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Results 31 - 40 of 42 for 8x10xf32 (0.16 sec)
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tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-prefer-tf2xla.mlir
// NOFALLBACK-LABEL: @xla_svd func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) { // NOFALLBACK: 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> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 15.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_space_to_depth_pass.mlir
%1:2 = "tf.IteratorGetNext"(%arg4) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> (tensor<2x224x224x3xf32>, tensor<2x1xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 37.4K 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/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/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/lite/experimental/tac/tests/target-annotation.mlir
%2 = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "CPU", tac.inference_type = "FLOAT" %3 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return } func.func @notAnnotateConst(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> { // CHECK-NOT: tac.device tac.inference_type
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/lite/tests/ops.mlir
%split_dim = arith.constant dense<0> : tensor<i32> // expected-error @+1 {{'tfl.split' op output #0 should be 'tensor<8x4xf32>'}} %0, %1 = "tfl.split"(%split_dim, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<16x4xf32>) -> (tensor<8x2xf32>, tensor<8x2xf32>) func.return %0, %1 : tensor<8x2xf32>, tensor<8x2xf32> } // -----
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/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/lite/tests/prepare-tf.mlir
%2 = "tf.Transpose"(%1, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32> func.return %2 : tensor<2x1xf32> // CHECK: %cst = arith.constant // CHECK: %[[trans:.*]] = "tf.Transpose" // CHECK-SAME: -> tensor<2x1xf32> // CHECK: %[[q:.*]] = "tfl.quantize"(%[[trans]]) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 1.000000e+00>>}>
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/tensorflow/tests/shape_inference.mlir
}) {is_stateless = false} : (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>) -> (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>) %elem_1 = "tf._SomeOtherOp"() : () -> tensor<8x1xf32> %tl_set_item = "tf.TensorListSetItem"(%while#1, %one, %elem_1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<8x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>> func.return }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0)