How to use freemem method in autotest

Best Python code snippet using autotest_python Github


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...17# The GC need to be enabled for those tests to work correctly.18if not getattr(mode_with_gpu.linker, 'allow_gc', False):19 mode_with_gpu.linker = copy.copy(mode_with_gpu.linker)20 mode_with_gpu.linker.allow_gc = True21def freemem(extra_alloc=0):22 """23 Return the free memory on the gpu in megabytes.24 """25 gc.collect()26 gc.collect()27 gc.collect()28 n_mallocs = cuda.cuda_ndarray.cuda_ndarray.outstanding_mallocs()29 if hasattr(cuda.cuda_ndarray.cuda_ndarray, "theano_allocated"):30 theano_alloc = cuda.cuda_ndarray.cuda_ndarray.theano_allocated()31 return ("(n malloc/theano mem allocated in KB)",32 n_mallocs + extra_alloc,33 int(theano_alloc / 1024))34 return ("n malloc on the gpu", n_mallocs + extra_alloc)35 # I don't use the following by default as if there is other stuff running36 # on the GPU, this won't work.37 mem_info = cuda.cuda_ndarray.cuda_ndarray.mem_info()38 gpu_used = (mem_info[1] - mem_info[0]) / 1024 ** 239 mem_info_msg = "(n malloc/gpu mem used in MB)"40 return (mem_info_msg, n_mallocs, int(gpu_used))41def test_memory():42 """43 We test that we do not keep link to memory between Theano function call44 and during Theano compilation45 The origin of this code come from Aaron Vandenoord and Sander Dieleman.46 I have their autorisation to put this in Theano with the Theano license.47 note::48 This test can fail if there is other process running on the gpu.49 """50 shapes = (200, 100)51 # more_alloc1 was different for each dtype in the past.52 # more_alloc2 is still currently not the same for both dtype.53 # when dtype is float32, the computation is done on the gpu.54 # This insert constant on the gpu during compilation55 # that raise the number of alloc.56 # When dtype is float64, only the shared is on the gpu and it is transferd57 # to the cpu for computation. So no extra alloc after compilation.58 # more_alloc1 if after the first compilation, more_alloc2 after the second.59 for dtype, more_alloc1, more_alloc2 in [("float32", 0, 3),60 ("float64", 0, 0)]:61 print(dtype)62 test_params = np.asarray(np.random.randn(, dtype)63 some_vector = tensor.vector('some_vector', dtype=dtype)64 some_matrix = some_vector.reshape(shapes)65 mem1 = freemem()66 print("Before shared variable", mem1)67 variables = cuda.shared_constructor(np.ones((shapes[1],),68 dtype='float32'))69 derp = tensor.sum([:shapes[0]], variables))70 print("Shared took ",71 borrow=True,73 return_internal_type=True).shape) *74 4 / 1024,75 "kB")76 mem2 = freemem()77 print("Before compilation", mem2)78 mem2_1 = freemem(extra_alloc=more_alloc1)79 mem2_2 = freemem(extra_alloc=more_alloc2)80 obj = theano.function([some_vector], derp, mode=mode_with_gpu)81 mem3 = freemem()82 print("After function compilation 1", mem3)83 assert mem2_1 == mem3, (mem2_1, mem3, dtype)84 grad_derp = tensor.grad(derp, some_vector)85 grad = theano.function([some_vector], grad_derp, mode=mode_with_gpu)86 mem4 = freemem()87 print("After function compilation 2", mem4)88 assert mem2_2 == mem4, (mem2_2, mem4, dtype)89 for i in range(3):90 obj(test_params)91 print("After function evaluation 1", freemem())92 assert mem2_2 == freemem(), (mem2_2, freemem())93 grad(test_params)94 print("After function evaluation 2", freemem())95 assert mem2_2 == freemem(), (mem2_2, freemem())96 del obj97 # print "After deleting function 1", freemem()98 # assert mem2 == freemem(), (mem2, freemem())99 del grad100 print("After deleting function 2", freemem())101 assert mem2 == freemem(), (mem2, freemem())102 del derp, variables, grad_derp103 print("After deleting shared variable and ref to it", freemem())104 assert mem1 == freemem(), (mem1, freemem())105@theano.configparser.change_flags(**{'vm.lazy': True})106def test_memory_lazy():107 """As test_memory, but with the ifelse op.108 We need to test it as the ifelse op with the [c]vm create op not109 executed in the graph. This mess with [c]vm gc implementation.110 """111 shapes = (50, 100)112 # more_alloc1 is not the same for both dtype.113 # when dtype is float32, the computation is done on the gpu.114 # This insert constant on the gpu during compilation115 # that raise the number of alloc.116 # When dtype is float64, only the shared is on the gpu and it is transferd117 # to the cpu for computation. So no extra alloc after compilation.118 # more_alloc1 if after the first compilation119 for dtype, more_alloc1 in [("float32", 1),120 ("float64", 0)]:121 print(dtype)122 test_params = np.asarray(np.random.randn(, dtype)123 some_vector = tensor.vector('some_vector', dtype=dtype)124 some_matrix = some_vector.reshape(shapes)125 branch_select = tensor.iscalar()126 mem1 = freemem()127 print("Before shared variable", mem1)128 variables = cuda.shared_constructor(np.ones((shapes[1],),129 dtype='float32'))130 derp = tensor.sum([:shapes[0]], variables))131 derp = ifelse.IfElse(1)(branch_select,132 derp, some_matrix[:shapes[0]].sum())133 derp += 1134 print("Shared took ",135 borrow=True,137 return_internal_type=True).shape) *138 4 / 1024,139 "kB")140 mem2 = freemem()141 print("Before compilation", mem2)142 mem2_1 = freemem(extra_alloc=more_alloc1)143 obj = theano.function([some_vector, branch_select], derp,144 mode=mode_with_gpu)145 # theano.printing.debugprint(obj, print_type=True)146 mem3 = freemem()147 print("After function compilation 1", mem3)148 assert mem2_1 == mem3, (mem2_1, mem3)149 for i in range(3):150 obj(test_params, 1)151 print("After function evaluation branch true", freemem())152 assert mem2_1 == freemem(), (mem2_1, freemem())153 obj(test_params, 0)154 print("After function evaluation branch false", freemem())155 assert mem2_1 == freemem(), (mem2_1, freemem())156 del obj157 print("After deleting function 1", freemem())158 assert mem2 == freemem(), (mem2, freemem())159 del derp, variables160 print("After deleting shared variable and ref to it", freemem())...

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