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Results 11 - 20 of 22 for 2x512xf32 (0.13 sec)
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tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
// CHECK-LABEL: hardcode_all func.func @hardcode_all(%arg0: tensor<2x2xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x2xf32> { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function="NONE"}: (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32> // CHECK: %[[q0:.*]] = "tfl.quantize"(%arg1) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir
} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> %2 = "quantfork.stats"(%1) {layerStats = dense<[0.000000e+00, 6.000000e+00]> : tensor<2xf32>} : (tensor<2x2xf32>) -> tensor<2x2xf32> return %2 : tensor<2x2xf32> } // CHECK-LABEL: composite_dot_general func.func private @composite_dot_general(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 26 07:48:15 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/reshape.mlir
// Confirm we can extract type info from reshape func.func @main() -> tensor<2x2xf32> { // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32> %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 730 bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops_large_constants.mlir
%6 = "tf.Mul"(%arg2, %arg4) : (tensor<f32>, tensor<512xf32>) -> tensor<512xf32> %7 = "tf.Div"(%6, %arg6) : (tensor<512xf32>, tensor<f32>) -> tensor<512xf32> %8 = "tf.Cast"(%5) {Truncate = false} : (tensor<1x2240x1120x512xi32>) -> tensor<1x2240x1120x512xf32> %9 = "tf.Mul"(%7, %8) : (tensor<512xf32>, tensor<1x2240x1120x512xf32>) -> tensor<1x2240x1120x512xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
} // CHECK-LABEL: pack func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/convert_func_to_bfloat16.mlir
func.func @constant_f32() -> tensor<2x2xf32> { // CHECK-NOT: f32 // CHECK{LITERAL}: stablehlo.constant dense<[[1.398440e+00, 0.000000e+00], [3.093750e+00, -2.001950e-01]]> : tensor<2x2xbf16> %0 = stablehlo.constant dense<[[1.4, 0.0], [3.1, -0.2]]> : tensor<2x2xf32> return %0 : tensor<2x2xf32> } // ----- func.func @constant_elided() -> tensor<2x2xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_update_embedding_enqueue_op_inputs.mlir
%2 = "tf.Const"() {value = dense<0.0> : tensor<2x2xf32>} : () -> tensor<2x2xf32> %3 = "tf.Const"() {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32> "tf.SendTPUEmbeddingGradients"(%2, %3) {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D", operandSegmentSizes = array<i32: 2, 0>} : (tensor<2x2xf32>, tensor<4x4xf32>) -> ()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/legalize_tf_quant_test.cc
constexpr char mlir_module_string[] = R"mlir( module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} { func.func @main(%arg0 : tensor<2x2xf32>) -> tensor<2x2xf32> { %max = "tf.Const"() { value = dense<12.0> : tensor<f32> } : () -> tensor<f32> %min = "tf.Const"() { value = dense<-25.0> : tensor<f32> } : () -> tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 29 18:43:55 UTC 2024 - 7.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/execution_metadata_exporter_test.cc
%3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %3 : tensor<2x1xf32> })"; const std::string kExpectedFB = CreateRuntimeMetadata(); mlir::DialectRegistry registry; registry.insert<mlir::TFL::TensorFlowLiteDialect, mlir::arith::ArithDialect,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 06:11:34 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/legacy_reshape.json
// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32> { "version": 3, "operator_codes": [ { "builtin_code": "RESHAPE" } ], "subgraphs": [ { "tensors": [ { "shape": [1, 4],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 986 bytes - Viewed (0)