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Results 1 - 6 of 6 for 40x8xf32 (1.53 sec)

  2. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/tpu_cluster_formation.mlir 

    // CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_1:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_2:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_3:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>)
!rtype = tensor<!tf_type.resource<tensor<10x3xf32>>>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 22:03:30 UTC 2024
    - 53.9K bytes
    - Viewed (0)
  3. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir 

        // CHECK-NEXT:  %[[RES:.*]] = "tf.SelectV2"(%[[PRED]], %[[SCALED_GRADIENTS]], %[[SELU_GRAD_VALUE]]) : (tensor<4x8xi1>, tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
    // CHECK-NEXT:  return %[[RES]] : tensor<4x8xf32>
    %2 = "tf.SeluGrad"(%gradients, %features) : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
    func.return %2 : tensor<4x8xf32>
}
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Fri Jan 05 18:35:42 UTC 2024
    - 92K bytes
    - Viewed (0)
  4. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tfrt/tests/mlrt/while_to_map_fn.mlir 

      %3 = "tf.TensorListStack"(%1#3, %cst_0) {device = "/job:localhost/replica:0/task:0/device:CPU:0", num_elements = 2 : i64} : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<1xi32>) -> tensor<2x8xf32>
  return %3, %2 : tensor<2x8xf32>, tensor<2x8xf32>
}


// -----

// Convert a while with multiple tensor array to map_fn
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue Apr 23 06:40:22 UTC 2024
    - 68.6K bytes
    - Viewed (0)
  5. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir 

          %cst_2, %cst_2, %cst_2, %cst_2) {cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", proj_clip = 0.000000e+00 : f32, time_major = false}
    : ( tensor<1x28x28xf32>,
        tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>,
        tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>,
        none, none, none,
        tensor<20xf32>, tensor<20xf32>, tensor<20xf32>, tensor<20xf32>,
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu May 02 09:41:17 UTC 2024
    - 52.6K bytes
    - Viewed (0)
  6. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/tensorflow/tests/tensor_array_ops_decomposition.mlir 

      %size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  // CHECK-NOT: tf.TensorArrayV3
  // CHECK: %[[TA_BUFFER:.*]] = "tf.MlirLocalVarOp"() : () -> tensor<!tf_type.resource<tensor<10x3xf32>>>
  // CHECK: "tf.AssignVariableOp"(%[[TA_BUFFER]]
  // CHECK-NOT: tf.TensorArrayV3
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Mon Oct 30 06:52:55 UTC 2023
    - 49K bytes
    - Viewed (0)
  7. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md 

    a manner will allow subsequent cluster formation pass to handle IR with both
data and model parallelism in an easier manner.

For example, the following:

```mlir
!rtype = type tensor<!tf_type.resource<tensor<10x3xf32>>>
func @data_and_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> !rtype {
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed Aug 02 02:26:39 UTC 2023
    - 96.4K bytes
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