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new 0dbba84 Replace the old adhoc method to iterate over gpu devices with
new mx.context.num_gpus (#11227)
0dbba84 is described below
commit 0dbba84c72f85e5db90161bd6282763472ce5f4f
Author: Deokjae Lee <36436141+asitsta...@users.noreply.github.com>
AuthorDate: Tue Jun 12 01:27:31 2018 +0900
Replace the old adhoc method to iterate over gpu devices with new
mx.context.num_gpus (#11227)
---
tests/python/unittest/test_random.py | 104 +++++++++++++++--------------------
1 file changed, 43 insertions(+), 61 deletions(-)
diff --git a/tests/python/unittest/test_random.py
b/tests/python/unittest/test_random.py
index 40723b2..7abbc99 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])
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