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Results 1 - 8 of 8 for 1x3x3x3xf32 (0.36 sec)

  1. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions_weight_only.mlir

        return %1 : tensor<1x3x4x2xf32>
      }
    
      func.func private @composite_conv_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32> attributes {_from_xla_call_module} {
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 09 05:56:10 UTC 2024
    - 9.4K bytes
    - Viewed (0)
  2. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/optimize_graph.mlir

      func.return %dequant : tensor<1x3x4x2xf32>
    }
    
    // -----
    
    // CHECK-LABEL: @dont_merge_quantization_followed_by_quantization
    // CHECK-SAME: %[[ARG_0:.*]]: tensor<1x3x4x3xf32>
    func.func @dont_merge_quantization_followed_by_quantization(%arg0: tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32> {
      // CHECK: %[[QUANT_ARG_0:.*]] = stablehlo.uniform_quantize %[[ARG_0]]
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Feb 08 22:40:14 UTC 2024
    - 2.6K bytes
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  3. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir

        %2 = stablehlo.convolution(%1, %0)
          dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
          window = {
            stride = [1, 1], pad = [[0, 0], [1, 1]],
            lhs_dilate = [1, 1],
            rhs_dilate = [1, 1]
          }
          {
            batch_group_count = 1 : i64,
            feature_group_count = 1 : i64
          } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>)
          -> tensor<1x2x2x2xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue Mar 26 07:48:15 UTC 2024
    - 8.6K bytes
    - Viewed (0)
  4. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_weight_only.mlir

        return %2 : tensor<1x3x4x2xf32>
      }
    
      func.func private @composite_conv_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32> attributes {_from_xla_call_module} {
        %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue May 14 17:10:32 UTC 2024
    - 4.8K bytes
    - Viewed (0)
  5. tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/insert_calibration_statistics_saver_with_skipping.mlir

      %output, %min, %max, %histogram = "tf.CustomAggregator"(%arg0) <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue May 14 06:31:57 UTC 2024
    - 6.3K bytes
    - Viewed (0)
  6. tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir

    func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
      %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
      %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>
    
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed May 08 19:32:28 UTC 2024
    - 6.4K bytes
    - Viewed (0)
  7. tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/import_json.json

    // CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>
    
    {
      "version": 3,
      "operator_codes": [
        {
          "builtin_code": "CONV_2D"
        }
      ],
      "subgraphs": [
        {
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 1.8K bytes
    - Viewed (0)
  8. tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/optional_input.json

    // CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>
    
    {
      "version": 3,
      "operator_codes": [
        {
          "builtin_code": "CONV_2D"
        }
      ],
      "subgraphs": [
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 1.8K bytes
    - Viewed (0)
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