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Results 61 - 70 of 166 for mat_mul (0.15 sec)
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tensorflow/compiler/mlir/tfrt/tests/saved_model/testdata/test.mlir
tf_saved_model.exported_names = ["serving_default"] } { %0 = "tf.ReadVariableOp"(%arg1) {device = ""} : (tensor<!tf_type.resource<tensor<3x1xi32>>>) -> tensor<3x1xi32> %1 = "tf.MatMul"(%arg0, %0) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x3xi32>, tensor<3x1xi32>) -> tensor<1x1xi32> func.return %1 : tensor<1x1xi32> } func.func @predict(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 11:03:04 UTC 2022 - 1.6K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_test.cc
EXPECT_EQ(*result_value, 4.0); TF_DeleteTensor(result_tensor); TF_DeleteAbstractTensor(result); TF_DeleteOutputList(o); TF_DeleteExecutionContext(ctx); } // MatMul Test TEST_P(UnifiedCAPI, TestBasicEagerMatMul) { std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status( TF_NewStatus(), TF_DeleteStatus); TFE_ContextOptions* opts = TFE_NewContextOptions();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 21:44:52 UTC 2023 - 39.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/runtime_fallback/runtime_fallback_ops.td
TFRT attributes are sorted alphabetically, passed in as positional attributes to the TFRT kernel, rather than as named attributes. Example: To run "tf.MatMul" op, which has two boolean attributes, 1. Set _name = "MatMul" 2. For each TF attribute, split it into two attributes, one for name of the TF attribute, and the other for the type and value of the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 23 19:35:12 UTC 2023 - 5.9K bytes - Viewed (0) -
tensorflow/c/experimental/ops/update_cpp_ops.sh
${generate} \ --category=array \ Identity \ IdentityN \ ZerosLike \ Shape \ ExpandDims \ OnesLike ${generate} \ --category=math \ Mul \ Conj \ AddV2 \ MatMul \ Neg \ Sum \ Sub \ Div \ DivNoNan \ Exp \ Sqrt \ SqrtGrad \ Log1p ${generate} \ --category=nn \ SparseSoftmaxCrossEntropyWithLogits \ ReluGrad \ Relu \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 17 17:54:34 UTC 2022 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/fused_kernel_matcher.mlir
%3 = "tf.Identity"(%2) : (tensor<*xf32>) -> tensor<*xf32> func.return %3 : tensor<*xf32> } //===----------------------------------------------------------------------===// // MatMul + BiasAdd + <Activation> fusions. //===----------------------------------------------------------------------===// // CHECK-LABEL: matmulBiasAdd
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
).astype(np.float32) class TwoMatmulModel(module.Module): """A model with two matmul ops.""" @def_function.function def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]: """Performs a matrix multiplication. Args: input_tensor: Input tensor to matmul with the filter. Returns: A 'output' -> output tensor mapping """
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/device_conversion.mlir
%arg1: tensor<1x3xf32> {tf_saved_model.index_path = [0]}) -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) { // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0" %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 645 bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
@def_function.function def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]: """Performs a matrix multiplication. Depending on self.has_bias and self.activation_fn, it may add a bias term or go through the activaction function. Args: input_tensor: Input tensor to matmul with the filter. Returns:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/compile_mlir_util/shape-inference.mlir
module attributes {tf.versions = {producer = 179 : i32}} { func.func @main(%arg0: tensor<*xf32>, %arg1: tensor<?x19xf32>) -> tensor<?x19xf32> { %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<?x19xf32>) -> tensor<?x19xf32> func.return %0 : tensor<?x19xf32> } } // CHECK-LABEL: HloModule main
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 23 18:56:13 UTC 2022 - 969 bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/attributes.mlir
// CHECK: {{%.*}} = tfrt_fallback_async.executeop {{.*}} device("/device:CPU:0") "tf.MatMul" // CHECK-SAME: {T = f32, transpose_a = false, transpose_b = false}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 4.8K bytes - Viewed (0)