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Results 1 - 10 of 61 for matmult (0.22 sec)
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src/runtime/proc_test.go
done1 := make(chan struct{}, 1) go matmult(done1, A, B, C, i0, i1, j0, mj, k0, k1, threshold) matmult(nil, A, B, C, i0, i1, mj, j1, k0, k1, threshold) <-done1 } else if dk >= threshold { // divide in two by "k" axis // deliberately not parallel because of data races mk := k0 + dk/2 matmult(nil, A, B, C, i0, i1, j0, j1, k0, mk, threshold) matmult(nil, A, B, C, i0, i1, j0, j1, mk, k1, threshold) } else {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Jun 14 00:03:57 UTC 2023 - 25.8K 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/quantization/tensorflow/tests/quantize_weights.mlir
// CHECK: %[[MATMUL_3:.*]] = "tf.MatMul"(%arg0, %[[ORIGINAL_IDENTITY]]) <{transpose_a = false, transpose_b = false}> {attr_map = "0:transpose_a,1:transpose_a", device = ""} : (tensor<1x2x2x2xf32>, tensor<2x1024xf32>) -> tensor<*xf32> // CHECK: return %[[MATMUL_1]], %[[MATMUL_2]], %[[MATMUL_3]] : tensor<*xf32>, tensor<*xf32>, tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 42K 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/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/cc/framework/gradients_test.cc
auto dv = Const(scope, {{1.0, 1.0}, {1.0, 1.0}}); auto dt = MatMul(scope, dv, u, MatMul::TransposeB(true)); auto du = MatMul(scope, t, dv, MatMul::TransposeA(true)); auto dz = Const(scope, {{1.0, 1.0}, {1.0, 1.0}}); auto dx = MatMul(scope, dz, y, MatMul::TransposeB(true)); auto dy = MatMul(scope, x, dz, MatMul::TransposeA(true)); } else { // Call AddSymbolicGradients.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 15 15:13:38 UTC 2023 - 25K bytes - Viewed (0) -
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/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)