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Results 71 - 80 of 278 for 5xi32 (0.05 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
func.func @QuantizeSlice(tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32> { ^bb0(%arg0: tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>): %0 = "tfl.dequantize"(%arg0) : (tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>) -> tensor<2x3x5xf32> %1 = "tfl.slice"(%0, %arg1, %arg2) : (tensor<2x3x5xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32> func.return %1 : tensor<?x3x5xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 67.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/convert_tpu_model_to_cpu.mlir
%cst_0 = "tf.Const"() {device = "", value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32> %cst_1 = "tf.Const"() {_tpu_replicate = "cluster", device = "", value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32> %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_xla.mlir
// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xf32> // CHECK: return %[[conv_quant]] // CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 25.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
} // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32> // CHECK: [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/composite-lowering.mlir
%14 = "mhlo.broadcast_in_dim"(%1) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32> %15 = mhlo.compare LT, %13, %14, SIGNED : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1> %16 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32> %17 = mhlo.add %13, %16 : tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 32.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/shape-inference.mlir
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> { %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32> // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32> %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32> func.return %0 : tensor<*xi32> } } // ----- // CHECK-LABEL: testReshapeShapeInferenceUnknownDim
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_canonicalize.td
// %0 = "tfl.transpose"(%arg0, %cst) : (tensor<56x8x56x1x1x1x7xf32>, tensor<7xi32>) -> tensor<1x1x8x56x56x7x1xf32> // to- // %0 = "tfl.reshape"(%arg0, %cst) : (tensor<56x8x56x1x1x1x7xf32>, tensor<4xi32>) -> tensor<56x8x56x7xf32> // %1 = "tfl.transpose"(%0, %cst_0) : (tensor<56x8x56x7xf32>, tensor<4xi32>) -> tensor<8x56x56x7xf32> // %2 = "tfl.reshape"(%1, %cst_1) : (tensor<8x56x56x7xf32>, tensor<7xi32>) -> tensor<1x1x8x56x56x7x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 13 20:41:03 UTC 2023 - 2.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/canonicalize.mlir
%0 = "tfr.cast"(%arg0) : (tensor<*xf32>) -> !tfr.tensor %1 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<*xi32> %2 = "tfr.cast"(%0) : (!tfr.tensor) -> tensor<2xi32> func.return %1, %2 : tensor<*xi32>, tensor<2xi32> // CHECK: %[[tf_cast_unranked:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<*xf32>) -> tensor<*xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
%0 = "tfl.pseudo_const"() {value = dense<[1, 4, 384, 32]> : tensor<4xi32>} : () -> tensor<4xi32> // CHECK-NOT: tac.device tac.inference_type %1 = "tfl.pseudo_const"() {value = dense<[4, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32> // CHECK: tac.device = "CPU", tac.inference_type = "QUANTIZED_INT8" %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>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
```mlir %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32> %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32> %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32> %5 = "tf.D"(%4) : (tensor<?xi32>) -> tensor<?xi32> ``` After the pass, we will have: ```mlir %0 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32> %1 = "tf_device.launch"() ( {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0)