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Results 11 - 20 of 166 for mat_mul (0.13 sec)
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tensorflow/compiler/aot/tests/tfcompile_test.cc
matmul.arg0(1, 0) = 4; matmul.arg0(1, 1) = 5; matmul.arg0(1, 2) = 6; matmul.arg1(0, 0) = 7; matmul.arg1(0, 1) = 8; matmul.arg1(1, 0) = 9; matmul.arg1(1, 1) = 10; matmul.arg1(2, 0) = 11; matmul.arg1(2, 1) = 12; EXPECT_TRUE(matmul.Run()); EXPECT_EQ(matmul.error_msg(), ""); const float results[4] = {58, 64, 139, 154}; for (int i = 0; i < 4; ++i) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 26.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/device_copy.mlir
func.func @fold_identity_n_test(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> (tensor<2x2xf32>, tensor<2x2xf32>) { // CHECK: tf.MatMul %outputs = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> %outputs_0 = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 5.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.td
(IsInt8ElementType $weight), (IsConstTensor $weight), (IsInt32ElementType $matmul), (HasStaticShapeConstraint $weight)], [], (addBenefit 10)>; // Convert Matmul with hybrid inputs (f32 activation/int8 weight) to XlaDotV2 def ConvertTFMatMulToXLADotV2OpWeightOnly : Pat< (TF_MatMulOp:$matmul $input, (TF_MulOp (TF_CastOp (TF_IdentityOp $weight), $truncate1), $scale),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Dec 10 05:52:02 UTC 2023 - 21.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/back2back_fake_quant.pbtxt
input: "sequential/quant_dense/MatMul/kquant/FakeQuantWithMinMaxVars/ReadVariableOp" input: "sequential/quant_dense/MatMul/kquant/FakeQuantWithMinMaxVars/ReadVariableOp_1" attr { key: "narrow_range" value { b: false } } attr { key: "num_bits" value { i: 8 } } } node { name: "sequential/quant_dense/MatMul/kquant/IdentityN"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 15 19:42:47 UTC 2021 - 25.9K bytes - Viewed (0) -
tensorflow/c/eager/c_api_remote_test_util.cc
TFE_OpAddInput(matmul, h0_task0, status); ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); TFE_OpAddInput(matmul, has_packed_input ? packed_handle : h1_task2, status); ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); } else { // Handles are on task0 (local), and task2, but op is on task1. matmul = MatMulOp(ctx, h0_task0, h1_task2); } if (remote) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Dec 11 22:56:03 UTC 2020 - 9.1K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
"gradients/MatMul", false, true); TF_Operation* matmul2 = MatMul(expected_graph_, s_, const0, const3, "gradients/MatMul_1", true, false); expected_grad_outputs[0] = {matmul1, 0}; expected_grad_outputs[1] = {matmul2, 0}; } TF_Tensor* FloatTensor2x2(const float* values) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 96.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/multi_arguments_results_v1.py
# CHECK-DAG: %[[MUL1:.*]] = "tf.MatMul"(%[[ARG0]], %[[ARG1]]) # CHECK-DAG: %[[MUL2:.*]] = "tf.MatMul"(%[[ARG1]], %[[ARG0]]) # CHECK: %[[IDENTITY:.*]]:2 = "tf.IdentityN"(%[[MUL1]], %[[MUL2]]) # CHECK: return %[[IDENTITY]]#1, %[[IDENTITY]]#0 def Test(): x = tf.constant(1.0, shape=(5, 3)) y = tf.constant(1.0, shape=(3, 5)) s = tf.matmul(x, y) t = tf.matmul(y, x) [t, s] = array_ops.identity_n([t, s])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.5K bytes - Viewed (0) -
tensorflow/c/eager/custom_device_test.cc
std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> matmul( MatMulOp(context, hcpu, hdevice), TFE_DeleteOp); TFE_OpSetDevice(matmul.get(), name, status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_TensorHandle* retval; int num_retvals = 1; TFE_Execute(matmul.get(), &retval, &num_retvals, status.get());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 27 23:39:24 UTC 2020 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/control_flow_v1.pbtxt
op: "Identity" input: "Placeholder_1" attr { key: "T" value { type: DT_BOOL } } } node { name: "cond/MatMul" op: "MatMul" input: "cond/MatMul/Switch:1" input: "cond/MatMul/Switch_1:1" attr { key: "T" value { type: DT_FLOAT } } attr { key: "transpose_a" value { b: false }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 23 21:23:31 UTC 2020 - 3.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0)