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Results 81 - 90 of 99 for 1x37xf32 (0.17 sec)
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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/vhlo.mlir
"vhlo.return_v1" (%421) : (tensor<1xf32>) -> () }) : (tensor<1x16x16x320xf32>, tensor<f32>) -> tensor<1x320xf32> return %0 : tensor<1x320xf32> } //CHECK:func.func private @reduce(%arg0: tensor<1x16x16x320xf32>, %arg1: tensor<f32>) -> tensor<1x320xf32> { //CHECK-NEXT: %0 = "vhlo.reduce_v1"(%arg0, %arg1) <{dimensions = #vhlo.tensor_v1<dense<[1, 2]> : tensor<2xi64>>}> ({
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 14 19:15:40 UTC 2024 - 31.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/canonicalize.mlir
%1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32> %2 = "tf.TensorListGetItem"(%0, %arg3, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 132.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
%8 = "tfl.concatenation"(%2, %0) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x1xf32>, tensor<1x1xf32>) -> tensor<1x2xf32> %9 = "quantfork.stats"(%8) {layerStats = dense<[-0.488159984, 0.189515018]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32> %10 = "tfl.concatenation"(%9, %7) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<1x4xf32>
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/cast_bf16_ops_to_f32.mlir
%2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x2xbf16>) -> tensor<1x2xf32> %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x2xf32>) -> tensor<1x2xf32> return %3 : tensor<1x2xf32> } // CHECK: func @cast_bf16_matmul_to_fp32 // CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32> // CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/fuse_tpu_compile_and_execute_ops.mlir
%1 = "tf.ReadVariableOp"(%0) {device = "/CPU:0"} : (tensor<!tf_type.resource<tensor<1x1xf32>>>) -> tensor<1x1xf32> %2:2 = "tf.Split"(%cst, %arg0) {device = "/CPU:0"} : (tensor<i32>, tensor<1x4xf32>) -> (tensor<1x2xf32>, tensor<1x2xf32>) %3:2 = "tf.Split"(%cst, %2#0) {device = "/CPU:0"} : (tensor<i32>, tensor<1x2xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
func.func private @batched_function(%arg0: tensor<1x3xf32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> { %0 = "tf.Identity"(%arg0) : (tensor<1x3xf32>) -> tensor<1x3xf32> func.return %0 : tensor<1x3xf32> } // ----- func.func @test_batch_function_with_invalid_symbol(%arg0: tensor<1x3xf32>, %arg1: tensor<!tf_type.resource<tensor<1x3xf32>>>) -> () {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
For example, if we have the code ```mlir %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> ``` then running this pass with 'default-device=foobar', we get: ```mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<1x3xi32>) -> tensor<1x3xf32> %11 = "tf.Mul"(%10, %cst) {device = ""} : (tensor<1x3xf32>, tensor<f32>) -> tensor<1x3xf32> %12 = "tf.Relu"(%11) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32> return %12 : tensor<1x3xf32> } // CHECK-LABEL: func @matmul_with_relu
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/optimize.mlir
%0 = "mhlo.reshape"(%arg0) : (tensor<1x1x512xf32>) -> tensor<1x512xf32> %1 = "mhlo.dot"(%0, %arg1) : (tensor<1x512xf32>, tensor<512x13x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x13xf32> %2 = "mhlo.reshape"(%1) : (tensor<1x13xf32>) -> tensor<1x1x13xf32> func.return %2 : tensor<1x1x13xf32> // CHECK: %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{ // CHECK-SAME: dot_dimension_numbers = #mhlo.dot<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 22.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/einsum.mlir
// CHECK: return %[[v3:.*]] : tensor<5x7xf32> } func.func @einsum_transpose_matmul(%arg0: tensor<2x5x7xf32>, %arg1: tensor<5x3x2xf32>) -> tensor<5x3x7xf32> { %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "lbh,bkl->bkh"}: (tensor<2x5x7xf32>, tensor<5x3x2xf32>) -> tensor<5x3x7xf32> func.return %0 : tensor<5x3x7xf32> // CHECK-LABEL: einsum_transpose_matmul
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 25.9K bytes - Viewed (0)