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e24a4fa7a0
Allow InvokeInst to have the second optional prof branch weight for its unwind branch. InvokeInst is a terminator with two successors. It might have its unwind branch taken many times. If so the BranchProbabilityInfo unwind branch heuristic can be inaccurate. This patch allows a higher accuracy calculated with both branch weights set. Changes: - A new section about InvokeInst is added to the BranchWeightMetadata page. It states the old information that missed in the doc and adds new about the second branch weight. - Verifier is changed to allow either 1 or 2 branch weights for InvokeInst. - A new test is written for BranchProbabilityInfo to demonstrate the main improvement of the simple fix in calcMetadataWeights(). - Several new testcases are created for Inliner. Those check that both weights are accounted for invoke instruction weight calculation. - PGOUseFunc::setBranchWeights() is fixed to be applicable to InvokeInst. Reviewers: davidxl, reames, xur, yamauchi Tags: #llvm Differential Revision: https://reviews.llvm.org/D80618
186 lines
5.5 KiB
ReStructuredText
186 lines
5.5 KiB
ReStructuredText
===========================
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LLVM Branch Weight Metadata
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===========================
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.. contents::
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:local:
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Introduction
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============
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Branch Weight Metadata represents branch weights as its likeliness to be taken
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(see :doc:`BlockFrequencyTerminology`). Metadata is assigned to an
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``Instruction`` that is a terminator as a ``MDNode`` of the ``MD_prof`` kind.
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The first operator is always a ``MDString`` node with the string
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"branch_weights". Number of operators depends on the terminator type.
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Branch weights might be fetch from the profiling file, or generated based on
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`__builtin_expect`_ instruction.
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All weights are represented as an unsigned 32-bit values, where higher value
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indicates greater chance to be taken.
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Supported Instructions
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======================
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``BranchInst``
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^^^^^^^^^^^^^^
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Metadata is only assigned to the conditional branches. There are two extra
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operands for the true and the false branch.
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.. code-block:: none
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!0 = metadata !{
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metadata !"branch_weights",
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i32 <TRUE_BRANCH_WEIGHT>,
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i32 <FALSE_BRANCH_WEIGHT>
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}
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``SwitchInst``
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^^^^^^^^^^^^^^
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Branch weights are assigned to every case (including the ``default`` case which
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is always case #0).
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.. code-block:: none
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!0 = metadata !{
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metadata !"branch_weights",
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i32 <DEFAULT_BRANCH_WEIGHT>
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[ , i32 <CASE_BRANCH_WEIGHT> ... ]
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}
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``IndirectBrInst``
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^^^^^^^^^^^^^^^^^^
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Branch weights are assigned to every destination.
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.. code-block:: none
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!0 = metadata !{
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metadata !"branch_weights",
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i32 <LABEL_BRANCH_WEIGHT>
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[ , i32 <LABEL_BRANCH_WEIGHT> ... ]
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}
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``CallInst``
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^^^^^^^^^^^^^^^^^^
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Calls may have branch weight metadata, containing the execution count of
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the call. It is currently used in SamplePGO mode only, to augment the
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block and entry counts which may not be accurate with sampling.
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.. code-block:: none
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!0 = metadata !{
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metadata !"branch_weights",
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i32 <CALL_BRANCH_WEIGHT>
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}
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``InvokeInst``
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^^^^^^^^^^^^^^^^^^
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Invoke instruction may have branch weight metadata with one or two weights.
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The second weight is optional and corresponds to the unwind branch.
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If only one weight is set then it contains the execution count of the call
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and used in SamplePGO mode only as described for the call instruction. If both
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weights are specified then the second weight contains count of unwind branch
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taken and the first weights contains the execution count of the call minus
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the count of unwind branch taken. Both weights specified are used to calculate
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BranchProbability as for BranchInst and for SamplePGO the sum of both weights
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is used.
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.. code-block:: none
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!0 = metadata !{
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metadata !"branch_weights",
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i32 <INVOKE_NORMAL_WEIGHT>
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[ , i32 <INVOKE_UNWIND_WEIGHT> ]
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}
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Other
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^^^^^
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Other terminator instructions are not allowed to contain Branch Weight Metadata.
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.. _\__builtin_expect:
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Built-in ``expect`` Instructions
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================================
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``__builtin_expect(long exp, long c)`` instruction provides branch prediction
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information. The return value is the value of ``exp``.
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It is especially useful in conditional statements. Currently Clang supports two
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conditional statements:
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``if`` statement
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^^^^^^^^^^^^^^^^
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The ``exp`` parameter is the condition. The ``c`` parameter is the expected
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comparison value. If it is equal to 1 (true), the condition is likely to be
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true, in other case condition is likely to be false. For example:
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.. code-block:: c++
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if (__builtin_expect(x > 0, 1)) {
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// This block is likely to be taken.
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}
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``switch`` statement
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^^^^^^^^^^^^^^^^^^^^
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The ``exp`` parameter is the value. The ``c`` parameter is the expected
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value. If the expected value doesn't show on the cases list, the ``default``
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case is assumed to be likely taken.
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.. code-block:: c++
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switch (__builtin_expect(x, 5)) {
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default: break;
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case 0: // ...
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case 3: // ...
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case 5: // This case is likely to be taken.
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}
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CFG Modifications
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=================
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Branch Weight Metatada is not proof against CFG changes. If terminator operands'
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are changed some action should be taken. In other case some misoptimizations may
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occur due to incorrect branch prediction information.
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Function Entry Counts
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=====================
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To allow comparing different functions during inter-procedural analysis and
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optimization, ``MD_prof`` nodes can also be assigned to a function definition.
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The first operand is a string indicating the name of the associated counter.
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Currently, one counter is supported: "function_entry_count". The second operand
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is a 64-bit counter that indicates the number of times that this function was
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invoked (in the case of instrumentation-based profiles). In the case of
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sampling-based profiles, this operand is an approximation of how many times
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the function was invoked.
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For example, in the code below, the instrumentation for function foo()
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indicates that it was called 2,590 times at runtime.
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.. code-block:: llvm
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define i32 @foo() !prof !1 {
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ret i32 0
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}
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!1 = !{!"function_entry_count", i64 2590}
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If "function_entry_count" has more than 2 operands, the later operands are
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the GUID of the functions that needs to be imported by ThinLTO. This is only
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set by sampling based profile. It is needed because the sampling based profile
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was collected on a binary that had already imported and inlined these functions,
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and we need to ensure the IR matches in the ThinLTO backends for profile
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annotation. The reason why we cannot annotate this on the callsite is that it
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can only goes down 1 level in the call chain. For the cases where
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foo_in_a_cc()->bar_in_b_cc()->baz_in_c_cc(), we will need to go down 2 levels
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in the call chain to import both bar_in_b_cc and baz_in_c_cc.
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