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Trying to fix unidirectional_sequence_lstm rank in prepare-quantize-post-training.mlir

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The unidirectional_sequence_lstm should have input/output as rank3 and cell_state/output_state as rank2

PiperOrigin-RevId: 358955726
Change-Id: I21513b7dd18d3c424228d180f738a7061694ed8d
This commit is contained in:
Renjie Liu 2021-02-22 19:29:54 -08:00 committed by TensorFlower Gardener
parent 439eb0899f
commit b64965ce3d
1 changed files with 8 additions and 8 deletions
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16
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
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@ -38,9 +38,9 @@ func @QuantizeLstmCellInput(%arg0: tensor<1x28x28xf32>) -> tensor<1x28x20xf32> {
}
// CHECK-LABEL: QuantizeWithoutNorm
func @QuantizeWithoutNorm(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
func @QuantizeWithoutNorm(%arg0: tensor<1x1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
%none = constant unit
%input = "quant.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x5xf32>) -> tensor<1x5xf32>
%input = "quant.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x1x5xf32>) -> tensor<1x1x5xf32>
%0 = "tfl.pseudo_const"() {value = dense<[[1.31760073, -0.78338623, 0.287265539, -0.383972764, -0.00321021513], [0.104248755, 1.07823908, 0.138089031, 0.76123321, -1.4124943]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
%1 = "tfl.pseudo_const"() {value = dense<[[2.32939887, -0.623641372, -0.0191893689, 0.326861918, 0.734137893], [0.499284297, 1.25277913, 0.60228157, -1.39478016, 0.115529917]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
%2 = "tfl.pseudo_const"() {value = dense<[[0.839470446, 0.564852297, -0.80136007, -0.0372898243, 0.57127893], [-5.516230e-01, -1.082380e+00, 1.41860521, -0.92541927, -1.13971734]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
@ -77,7 +77,7 @@ func @QuantizeWithoutNorm(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {t
effective_hidden_scale_intermediate = tensor<!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>,
fused_activation_function = "TANH",
proj_clip = 0.000000e+00 : f32, time_major = false} : (
tensor<1x5xf32>,
tensor<1x1x5xf32>,
tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>,
tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>,
tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
@ -88,7 +88,7 @@ func @QuantizeWithoutNorm(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {t
%24 = "quant.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
return %24 : tensor<*xf32>
// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x5xf32>
// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x1x5xf32>
// CHECK-DAG: %[[input_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011122002376346137>>) -> tensor<2x5xf32>
// CHECK-DAG: %[[input_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.018341723389512912>>) -> tensor<2x5xf32>
// CHECK-DAG: %[[input_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011170119751156785>>) -> tensor<2x5xf32>
@ -199,8 +199,8 @@ func @QuantizeLstmCifg(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {tf.e
}
// CHECK-LABEL: QuantizeUnidirectionalLstmFull
func @QuantizeUnidirectionalLstmFull(%arg0: tensor<1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
%input = "quant.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x5xf32>) -> tensor<1x5xf32>
func @QuantizeUnidirectionalLstmFull(%arg0: tensor<1x1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
%input = "quant.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x1x5xf32>) -> tensor<1x1x5xf32>
%0 = "tfl.pseudo_const"() {value = dense<[[1.31760073, -0.78338623, 0.287265539, -0.383972764, -0.00321021513], [0.104248755, 1.07823908, 0.138089031, 0.76123321, -1.4124943]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
%1 = "tfl.pseudo_const"() {value = dense<[[2.32939887, -0.623641372, -0.0191893689, 0.326861918, 0.734137893], [0.499284297, 1.25277913, 0.60228157, -1.39478016, 0.115529917]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
%2 = "tfl.pseudo_const"() {value = dense<[[0.839470446, 0.564852297, -0.80136007, -0.0372898243, 0.57127893], [-5.516230e-01, -1.082380e+00, 1.41860521, -0.92541927, -1.13971734]]> : tensor<2x5xf32>} : () -> tensor<2x5xf32>
@ -241,7 +241,7 @@ func @QuantizeUnidirectionalLstmFull(%arg0: tensor<1x5xf32>) -> tensor<*xf32> at
input_to_input_intermediate = tensor<!quant.calibrated<f32<-3.200000e+01:3.200000e+01>>>,
input_to_output_intermediate = tensor<!quant.calibrated<f32<-1.000000e+00:1.000000e+00>>>,
proj_clip = 0.000000e+00 : f32, time_major = false} : (
tensor<1x5xf32>,
tensor<1x1x5xf32>,
tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>,
tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>,
tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
@ -252,7 +252,7 @@ func @QuantizeUnidirectionalLstmFull(%arg0: tensor<1x5xf32>) -> tensor<*xf32> at
%24 = "quant.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
return %24 : tensor<*xf32>
// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x5xf32>
// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x1x5xf32>
// CHECK-DAG: %[[input_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011122002376346137>>) -> tensor<2x5xf32>
// CHECK-DAG: %[[input_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.018341723389512912>>) -> tensor<2x5xf32>
// CHECK-DAG: %[[input_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011170119751156785>>) -> tensor<2x5xf32>