def test_mkl_simple_rewrite(self, gen):
cpu_model, shape = gen()
cpu_model = deterministic_io(cpu_model)
mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
X = np.random.randn(*shape).astype(np.float32)
def run(model):
self.ws.run(model.InitProto())
self.ws.create_blob(model.Proto().external_input[0]).feed(X)
self.ws.run(model.Proto())
return self.ws.blobs[model.Proto().external_output[0]].fetch()
np.testing.assert_allclose(run(cpu_model), run(mkl_model),
atol=1e-4, rtol=1e-4)
def test_mkl_multi_output_rewrite(self):
cpu_model, shapes = double_matmul()
cpu_model = deterministic_io(cpu_model)
mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
np.random.seed(1701)
Xs = [np.random.randn(*shape).astype(np.float32) for shape in shapes]
def run(model):
self.ws.run(model.InitProto())
for (name, X) in zip(model.Proto().external_input, Xs):
self.ws.create_blob(name).feed(X)
print(model.Proto())
self.ws.run(model.Proto())
return [self.ws.blobs[name].fetch()
for name in model.Proto().external_output]
run(mkl_model)
np.testing.assert_allclose(run(cpu_model), run(mkl_model),
atol=1e-4, rtol=1e-4)
def test_mkl_multi_output_rewrite(self):
cpu_model, shapes = double_matmul()
cpu_model = deterministic_io(cpu_model)
mkl_model = rewrite_graph.rewrite_model_helper_simple(cpu_model)
np.random.seed(1701)
Xs = [np.random.randn(*shape).astype(np.float32) for shape in shapes]
def run(model):
self.ws.run(model.InitProto())
for (name, X) in zip(model.Proto().external_input, Xs):
self.ws.create_blob(name).feed(X)
print(model.Proto())
self.ws.run(model.Proto())
return [self.ws.blobs[name].fetch()
for name in model.Proto().external_output]
run(mkl_model)
np.testing.assert_allclose(run(cpu_model), run(mkl_model),
atol=1e-4, rtol=1e-4)
