除了本地/全局变量存储时间,操作码预测使函数更快。
正如其他答案所解释的,函数在循环中使用STORE_FAST操作码。下面是函数循环的字节码:>> 13 FOR_ITER 6 (to 22) # get next value from iterator
16 STORE_FAST 0 (x) # set local variable
19 JUMP_ABSOLUTE 13 # back to FOR_ITER
通常,当程序运行时,Python会一个接一个地执行每个操作码,跟踪堆栈,并在执行每个操作码后对堆栈帧执行其他检查。操作码预测意味着在某些情况下,Python能够直接跳转到下一个操作码,从而避免了一些这种开销。
在这种情况下,每当Python看到FOR_ITER(循环的顶部)时,它就会“预测”到STORE_FAST是它必须执行的下一个操作码。然后,Python查看下一个操作码,如果预测正确,它将直接跳到STORE_FAST。这会将两个操作码压缩为一个操作码。
另一方面,在全局级别的循环中使用STORE_NAME操作码。Python看到这个操作码时,确实会做出类似的预测。相反,它必须返回到计算循环的顶部,这对循环的执行速度有明显的影响。
为了提供有关此优化的更多技术细节,这里引用了^{}文件(Python虚拟机的“引擎”)中的一段话:Some opcodes tend to come in pairs thus making it possible to
predict the second code when the first is run. For example,
GET_ITER is often followed by FOR_ITER. And FOR_ITER is often
followed by STORE_FAST or UNPACK_SEQUENCE.
Verifying the prediction costs a single high-speed test of a register
variable against a constant. If the pairing was good, then the
processor's own internal branch predication has a high likelihood of
success, resulting in a nearly zero-overhead transition to the
next opcode. A successful prediction saves a trip through the eval-loop
including its two unpredictable branches, the HAS_ARG test and the
switch-case. Combined with the processor's internal branch prediction,
a successful PREDICT has the effect of making the two opcodes run as if
they were a single new opcode with the bodies combined.
在^{}操作码的源代码中,我们可以看到STORE_FAST的预测是在哪里进行的:case FOR_ITER: // the FOR_ITER opcode case
v = TOP();
x = (*v->ob_type->tp_iternext)(v); // x is the next value from iterator
if (x != NULL) {
PUSH(x); // put x on top of the stack
PREDICT(STORE_FAST); // predict STORE_FAST will follow - success!
PREDICT(UNPACK_SEQUENCE); // this and everything below is skipped
continue;
}
// error-checking and more code for when the iterator ends normally
PREDICT函数扩展为if (*next_instr == op) goto PRED_##op,也就是说,我们只是跳到预测操作码的开头。在这种情况下,我们跳到这里:PREDICTED_WITH_ARG(STORE_FAST);
case STORE_FAST:
v = POP(); // pop x back off the stack
SETLOCAL(oparg, v); // set it as the new local variable
goto fast_next_opcode;
现在设置了局部变量,下一个操作码准备执行。Python继续遍历iterable,直到它到达末尾,每次都进行成功的预测。
Python wiki page有更多关于CPython的虚拟机如何工作的信息。