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Fix shape for conv2d in post-quantize.mlir

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PiperOrigin-RevId: 358603186
Change-Id: I53afbd10a16a5d22784d4fb9c128213494881bd6
This commit is contained in:
Renjie Liu 2021-02-20 12:49:41 -08:00 committed by TensorFlower Gardener
parent 8eb1ba5d83
commit a7e6b1b379
1 changed files with 2 additions and 2 deletions
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tensorflow/compiler/mlir/lite/tests/post-quantize.mlir
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@ -34,7 +34,7 @@ func @main(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> {
%0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>> %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>
%1 = "tfl.pseudo_qconst"() {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>} : () -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>> %1 = "tfl.pseudo_qconst"() {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>} : () -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>
%2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>> %2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>
%3 = "tfl.conv_2d"(%0, %1, %2) {dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>, tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>> %3 = "tfl.conv_2d"(%0, %1, %2) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>, tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
%4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>> %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>
%5 = "tfl.softmax"(%4) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>> %5 = "tfl.softmax"(%4) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>
%6 = "tfl.dequantize"(%5) : (tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>) -> tensor<1x401408xf32> %6 = "tfl.dequantize"(%5) : (tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>) -> tensor<1x401408xf32>
@ -53,7 +53,7 @@ func @main2(%arg0: tensor<2x4xf32>, %arg1: tensor<2x4xf32>) -> tensor<2x4xf32> {
// CHECK-NEXT: %[[cst:.*]] = constant dense<[1, 401408]> : tensor<2xi32> // CHECK-NEXT: %[[cst:.*]] = constant dense<[1, 401408]> : tensor<2xi32>
// CHECK-NEXT: %[[q_cst_0:.*]] = "tfl.pseudo_qconst"() {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>} // CHECK-NEXT: %[[q_cst_0:.*]] = "tfl.pseudo_qconst"() {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>}
// CHECK-NEXT: %[[q_cst_1:.*]] = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} // CHECK-NEXT: %[[q_cst_1:.*]] = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>}
// CHECK-NEXT: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[q_cst_0]], %[[q_cst_1]]) {dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32} // CHECK-NEXT: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[q_cst_0]], %[[q_cst_1]]) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}
// CHECK-NEXT: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[cst]]) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) // CHECK-NEXT: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[cst]]) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>)
// CHECK-NEXT: %[[softmax:.*]] = "tfl.softmax"(%[[reshape]]) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>) // CHECK-NEXT: %[[softmax:.*]] = "tfl.softmax"(%[[reshape]]) {beta = 1.000000e+00 : f32} : (tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>)
// CHECK-NEXT: return %[[softmax]] : tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>> // CHECK-NEXT: return %[[softmax]] : tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>