diff --git a/python/paddle/distribution.py b/python/paddle/distribution.py index 49e98805d24f3f8f5dc1cfcbf3ddc8d9fb835fde..35204affb3fd168b8bd137d78c3413a08885e2bb 100644 --- a/python/paddle/distribution.py +++ b/python/paddle/distribution.py @@ -102,21 +102,24 @@ class Distribution(object): tmp = 0. for arg in args: - valid_arg = False - for cls in [float, list, np.ndarray, tensor.Variable]: - if isinstance(arg, cls): - valid_arg = True - break - assert valid_arg, "type of input args must be float, list, numpy.ndarray or Tensor." if isinstance(arg, float): - arg = np.zeros(1) + arg + arg = [arg] + if not isinstance(arg, (list, np.ndarray, tensor.Variable)): + raise TypeError( + "Type of input args must be float, list, numpy.ndarray or Tensor, but received type {}". + format(type(arg))) + arg_np = np.array(arg) arg_dtype = arg_np.dtype - if str(arg_dtype) not in ['float32']: - warnings.warn( - "data type of argument only support float32, your argument will be convert to float32." - ) + if str(arg_dtype) != 'float32': + if str(arg_dtype) != 'float64': + # "assign" op doesn't support float64. if dtype is float64, float32 variable will be generated + # and converted to float64 later using "cast". + warnings.warn( + "data type of argument only support float32 and float64, your argument will be convert to float32." + ) arg_np = arg_np.astype('float32') + # tmp is used to support broadcast, it summarizes shapes of all the args and get the mixed shape. tmp = tmp + arg_np numpy_args.append(arg_np) @@ -129,6 +132,37 @@ class Distribution(object): return tuple(variable_args) + def _check_values_dtype_in_probs(self, param, value): + """ + Log_prob and probs methods have input ``value``, if value's dtype is different from param, + convert value's dtype to be consistent with param's dtype. + + Args: + param (Tensor): low and high in Uniform class, loc and scale in Normal class. + value (Tensor): The input tensor. + + Returns: + value (Tensor): Change value's dtype if value's dtype is different from param. + """ + if in_dygraph_mode(): + if value.dtype != param.dtype and convert_dtype( + value.dtype) in ['float32', 'float64']: + warnings.warn( + "dtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted." + ) + return core.ops.cast(value, 'in_dtype', value.dtype, + 'out_dtype', param.dtype) + return value + + check_variable_and_dtype(value, 'value', ['float32', 'float64'], + 'log_prob') + if value.dtype != param.dtype: + warnings.warn( + "dtype of input 'value' needs to be the same as parameters of distribution class. dtype of 'value' will be converted." + ) + return tensor.cast(value, dtype=param.dtype) + return value + class Uniform(Distribution): """Uniform distribution with `low` and `high` parameters. @@ -155,8 +189,8 @@ class Uniform(Distribution): [broadcasting](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basic_concept/broadcasting_en.html) (e.g., `high - low` is a valid operation). Args: - low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is int, float32, list, numpy.ndarray or Tensor - high(int|float|list|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is int, float32, list, numpy.ndarray or Tensor + low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is int, float, list, numpy.ndarray or Tensor + high(int|float|list|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is int, float, list, numpy.ndarray or Tensor name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Examples: @@ -206,6 +240,7 @@ class Uniform(Distribution): self.all_arg_is_float = False self.batch_size_unknown = False self.name = name if name is not None else 'Uniform' + self.dtype = 'float32' if isinstance(low, int): low = float(low) @@ -216,10 +251,22 @@ class Uniform(Distribution): self.batch_size_unknown = True self.low = low self.high = high + self.dtype = convert_dtype(low.dtype) else: if isinstance(low, float) and isinstance(high, float): self.all_arg_is_float = True + if isinstance( + low, + np.ndarray) and str(low.dtype) in ['float32', 'float64']: + self.dtype = low.dtype + elif isinstance( + high, + np.ndarray) and str(high.dtype) in ['float32', 'float64']: + self.dtype = high.dtype self.low, self.high = self._to_tensor(low, high) + if self.dtype != convert_dtype(self.low.dtype): + self.low = tensor.cast(self.low, dtype=self.dtype) + self.high = tensor.cast(self.high, dtype=self.dtype) def sample(self, shape, seed=0): """Generate samples of the specified shape. @@ -241,11 +288,11 @@ class Uniform(Distribution): if self.batch_size_unknown: output_shape = shape + batch_shape zero_tmp = tensor.fill_constant_batch_size_like( - self.low + self.high, batch_shape + shape, self.low.dtype, 0.) + self.low + self.high, batch_shape + shape, self.dtype, 0.) uniform_random_tmp = nn.uniform_random_batch_size_like( zero_tmp, zero_tmp.shape, - dtype=convert_dtype(zero_tmp.dtype), + dtype=self.dtype, min=0., max=1., seed=seed) @@ -259,9 +306,8 @@ class Uniform(Distribution): else: output_shape = shape + batch_shape output = nn.uniform_random( - output_shape, seed=seed) * (tensor.zeros( - output_shape, dtype=self.low.dtype) + - (self.high - self.low)) + output_shape, seed=seed, dtype=self.dtype) * (tensor.zeros( + output_shape, dtype=self.dtype) + (self.high - self.low)) output = elementwise_add(output, self.low, name=name) if self.all_arg_is_float: return nn.reshape(output, shape, name=name) @@ -279,22 +325,20 @@ class Uniform(Distribution): """ name = self.name + '_log_prob' + value = self._check_values_dtype_in_probs(self.low, value) if in_dygraph_mode(): + # ensure value in [low, high] lb_bool = self.low < value ub_bool = value < self.high - dtype = value.dtype lb = core.ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype', - dtype) + value.dtype) ub = core.ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype', - dtype) + value.dtype) return nn.log(lb * ub) - nn.log(self.high - self.low) - check_variable_and_dtype(value, 'value', ['float32', 'float64'], - 'log_prob') - - lb_bool = control_flow.less_than(self.low, value) - ub_bool = control_flow.less_than(value, self.high) + lb_bool = self.low < value + ub_bool = value < self.high lb = tensor.cast(lb_bool, dtype=value.dtype) ub = tensor.cast(ub_bool, dtype=value.dtype) return elementwise_sub( @@ -311,22 +355,19 @@ class Uniform(Distribution): """ name = self.name + '_probs' + value = self._check_values_dtype_in_probs(self.low, value) if in_dygraph_mode(): lb_bool = self.low < value ub_bool = value < self.high - dtype = value.dtype lb = core.ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype', - dtype) + value.dtype) ub = core.ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype', - dtype) + value.dtype) return (lb * ub) / (self.high - self.low) - check_variable_and_dtype(value, 'value', ['float32', 'float64'], - 'log_prob') - - lb_bool = control_flow.less_than(self.low, value) - ub_bool = control_flow.less_than(value, self.high) + lb_bool = self.low < value + ub_bool = value < self.high lb = tensor.cast(lb_bool, dtype=value.dtype) ub = tensor.cast(ub_bool, dtype=value.dtype) return elementwise_div((lb * ub), (self.high - self.low), name=name) @@ -334,6 +375,12 @@ class Uniform(Distribution): def entropy(self): """Shannon entropy in nats. + The entropy is + + .. math:: + + entropy(low, high) = \\log (high - low) + Returns: Tensor: Shannon entropy of uniform distribution.The data type is float32. @@ -364,8 +411,8 @@ class Normal(Distribution): * :math:`Z`: is the normalization constant. Args: - loc(int|float|list|numpy.ndarray|Tensor): The mean of normal distribution.The data type is int, float32, list, numpy.ndarray or Tensor. - scale(int|float|list|numpy.ndarray|Tensor): The std of normal distribution.The data type is int, float32, list, numpy.ndarray or Tensor. + loc(int|float|list|numpy.ndarray|Tensor): The mean of normal distribution.The data type is int, float, list, numpy.ndarray or Tensor. + scale(int|float|list|numpy.ndarray|Tensor): The std of normal distribution.The data type is int, float, list, numpy.ndarray or Tensor. name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Examples: @@ -418,6 +465,7 @@ class Normal(Distribution): self.batch_size_unknown = False self.all_arg_is_float = False self.name = name if name is not None else 'Normal' + self.dtype = 'float32' if isinstance(loc, int): loc = float(loc) @@ -428,10 +476,22 @@ class Normal(Distribution): self.batch_size_unknown = True self.loc = loc self.scale = scale + self.dtype = convert_dtype(loc.dtype) else: if isinstance(loc, float) and isinstance(scale, float): self.all_arg_is_float = True + if isinstance( + loc, + np.ndarray) and str(loc.dtype) in ['float32', 'float64']: + self.dtype = loc.dtype + elif isinstance( + scale, + np.ndarray) and str(scale.dtype) in ['float32', 'float64']: + self.dtype = scale.dtype self.loc, self.scale = self._to_tensor(loc, scale) + if self.dtype != convert_dtype(self.loc.dtype): + self.loc = tensor.cast(self.loc, dtype=self.dtype) + self.scale = tensor.cast(self.scale, dtype=self.dtype) def sample(self, shape, seed=0): """Generate samples of the specified shape. @@ -454,22 +514,18 @@ class Normal(Distribution): if self.batch_size_unknown: output_shape = shape + batch_shape zero_tmp = tensor.fill_constant_batch_size_like( - self.loc + self.scale, batch_shape + shape, self.loc.dtype, 0.) + self.loc + self.scale, batch_shape + shape, self.dtype, 0.) zero_tmp_reshape = nn.reshape(zero_tmp, output_shape) zero_tmp_shape = nn.shape(zero_tmp_reshape) normal_random_tmp = nn.gaussian_random( - zero_tmp_shape, - mean=0., - std=1., - seed=seed, - dtype=convert_dtype(self.loc.dtype)) + zero_tmp_shape, mean=0., std=1., seed=seed, dtype=self.dtype) output = normal_random_tmp * (zero_tmp_reshape + self.scale) output = elementwise_add(output, self.loc, name=name) return output else: output_shape = shape + batch_shape - output = nn.gaussian_random(output_shape, mean=0., std=1., seed=seed) * \ - (tensor.zeros(output_shape, dtype=self.loc.dtype) + self.scale) + output = nn.gaussian_random(output_shape, mean=0., std=1., seed=seed, dtype=self.dtype) * \ + (tensor.zeros(output_shape, dtype=self.dtype) + self.scale) output = elementwise_add(output, self.loc, name=name) if self.all_arg_is_float: return nn.reshape(output, shape, name=name) @@ -479,6 +535,16 @@ class Normal(Distribution): def entropy(self): """Shannon entropy in nats. + The entropy is + + .. math:: + + entropy(\sigma) = 0.5 \\log (2 \pi e \sigma^2) + + In the above equation: + + * :math:`scale = \sigma`: is the std. + Returns: Tensor: Shannon entropy of normal distribution.The data type is float32. @@ -486,7 +552,7 @@ class Normal(Distribution): name = self.name + '_entropy' batch_shape = list((self.loc + self.scale).shape) zero_tmp = tensor.fill_constant_batch_size_like( - self.loc + self.scale, batch_shape, self.loc.dtype, 0.) + self.loc + self.scale, batch_shape, self.dtype, 0.) return elementwise_add( 0.5 + zero_tmp, 0.5 * math.log(2 * math.pi) + nn.log((self.scale + zero_tmp)), @@ -502,11 +568,9 @@ class Normal(Distribution): Tensor: log probability.The data type is same with value. """ - if not in_dygraph_mode(): - check_variable_and_dtype(value, 'value', ['float32', 'float64'], - 'log_prob') - name = self.name + '_log_prob' + value = self._check_values_dtype_in_probs(self.loc, value) + var = self.scale * self.scale log_scale = nn.log(self.scale) return elementwise_sub( @@ -524,11 +588,9 @@ class Normal(Distribution): Tensor: probability.The data type is same with value. """ - if not in_dygraph_mode(): - check_variable_and_dtype(value, 'value', ['float32', 'float64'], - 'log_prob') - name = self.name + '_probs' + value = self._check_values_dtype_in_probs(self.loc, value) + var = self.scale * self.scale return elementwise_div( ops.exp(-1. * ((value - self.loc) * (value - self.loc)) / @@ -538,6 +600,29 @@ class Normal(Distribution): def kl_divergence(self, other): """The KL-divergence between two normal distributions. + The probability density function (pdf) is + + .. math:: + + KL\_divergence(\mu_0, \sigma_0; \mu_1, \sigma_1) = 0.5 (ratio^2 + (\\frac{diff}{\sigma_1})^2 - 1 - 2 \\ln {ratio}) + + .. math:: + + ratio = \\frac{\sigma_0}{\sigma_1} + + .. math:: + + diff = \mu_1 - \mu_0 + + In the above equation: + + * :math:`loc = \mu_0`: is the mean of current Normal distribution. + * :math:`scale = \sigma_0`: is the std of current Normal distribution. + * :math:`loc = \mu_1`: is the mean of other Normal distribution. + * :math:`scale = \sigma_1`: is the std of other Normal distribution. + * :math:`ratio`: is the ratio of scales. + * :math:`diff`: is the difference between means. + Args: other (Normal): instance of Normal. diff --git a/python/paddle/fluid/tests/unittests/test_distribution.py b/python/paddle/fluid/tests/unittests/test_distribution.py index 533ad9604cf0d879371796fb197e61e931fb479f..47a1c407230527d53327ba57d7b5d7a979bd7d49 100644 --- a/python/paddle/fluid/tests/unittests/test_distribution.py +++ b/python/paddle/fluid/tests/unittests/test_distribution.py @@ -40,8 +40,11 @@ class DistributionNumpy(): class UniformNumpy(DistributionNumpy): def __init__(self, low, high): - self.low = np.array(low).astype('float32') - self.high = np.array(high).astype('float32') + self.low = np.array(low) + self.high = np.array(high) + if str(self.low.dtype) not in ['float32', 'float64']: + self.low = self.low.astype('float32') + self.high = self.high.astype('float32') def sample(self, shape): shape = tuple(shape) + (self.low + self.high).shape @@ -49,13 +52,13 @@ class UniformNumpy(DistributionNumpy): (self.high - self.low)) def log_prob(self, value): - lb = np.less(self.low, value).astype('float32') - ub = np.less(value, self.high).astype('float32') + lb = np.less(self.low, value).astype(self.low.dtype) + ub = np.less(value, self.high).astype(self.low.dtype) return np.log(lb * ub) - np.log(self.high - self.low) def probs(self, value): - lb = np.less(self.low, value).astype('float32') - ub = np.less(value, self.high).astype('float32') + lb = np.less(self.low, value).astype(self.low.dtype) + ub = np.less(value, self.high).astype(self.low.dtype) return (lb * ub) / (self.high - self.low) def entropy(self): @@ -64,8 +67,11 @@ class UniformNumpy(DistributionNumpy): class NormalNumpy(DistributionNumpy): def __init__(self, loc, scale): - self.loc = np.array(loc).astype('float32') - self.scale = np.array(scale).astype('float32') + self.loc = np.array(loc) + self.scale = np.array(scale) + if str(self.loc.dtype) not in ['float32', 'float64']: + self.loc = self.loc.astype('float32') + self.scale = self.scale.astype('float32') def sample(self, shape): shape = tuple(shape) + (self.loc + self.scale).shape @@ -83,8 +89,8 @@ class NormalNumpy(DistributionNumpy): (2. * var)) / (math.sqrt(2 * math.pi) * self.scale) def entropy(self): - return 0.5 + 0.5 * np.log(np.array(2. * math.pi).astype( - 'float32')) + np.log(self.scale) + return 0.5 + 0.5 * np.log( + np.array(2. * math.pi).astype(self.loc.dtype)) + np.log(self.scale) def kl_divergence(self, other): var_ratio = (self.scale / other.scale) @@ -94,724 +100,571 @@ class NormalNumpy(DistributionNumpy): return 0.5 * (var_ratio + t1 - 1 - np.log(var_ratio)) -class DistributionTest(unittest.TestCase): - def setUp(self, use_gpu=False): +class UniformTest(unittest.TestCase): + def setUp(self, use_gpu=False, batch_size=5, dims=6): self.use_gpu = use_gpu if not use_gpu: - place = fluid.CPUPlace() + self.place = fluid.CPUPlace() self.gpu_id = -1 else: - place = fluid.CUDAPlace(0) + self.place = fluid.CUDAPlace(0) self.gpu_id = 0 - self.executor = fluid.Executor(place) - - def build_normal_common_net(self, batch_size, dims, sample_shape, loc_float, - scale_float, other_loc_float, other_scale_float, - scale_np, other_scale_np, loc_np, other_loc_np, - loc, scale, other_loc, other_scale, values): - """Generate Normal object and get the output of its methods including - ``sample``, ``entropy``, ``log_prob``, ``probs`` and ``kl_divergence``. - Parameters ``loc`` and ``scale`` have different data types to test different situations. - - Args: - batch_size(int): The first dimension of the shape of parameters(loc and scale). - dims(int): The second dimension of the shape of parameters. - sample_shape(int): The sample value used in ``sample`` method. - loc_float(float): Generated in function ``get_normal_random_input``, loc is a float number. - scale_float(float): Generated in function ``get_normal_random_input``, scale is a float number. - other_loc_float(float): Generated in function ``get_normal_random_input``, other_loc is a - float number. It is the first parameter in another Normal object used in ``kl_divergence`` - method. - other_scale_float(float): Generated in function ``get_normal_random_input``, other_scale is a - float number. It is the second parameter in another Normal object used in ``kl_divergence`` - method. - scale_np(numpy.ndarray): Generated in function ``get_normal_random_input``, An numpy array - whose shape is [batch_size, dims]. - other_scale_np(numpy.ndarray): Generated in function ``get_normal_random_input``, other_scale_np - is an numpy array. It is the second parameter in another Normal object used in ``kl_divergence`` - method. - loc_np(numpy.ndarray): Generated in function ``get_normal_random_input``, An numpy array - whose shape is [batch_size, dims]. - other_loc_np(numpy.ndarray): Generated in function ``get_normal_random_input``, other_loc_np - is an numpy array. It is the first parameter in another Normal object used in ``kl_divergence`` - method. - loc(Tensor): In dynamic mode, loc is generated in ``build_normal_dygraph``, it's a Tensor filled - with ``loc_np`` data. In static mode, loc is generated in ``build_normal_static``, ``layers.data`` - method is used to get a Placeholder whose shape is [dims]. - scale(Tensor): In dynamic mode, scale is generated in ``build_normal_dygraph``, it's a Tensor filled - with ``scale_np`` data. In static mode, scale is generated in ``build_normal_static``, ``layers.data`` - method is used to get a Placeholder whose shape is [dims]. - other_loc(Tensor): In dynamic mode, other_loc is generated in ``build_normal_dygraph``, it's a Tensor - filled with ``other_loc_np`` data. In static mode, other_loc is generated in ``build_normal_static``, - ``layers.data`` method is used to get a Placeholder whose shape is [dims]. It is the first parameter - in another Normal object used in ``kl_divergence`` method. - other_scale(Tensor): In dynamic mode, other_scale is generated in ``build_normal_dygraph``, it's a Tensor - filled with ``other_scale_np`` data. In static mode, other_scale is generated in ``build_normal_static``, - ``layers.data`` method is used to get a Placeholder whose shape is [dims]. It is the second parameter - in another Normal object used in ``kl_divergence`` method. - values(Tensor): In dynamic mode, values is generated in ``build_normal_dygraph``, it's a Tensor filled with - ``values_np`` data. In static mode, values is generated in ``build_normal_static``, ``layers.data`` - method is used to get a Placeholder whose shape is [dims]. - - Returns: - List: The elements of the list are the output of sample, entropy, log_prob, probs, kl_divergence methods. - The inputs' type of these methods can be float, np.ndarray and Tensor. And broadcast will be considered. - - """ - normal_int = Normal(int(loc_float), int(scale_float)) - normal_float = Normal(loc_float, scale_float) - other_normal_float = Normal(other_loc_float, other_scale_float) - - normal_float_np_broadcast = Normal(loc_float, scale_np) - other_normal_float_np_broadcast = Normal(other_loc_float, - other_scale_np) - - normal_np = Normal(loc_np, scale_np) - other_normal_np = Normal(other_loc_np, other_scale_np) - - normal_variable = Normal(loc, scale) - other_normal_variable = Normal(other_loc, other_scale) - - sample_int = normal_int.sample([batch_size, dims]) - sample_float = normal_float.sample([batch_size, dims]) - sample_float_np_broadcast = normal_float_np_broadcast.sample( - [batch_size, dims]) - sample_np = normal_np.sample([batch_size, dims]) - sample_variable = normal_variable.sample([batch_size, dims]) - - sample_int_diff = normal_int.sample([sample_shape]) - sample_float_diff = normal_float.sample([sample_shape]) - sample_float_np_broadcast_diff = normal_float_np_broadcast.sample( - [sample_shape]) - sample_np_diff = normal_np.sample([sample_shape]) - sample_variable_diff = normal_variable.sample([sample_shape]) - - entropy_int = normal_int.entropy() - entropy_float = normal_float.entropy() - entropy_float_np_broadcast = normal_float_np_broadcast.entropy() - entropy_np = normal_np.entropy() - entropy_variable = normal_variable.entropy() - - lp_float_np_broadcast = normal_float_np_broadcast.log_prob(values) - lp_np = normal_np.log_prob(values) - lp_variable = normal_variable.log_prob(values) - - p_float_np_broadcast = normal_float_np_broadcast.probs(values) - p_np = normal_np.probs(values) - p_variable = normal_variable.probs(values) - - kl_float = normal_float.kl_divergence(other_normal_float) - kl_float_np_broadcast = normal_float_np_broadcast.kl_divergence( - other_normal_float_np_broadcast) - kl_np = normal_np.kl_divergence(other_normal_np) - kl_variable = normal_variable.kl_divergence(other_normal_variable) - - fetch_list = [ - sample_int, sample_float, sample_float_np_broadcast, sample_np, - sample_variable, sample_int_diff, sample_float_diff, - sample_float_np_broadcast_diff, sample_np_diff, - sample_variable_diff, entropy_int, entropy_float, - entropy_float_np_broadcast, entropy_np, entropy_variable, - lp_float_np_broadcast, lp_np, lp_variable, p_float_np_broadcast, - p_np, p_variable, kl_float, kl_float_np_broadcast, kl_np, - kl_variable - ] - return fetch_list - - def build_normal_static(self, test_program, batch_size, dims, sample_shape, - loc_float, scale_float, other_loc_float, - other_scale_float, scale_np, other_scale_np, loc_np, - other_loc_np, values_np): - """ - In static mode, generate feed data of Normal network, and get output fetch_list using - ``build_normal_common_net``. - - Args: - test_program: In static mode, the Program object. - other args can refer to function ``build_normal_common_net``. - - Returns: - feed_vars: The feed data of Normal network in static mode. - fetch_list: The output is generated by function ``build_normal_common_net``. - """ - with fluid.program_guard(test_program): - loc = layers.data(name='loc', shape=[dims], dtype='float32') - scale = layers.data(name='scale', shape=[dims], dtype='float32') - - other_loc = layers.data( - name='other_loc', shape=[dims], dtype='float32') - other_scale = layers.data( - name='other_scale', shape=[dims], dtype='float32') - values = layers.data(name='values', shape=[dims], dtype='float32') + self.init_numpy_data(batch_size, dims) - fetch_list = self.build_normal_common_net( - batch_size, dims, sample_shape, loc_float, scale_float, - other_loc_float, other_scale_float, scale_np, other_scale_np, - loc_np, other_loc_np, loc, scale, other_loc, other_scale, - values) + paddle.disable_static(self.place) + self.init_dynamic_data(batch_size, dims) - feed_vars = { - 'loc': loc_np, - 'scale': scale_np, - 'other_loc': other_loc_np, - 'other_scale': other_scale_np, - 'values': values_np - } - return feed_vars, fetch_list - - def build_normal_dygraph(self, batch_size, dims, sample_shape, loc_float, - scale_float, other_loc_float, other_scale_float, - scale_np, other_scale_np, loc_np, other_loc_np, - values_np): - """ - In dynamic mode, generate input data of Normal network, and get output fetch_list using - ``build_normal_common_net``. - - Args: - refer to function ``build_normal_common_net``. - - Returns: - fetch_list_numpy: The output is generated by function ``build_normal_common_net``. Transform - these tensor to numpy.ndarray. - """ - loc = paddle.to_tensor(loc_np) - scale = paddle.to_tensor(scale_np) - other_loc = paddle.to_tensor(other_loc_np) - other_scale = paddle.to_tensor(other_scale_np) - values = paddle.to_tensor(values_np) - - fetch_list = self.build_normal_common_net( - batch_size, dims, sample_shape, loc_float, scale_float, - other_loc_float, other_scale_float, scale_np, other_scale_np, - loc_np, other_loc_np, loc, scale, other_loc, other_scale, values) - fetch_list_numpy = [t.numpy() for t in fetch_list] - return fetch_list_numpy - - def get_normal_random_input(self, batch_size, dims): - """ - Generate input data ``loc`` and ``scale`` used in Normal network. - - Args: - refer to function ``build_normal_common_net``. - - Returns: - List: Different data type of ``loc`` and ``scale``, including float, numpy.ndarray. - By the way, ``other_loc`` and ``other_scale`` are used in ``kl_divergence`` method. - refer to ``args`` in function ``build_normal_common_net``. - """ - loc_np = np.random.randn(batch_size, dims).astype('float32') - other_loc_np = np.random.randn(batch_size, dims).astype('float32') - - loc_float = (np.random.ranf() - 0.5) * 4 - scale_float = (np.random.ranf() - 0.5) * 4 - while scale_float < 0: - scale_float = (np.random.ranf() - 0.5) * 4 - - other_loc_float = (np.random.ranf() - 0.5) * 4 - other_scale_float = (np.random.ranf() - 0.5) * 4 - while other_scale_float < 0: - other_scale_float = (np.random.ranf() - 0.5) * 4 - - scale_np = np.random.randn(batch_size, dims).astype('float32') - other_scale_np = np.random.randn(batch_size, dims).astype('float32') - values_np = np.random.randn(batch_size, dims).astype('float32') - - while not np.all(scale_np > 0): - scale_np = np.random.randn(batch_size, dims).astype('float32') - while not np.all(other_scale_np > 0): - other_scale_np = np.random.randn(batch_size, dims).astype('float32') - return [ - loc_np, other_loc_np, loc_float, scale_float, other_loc_float, - other_scale_float, scale_np, other_scale_np, values_np - ] - - def compare_normal_with_numpy(self, - data_list, - output_list, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Compare the outputs of Normal's methods in paddle and numpy. If the outputs are not consistent, - raise errors. - - Args: - data_list: Input data generated by function ``get_normal_random_input``. - output_list: The outputs of Normal's methods in static or dynamic mode. - batch_size(int): The first dimension of the shape of parameters(loc and scale). - dims(int): The second dimension of the shape of parameters. - sample_shape(int): The sample value used in ``sample`` method. - tolerance(float): The tolerance of the error. - """ - loc_np, other_loc_np, loc_float, scale_float, other_loc_float, other_scale_float, scale_np, other_scale_np, values_np = data_list - - np_normal_int = NormalNumpy(int(loc_float), int(scale_float)) - np_normal_float = NormalNumpy(loc_float, scale_float) - np_other_normal_float = NormalNumpy(other_loc_float, other_scale_float) - np_normal_float_np_broadcast = NormalNumpy(loc_float, scale_np) - np_other_normal_float_np_broadcast = NormalNumpy(other_loc_float, - other_scale_np) - np_normal = NormalNumpy(loc_np, scale_np) - np_other_normal = NormalNumpy(other_loc_np, other_scale_np) - - gt_sample_int = np_normal_int.sample([batch_size, dims]) - gt_sample_float = np_normal_float.sample([batch_size, dims]) - gt_sample_float_np_broadcast = np_normal_float_np_broadcast.sample( - [batch_size, dims]) - gt_sample_np = np_normal.sample([batch_size, dims]) - - gt_sample_int_diff = np_normal_int.sample([sample_shape]) - gt_sample_float_diff = np_normal_float.sample([sample_shape]) - gt_sample_float_np_broadcast_diff = np_normal_float_np_broadcast.sample( - [sample_shape]) - gt_sample_np_diff = np_normal.sample([sample_shape]) - - gt_entropy_int = np_normal_int.entropy() - gt_entropy_float = np_normal_float.entropy() - gt_entropy_float_np_broadcast = np_normal_float_np_broadcast.entropy() - gt_entropy = np_normal.entropy() - gt_lp_float_np_broadcast = np_normal_float_np_broadcast.log_prob( - values_np) - gt_lp = np_normal.log_prob(values_np) - gt_p_float_np_broadcast = np_normal_float_np_broadcast.probs(values_np) - gt_p = np_normal.probs(values_np) - gt_kl_float = np_normal_float.kl_divergence(np_other_normal_float) - gt_kl_float_np_broadcast = np_normal_float_np_broadcast.kl_divergence( - np_other_normal_float_np_broadcast) - gt_kl = np_normal.kl_divergence(np_other_normal) - - [ - output_sample_int, output_sample_float, - output_sample_float_np_broadcast, output_sample_np, - output_sample_variable, output_sample_int_diff, - output_sample_float_diff, output_sample_float_np_broadcast_diff, - output_sample_np_diff, output_sample_variable_diff, - output_entropy_int, output_entropy_float, - output_entropy_float_np_broadcast, output_entropy_np, - output_entropy_variable, output_lp_float_np_broadcast, output_lp_np, - output_lp_variable, output_p_float_np_broadcast, output_p_np, - output_p_variable, output_kl_float, output_kl_float_np_broadcast, - output_kl_np, output_kl_variable - ] = output_list - - np.testing.assert_equal(output_sample_int.shape, gt_sample_int.shape) - np.testing.assert_equal(output_sample_float.shape, - gt_sample_float.shape) - np.testing.assert_equal(output_sample_float_np_broadcast.shape, - gt_sample_float_np_broadcast.shape) - np.testing.assert_equal(output_sample_np.shape, gt_sample_np.shape) - np.testing.assert_equal(output_sample_variable.shape, - gt_sample_np.shape) - np.testing.assert_equal(output_sample_int_diff.shape, - gt_sample_int_diff.shape) - np.testing.assert_equal(output_sample_float_diff.shape, - gt_sample_float_diff.shape) - np.testing.assert_equal(output_sample_float_np_broadcast_diff.shape, - gt_sample_float_np_broadcast_diff.shape) - np.testing.assert_equal(output_sample_np_diff.shape, - gt_sample_np_diff.shape) - np.testing.assert_equal(output_sample_variable_diff.shape, - gt_sample_np_diff.shape) - np.testing.assert_allclose( - output_entropy_int, gt_entropy_int, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_entropy_float, - gt_entropy_float, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_entropy_float_np_broadcast, - gt_entropy_float_np_broadcast, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_entropy_np, gt_entropy, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_entropy_variable, gt_entropy, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_lp_float_np_broadcast, - gt_lp_float_np_broadcast, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_lp_np, gt_lp, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_lp_variable, gt_lp, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_p_float_np_broadcast, - gt_p_float_np_broadcast, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_p_np, gt_p, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_p_variable, gt_p, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_kl_float, gt_kl_float, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_kl_float_np_broadcast, - gt_kl_float_np_broadcast, - rtol=tolerance, - atol=tolerance) + paddle.enable_static() + self.test_program = fluid.Program() + self.executor = fluid.Executor(self.place) + self.init_static_data(batch_size, dims) + + def init_numpy_data(self, batch_size, dims): + # low ans high are 'float' + self.low_np = np.random.uniform(-2, 1) + self.high_np = np.random.uniform(1, 3) + self.values_np = np.array([1.0]).astype('float32') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_low = self.low_np + self.dynamic_high = self.high_np + self.dynamic_values = paddle.to_tensor(self.values_np) + + def init_static_data(self, batch_size, dims): + self.static_low = self.low_np + self.static_high = self.high_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[], dtype='float32') + + def compare_with_numpy(self, fetch_list, sample_shape=7, tolerance=1e-6): + sample, entropy, log_prob, probs = fetch_list + + np_uniform = UniformNumpy(self.low_np, self.high_np) + np_sample = np_uniform.sample([sample_shape]) + np_entropy = np_uniform.entropy() + np_lp = np_uniform.log_prob(self.values_np) + np_p = np_uniform.probs(self.values_np) + + np.testing.assert_equal(sample.shape, np_sample.shape) np.testing.assert_allclose( - output_kl_np, gt_kl, rtol=tolerance, atol=tolerance) + entropy, np_entropy, rtol=tolerance, atol=tolerance) np.testing.assert_allclose( - output_kl_variable, gt_kl, rtol=tolerance, atol=tolerance) - - def test_normal_distribution_static(self, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Test Normal's methods in static mode. - - Args: - refer to ``compare_normal_with_numpy`` function. - """ - test_program = fluid.Program() - data_list = self.get_normal_random_input(batch_size, dims) - loc_np, other_loc_np, loc_float, scale_float, other_loc_float, other_scale_float, scale_np, other_scale_np, values_np = data_list - - feed_vars, fetch_list = self.build_normal_static( - test_program, batch_size, dims, sample_shape, loc_float, - scale_float, other_loc_float, other_scale_float, scale_np, - other_scale_np, loc_np, other_loc_np, values_np) - self.executor.run(fluid.default_startup_program()) + log_prob, np_lp, rtol=tolerance, atol=tolerance) + np.testing.assert_allclose(probs, np_p, rtol=tolerance, atol=tolerance) - output_list = self.executor.run(program=test_program, - feed=feed_vars, - fetch_list=fetch_list) - - self.compare_normal_with_numpy(data_list, output_list, batch_size, dims, - sample_shape, tolerance) - - def test_normal_distribution_dygraph(self, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Test Normal's methods in dynamic mode. - - Args: - refer to ``compare_normal_with_numpy`` function. - """ - paddle.disable_static() - data_list = self.get_normal_random_input(batch_size, dims) - loc_np, other_loc_np, loc_float, scale_float, other_loc_float, other_scale_float, scale_np, other_scale_np, values_np = data_list - - output_list = self.build_normal_dygraph( - batch_size, dims, sample_shape, loc_float, scale_float, - other_loc_float, other_scale_float, scale_np, other_scale_np, - loc_np, other_loc_np, values_np) - - self.compare_normal_with_numpy(data_list, output_list, batch_size, dims, - sample_shape, tolerance) + def test_uniform_distribution_dygraph(self, sample_shape=7, tolerance=1e-6): + paddle.disable_static(self.place) + uniform = Uniform(self.dynamic_low, self.dynamic_high) + sample = uniform.sample([sample_shape]).numpy() + entropy = uniform.entropy().numpy() + log_prob = uniform.log_prob(self.dynamic_values).numpy() + probs = uniform.probs(self.dynamic_values).numpy() + fetch_list = [sample, entropy, log_prob, probs] + + self.compare_with_numpy(fetch_list) + + def test_uniform_distribution_static(self, sample_shape=7, tolerance=1e-6): paddle.enable_static() + with fluid.program_guard(self.test_program): + uniform = Uniform(self.static_low, self.static_high) + sample = uniform.sample([sample_shape]) + entropy = uniform.entropy() + log_prob = uniform.log_prob(self.static_values) + probs = uniform.probs(self.static_values) + fetch_list = [sample, entropy, log_prob, probs] - def build_uniform_common_net(self, batch_size, dims, sample_shape, - low_float, high_float, high_np, low_np, - values_np, low, high, values): - """Generate Uniform object and get the output of its methods including ``sample``, ``entropy``, - ``log_prob`` and ``probs``. - Parameters ``low`` and ``high`` have different data types to test different situations. - - Args: - batch_size(int): The first dimension of the shape of parameters(low and high). - dims(int): The second dimension of the shape of parameters. - sample_shape(int): The sample value used in ``sample`` method. - low_float(float): Parameter ``low`` is a float number. - high_float(float): Parameter ``high`` is a float number. - high_np(numpy.ndarray): An numpy array whose shape is [batch_size, dims]. - low_np(numpy.ndarray): An numpy array whose shape is [batch_size, dims]. - values_np(numpy.ndarray): The input of ``log_prob`` and ``probs`` methods. An numpy array whose - shape is [batch_size, dims]. - low(Tensor): In dynamic mode, low is generated in ``build_uniform_dygraph``, it's a Tensor filled - with ``low_np`` data. In static mode, low is generated in ``build_uniform_static``. - high(Tensor): In dynamic mode, high is generated in ``build_uniform_dygraph``, it's a Tensor filled - with ``high_np`` data. In static mode, high is generated in ``build_uniform_static``. - values(Tensor): In dynamic mode, values is generated in ``build_uniform_dygraph``, it's a Tensor - filled with ``values_np`` data. In static mode, values is generated in ``build_uniform_static``. - - Returns: - List: The elements of the list are the output of sample, entropy, log_prob, probs methods. - The inputs' type of these methods can be float, np.ndarray and Tensor. And broadcast will be - considered. - - """ - uniform_int = Uniform(int(low_float), int(high_float)) - uniform_float = Uniform(low_float, high_float) - uniform_float_np_broadcast = Uniform(low_float, high_np) - uniform_np = Uniform(low_np, high_np) - uniform_variable = Uniform(low, high) - - sample_int = uniform_int.sample([batch_size, dims]) - sample_float = uniform_float.sample([batch_size, dims]) - sample_float_np_broadcast = uniform_float_np_broadcast.sample( - [batch_size, dims]) - sample_np = uniform_np.sample([batch_size, dims]) - sample_variable = uniform_variable.sample([batch_size, dims]) - - sample_int_diff = uniform_int.sample([sample_shape]) - sample_float_diff = uniform_float.sample([sample_shape]) - sample_float_np_broadcast_diff = uniform_float_np_broadcast.sample( - [sample_shape]) - sample_np_diff = uniform_np.sample([sample_shape]) - sample_variable_diff = uniform_variable.sample([sample_shape]) - - entropy_int = uniform_int.entropy() - entropy_float = uniform_float.entropy() - entropy_float_np_broadcast = uniform_float_np_broadcast.entropy() - entropy_np = uniform_np.entropy() - entropy_variable = uniform_variable.entropy() - - lp_float_np_broadcast = uniform_float_np_broadcast.log_prob(values) - lp_np = uniform_np.log_prob(values) - lp_variable = uniform_variable.log_prob(values) - - p_float_np_broadcast = uniform_float_np_broadcast.probs(values) - p_np = uniform_np.probs(values) - p_variable = uniform_variable.probs(values) - - fetch_list = [ - sample_int, sample_float, sample_float_np_broadcast, sample_np, - sample_variable, sample_int_diff, sample_float_diff, - sample_float_np_broadcast_diff, sample_np_diff, - sample_variable_diff, entropy_int, entropy_float, - entropy_float_np_broadcast, entropy_np, entropy_variable, - lp_float_np_broadcast, lp_np, lp_variable, p_float_np_broadcast, - p_np, p_variable - ] - return fetch_list - - def build_uniform_static(self, test_program, batch_size, dims, sample_shape, - low_float, high_float, high_np, low_np, values_np): - """ - In static mode, generate feed data of Uniform network, and get output fetch_list using - ``build_uniform_common_net``. - - Args: - test_program: In static mode, the Program object. - other args can refer to function ``build_uniform_common_net``. - - Returns: - feed_vars: The feed data of Uniform network in static mode. - fetch_list: The output is generated by function ``build_uniform_common_net``. - """ - with fluid.program_guard(test_program): - low = layers.data(name='low', shape=[dims], dtype='float32') - high = layers.data(name='high', shape=[dims], dtype='float32') - - values = layers.data(name='values', shape=[dims], dtype='float32') - - fetch_list = self.build_uniform_common_net( - batch_size, dims, sample_shape, low_float, high_float, high_np, - low_np, values_np, low, high, values) - - feed_vars = {'low': low_np, 'high': high_np, 'values': values_np} - return feed_vars, fetch_list - - def build_uniform_dygraph(self, batch_size, dims, sample_shape, low_float, - high_float, high_np, low_np, values_np): - """ - In dynamic mode, generate input data of Uniform network, and get output fetch_list using - ``build_uniform_common_net``. - - Args: - refer to function ``build_uniform_common_net``. - - Returns: - fetch_list_numpy: The output is generated by function ``build_uniform_common_net``. Transform - these tensor to numpy.ndarray. - """ - low = paddle.to_tensor(low_np) - high = paddle.to_tensor(high_np) - values = paddle.to_tensor(values_np) - - fetch_list = self.build_uniform_common_net( - batch_size, dims, sample_shape, low_float, high_float, high_np, - low_np, values_np, low, high, values) - fetch_list_numpy = [t.numpy() for t in fetch_list] - return fetch_list_numpy - - def compare_uniform_with_numpy(self, - data_list, - output_list, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Compare the outputs of Uniform's methods in paddle and numpy. If the outputs are not consistent, - raise errors. - - Args: - data_list: Input data including float and numpy.ndarray type of ``low`` and ``high`` parameters. - output_list: The outputs of Uniform's methods in static or dynamic mode. - batch_size(int): The first dimension of the shape of parameters(low and high). - dims(int): The second dimension of the shape of parameters. - sample_shape(int): The sample value used in ``sample`` method. - tolerance(float): The tolerance of the error. - """ - [low_np, low_float, high_float, high_np, values_np] = data_list - - np_uniform_int = UniformNumpy(int(low_float), int(high_float)) - np_uniform_float = UniformNumpy(low_float, high_float) - np_uniform_float_np_broadcast = UniformNumpy(low_float, high_np) - np_uniform = UniformNumpy(low_np, high_np) - - gt_sample_int = np_uniform_int.sample([batch_size, dims]) - gt_sample_float = np_uniform_float.sample([batch_size, dims]) - gt_sample_float_np_broadcast = np_uniform_float_np_broadcast.sample( - [batch_size, dims]) - gt_sample_np = np_uniform.sample([batch_size, dims]) - gt_sample_int_diff = np_uniform_int.sample([sample_shape]) - gt_sample_float_diff = np_uniform_float.sample([sample_shape]) - gt_sample_float_np_broadcast_diff = np_uniform_float_np_broadcast.sample( - [sample_shape]) - gt_sample_np_diff = np_uniform.sample([sample_shape]) - gt_entropy_int = np_uniform_int.entropy() - gt_entropy_float = np_uniform_float.entropy() - gt_entropy_float_np_broadcast = np_uniform_float_np_broadcast.entropy() - gt_entropy = np_uniform.entropy() - gt_lp_float_np_broadcast = np_uniform_float_np_broadcast.log_prob( - values_np) - gt_lp = np_uniform.log_prob(values_np) - gt_p_float_np_broadcast = np_uniform_float_np_broadcast.probs(values_np) - gt_p = np_uniform.probs(values_np) - - [ - output_sample_int, output_sample_float, - output_sample_float_np_broadcast, output_sample_np, - output_sample_variable, output_sample_int_diff, - output_sample_float_diff, output_sample_float_np_broadcast_diff, - output_sample_np_diff, output_sample_variable_diff, - output_entropy_int, output_entropy_float, - output_entropy_float_np_broadcast, output_entropy_np, - output_entropy_variable, output_lp_float_np_broadcast, output_lp_np, - output_lp_variable, output_p_float_np_broadcast, output_p_np, - output_p_variable - ] = output_list - - np.testing.assert_equal(output_sample_int.shape, gt_sample_int.shape) - np.testing.assert_equal(output_sample_float.shape, - gt_sample_float.shape) - np.testing.assert_equal(output_sample_float_np_broadcast.shape, - gt_sample_float_np_broadcast.shape) - np.testing.assert_equal(output_sample_np.shape, gt_sample_np.shape) - np.testing.assert_equal(output_sample_variable.shape, - gt_sample_np.shape) - np.testing.assert_equal(output_sample_int_diff.shape, - gt_sample_int_diff.shape) - np.testing.assert_equal(output_sample_float_diff.shape, - gt_sample_float_diff.shape) - np.testing.assert_equal(output_sample_float_np_broadcast_diff.shape, - gt_sample_float_np_broadcast_diff.shape) - np.testing.assert_equal(output_sample_np_diff.shape, - gt_sample_np_diff.shape) - np.testing.assert_equal(output_sample_variable_diff.shape, - gt_sample_np_diff.shape) - np.testing.assert_allclose( - output_entropy_int, gt_entropy_int, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_entropy_float, - gt_entropy_float, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_entropy_float_np_broadcast, - gt_entropy_float_np_broadcast, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_entropy_np, gt_entropy, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_entropy_variable, gt_entropy, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_lp_float_np_broadcast, - gt_lp_float_np_broadcast, - rtol=tolerance, - atol=tolerance) - np.testing.assert_allclose( - output_lp_np, gt_lp, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_lp_variable, gt_lp, rtol=tolerance, atol=tolerance) - np.testing.assert_allclose( - output_p_float_np_broadcast, - gt_p_float_np_broadcast, - rtol=tolerance, - atol=tolerance) + feed_vars = { + 'low': self.low_np, + 'high': self.high_np, + 'values': self.values_np + } + + self.executor.run(fluid.default_startup_program()) + fetch_list = self.executor.run(program=self.test_program, + feed=feed_vars, + fetch_list=fetch_list) + + self.compare_with_numpy(fetch_list) + + +class UniformTest2(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low ans high are 'int' + self.low_np = int(np.random.uniform(-2, 1)) + self.high_np = int(np.random.uniform(1, 3)) + self.values_np = np.array([1.0]).astype('float32') + + +class UniformTest3(UniformTest): + def init_numpy_data(self, batch_size, dims): + # test broadcast: low is float, high is numpy.ndarray with dtype 'float32'. + self.low_np = np.random.uniform(-2, 1) + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + + def init_static_data(self, batch_size, dims): + self.static_low = self.low_np + self.static_high = self.high_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class UniformTest4(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low and high are numpy.ndarray with dtype 'float32'. + self.low_np = np.random.randn(batch_size, dims).astype('float32') + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + + def init_static_data(self, batch_size, dims): + self.static_low = self.low_np + self.static_high = self.high_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class UniformTest5(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low and high are numpy.ndarray with dtype 'float64'. + self.low_np = np.random.randn(batch_size, dims).astype('float64') + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float64') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_low = self.low_np + self.dynamic_high = self.high_np + self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64') + + def init_static_data(self, batch_size, dims): + self.static_low = self.low_np + self.static_high = self.high_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float64') + + +class UniformTest6(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low and high are Tensor with dtype 'VarType.FP32'. + self.low_np = np.random.randn(batch_size, dims).astype('float32') + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_low = paddle.to_tensor(self.low_np) + self.dynamic_high = paddle.to_tensor(self.high_np) + self.dynamic_values = paddle.to_tensor(self.values_np) + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_low = layers.data( + name='low', shape=[dims], dtype='float32') + self.static_high = layers.data( + name='high', shape=[dims], dtype='float32') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class UniformTest7(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low and high are Tensor with dtype 'VarType.FP64'. + self.low_np = np.random.randn(batch_size, dims).astype('float64') + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float64') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64') + self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64') + self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64') + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_low = layers.data( + name='low', shape=[dims], dtype='float64') + self.static_high = layers.data( + name='high', shape=[dims], dtype='float64') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float64') + + +class UniformTest8(UniformTest): + def init_numpy_data(self, batch_size, dims): + # low and high are Tensor with dtype 'VarType.FP64'. value's dtype is 'VarType.FP32'. + self.low_np = np.random.randn(batch_size, dims).astype('float64') + self.high_np = np.random.uniform(-5.0, 5.0, + (batch_size, dims)).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64') + self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64') + self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float32') + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_low = layers.data( + name='low', shape=[dims], dtype='float64') + self.static_high = layers.data( + name='high', shape=[dims], dtype='float64') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class NormalTest(unittest.TestCase): + def setUp(self, use_gpu=False, batch_size=2, dims=3): + self.use_gpu = use_gpu + if not use_gpu: + self.place = fluid.CPUPlace() + self.gpu_id = -1 + else: + self.place = fluid.CUDAPlace(0) + self.gpu_id = 0 + + self.init_numpy_data(batch_size, dims) + + paddle.disable_static(self.place) + self.init_dynamic_data(batch_size, dims) + + paddle.enable_static() + self.test_program = fluid.Program() + self.executor = fluid.Executor(self.place) + self.init_static_data(batch_size, dims) + + def init_numpy_data(self, batch_size, dims): + # loc ans scale are 'float' + self.loc_np = (np.random.ranf() - 0.5) * 4 + self.scale_np = (np.random.ranf() - 0.5) * 4 + while self.scale_np < 0: + self.scale_np = (np.random.ranf() - 0.5) * 4 + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = (np.random.ranf() - 0.5) * 4 + self.other_scale_np = (np.random.ranf() - 0.5) * 4 + while self.other_scale_np < 0: + self.other_scale_np = (np.random.ranf() - 0.5) * 4 + self.values_np = np.random.ranf(1).astype('float32') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_loc = self.loc_np + self.dynamic_scale = self.scale_np + self.dynamic_other_loc = self.other_loc_np + self.dynamic_other_scale = self.other_scale_np + self.dynamic_values = paddle.to_tensor(self.values_np) + + def init_static_data(self, batch_size, dims): + self.static_loc = self.loc_np + self.static_scale = self.scale_np + self.static_other_loc = self.other_loc_np + self.static_other_scale = self.other_scale_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[], dtype='float32') + + def compare_with_numpy(self, fetch_list, sample_shape=7, tolerance=1e-6): + sample, entropy, log_prob, probs, kl = fetch_list + + np_normal = NormalNumpy(self.loc_np, self.scale_np) + np_sample = np_normal.sample([sample_shape]) + np_entropy = np_normal.entropy() + np_lp = np_normal.log_prob(self.values_np) + np_p = np_normal.probs(self.values_np) + np_other_normal = NormalNumpy(self.other_loc_np, self.other_scale_np) + np_kl = np_normal.kl_divergence(np_other_normal) + + np.testing.assert_equal(sample.shape, np_sample.shape) np.testing.assert_allclose( - output_p_np, gt_p, rtol=tolerance, atol=tolerance) + entropy, np_entropy, rtol=tolerance, atol=tolerance) np.testing.assert_allclose( - output_p_variable, gt_p, rtol=tolerance, atol=tolerance) - - def test_uniform_distribution_static(self, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Test Uniform's methods in static mode. - - Args: - refer to ``compare_uniform_with_numpy`` function. - """ - test_program = fluid.Program() - - low_np = np.random.randn(batch_size, dims).astype('float32') - low_float = np.random.uniform(-2, 1) - high_float = np.random.uniform(1, 3) - high_np = np.random.uniform(-5.0, 5.0, - (batch_size, dims)).astype('float32') - values_np = np.random.randn(batch_size, dims).astype('float32') - - data_list = [low_np, low_float, high_float, high_np, values_np] - - feed_vars, fetch_list = self.build_uniform_static( - test_program, batch_size, dims, sample_shape, low_float, high_float, - high_np, low_np, values_np) + log_prob, np_lp, rtol=tolerance, atol=tolerance) + np.testing.assert_allclose(probs, np_p, rtol=tolerance, atol=tolerance) + np.testing.assert_allclose(kl, np_kl, rtol=tolerance, atol=tolerance) - self.executor.run(fluid.default_startup_program()) + def test_normal_distribution_dygraph(self, sample_shape=7, tolerance=1e-6): + paddle.disable_static(self.place) + normal = Normal(self.dynamic_loc, self.dynamic_scale) + + sample = normal.sample([sample_shape]).numpy() + entropy = normal.entropy().numpy() + log_prob = normal.log_prob(self.dynamic_values).numpy() + probs = normal.probs(self.dynamic_values).numpy() + other_normal = Normal(self.dynamic_other_loc, self.dynamic_other_scale) + kl = normal.kl_divergence(other_normal).numpy() - # result calculated by paddle - output_list = self.executor.run(program=test_program, - feed=feed_vars, - fetch_list=fetch_list) - self.compare_uniform_with_numpy(data_list, output_list, batch_size, - dims, sample_shape, tolerance) - - def test_uniform_distribution_dygraph(self, - batch_size=2, - dims=3, - sample_shape=7, - tolerance=1e-6): - """ - Test Uniform's methods in dynamic mode. - - Args: - refer to ``compare_uniform_with_numpy`` function. - """ - paddle.disable_static() - - low_np = np.random.randn(batch_size, dims).astype('float32') - low_float = np.random.uniform(-2, 1) - high_float = np.random.uniform(1, 3) - high_np = np.random.uniform(-5.0, 5.0, - (batch_size, dims)).astype('float32') - values_np = np.random.randn(batch_size, dims).astype('float32') - - data_list = [low_np, low_float, high_float, high_np, values_np] - output_list = self.build_uniform_dygraph(batch_size, dims, sample_shape, - low_float, high_float, high_np, - low_np, values_np) - - self.compare_uniform_with_numpy(data_list, output_list, batch_size, - dims, sample_shape, tolerance) + fetch_list = [sample, entropy, log_prob, probs, kl] + self.compare_with_numpy(fetch_list) + + def test_normal_distribution_static(self, sample_shape=7, tolerance=1e-6): paddle.enable_static() + with fluid.program_guard(self.test_program): + normal = Normal(self.static_loc, self.static_scale) + + sample = normal.sample([sample_shape]) + entropy = normal.entropy() + log_prob = normal.log_prob(self.static_values) + probs = normal.probs(self.static_values) + other_normal = Normal(self.static_other_loc, + self.static_other_scale) + kl = normal.kl_divergence(other_normal) + + fetch_list = [sample, entropy, log_prob, probs, kl] + + feed_vars = { + 'loc': self.loc_np, + 'scale': self.scale_np, + 'values': self.values_np, + 'other_loc': self.other_loc_np, + 'other_scale': self.other_scale_np + } + + self.executor.run(fluid.default_startup_program()) + fetch_list = self.executor.run(program=self.test_program, + feed=feed_vars, + fetch_list=fetch_list) + + self.compare_with_numpy(fetch_list) + + +class NormalTest2(NormalTest): + def init_numpy_data(self, batch_size, dims): + # loc ans scale are 'int' + self.loc_np = int((np.random.ranf() - 0.5) * 8) + self.scale_np = int((np.random.ranf() - 0.5) * 8) + while self.scale_np < 0: + self.scale_np = int((np.random.ranf() - 0.5) * 8) + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = int((np.random.ranf() - 0.5) * 8) + self.other_scale_np = int((np.random.ranf() - 0.5) * 8) + while self.other_scale_np < 0: + self.other_scale_np = int((np.random.ranf() - 0.5) * 8) + self.values_np = np.random.ranf(1).astype('float32') + + +class NormalTest3(NormalTest): + def init_numpy_data(self, batch_size, dims): + # test broadcast: loc is float, scale is numpy.ndarray with dtype 'float32'. + self.loc_np = (np.random.ranf() - 0.5) * 4 + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = (np.random.ranf() - 0.5) * 4 + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + + def init_static_data(self, batch_size, dims): + self.static_loc = self.loc_np + self.static_scale = self.scale_np + self.static_other_loc = self.other_loc_np + self.static_other_scale = self.other_scale_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class NormalTest4(NormalTest): + def init_numpy_data(self, batch_size, dims): + # loc and scale are numpy.ndarray with dtype 'float32'. + self.loc_np = np.random.randn(batch_size, dims).astype('float32') + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float32') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + + def init_static_data(self, batch_size, dims): + self.static_loc = self.loc_np + self.static_scale = self.scale_np + self.static_other_loc = self.other_loc_np + self.static_other_scale = self.other_scale_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + + +class NormalTest5(NormalTest): + def init_numpy_data(self, batch_size, dims): + # loc and scale are numpy.ndarray with dtype 'float64'. + self.loc_np = np.random.randn(batch_size, dims).astype('float64') + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float64') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float64') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_loc = self.loc_np + self.dynamic_scale = self.scale_np + self.dynamic_other_loc = self.other_loc_np + self.dynamic_other_scale = self.other_scale_np + self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64') + + def init_static_data(self, batch_size, dims): + self.static_loc = self.loc_np + self.static_scale = self.scale_np + self.static_other_loc = self.other_loc_np + self.static_other_scale = self.other_scale_np + with fluid.program_guard(self.test_program): + self.static_values = layers.data( + name='values', shape=[dims], dtype='float64') + + +class NormalTest6(NormalTest): + def init_data(self, batch_size=2, dims=3): + # loc and scale are Tensor with dtype 'VarType.FP32'. + self.loc_np = np.random.randn(batch_size, dims).astype('float32') + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + self.loc = paddle.to_tensor(self.loc_np) + self.scale = paddle.to_tensor(self.scale_np) + self.values = paddle.to_tensor(self.values_np) + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float32') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + self.other_loc = paddle.to_tensor(self.other_loc_np) + self.other_scale = paddle.to_tensor(self.other_scale_np) + + def init_numpy_data(self, batch_size, dims): + # loc and scale are Tensor with dtype 'VarType.FP32'. + self.loc_np = np.random.randn(batch_size, dims).astype('float32') + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float32') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float32') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float32') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_loc = paddle.to_tensor(self.loc_np) + self.dynamic_scale = paddle.to_tensor(self.scale_np) + self.dynamic_values = paddle.to_tensor(self.values_np) + self.dynamic_other_loc = paddle.to_tensor(self.other_loc_np) + self.dynamic_other_scale = paddle.to_tensor(self.other_scale_np) + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_loc = layers.data( + name='loc', shape=[dims], dtype='float32') + self.static_scale = layers.data( + name='scale', shape=[dims], dtype='float32') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + self.static_other_loc = layers.data( + name='other_loc', shape=[dims], dtype='float32') + self.static_other_scale = layers.data( + name='other_scale', shape=[dims], dtype='float32') + + +class NormalTest7(NormalTest): + def init_numpy_data(self, batch_size, dims): + # loc and scale are Tensor with dtype 'VarType.FP64'. + self.loc_np = np.random.randn(batch_size, dims).astype('float64') + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float64') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float64') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_loc = paddle.to_tensor(self.loc_np, dtype='float64') + self.dynamic_scale = paddle.to_tensor(self.scale_np, dtype='float64') + self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64') + self.dynamic_other_loc = paddle.to_tensor( + self.other_loc_np, dtype='float64') + self.dynamic_other_scale = paddle.to_tensor( + self.other_scale_np, dtype='float64') + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_loc = layers.data( + name='loc', shape=[dims], dtype='float64') + self.static_scale = layers.data( + name='scale', shape=[dims], dtype='float64') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float64') + self.static_other_loc = layers.data( + name='other_loc', shape=[dims], dtype='float64') + self.static_other_scale = layers.data( + name='other_scale', shape=[dims], dtype='float64') + + +class NormalTest8(NormalTest): + def init_numpy_data(self, batch_size, dims): + # loc and scale are Tensor with dtype 'VarType.FP64'. value's dtype is 'VarType.FP32'. + self.loc_np = np.random.randn(batch_size, dims).astype('float64') + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + while not np.all(self.scale_np > 0): + self.scale_np = np.random.randn(batch_size, dims).astype('float64') + self.values_np = np.random.randn(batch_size, dims).astype('float32') + # used to construct another Normal object to calculate kl_divergence + self.other_loc_np = np.random.randn(batch_size, dims).astype('float64') + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + while not np.all(self.scale_np > 0): + self.other_scale_np = np.random.randn(batch_size, + dims).astype('float64') + + def init_dynamic_data(self, batch_size, dims): + self.dynamic_loc = paddle.to_tensor(self.loc_np, dtype='float64') + self.dynamic_scale = paddle.to_tensor(self.scale_np, dtype='float64') + self.dynamic_values = paddle.to_tensor(self.values_np) + self.dynamic_other_loc = paddle.to_tensor( + self.other_loc_np, dtype='float64') + self.dynamic_other_scale = paddle.to_tensor( + self.other_scale_np, dtype='float64') + + def init_static_data(self, batch_size, dims): + with fluid.program_guard(self.test_program): + self.static_loc = layers.data( + name='loc', shape=[dims], dtype='float64') + self.static_scale = layers.data( + name='scale', shape=[dims], dtype='float64') + self.static_values = layers.data( + name='values', shape=[dims], dtype='float32') + self.static_other_loc = layers.data( + name='other_loc', shape=[dims], dtype='float64') + self.static_other_scale = layers.data( + name='other_scale', shape=[dims], dtype='float64') class DistributionTestError(unittest.TestCase):