def init_output_module(self):
self.emb_out = LookupTable(self.voc_sz, self.out_dim)
p = Parallel()
p.add(self.emb_out)
p.add(Identity())
self.mod_out = Sequential()
self.mod_out.add(p)
self.mod_out.add(MatVecProd(False))
def init_query_module(self):
self.emb_query = LookupTable(self.voc_sz, self.in_dim)
p = Parallel()
p.add(self.emb_query)
p.add(Identity())
self.mod_query = Sequential()
self.mod_query.add(p)
self.mod_query.add(MatVecProd(True))
self.mod_query.add(Softmax())
def init_output_module(self):
self.emb_out = LookupTable(self.voc_sz, self.out_dim)
s = Sequential()
s.add(self.emb_out)
s.add(ElemMult(self.config["weight"]))
s.add(Sum(dim=1))
p = Parallel()
p.add(s)
p.add(Identity())
self.mod_out = Sequential()
self.mod_out.add(p)
self.mod_out.add(MatVecProd(False))
def init_query_module(self):
self.emb_query = LookupTable(self.voc_sz, self.in_dim)
s = Sequential()
s.add(self.emb_query)
s.add(ElemMult(self.config["weight"]))
s.add(Sum(dim=1))
p = Parallel()
p.add(s)
p.add(Identity())
self.mod_query = Sequential()
self.mod_query.add(p)
self.mod_query.add(MatVecProd(True))
self.mod_query.add(Softmax())
except AssertionError:
tests[1] = False
if TEST2:
tests[2] = True
for i in range(10):
M = np.random.rand(*matrix_batch_dim)
V = np.random.rand(*vect_batch_dim)
input_data = [M, V]
input_data_torch = [
torch.from_numpy(M).type(FloatTensor),
torch.from_numpy(V).type(FloatTensor)
]
transpose = i % 2 == 0
mvp = MatVecProd(transpose)
mvp_pt = MatVecProdPytorch(transpose)
result_1 = mvp.fprop(input_data)
result_2 = mvp_pt.forward(input_data_torch)
try:
result_2_np = result_2.data.numpy()
assert np.allclose(result_1, result_2_np)
except AssertionError:
tests[2] = False
if TEST3:
tests[3] = True
input = [
np.random.rand(50, 32).astype('f'),
np.random.rand(50, 32).astype('f')
]