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Results 41 - 50 of 55 for 4x16xf32 (5.54 sec)
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tensorflow/compiler/mlir/tensorflow/tests/group_by_dialect.mlir
%one = "glue.constant"() { value = 1: i32 } : () -> i32 %done = "glue.compare" (%one, %one) { predicate = #glue<"compare LTE"> } : (i32, i32) -> i1 %2 = mhlo.constant dense<[[1.1]]> : tensor<1x1xf32> %3 = mhlo.multiply %2, %2 : tensor<1x1xf32> %cst = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32> %0 = "tf.AddV2"(%arg0, %cst) {device = "/device:CPU:0"} : (tensor<f32>, tensor<f32>) -> tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 28 23:43:21 UTC 2022 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/defer_activation_transpose.mlir
func.func @add_with_activation_transpose_rank_two(%arg0: tensor<1x2xf32>) -> tensor<2x1xf32> { %0 = stablehlo.constant dense<2.000000e+00> : tensor<2x1xf32> %1 = stablehlo.transpose %arg0, dims = [1, 0] : (tensor<1x2xf32>) -> tensor<2x1xf32> %2 = stablehlo.add %1, %0 : tensor<2x1xf32> return %2 : tensor<2x1xf32> } // CHECK: %[[TRANSPOSE_0:.+]] = stablehlo.transpose
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 14.6K bytes - Viewed (0) -
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) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/cast_bf16_ops_to_f32.mlir
// CHECK: return %[[identity]] : tensor<1x3x2x2xf32> func.func @cast_bf16_matmul_to_fp32(%arg0: tensor<1x10xf32>) -> (tensor<1x2xf32>) { %cst = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<10x2xbf16>} : () -> tensor<10x2xbf16> %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x10xf32>) -> tensor<1x10xbf16>
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/lite/tests/quantize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/while_to_map_fn.mlir
%outputs_6 = "tf.Identity"(%outputs_2) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>) -> tensor<i32> %outputs_8 = "tf.MatMul"(%arg5, %outputs_4) {device = "/job:localhost/replica:0/task:0/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<?x3xf32>, tensor<3x1xf32>) -> tensor<?x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 23 06:40:22 UTC 2024 - 68.6K 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/transforms/tf_passes.td
```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 Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
%1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32> // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %2 = "tfl.conv_2d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32>
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/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
// CHECK-LABEL: @xla_svd func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) { // CHECK-NOT: XlaSvd %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32> } func.func @identity(%arg0: f32) -> f32 {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (1)