22
5 changed files with 19 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
tensorflow/compiler/mlir/lite/quantization/quantization_utils.h
View File

@ -152,6 +152,8 @@ struct ConvertStatsToQDQs : public OpRewritePattern<quant::StatisticsOp> {
rewriter.setInsertionPointAfter(op.getOperation());
Type result_type = quant_type.castFromExpressedType(op.getType());
auto q = rewriter.create<Q>(op.getLoc(), result_type, op.arg());
q->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
auto dq = rewriter.create<DQ>(op.getLoc(), op.getType(), q);
op.getResult().replaceAllUsesWith(dq);
q.getOperation()->replaceUsesOfWith(dq, op.arg());

10
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
View File

@ -112,7 +112,7 @@ func @QuantizeWithoutNorm(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {t
// CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]]
// CHECK-SAME: effective_hidden_scale_intermediate = tensor<!quant.uniform<i8:f32, 0.0039215686274509803:-1>>
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>}
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>, volatile}
}
// CHECK-LABEL: QuantizeLstmCifg
@ -194,7 +194,7 @@ func @QuantizeLstmCifg(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {tf.e
// CHECK-SAME: input_to_forget_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 4.8829615161595508E-4>>
// CHECK-SAME: input_to_output_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 3.0518509475997192E-5>>
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>}
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>, volatile}
}
// CHECK-LABEL: QuantizeUnidirectionalLstmFull
@ -285,7 +285,7 @@ func @QuantizeUnidirectionalLstmFull(%arg0: tensor<1x5xf32>) -> tensor<*xf32> at
// CHECK-SAME: input_to_input_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 9.7659230323191015E-4>>
// CHECK-SAME: input_to_output_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 3.0518509475997192E-5>>
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>}
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>, volatile}
}
// CHECK-LABEL: QuantizeLstmFull
@ -377,7 +377,7 @@ func @QuantizeLstmFull(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {tf.e
// CHECK-SAME: input_to_input_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 9.7659230323191015E-4>>
// CHECK-SAME: input_to_output_intermediate = tensor<!quant.uniform<i16<-32767:32767>:f32, 3.0518509475997192E-5>>
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>}
// CHECK: "tfl.quantize"(%[[lstm]]) {qtype = tensor<*x!quant.uniform<i8:f32, 0.015686274509803921:-1>>, volatile}
}
// CHECK-LABEL: QuantizeSVDF
@ -398,7 +398,7 @@ func @QuantizeSVDF(%arg0: tensor<1x3xf32>) -> tensor<1x2xf32> {
// CHECK-DAG: %[[input_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x!quant.uniform<i32:f32, 1.3900876031311922E-5>>)
// CHECK-DAG: %[[input_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x4x!quant.uniform<i16<-32767:32767>:f32, 0.0037514108011770368>>)
// CHECK: %[[svdf:.*]] = "tfl.svdf"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]])
// CHECK: %[[q:.*]] = "tfl.quantize"(%[[svdf]]) {qtype = tensor<1x2x!quant.uniform<i8:f32, 0.12954867493872549:-128>>}
// CHECK: %[[q:.*]] = "tfl.quantize"(%[[svdf]]) {qtype = tensor<1x2x!quant.uniform<i8:f32, 0.12954867493872549:-128>>, volatile}
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%11)
// CHECK: return %[[dq]]
}

8
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
View File

@ -49,9 +49,9 @@ func @prepareStatistics(%arg0: tensor<8x4x3xf32>) -> tensor<8x4x3xf32> {
} : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
return %1 : tensor<8x4x3xf32>
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<i8:f32, 0.0078431372549019607:-1>>}
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<i8:f32, 0.0078431372549019607:-1>>, volatile}
// CHECK: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]])
// CHECK: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<i8:f32:2, {0.0078431372549019607:-1,0.062745098039215685:-1,0.0039215686274509803:-1}>>}
// CHECK: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<i8:f32:2, {0.0078431372549019607:-1,0.062745098039215685:-1,0.0039215686274509803:-1}>>, volatile}
// CHECK: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]])
// CHECK: return %[[dq2]]
}
@ -71,9 +71,9 @@ func @prepareStatisticsNudge(%arg0: tensor<8x4x3xf32>) -> tensor<8x4x3xf32> {
} : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
return %1 : tensor<8x4x3xf32>
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<i8:f32, 0.0039215686274509803:-128>>}
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<i8:f32, 0.0039215686274509803:-128>>, volatile}
// 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: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<i8:f32:2, {0.0078431372549019607:-1,0.031372549019607843:127,0.0039215686274509803:-1}>>, volatile}
// CHECK: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]])
// CHECK: return %[[dq2]]
}

4
tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
View File

@ -48,9 +48,9 @@ func @prepareStatistics(%arg0: tensor<8x4x3xf32>) -> tensor<8x4x3xf32> {
} : (tensor<8x4x3xf32>) -> tensor<8x4x3xf32>
return %1 : tensor<8x4x3xf32>
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}
// CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) {qtype = tensor<8x4x3x!quant.uniform<u8:f32, 0.0078431372549019607:128>>, volatile}
// CHECK: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]])
// CHECK: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<u8:f32:2, {0.0078431372549019607:128,0.062745098039215685:128,0.0039215686274509803:128}>>}
// CHECK: %[[q2:.*]] = "tfl.quantize"(%[[dq1]]) {qtype = tensor<8x4x3x!quant.uniform<u8:f32:2, {0.0078431372549019607:128,0.062745098039215685:128,0.0039215686274509803:128}>>, volatile}
// CHECK: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]])
// CHECK: return %[[dq2]]
}

6
tensorflow/compiler/mlir/lite/transforms/post_quantize.cc
View File

@ -132,6 +132,12 @@ struct RemoveVolatileOps : public OpRewritePattern<DequantizeOp> {
if (auto q = llvm::dyn_cast_or_null<QuantizeOp>(input_op)) {
if (!q->getAttr(mlir::quant::kVolatileOpAttrName)) return failure();
// Don't remove leading and tailing QDQ for PQT workflow, so the io
// modifying lib can work correctly.
if (!q.input().getDefiningOp()) return failure();
if (op->hasOneUse() && op->user_begin()->isKnownTerminator())
return failure();
op.replaceAllUsesWith(q.input());
return success();
}