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Results 1 - 10 of 92 for shade (0.06 sec)
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src/runtime/mgcmark.go
obj := span.base() + idx*span.elemsize greyobject(obj, b, i, span, gcw, idx) } } // Shade the object if it isn't already. // The object is not nil and known to be in the heap. // Preemption must be disabled. // //go:nowritebarrier func shade(b uintptr) { if obj, span, objIndex := findObject(b, 0, 0); obj != 0 { gcw := &getg().m.p.ptr().gcw
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Apr 18 21:25:11 UTC 2024 - 52.5K bytes - Viewed (0) -
maven-core/src/main/java/org/apache/maven/project/MavenProject.java
* 3.0.x. Historically, it logged and ignored a second addition of the same g/a/v/c/t. Now it replaces the file for * the artifact, so that plugins (e.g. shade) can change the pathname of the file for a particular set of * coordinates. * * @param artifact the artifact to add or replace. * @deprecated Please use {@link MavenProjectHelper}
Registered: Wed Jun 12 09:55:16 UTC 2024 - Last Modified: Fri Mar 01 17:18:13 UTC 2024 - 56.6K bytes - Viewed (0) -
src/runtime/mgc.go
// drive GC to completion. // // It is explicitly okay to have write barriers in this function. If // it does transition to mark termination, then all reachable objects // have been marked, so the write barrier cannot shade any more // objects. func gcMarkDone() { // Ensure only one thread is running the ragged barrier at a // time. semacquire(&work.markDoneSema) top:
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 29 16:25:21 UTC 2024 - 62K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
weight_row = array_ops.ones( shape=array_ops.shape(input_vocabs_placeholder), dtype=dtypes.float32 ) # shape: (?, 2) weight = array_ops.transpose_v2( array_ops_stack.stack([weight_row, weight_row]) ) # shape: (2, 2) output_tensor = math_ops.matmul(matmul_input, weight) return input_vocabs_placeholder, lookup_vals, output_tensor
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/lite/transforms/optimize_patterns.td
// tail of the other operand and the intermediate result isn't used by other // ops. // $rhs is required to be the tail shape of $lhs, so after transformation the // shape of the binary op result is valid. For example, assume the shapes of // $input, $lhs and $rhs are [1600], [1,40,40] and [40x1]. After the // transformation, the shape of the binary op result is [40x1600], which
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tensor_array_ops_decomposition.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 49K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad.cc
// Reduce along the broadcasted batch dimensions. Output sx = Shape(scope, op.input(0)); Output sy = Shape(scope, op.input(1)); Output x_batch_shape = Slice(scope, sx, {0}, Sub(scope, Shape(scope, sx), 2)); Output y_batch_shape = Slice(scope, sy, {0}, Sub(scope, Shape(scope, sy), 2)); auto reduce = internal::BroadcastGradientArgs(scope, x_batch_shape, y_batch_shape);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 50.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
auto ranked_ty = mlir::dyn_cast<RankedTensorType>(ty); // Unranked type. if (!ranked_ty) return ty; auto shape = llvm::to_vector<4>(ranked_ty.getShape()); if (axis < 0) axis += ranked_ty.getRank() + 1; shape.insert(shape.begin() + axis, 1); return tensorflow::GetTypeFromTFTensorShape(shape, ranked_ty.getElementType()); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/lower_static_tensor_list.cc
} SmallVector<int64_t, 4> result_shape = {leading_dim_v}; ArrayRef<int64_t> shape = element_type.getShape(); result_shape.append(shape.begin(), shape.end()); result_type = tensorflow::GetTypeFromTFTensorShape(result_shape, element_dtype); } // Create a 1-D RankedTensorType for result's shape. Number of elements in // it is equal to the rank of the result, if known. Otherwise, the number of
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 70.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
%4 = shape.shape_of %3 : tensor<?x28x28x16xf32> -> tensor<4xindex> %5 = stablehlo.dynamic_broadcast_in_dim %1, %4, dims = [3] : (tensor<16xf32>, tensor<4xindex>) -> tensor<?x28x28x16xf32> %6 = stablehlo.add %3, %5 : tensor<?x28x28x16xf32> %7 = shape.shape_of %6 : tensor<?x28x28x16xf32> -> tensor<4xindex>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0)