update annotation doc; update smartparam sync

docs/en_US/Tutorial/AnnotationSpec.md

@@ -41,11 +41,11 @@ There are 10 types to express your search space as follows:
 * `@nni.variable(nni.uniform(low, high),name=variable)`
   Which means the variable value is a value uniformly between low and high.
 * `@nni.variable(nni.quniform(low, high, q),name=variable)`
-  Which means the variable value is a value like round(uniform(low, high) / q) * q
+  Which means the variable value is a value like randint(floor((high-low)/q)+1)*q + low
 * `@nni.variable(nni.loguniform(low, high),name=variable)`
   Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
 * `@nni.variable(nni.qloguniform(low, high, q),name=variable)`
-  Which means the variable value is a value like round(exp(uniform(low, high)) / q) * q
+  Which means the variable value is a value like floor((loguniform(low, high) - low) / q) * q + low
 * `@nni.variable(nni.normal(mu, sigma),name=variable)`
   Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma.
 * `@nni.variable(nni.qnormal(mu, sigma, q),name=variable)`

src/sdk/pynni/nni/parameter_expressions.py

@@ -57,6 +57,7 @@ def quniform(low, high, q, random_state):
     q: sample step
     random_state: an object of numpy.random.RandomState
     '''
+    assert high > low, 'Upper bound must be larger than lower bound'
     return randint(np.floor((high-low)/q)+1, random_state) * q + low
 
 

src/sdk/pynni/nni/smartparam.py

@@ -57,14 +57,14 @@ if trial_env_vars.NNI_PLATFORM is None:
 
     def quniform(low, high, q, name=None):
         assert high > low, 'Upper bound must be larger than lower bound'
-        return round(random.uniform(low, high) / q) * q
+        return randint(np.floor((high-low)/q)+1) * q + low
 
     def loguniform(low, high, name=None):
         assert low > 0, 'Lower bound must be positive'
         return np.exp(random.uniform(np.log(low), np.log(high)))
 
     def qloguniform(low, high, q, name=None):
-        return round(loguniform(low, high) / q) * q
+        return np.floor((loguniform(low, high) - low) / q) * q + low
 
     def normal(mu, sigma, name=None):
         return random.gauss(mu, sigma)