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Python yield与实现方法代码分析

2020-01-04 15:57:15
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yield的功能类似于return,但是不同之处在于它返回的是生成器。

生成器

生成器是通过一个或多个yield表达式构成的函数,每一个生成器都是一个迭代器(但是迭代器不一定是生成器)。

如果一个函数包含yield关键字,这个函数就会变为一个生成器。

生成器并不会一次返回所有结果,而是每次遇到yield关键字后返回相应结果,并保留函数当前的运行状态,等待下一次的调用。

由于生成器也是一个迭代器,那么它就应该支持next方法来获取下一个值。

基本操作

# 通过`yield`来创建生成器def func(): for i in xrange(10);  yield i
# 通过列表来创建生成器[i for i in xrange(10)]# 通过`yield`来创建生成器def func(): for i in xrange(10);  yield i# 通过列表来创建生成器[i for i in xrange(10)]Python# 调用如下>>> f = func()>>> f # 此时生成器还没有运行<generator object func at 0x7fe01a853820>>>> f.next() # 当i=0时,遇到yield关键字,直接返回>>> f.next() # 继续上一次执行的位置,进入下一层循环...>>> f.next()>>> f.next() # 当执行完最后一次循环后,结束yield语句,生成StopIteration异常Traceback (most recent call last): File "<stdin>", line 1, in <module>StopIteration>>># 调用如下>>> f = func()>>> f # 此时生成器还没有运行<generator object func at 0x7fe01a853820>>>> f.next() # 当i=0时,遇到yield关键字,直接返回>>> f.next() # 继续上一次执行的位置,进入下一层循环...>>> f.next()>>> f.next() # 当执行完最后一次循环后,结束yield语句,生成StopIteration异常Traceback (most recent call last): File "<stdin>", line 1, in <module>StopIteration>>>

除了next函数,生成器还支持send函数。该函数可以向生成器传递参数。

>>> def func():...  n = 0...  while 1:...   n = yield n #可以通过send函数向n赋值... >>> f = func()>>> f.next() # 默认情况下n为0>>> f.send(1) #n赋值1>>> f.send(2)>>> >>> def func():...  n = 0...  while 1:...   n = yield n #可以通过send函数向n赋值... >>> f = func()>>> f.next() # 默认情况下n为0>>> f.send(1) #n赋值1>>> f.send(2)>>> 

应用

最经典的例子,生成无限序列。

常规的解决方法是,生成一个满足要求的很大的列表,这个列表需要保存在内存中,很明显内存限制了这个问题。

def get_primes(start): for element in magical_infinite_range(start):  if is_prime(element):   return elementdef get_primes(start): for element in magical_infinite_range(start):  if is_prime(element):   return element

如果使用生成器就不需要返回整个列表,每次都只是返回一个数据,避免了内存的限制问题。

def get_primes(number): while True:  if is_prime(number):   yield number  number += 1def get_primes(number): while True:  if is_prime(number):   yield number  number += 1

生成器源码分析

生成器的源码在Objects/genobject.c。

调用栈

在解释生成器之前,需要讲解一下Python虚拟机的调用原理。

Python虚拟机有一个栈帧的调用栈,其中栈帧的是PyFrameObject,位于Include/frameobject.h。

typedef struct _frame { PyObject_VAR_HEAD struct _frame *f_back; /* previous frame, or NULL */ PyCodeObject *f_code; /* code segment */ PyObject *f_builtins; /* builtin symbol table (PyDictObject) */ PyObject *f_globals; /* global symbol table (PyDictObject) */ PyObject *f_locals;  /* local symbol table (any mapping) */ PyObject **f_valuestack; /* points after the last local */ /* Next free slot in f_valuestack. Frame creation sets to f_valuestack.  Frame evaluation usually NULLs it, but a frame that yields sets it  to the current stack top. */ PyObject **f_stacktop; PyObject *f_trace;  /* Trace function */ /* If an exception is raised in this frame, the next three are used to  * record the exception info (if any) originally in the thread state. See  * comments before set_exc_info() -- it's not obvious.  * Invariant: if _type is NULL, then so are _value and _traceback.  * Desired invariant: all three are NULL, or all three are non-NULL. That  * one isn't currently true, but "should be".  */ PyObject *f_exc_type, *f_exc_value, *f_exc_traceback; PyThreadState *f_tstate; int f_lasti;  /* Last instruction if called */ /* Call PyFrame_GetLineNumber() instead of reading this field  directly. As of 2.3 f_lineno is only valid when tracing is  active (i.e. when f_trace is set). At other times we use  PyCode_Addr2Line to calculate the line from the current  bytecode index. */ int f_lineno;  /* Current line number */ int f_iblock;  /* index in f_blockstack */ PyTryBlock f_blockstack[CO_MAXBLOCKS]; /* for try and loop blocks */ PyObject *f_localsplus[1]; /* locals+stack, dynamically sized */} PyFrameObject;typedef struct _frame { PyObject_VAR_HEAD struct _frame *f_back; /* previous frame, or NULL */ PyCodeObject *f_code; /* code segment */ PyObject *f_builtins; /* builtin symbol table (PyDictObject) */ PyObject *f_globals; /* global symbol table (PyDictObject) */ PyObject *f_locals;  /* local symbol table (any mapping) */ PyObject **f_valuestack; /* points after the last local */ /* Next free slot in f_valuestack. Frame creation sets to f_valuestack.  Frame evaluation usually NULLs it, but a frame that yields sets it  to the current stack top. */ PyObject **f_stacktop; PyObject *f_trace;  /* Trace function */ /* If an exception is raised in this frame, the next three are used to  * record the exception info (if any) originally in the thread state. See  * comments before set_exc_info() -- it's not obvious.  * Invariant: if _type is NULL, then so are _value and _traceback.  * Desired invariant: all three are NULL, or all three are non-NULL. That  * one isn't currently true, but "should be".  */ PyObject *f_exc_type, *f_exc_value, *f_exc_traceback;  PyThreadState *f_tstate; int f_lasti;  /* Last instruction if called */ /* Call PyFrame_GetLineNumber() instead of reading this field  directly. As of 2.3 f_lineno is only valid when tracing is  active (i.e. when f_trace is set). At other times we use  PyCode_Addr2Line to calculate the line from the current  bytecode index. */ int f_lineno;  /* Current line number */ int f_iblock;  /* index in f_blockstack */ PyTryBlock f_blockstack[CO_MAXBLOCKS]; /* for try and loop blocks */ PyObject *f_localsplus[1]; /* locals+stack, dynamically sized */} PyFrameObject;

栈帧保存了给出代码的的信息和上下文,其中包含最后执行的指令,全局和局部命名空间,异常状态等信息。f_valueblock保存了数据,b_blockstack保存了异常和循环控制方法。

举一个例子来说明,

def foo(): x = 1 def bar(y):  z = y + 2 # def foo(): x = 1 def bar(y):  z = y + 2 # 

那么,相应的调用栈如下,一个py文件,一个类,一个函数都是一个代码块,对应者一个Frame,保存着上下文环境以及字节码指令。

c ---------------------------a | bar Frame     | -> block stack: []l |  (newest)    | -> data stack: [1, 2]l --------------------------- | foo Frame     | -> block stack: []s |       | -> data stack: [.bar at 0x10d389680>, 1]t ---------------------------a | main (module) Frame  | -> block stack: []c |  (oldest)   | -> data stack: []k ---------------------------c ---------------------------a | bar Frame     | -> block stack: []l |  (newest)    | -> data stack: [1, 2]l --------------------------- | foo Frame     | -> block stack: []s |       | -> data stack: [.bar at 0x10d389680>, 1]t ---------------------------a | main (module) Frame  | -> block stack: []c |  (oldest)   | -> data stack: []k ---------------------------

每一个栈帧都拥有自己的数据栈和block栈,独立的数据栈和block栈使得解释器可以中断和恢复栈帧(生成器正式利用这点)。

Python代码首先被编译为字节码,再由Python虚拟机来执行。一般来说,一条Python语句对应着多条字节码(由于每条字节码对应着一条C语句,而不是一个机器指令,所以不能按照字节码的数量来判断代码性能)。

调用dis模块可以分析字节码,

from dis import disdis(foo)    0 LOAD_CONST    1 (1) # 加载常量1    3 STORE_FAST    0 (x) # x赋值为1   6 LOAD_CONST    2 (<code>) # 加载常量2    9 MAKE_FUNCTION   0 # 创建函数    12 STORE_FAST    1 (bar)    15 LOAD_FAST    1 (bar)     18 LOAD_FAST    0 (x)    21 CALL_FUNCTION   1 # 调用函数    24 RETURN_VALUE  </code>from dis import dis dis(foo)    0 LOAD_CONST    1 (1) # 加载常量1    3 STORE_FAST    0 (x) # x赋值为1   6 LOAD_CONST    2 (<code>) # 加载常量2    9 MAKE_FUNCTION   0 # 创建函数    12 STORE_FAST    1 (bar)    15 LOAD_FAST    1 (bar)     18 LOAD_FAST    0 (x)    21 CALL_FUNCTION   1 # 调用函数    24 RETURN_VALUE  </code>

其中,

第一行为代码行号;
第二行为偏移地址;
第三行为字节码指令;
第四行为指令参数;
第五行为参数解释。

第一行为代码行号;
第二行为偏移地址;
第三行为字节码指令;
第四行为指令参数;
第五行为参数解释。

生成器源码分析

由了上面对于调用栈的理解,就可以很容易的明白生成器的具体实现。

生成器的源码位于object/genobject.c。

生成器的创建

PyObject *PyGen_New(PyFrameObject *f){ PyGenObject *gen = PyObject_GC_New(PyGenObject, &PyGen_Type); # 创建生成器对象 if (gen == NULL) {  Py_DECREF(f);  return NULL; } gen->gi_frame = f; # 赋予代码块 Py_INCREF(f->f_code); # 引用计数+1 gen->gi_code = (PyObject *)(f->f_code); gen->gi_running = 0; # 0表示为执行,也就是生成器的初始状态 gen->gi_weakreflist = NULL; _PyObject_GC_TRACK(gen); # GC跟踪 return (PyObject *)gen;}PyObject *PyGen_New(PyFrameObject *f){ PyGenObject *gen = PyObject_GC_New(PyGenObject, &PyGen_Type); # 创建生成器对象 if (gen == NULL) {  Py_DECREF(f);  return NULL; } gen->gi_frame = f; # 赋予代码块 Py_INCREF(f->f_code); # 引用计数+1 gen->gi_code = (PyObject *)(f->f_code); gen->gi_running = 0; # 0表示为执行,也就是生成器的初始状态 gen->gi_weakreflist = NULL; _PyObject_GC_TRACK(gen); # GC跟踪 return (PyObject *)gen;}

send与next

next与send函数,如下

static PyObject *gen_iternext(PyGenObject *gen){ return gen_send_ex(gen, NULL, 0);}static PyObject *gen_send(PyGenObject *gen, PyObject *arg){ return gen_send_ex(gen, arg, 0);}static PyObject *gen_iternext(PyGenObject *gen){ return gen_send_ex(gen, NULL, 0);}static PyObject *gen_send(PyGenObject *gen, PyObject *arg){ return gen_send_ex(gen, arg, 0);}

从上面的代码中可以看到,send和next都是调用的同一函数gen_send_ex,区别在于是否带有参数。

static PyObject *gen_send_ex(PyGenObject *gen, PyObject *arg, int exc){ PyThreadState *tstate = PyThreadState_GET(); PyFrameObject *f = gen->gi_frame; PyObject *result; if (gen->gi_running) { # 判断生成器是否已经运行  PyErr_SetString(PyExc_ValueError,      "generator already executing");  return NULL; } if (f==NULL || f->f_stacktop == NULL) { # 如果代码块为空或调用栈为空,则抛出StopIteration异常  /* Only set exception if called from send() */  if (arg && !exc)   PyErr_SetNone(PyExc_StopIteration);  return NULL; } if (f->f_lasti == -1) { # f_lasti=1 代表首次执行  if (arg && arg != Py_None) { # 首次执行不允许带有参数   PyErr_SetString(PyExc_TypeError,       "can't send non-None value to a "       "just-started generator");   return NULL;  } } else {  /* Push arg onto the frame's value stack */  result = arg ? arg : Py_None;  Py_INCREF(result); # 该参数引用计数+1  *(f->f_stacktop++) = result; # 参数压栈 } /* Generators always return to their most recent caller, not  * necessarily their creator. */ f->f_tstate = tstate; Py_XINCREF(tstate->frame); assert(f->f_back == NULL); f->f_back = tstate->frame; gen->gi_running = 1; # 修改生成器执行状态 result = PyEval_EvalFrameEx(f, exc); # 执行字节码 gen->gi_running = 0; # 恢复为未执行状态 /* Don't keep the reference to f_back any longer than necessary. It  * may keep a chain of frames alive or it could create a reference  * cycle. */ assert(f->f_back == tstate->frame); Py_CLEAR(f->f_back); /* Clear the borrowed reference to the thread state */ f->f_tstate = NULL; /* If the generator just returned (as opposed to yielding), signal  * that the generator is exhausted. */ if (result == Py_None && f->f_stacktop == NULL) {  Py_DECREF(result);  result = NULL;  /* Set exception if not called by gen_iternext() */  if (arg)   PyErr_SetNone(PyExc_StopIteration); } if (!result || f->f_stacktop == NULL) {  /* generator can't be rerun, so release the frame */  Py_DECREF(f);  gen->gi_frame = NULL; } return result;}static PyObject *gen_send_ex(PyGenObject *gen, PyObject *arg, int exc){ PyThreadState *tstate = PyThreadState_GET(); PyFrameObject *f = gen->gi_frame; PyObject *result; if (gen->gi_running) { # 判断生成器是否已经运行  PyErr_SetString(PyExc_ValueError,      "generator already executing");  return NULL; } if (f==NULL || f->f_stacktop == NULL) { # 如果代码块为空或调用栈为空,则抛出StopIteration异常  /* Only set exception if called from send() */  if (arg && !exc)   PyErr_SetNone(PyExc_StopIteration);  return NULL; } if (f->f_lasti == -1) { # f_lasti=1 代表首次执行  if (arg && arg != Py_None) { # 首次执行不允许带有参数   PyErr_SetString(PyExc_TypeError,       "can't send non-None value to a "       "just-started generator");   return NULL;  } } else {  /* Push arg onto the frame's value stack */  result = arg ? arg : Py_None;  Py_INCREF(result); # 该参数引用计数+1  *(f->f_stacktop++) = result; # 参数压栈 } /* Generators always return to their most recent caller, not  * necessarily their creator. */ f->f_tstate = tstate; Py_XINCREF(tstate->frame); assert(f->f_back == NULL); f->f_back = tstate->frame; gen->gi_running = 1; # 修改生成器执行状态 result = PyEval_EvalFrameEx(f, exc); # 执行字节码 gen->gi_running = 0; # 恢复为未执行状态 /* Don't keep the reference to f_back any longer than necessary. It  * may keep a chain of frames alive or it could create a reference  * cycle. */ assert(f->f_back == tstate->frame); Py_CLEAR(f->f_back); /* Clear the borrowed reference to the thread state */ f->f_tstate = NULL; /* If the generator just returned (as opposed to yielding), signal  * that the generator is exhausted. */ if (result == Py_None && f->f_stacktop == NULL) {  Py_DECREF(result);  result = NULL;  /* Set exception if not called by gen_iternext() */  if (arg)   PyErr_SetNone(PyExc_StopIteration); } if (!result || f->f_stacktop == NULL) {  /* generator can't be rerun, so release the frame */  Py_DECREF(f);  gen->gi_frame = NULL; } return result;}

字节码的执行

PyEval_EvalFrameEx函数的功能为执行字节码并返回结果。

# 主要流程如下,for (;;) { switch(opcode) { # opcode为操作码,对应着各种操作  case NOP:   goto fast_next_opcode;  ...  ...  case YIELD_VALUE: # 如果操作码是yield   retval = POP();    f->f_stacktop = stack_pointer;   why = WHY_YIELD;   goto fast_yield; # 利用goto跳出循环 }}fast_yield: ... return vetval; # 返回结果# 主要流程如下,for (;;) { switch(opcode) { # opcode为操作码,对应着各种操作  case NOP:   goto fast_next_opcode;  ...  ...  case YIELD_VALUE: # 如果操作码是yield   retval = POP();    f->f_stacktop = stack_pointer;   why = WHY_YIELD;   goto fast_yield; # 利用goto跳出循环 }}fast_yield: ... return vetval; # 返回结果

举一个例子,f_back上一个Frame,f_lasti上一次执行的指令的偏移量,

import sysfrom dis import disdef func(): f = sys._getframe(0) print f.f_lasti print f.f_back yield 1 print f.f_lasti print f.f_back yield 2a = func()dis(func)a.next()a.next()import sysfrom dis import disdef func(): f = sys._getframe(0) print f.f_lasti print f.f_back yield 1 print f.f_lasti print f.f_back yield 2a = func()dis(func)a.next()a.next()

结果如下,其中第三行的英文为操作码,对应着上面的opcode,每次switch都是在不同的opcode之间进行选择。

Python   0 LOAD_GLOBAL    0 (sys)    3 LOAD_ATTR    1 (_getframe)    6 LOAD_CONST    1 (0)    9 CALL_FUNCTION   1    12 STORE_FAST    0 (f)   15 LOAD_FAST    0 (f)    18 LOAD_ATTR    2 (f_lasti)     21 PRINT_ITEM       22 PRINT_NEWLINE     23 LOAD_FAST    0 (f)    26 LOAD_ATTR    3 (f_back)    29 PRINT_ITEM       30 PRINT_NEWLINE    31 LOAD_CONST    2 (1)    34 YIELD_VALUE  # 此时操作码为YIELD_VALUE,直接跳转上述goto语句,此时f_lasti为当前指令,f_back为当前frame    35 POP_TOP      36 LOAD_FAST    0 (f)    39 LOAD_ATTR    2 (f_lasti)    42 PRINT_ITEM       43 PRINT_NEWLINE     44 LOAD_FAST    0 (f)    47 LOAD_ATTR    3 (f_back)    50 PRINT_ITEM       51 PRINT_NEWLINE     52 LOAD_CONST    3 (2)    55 YIELD_VALUE       56 POP_TOP        57 LOAD_CONST    0 (None)    60 RETURN_VALUE  <frame object at 0x7fa75fcebc20> #和下面的frame相同,属于同一个frame,也就是说在同一个函数(命名空间)内,frame是同一个。<frame object at 0x7fa75fcebc20>   0 LOAD_GLOBAL    0 (sys)    3 LOAD_ATTR    1 (_getframe)    6 LOAD_CONST    1 (0)    9 CALL_FUNCTION   1    12 STORE_FAST    0 (f)   15 LOAD_FAST    0 (f)    18 LOAD_ATTR    2 (f_lasti)     21 PRINT_ITEM       22 PRINT_NEWLINE     23 LOAD_FAST    0 (f)    26 LOAD_ATTR    3 (f_back)    29 PRINT_ITEM       30 PRINT_NEWLINE     31 LOAD_CONST    2 (1)    34 YIELD_VALUE  # 此时操作码为YIELD_VALUE,直接跳转上述goto语句,此时f_lasti为当前指令,f_back为当前frame    35 POP_TOP       36 LOAD_FAST    0 (f)    39 LOAD_ATTR    2 (f_lasti)    42 PRINT_ITEM       43 PRINT_NEWLINE     44 LOAD_FAST    0 (f)    47 LOAD_ATTR    3 (f_back)    50 PRINT_ITEM       51 PRINT_NEWLINE     52 LOAD_CONST    3 (2)    55 YIELD_VALUE       56 POP_TOP        57 LOAD_CONST    0 (None)    60 RETURN_VALUE  <frame object at 0x7fa75fcebc20> #和下面的frame相同,属于同一个frame,也就是说在同一个函数(命名空间)内,frame是同一个。<frame object at 0x7fa75fcebc20>

总结

以上所述是小编给大家介绍的Python yield与实现方法代码分析,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对VEVB武林网网站的支持!


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