haojin2 commented on a change in pull request #14617: PDF operators for the
random samplers, and also the Dirichlet
URL: https://github.com/apache/incubator-mxnet/pull/14617#discussion_r280228721
##########
File path: tests/python/unittest/test_random.py
##########
@@ -290,11 +315,84 @@ def check_with_device(device, dtype):
for check_name, check_func, tol in symbdic['checks']:
assert np.abs(check_func(samples, params)) < tol,
"symbolic test: %s check for `%s` did not pass" % (check_name, name)
-@with_seed()
+ # check pdfs with only a subset of the generated samples
+ un1 = np.resize(un1, (un1.shape[0], un1.shape[1], pdfshape[0],
pdfshape[1]))
+ print(name)
+ symbol = symbdic['pdfsymbol']
+ pdffunc = symbdic['pdffunc']
+ v0 = mx.sym.Variable('v0')
+ v1 = mx.sym.Variable('v1')
+ v2 = mx.sym.Variable('v2')
+ p1 = np.array(symbdic['inputs'][0][1])
+ p2 = None if single_param else np.array(symbdic['inputs'][1][1])
+ # Move samples away from boundaries of support
+ if name == 'gamma' or name == 'exponential':
+ un1 = np.maximum(un1, 1e-1)
+ if name == 'uniform':
+ un1 =
np.minimum(np.maximum(un1.reshape((un1.shape[0],un1.shape[1],-1)),
p1.reshape((p1.shape[0],p1.shape[1],-1))+1e-4),
+
p2.reshape((p2.shape[0],p2.shape[1],-1))-1e-4).reshape(un1.shape)
+ for use_log in [False, True]:
+ test_pdf = symbol(v0, v1, is_log=use_log) if single_param else
symbol(v0, v1, v2, is_log=use_log)
+ forw_atol = 1e-7 if dtype != np.float16 else 1e-3
+ forw_rtol = 1e-4 if dtype != np.float16 else 5e-2
+ backw_atol = 1e-3
+ backw_rtol = 5e-2
+ if single_param:
+ res = pdffunc(un1.reshape((un1.shape[0],un1.shape[1],-1)),
+
p1.reshape((p1.shape[0],p1.shape[1],-1))).reshape(un1.shape)
+ if use_log:
+ res = np.log(res)
+ check_symbolic_forward(test_pdf, [un1, p1], [res],
atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+ if dtype == np.float64:
+ grad_nodes = ['v1'] if symbdic['discrete'] else ['v0', 'v1']
+ check_numeric_gradient(test_pdf, [un1, p1],
grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
+ else:
+ res = pdffunc(un1.reshape((un1.shape[0],un1.shape[1],-1)),
+ p1.reshape((p1.shape[0],p1.shape[1],-1)),
+
p2.reshape((p2.shape[0],p2.shape[1],-1))).reshape(un1.shape)
+ if use_log:
+ res = np.log(res)
+ check_symbolic_forward(test_pdf, [un1, p1, p2], [res],
atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+ if dtype == np.float64:
+ grad_nodes = ['v1', 'v2'] if symbdic['discrete'] else ['v0',
'v1', 'v2']
+ check_numeric_gradient(test_pdf, [un1, p1, p2],
grad_nodes=grad_nodes, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
+
+@with_seed(1000)
+def test_dirichlet():
+ num_classes = 2
+ num = 100
+ alpha = np.random.uniform(low=0.5, high=10, size=(4, num_classes))
+
+ samples = []
+ results = []
+ for a in alpha:
+ v = ss.dirichlet.rvs(a, size=num)
+ samples.append(v)
+ results.append(ss.dirichlet.logpdf(v.transpose(), a))
+ samples = np.concatenate(samples, axis=0).reshape((2, 2, num, num_classes))
+ results = np.concatenate(results, axis=0).reshape((2, 2, num))
+
+ alpha = alpha.reshape((2, 2, num_classes))
+
+ for dtype in [np.float32, np.float64]:
+ forw_atol = 1e-5
+ forw_rtol = 1e-4
+ backw_atol = 1e-5 if dtype == np.float64 else 1e-3
+ backw_rtol = 1e-4 if dtype == np.float64 else 5e-2
+ for use_log in [False, True]:
+ print("use_log",use_log)
+ v0 = mx.sym.Variable('v0')
+ v1 = mx.sym.Variable('v1')
+ test_pdf = mx.sym.random_pdf_dirichlet(v0, v1, is_log=use_log)
+ res = results if use_log else np.exp(results)
+ check_symbolic_forward(test_pdf, [samples, alpha], [res],
atol=forw_atol, rtol=forw_rtol, dtype=dtype)
+ if dtype == 'float64':
+ check_numeric_gradient(test_pdf, [samples, alpha],
numeric_eps=1e-7, atol=backw_atol, rtol=backw_rtol, dtype=dtype)
Review comment:
I saw that numeric gradient is not checked for fp32, what is the reason
behind that? I think we should have coverage for the most commonly used data
type. And can you also add the symbolic backward check using
`check_symbolic_backward` ?
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