forked from tinygrad/tinygrad
add some dels to jit for OOM fixes (#17566)
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@@ -269,10 +269,14 @@ class _TinyJit(Generic[ReturnType]):
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big_linear, onetime_linear = prune_linear(big_linear, set(input_buf_uops))
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if DEBUG >= 1: print(f"pruned from {len(big_linear.src) + len(onetime_linear.src)} -> {len(big_linear.src)} kernels")
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run_linear(onetime_linear, var_vals)
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del onetime_linear
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# hold all buffers reachable from live Tensors (e.g. lazy .grad created during capture), the memory planner can't suballocate those
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held_bufs = set(buffers) | {u for tref in list(all_tensors) if (t:=tref()) is not None for u in t.uop.toposort() if u.op is Ops.BUFFER}
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linear = jit_lower(big_linear, held_bufs, input_buf_uops)
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# drop the pre-planning graph: it keeps the whole capture-time working set allocated (big_linear) or referenced (held_bufs).
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# the planned linear only uses the arena/held buffers, so the intermediates must be freed before linking and first exec
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del big_linear, held_bufs
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self.captured = CapturedJit(ret, linear, names, expected_input_info)
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ret = self.captured(input_buf_uops, var_vals)
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elif self.cnt >= 2:
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+3
-2
@@ -242,9 +242,10 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
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arg:Any = None
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tag:Any = None
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def __del__(self):
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if Ops is not None and self.op is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)
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# NOTE: getattr because this object may be partially constructed (e.g. if __init__ raised, like the BEAM timeout SIGALRM)
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if Ops is not None and getattr(self, 'op', None) is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)
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try: del UOpMetaClass.ucache[(self.op, self.dtype, self.src, self.arg, self.tag)]
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except AttributeError: pass
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except (AttributeError, KeyError): pass
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def __reduce__(self):
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args = [self.op, self.dtype, self.src, self.arg, self.tag, self.metadata]
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if self.op is Ops.BUFFER and self.realized is not None: args.append(self.realized)
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