You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/11 16:27:33 UTC
[GitHub] marcoabreu closed pull request #11227: Minor improvement in the
random seed test
marcoabreu closed pull request #11227: Minor improvement in the random seed test
URL: https://github.com/apache/incubator-mxnet/pull/11227
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 40723b270a3..7abbc9918c5 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -293,31 +293,22 @@ def test_random_seed_setting_for_context():
samples_imp = []
samples_sym = []
# Collect random number samples from the generators of all devices, each seeded with the same number.
- for dev_id in range(0, 16 if dev_type == 'gpu' else 1):
- # Currently python API does not provide a method to get the number of gpu devices.
- # Waiting for PR #10354, which provides the method, to be merged.
- # As a temporal workaround, try first and catch the exception caused by the absence of the device with `dev_id`.
- try:
- with mx.Context(dev_type, dev_id):
- ctx = mx.context.current_context()
- seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
-
- # Check imperative. `multinomial` uses non-parallel rng.
- rnds = mx.nd.random.multinomial(data=mx.nd.array(probs, dtype=dtype), shape=num_samples)
- samples_imp.append(rnds.asnumpy())
-
- # Check symbolic. `multinomial` uses non-parallel rng.
- P = mx.sym.Variable("P")
- X = mx.sym.random.multinomial(data=P, shape=num_samples, get_prob=False)
- exe = X.bind(ctx, {"P": mx.nd.array(probs, dtype=dtype)})
- set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
- exe.forward()
- samples_sym.append(exe.outputs[0].asnumpy())
- except mx.MXNetError as e:
- if str(e).find("invalid device ordinal") != -1:
- break
- else:
- raise e
+ for dev_id in range(0, mx.context.num_gpus() if dev_type == 'gpu' else 1):
+ with mx.Context(dev_type, dev_id):
+ ctx = mx.context.current_context()
+ seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
+
+ # Check imperative. `multinomial` uses non-parallel rng.
+ rnds = mx.nd.random.multinomial(data=mx.nd.array(probs, dtype=dtype), shape=num_samples)
+ samples_imp.append(rnds.asnumpy())
+
+ # Check symbolic. `multinomial` uses non-parallel rng.
+ P = mx.sym.Variable("P")
+ X = mx.sym.random.multinomial(data=P, shape=num_samples, get_prob=False)
+ exe = X.bind(ctx, {"P": mx.nd.array(probs, dtype=dtype)})
+ set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
+ exe.forward()
+ samples_sym.append(exe.outputs[0].asnumpy())
# The samples should be identical across different gpu devices.
for i in range(1, len(samples_imp)):
assert same(samples_imp[i - 1], samples_imp[i])
@@ -333,42 +324,33 @@ def test_parallel_random_seed_setting_for_context():
samples_imp = []
samples_sym = []
# Collect random number samples from the generators of all devices, each seeded with the same number.
- for dev_id in range(0, 16 if dev_type == 'gpu' else 1):
- # Currently python API does not provide a method to get the number of gpu devices.
- # Waiting for PR #10354, which provides the method, to be merged.
- # As a temporal workaround, try first and catch the exception caused by the absence of the device with `dev_id`.
- try:
- with mx.Context(dev_type, dev_id):
- ctx = mx.context.current_context()
- # Avoid excessive test cpu runtimes.
- num_temp_seeds = 25 if dev_type == 'gpu' else 1
- # To flush out a possible race condition, run multiple times.
- for _ in range(20):
- # Create enough samples such that we get a meaningful distribution.
- shape = (200, 200)
- params = { 'low': -1.5, 'high': 3.0 }
- params.update(shape=shape, dtype=dtype)
-
- # Check imperative. `uniform` uses parallel rng.
- seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
- rnds = mx.nd.random.uniform(**params)
- samples_imp.append(rnds.asnumpy())
-
- # Check symbolic. `uniform` uses parallel rng.
- X = mx.sym.Variable("X")
- Y = mx.sym.random.uniform(**params) + X
- x = mx.nd.zeros(shape, dtype=dtype)
- xgrad = mx.nd.zeros(shape, dtype=dtype)
- yexec = Y.bind(ctx, {'X' : x}, {'X': xgrad})
- set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
- yexec.forward(is_train=True)
- yexec.backward(yexec.outputs[0])
- samples_sym.append(yexec.outputs[0].asnumpy())
- except mx.MXNetError as e:
- if str(e).find("invalid device ordinal") != -1:
- break
- else:
- raise e
+ for dev_id in range(0, mx.context.num_gpus() if dev_type == 'gpu' else 1):
+ with mx.Context(dev_type, dev_id):
+ ctx = mx.context.current_context()
+ # Avoid excessive test cpu runtimes.
+ num_temp_seeds = 25 if dev_type == 'gpu' else 1
+ # To flush out a possible race condition, run multiple times.
+ for _ in range(20):
+ # Create enough samples such that we get a meaningful distribution.
+ shape = (200, 200)
+ params = { 'low': -1.5, 'high': 3.0 }
+ params.update(shape=shape, dtype=dtype)
+
+ # Check imperative. `uniform` uses parallel rng.
+ seed = set_seed_variously_for_context(ctx, 1, num_temp_seeds, seed_to_test)
+ rnds = mx.nd.random.uniform(**params)
+ samples_imp.append(rnds.asnumpy())
+
+ # Check symbolic. `uniform` uses parallel rng.
+ X = mx.sym.Variable("X")
+ Y = mx.sym.random.uniform(**params) + X
+ x = mx.nd.zeros(shape, dtype=dtype)
+ xgrad = mx.nd.zeros(shape, dtype=dtype)
+ yexec = Y.bind(ctx, {'X' : x}, {'X': xgrad})
+ set_seed_variously_for_context(ctx, seed, num_temp_seeds, seed_to_test)
+ yexec.forward(is_train=True)
+ yexec.backward(yexec.outputs[0])
+ samples_sym.append(yexec.outputs[0].asnumpy())
# The samples should be identical across different gpu devices.
for i in range(1, len(samples_imp)):
assert same(samples_imp[i - 1], samples_imp[i])
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services