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Results 1 - 10 of 12 for operand1 (0.2 sec)
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tensorflow/compiler/mlir/lite/transforms/lower_static_tensor_list.cc
Value GetNumElements(TF::TensorListReserveOp op, ValueRange operands, PatternRewriter *rewriter) const override { Value scalar_zero = CreateI32SplatConst(op.getLoc(), rewriter, {}, 0); Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType()); Value num_elements = operands[1]; IntegerAttr attr; if (matchPattern(num_elements, m_Constant(&attr))) {
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/lite/flatbuffer_export.cc
if (mlir::isa<NoneType>(operand.getType())) operands.push_back(kTfLiteOptionalTensor); else if (auto stats_op = llvm::dyn_cast_or_null<mlir::quantfork::StatisticsOp>( operand.getDefiningOp())) operands.push_back(tensor_index_map.lookup(stats_op.getArg())); else operands.push_back(tensor_index_map.lookup(operand)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:41:49 UTC 2024 - 164.5K bytes - Viewed (2) -
tensorflow/compiler/mlir/tf2xla/internal/passes/xla_broadcast.cc
for (OpOperand& operand : op->getOpOperands()) { if (orig_to_new.count(operand.get())) { operand.assign(orig_to_new[operand.get()]); } } return WalkResult::advance(); }); return success(); } void XlaBroadcast::runOnOperation() { FuncOp func = getOperation(); mlir::ModuleOp module = func->getParentOfType<mlir::ModuleOp>(); if (!module) return signalPassFailure();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 18:52:07 UTC 2024 - 13.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
// // All HLO infeed ops expect a HLO token type operand and produce a tuple // containing a token. This HLO token type is used to order multiple infeed // operations within a computation. The token type can come from other // infeed/outfeed/send/recv ops or can be generated using create_token op with // no operands. Here we emit a create_token op to generate the token type
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (1) -
analysis/analysis-api-fir/src/org/jetbrains/kotlin/analysis/api/fir/components/KtFirExpressionTypeProvider.kt
private fun getExpectedTypeOfElvisOperand(expression: PsiElement): KaType? { val binaryExpression = expression.unwrapQualified<KtBinaryExpression> { binaryExpression, operand -> binaryExpression.operationToken == KtTokens.ELVIS && (operand == binaryExpression.left || operand == binaryExpression.right) } ?: return null if (expression !is KtExpression) return null
Registered: Wed Jun 12 09:53:16 UTC 2024 - Last Modified: Tue Jun 11 15:45:42 UTC 2024 - 24.4K bytes - Viewed (0) -
pkg/scheduler/internal/queue/scheduling_queue_test.go
} tests := []struct { name string operations []operation operands []*framework.QueuedPodInfo expected []*framework.QueuedPodInfo }{ { name: "add two pod to activeQ and sort them by the timestamp", operations: []operation{ addPodActiveQ, addPodActiveQ, }, operands: []*framework.QueuedPodInfo{pInfo2, pInfo1},
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Wed Jun 12 13:26:09 UTC 2024 - 146.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc
import_config.control_outputs = *control_ret_node_names; import_config.upgrade_legacy = true; // Disable shape inference during import as some TensorFlow op fails during // shape inference with dynamic shaped operands. This in turn causes the // import to fail. Shape inference during import is going to be removed and // the shape inference pass is run early in the pass pipeline, shape inference // during import is not necessary.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 22:19:26 UTC 2024 - 18.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
*Match* means the axis must match when adding, regarding the broadcasting. i.e. For both operands `lhs` and `rhs`, if `operand.quantization_axis` >= 0 and `output.quantization_axis` >= 0, `operand.dims` - `operand.quantization_axis` must be equal to `output.dims` - `output.quantization_axis`. }]; let arguments = (ins
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/passes.h
llvm::cl::desc("Force data format for all layout sensitive ops")}; Option<bool> skip_fold_transpose_in_ops{ *this, "skip-fold-transpose-in-ops", llvm::cl::desc("Skip folding transpose operands in Ops which can support " "different layouts.")}; }; // Layout optimization assigns optimal data layout for layout sensitive // operations, and cancels all redundant transposes.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 31.8K bytes - Viewed (0) -
src/go/types/unify.go
} if panicAtUnificationDepthLimit { panic("unification reached recursion depth limit") } return false } // Unification is symmetric, so we can swap the operands. // Ensure that if we have at least one // - defined type, make sure one is in y // - type parameter recorded with u, make sure one is in x if asNamed(x) != nil || u.asBoundTypeParam(y) != nil {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Jun 11 16:24:39 UTC 2024 - 27.9K bytes - Viewed (0)