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Results 1 - 10 of 75 for matmult (0.34 sec)
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tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
// done in parallel. // // This graph is: // (Const0, Const0) -> MatMul0 // (Const1, Const1) -> MatMul1 // (MatMul0, MatMul1) -> MatMulCombined // // Device0: [Const0, Const0, MatMul0] // Device1: [Const1, Const1, MatMul1, MatMulCombined] // // Cluster0: [Const0, Const0, MatMul0] // Cluster1: [Const1, Const1, MatMul1] // Cluster2: [MatMulCombined]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 10:11:10 UTC 2024 - 79.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op.mlir
// WholeModel-DAG: "tf.DumpTensor"(%[[m1]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "matmul2", log_dir_path = "/tmp/dumps/composite_matmul_fn_1", node_name = "MatMul_1"} // WholeModel-DAG: return %[[m1]] // IntPerLayer-LABEL: func @matmul2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 37.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.cc
std::vector<Value> sliced_rhs = sliceInput(input_rhs, bcast.y_batch_size(), loc, rewriter); // Compute (single batch) MatMul for each output batch. std::vector<Value> matmuls; matmuls.reserve(bcast.output_batch_size()); for (int batch_idx : llvm::seq<int>(0, bcast.output_batch_size())) { int lhs_batch_idx, rhs_batch_idx; if (bcast.IsBroadcastingRequired()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fused_kernel_matcher.cc
if ((matmul->hasAttr("grad_a") && mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) || (matmul->hasAttr("grad_b") && mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) { (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) { diag << "FusedMatMul kernel does not support grad_a/grad_b attrs"; }); return false; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/matmul.mlir
Christian Sigg <******@****.***> 1714640622 -0700
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_activity_listener_test.cc
"/job:localhost/replica:0/task:0/device:CPU:0"); Output a = ops::Placeholder(root.WithOpName("A"), DT_FLOAT); for (int i = 0; i < 5; i++) { a = ops::MatMul(root.WithOpName(absl::StrCat("matmul_", i)), a, a); a = ops::Add(root.WithOpName(absl::StrCat("add_", i)), a, a); } GraphDef graph_def; root.graph()->ToGraphDef(&graph_def); return graph_def; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 08:47:20 UTC 2024 - 5.9K 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/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) -
tensorflow/c/eager/c_api_distributed_test.cc
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1); TFE_OpSetDevice(matmul, remote_device_name, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_TensorHandle* retvals[1]; int num_retvals = 1; TFE_Execute(matmul, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 15 09:49:45 UTC 2024 - 23.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.cc
} return ConstantFoldOpIfPossible(value.getDefiningOp()).front(); } // Matches convolution op with "NHWC" data format or matmul op with false adj_y. // The list of supported ops in this function is: // - Conv2DOp // - Conv3DOp // - DepthwiseConv2dNativeOp // - MatMulOp // - BatchMatMulV2Op LogicalResult MatchSupportedAffineOp(Operation* op, Value& binding_output,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0)