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Results 1 - 10 of 23 for 3x64xf32 (0.2 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/replace_stablehlo_ops_in_main_function_with_xla_call_module_ops.mlir
%0 = stablehlo.constant dense<0.000000e+00> : tensor<1x64xf32> %1 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x3xf32>, tensor<3x64xf32>) -> tensor<1x64xf32> %2 = stablehlo.add %1, %arg2 : tensor<1x64xf32> %3 = stablehlo.maximum %2, %0 : tensor<1x64xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 39.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
%0 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<2xi32>) -> tensor<16x4xf32> %1 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32> %2 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32> %3 = arith.addf %1, %2 : tensor<64xf32> func.return %3 : tensor<64xf32> // CHECK-LABEL: func @reshape_removeAdjacentWithMultipleUse
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x5xf32>, tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>, tensor<2x4xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<4x2xf32>, tensor<4xf32>, tensor<1x4xf32>, tensor<1x2xf32>, none, none, none, none) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
%4 = "tf.MatMul"(%arg0, %3) {device = "", transpose_a = false, transpose_b = false} : (tensor<2x3xf32>, tensor<3x4xf32>) -> tensor<2x4xf32> %5 = "tf.Identity"(%4) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> %6 = "tf.Identity"(%5) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> func.return %6 : tensor<2x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize_jax_random.mlir
func.func @tfl_wrapped_jax_random_normal(%arg0: tensor<2xui32>) -> tuple<tensor<3x4xf32>> { // This is a fake jax random normal body. %0 = stablehlo.constant dense<0.0> : tensor<12xf32> %1 = "stablehlo.reshape"(%0) : (tensor<12xf32>) -> tensor<3x4xf32> %2 = "stablehlo.tuple"(%1) : (tensor<3x4xf32>) -> tuple<tensor<3x4xf32>> func.return %2 : tuple<tensor<3x4xf32>> } // CHECK-LABEL: func @tfl_wrapped_jax_random_uniform(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
func.func @concat_v2_1d_axis(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<3x6xf32> { // CHECK: "mhlo.concatenate"({{.*}}) <{dimension = 1 : i64}> : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x6xf32> %axis = "tf.Const"() { value = dense<[1]> : tensor<1xi64> } : () -> tensor<1xi64> %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<1xi64>) -> tensor<3x6xf32> func.return %1 : tensor<3x6xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%cst = arith.constant dense<[3, 4]> : tensor<2xi32> %cst_0 = arith.constant dense<1.000000e+00> : tensor<3x4xf32> %0 = "tfl.reshape"(%arg0, %cst) : (tensor<*xf32>, tensor<2xi32>) -> tensor<3x4xf32> %1 = "tfl.add"(%0, %cst_0) {fused_activation_function = "NONE"} : (tensor<3x4xf32>, tensor<3x4xf32>) -> tensor<3x4xf32> func.return %1 : tensor<3x4xf32> // CHECK-LABEL: ReshapeAddUnknownShape
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
%4 = "tfl.concatenation"(%1, %3) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<2x1xf32>, tensor<2x3xf32>) -> tensor<2x4xf32> %5 = "tfl.add"(%4, %arg2) {fused_activation_function = "NONE"} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32> func.return %5: tensor<2x4xf32> // MixedPrecision-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0) // MixedPrecision-NEXT: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
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/lite/tests/legalize-tf.mlir
func.func @cumsum(%arg0: tensor<3x3xf32>, %arg1: tensor<i32>) -> tensor<3x3xf32> { %0 = "tf.Cumsum"(%arg0, %arg1) {exclusive = false, reverse = false} : (tensor<3x3xf32>, tensor<i32>) -> tensor<3x3xf32> func.return %0 : tensor<3x3xf32> // CHECK-LABEL: cumsum // CHECK: "tfl.cumsum"(%arg0, %arg1) <{exclusive = false, reverse = false}> : (tensor<3x3xf32>, tensor<i32>) -> tensor<3x3xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0)