change qtype format to conforme with BOHB

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 randint(floor((high-low)/q)+1)*q + low
+  Which means the variable value is a value like clip(round(uniform(low, high) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound.
 * `@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 floor((loguniform(low, high) - low) / q) * q + low
+  Which means the variable value is a value like clip(round(loguniform(low, high) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound.
 * `@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)`

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -45,7 +45,7 @@ All types of sampling strategies and their parameter are listed here:
   * When optimizing, this variable is constrained to a two-sided interval.
 
 * {"_type":"quniform","_value":[low, high, q]}
-  * Which means the variable value is a value like randint(floor((high-low)/q)+1)*q + low. For example, for _value specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For _value specified as [2, 10, 5], possible values are [2, 7]. All possible values have the same possibility to be selected.   
+  * Which means the variable value is a value like clip(round(uniform(low, high) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound. For example, for _value specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For _value specified as [2, 10, 5], possible values are [2, 5, 10].
 
   * Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose integer from a range [low, high], you can write `_value` like this: `[low, high, 1]`.
 
@@ -54,7 +54,7 @@ All types of sampling strategies and their parameter are listed here:
   * When optimizing, this variable is constrained to be positive.
 
 * {"_type":"qloguniform","_value":[low, high, q]}
-  * Which means the variable value is a value like floor((loguniform(low, high) - low) / q) * q + low
+  * Which means the variable value is a value like clip(round(loguniform(low, high)) / q) * q, low, high), where the clip operation is used to constraint the generated value in the bound.
   * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
 
 * {"_type":"normal","_value":[mu, sigma]}

src/sdk/pynni/nni/parameter_expressions.py

@@ -57,8 +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
+    return np.clip(np.round(uniform(low, high, random_state) / q) * q, low, high)
 
 
 def loguniform(low, high, random_state):
@@ -78,7 +77,7 @@ def qloguniform(low, high, q, random_state):
     q: sample step
     random_state: an object of numpy.random.RandomState
     '''
-    return np.floor((loguniform(low, high, random_state) - low) / q) * q + low
+    return np.clip(np.round(loguniform(low, high, random_state) / q) * q, low, high)
 
 
 def normal(mu, sigma, random_state):

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 randint(np.floor((high-low)/q)+1) * q + low
+        return np.clip(round(random.uniform(low, high) / q) * q, low, high)
 
     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 np.floor((loguniform(low, high) - low) / q) * q + low
+        return np.clip(round(loguniform(low, high) / q) * q, low, high)
 
     def normal(mu, sigma, name=None):
         return random.gauss(mu, sigma)