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Results 11 - 20 of 25 for 3x2x6x4xf32 (0.12 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> { %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32> %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32> func.return %1: tensor<1x3x3x4xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-include-tf2xla-fallback.mlir
// fallback lowering is preferred for static shaped operands when available. // CHECK-LABEL: batchmatmulv2 func.func @batchmatmulv2(%arg0: tensor<1x4x2xf32>, %arg1: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> { // NO_FALLBACK: mhlo.dynamic_broadcast_in_dim // NO_FALLBACK: mhlo.dot_general // SUPPORTED_FALLBACK_DEVICE: mhlo.reduce // SUPPORTED_FALLBACK_DEVICE: mhlo.dot_general
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 16 19:04:03 UTC 2023 - 3.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_tf_graph_test.cc
func.func @main() -> (tensor<32x64xf32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"}) { %cst = "tf.Const"() {value = dense<[524170, 523952]> : tensor<2xi32>} : () -> tensor<2xi32> %cst_0 = "tf.Const"() {value = dense<[32, 64]> : tensor<2xi32>} : () -> tensor<2xi32> %0 = "tf.StatelessRandomNormal"(%cst_0, %cst) : (tensor<2xi32>, tensor<2xi32>) -> tensor<32x64xf32> return %0 : tensor<32x64xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 08:08:57 UTC 2024 - 11.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/compose-uniform-quantized-type.mlir
%9 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32> %10 = stablehlo.convolution(%8, %9) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32> %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 37K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/tfl_legalize_hlo.mlir
>, precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>] } : (tensor<3x2x6x5x1xf32>, tensor<3x2x4x6xf32>) -> tensor<3x5x1x4xf32> func.return %0 : tensor<3x5x1x4xf32> // CHECK-LABEL: convert_dot_general // CHECK: %[[TRANSPOSED_0:.*]] = "tfl.transpose" // CHECK: %[[TRANSPOSED_1:.*]] = "tfl.transpose" // CHECK-NEXT: %[[RESHAPED_0:.*]] = mhlo.reshape %[[TRANSPOSED_0]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 40.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/defer_activation_transpose.mlir
func.func @add_with_activation_transpose_permutation_mismatch( %arg0: tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32> { %0 = stablehlo.constant dense<2.000000e+00> : tensor<1x3x2x4xf32> %1 = stablehlo.transpose %arg0, dims = [0, 2, 1, 3] : (tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32> %2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32> return %2 : tensor<1x3x2x4xf32> }
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/tests/prepare-tf.mlir
} func.func @depthwiseConv2D(tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x30x30x12xf32>, tensor<256x12x30x30xf32>, tensor<256x30x30x12xf32>, tensor<256x30x30x12xf32>) { ^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>, %arg2: tensor<256x3x32x32xf32>) : // OK
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/prepare-tf-fake-quant-4bit.mlir
%fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 4, narrow_range = true} : (tensor<3x3x3x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<3x3x3x4xf32> %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x8x7x4xf32> func.return %rst : tensor<256x8x7x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
// CHECK: return } func.func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> { %0 = "tf.ReverseV2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32> func.return %0 : tensor<1x2x3x4xf32> // CHECK-LABEL:reverse_v2 // CHECK: "tfl.reverse_v2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32> // CHECK: return }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
} // ----- // CHECK-LABEL: testReverseV2 func.func @testReverseV2(%arg0: tensor<1x2x3x4xf32>, %arg1 : tensor<2xi32>) -> tensor<1x2x3x4xf32> { // CHECK: "tfl.reverse_v2"(%arg0, %arg1) %0 = "tfl.reverse_v2"(%arg0, %arg1): (tensor<1x2x3x4xf32>, tensor<2xi32>) -> tensor<1x2x3x4xf32> func.return %0 : tensor<1x2x3x4xf32> } // ----- // test select // CHECK-LABEL: testSelect
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0)