kopia lustrzana https://github.com/micropython/micropython
py/vm.c: Avoid heap-allocating slices for built-ins.
Fast path optimisation for when a BUILD_SLICE is immediately followed by a LOAD/STORE_SUBSCR for a native type to avoid needing to allocate the slice on the heap. In some cases (e.g. `a[1:3] = x`) this can result in no allocations at all. We can't do this for instance types because the get/set/delattr implementation may keep a reference to the slice. Adds more tests to the basic slice tests to ensure that a stack-allocated slice never makes it to Python, and also a heapalloc test that verifies (when using bytecode) that assigning to a slice is no-alloc. This work was funded through GitHub Sponsors. Signed-off-by: Jim Mussared <jim.mussared@gmail.com>pull/12518/head
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py/vm.c
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py/vm.c
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@ -850,8 +850,26 @@ unwind_jump:;
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step = POP();
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}
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mp_obj_t stop = POP();
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mp_obj_t start = TOP();
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SET_TOP(mp_obj_new_slice(start, stop, step));
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mp_obj_t start = POP();
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if ((*ip == MP_BC_LOAD_SUBSCR || *ip == MP_BC_STORE_SUBSCR) && mp_obj_is_native_type(mp_obj_get_type(TOP()))) {
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// Fast path optimisation for when the BUILD_SLICE is
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// immediately followed by a LOAD/STORE_SUBSCR for a
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// native type to avoid needing to allocate the slice
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// on the heap. In some cases (e.g. a[1:3] = x) this
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// can result in no allocations at all. We can't do
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// this for instance types because the get/set/delattr
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// implementation may keep a reference to the slice.
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byte op = *ip++;
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mp_obj_slice_t slice = { .base = { .type = &mp_type_slice }, .start = start, .stop = stop, .step = step };
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if (op == MP_BC_LOAD_SUBSCR) {
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SET_TOP(mp_obj_subscr(TOP(), MP_OBJ_FROM_PTR(&slice), MP_OBJ_SENTINEL));
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} else { // MP_BC_STORE_SUBSCR
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mp_obj_subscr(TOP(), MP_OBJ_FROM_PTR(&slice), sp[-1]);
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sp -= 2;
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}
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} else {
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PUSH(mp_obj_new_slice(start, stop, step));
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}
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DISPATCH();
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}
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#endif
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@ -1,11 +1,44 @@
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# test builtin slice
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# ensures that slices passed to user types are heap-allocated and can be
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# safely stored as well as not overriden by subsequent slices.
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# print slice
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class A:
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def __getitem__(self, idx):
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print(idx)
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print("get", idx)
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print("abc"[1:])
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print("get", idx)
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return idx
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s = A()[1:2:3]
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def __setitem__(self, idx, value):
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print("set", idx)
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print("abc"[1:])
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print("set", idx)
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self.saved_idx = idx
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return idx
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def __delitem__(self, idx):
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print("del", idx)
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print("abc"[1:])
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print("del", idx)
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return idx
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a = A()
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s = a[1:2:3]
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a[4:5:6] = s
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del a[7:8:9]
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print(a.saved_idx)
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# nested slicing
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print(A()[1:A()[A()[2:3:4]:5]])
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# tuple slicing
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a[1:2,4:5,7:8]
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a[1,4:5,7:8,2]
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a[1:2, a[3:4], 5:6]
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# check type
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print(type(s) is slice)
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@ -0,0 +1,18 @@
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# slice operations that don't require allocation
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try:
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from micropython import heap_lock, heap_unlock
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except (ImportError, AttributeError):
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heap_lock = heap_unlock = lambda: 0
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b = bytearray(range(10))
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m = memoryview(b)
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heap_lock()
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b[3:5] = b"aa"
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m[5:7] = b"bb"
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heap_unlock()
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print(b)
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@ -639,6 +639,9 @@ def run_tests(pyb, tests, args, result_dir, num_threads=1):
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skip_tests.add(
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"micropython/emg_exc.py"
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) # because native doesn't have proper traceback info
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skip_tests.add(
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"micropython/heapalloc_slice.py"
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) # because native doesn't do the stack-allocated slice optimisation
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skip_tests.add(
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"micropython/heapalloc_traceback.py"
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) # because native doesn't have proper traceback info
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