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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/optimize_graph.mlir

  %requant = stablehlo.uniform_quantize %conv : (tensor<1x3x4x2x!quant.uniform<i32:f32, 5.8949912267181218E-5>>) -> tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir

  %1 = "tfl.quantize"(%arg0) {qtype = tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>} : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>) -> tensor<2x2xf32>
  func.return %2 : tensor<2x2xf32>

// CHECK-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00>>}> : (tensor<2x2xf32>)

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.mlir

tensor<2048x!quant.uniform<i32:f32, 1.523437447303877E-7>>, %arg13: tensor<640x2048x!quant.uniform<i8<-127:127>:f32, 0.021174000576138496>>, %arg14: tensor<640x!quant.uniform<i32:f32, 1.601389680352559E-4>>, %arg15: tensor<2048x!quant.uniform<i16:f32, 4.3700000969693065E-4>>, %arg16: tensor<2048x!quant.uniform<i16:f32, 1.1000000085914508E-4>>, %arg17: tensor<2048x!quant.uniform<i16:f32, 1.6799999866634607E-4>>, %arg18: tensor<2048x!quant.uniform<i16:f32, 1.55999994603917E-4>>, %arg19: tensor<1x640x!quant.uniform<i8:f32,...

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td

    : QuantizedType<"Uniform",
                        [8, zero_pt, smantissa, sexp, -128, 127], 1>;

// General uniform quantized types. The definitions can be used to specify
// operand's tensor types.
def QI4 : QuantizedType<"Uniform", [4], 1>;
def QUI8 : QuantizedType<"Uniform", [8], 0>;
def QI8 : QuantizedType<"Uniform", [8], 1>;
def QUI16 : QuantizedType<"Uniform", [16], 0>;

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir

  %5 = "tfl.add"(%4, %arg3) {tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8", fused_activation_function = "NONE"} : (tensor<1x384x128x!quant.uniform<i8:f32, 0.3:3>>, tensor<128x!quant.uniform<i8:f32, 0.2:-4>>) -> tensor<1x384x128x!quant.uniform<i8:f32, 0.3:-3>>
  func.return %5 : tensor<1x384x128x!quant.uniform<i8:f32, 0.3:-3>>
}

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/post-quantize-dynamic-range.mlir

  %q_w = "tfl.pseudo_qconst"() {qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>} : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%q_w) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>

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tensorflow/compiler/mlir/lite/tests/ops.mlir

tensor<2048x!quant.uniform<i32:f32, 1.523437447303877E-7>>, %arg13: tensor<640x2048x!quant.uniform<i8<-127:127>:f32, 0.021174000576138496>>, %arg14: tensor<640x!quant.uniform<i32:f32, 1.601389680352559E-4>>, %arg15: tensor<2048x!quant.uniform<i16:f32, 4.3700000969693065E-4>>, %arg16: tensor<2048x!quant.uniform<i16:f32, 1.1000000085914508E-4>>, %arg17: tensor<2048x!quant.uniform<i16:f32, 1.6799999866634607E-4>>, %arg18: tensor<2048x!quant.uniform<i16:f32, 1.55999994603917E-4>>, %arg19: tensor<1x640x!quant.uniform<i8:f32,...

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tfr/tests/rewrite_quantized_io.mlir

func.func @remove_quantized_io(
  %arg0: tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>,
  %arg1: tensor<1x5xf32>) -> (tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>) {
  %0 = "tf.MyRequantize"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>
  %1 = "tf.Intermediate"(%arg1) : (tensor<1x5xf32>) -> tensor<1x5xf32>
  func.return %0, %1 : tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>

Registered: Sun Jun 16 05:45:23 UTC 2024

- Last Modified: Mon Oct 30 06:52:55 UTC 2023

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize.mlir

  // CHECK-SAME: quant.uniform<i8:f32, 0.023529411764705882:-128>
  // CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])
  // CHECK-SAME: quant.uniform<i8:f32, 0.023529411764705882:-128>
  // CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[cst3]])
  // CHECK-SAME: quant.uniform<i8:f32, 3.9215686274509805E-9>
  // CHECK: %[[dq3:.*]] = "quantfork.dcast"(%[[q3]])
  // CHECK-SAME: quant.uniform<i8:f32, 3.9215686274509805E-9>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir

  %2 = "tfl.reshape"(%arg0, %0) : (tensor<4x384x32x!quant.uniform<i8:f32, 0.2:-3>>, tensor<4xi32>) -> tensor<1x4x384x32x!quant.uniform<i8:f32, 0.2:-3>>
  // CHECK-NOT: tac.device tac.inference_type
  %3 = "tfl.quantize"(%2) {qtype = tensor<1x4x384x32x!quant.uniform<i8:f32, 0.19:1>>} : (tensor<1x4x384x32x!quant.uniform<i8:f32, 0.2:-3>>) -> tensor<1x4x384x32x!quant.uniform<i8:f32, 0.19:1>>

Registered: Sun Jun 16 05:45:23 UTC 2024

- Last Modified: Fri May 19 19:32:06 UTC 2023

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