add distributed

Author: lugimzzz

paddlenlp/quantization/hadamard_utils.py

@@ -12,45 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# import paddle
-
-
-# def matmul_hadU(X):
-
-#     input = X.clone().reshape((-1, X.shape[-1], 1))
-#     output = input.clone()
-#     while input.shape[1] > 1:
-#         input = input.reshape((input.shape[0], input.shape[1] // 2, 2, input.shape[2]))
-#         output = output.reshape(input.shape)
-#         output[:, :, 0, :] = input[:, :, 0, :] + input[:, :, 1, :]
-#         output[:, :, 1, :] = input[:, :, 0, :] - input[:, :, 1, :]
-#         output = output.reshape((input.shape[0], input.shape[1], -1))
-#         (input, output) = (output, input)
-#     del output
-
-#     return input.reshape(X.shape)
-
-
-# def random_hadamard_matrix(size, dtype, is_block=False):
-#     if not is_block:
-#         A = paddle.randint(low=0, high=2, shape=[size, size]).astype("float32") * 2 - 1
-#         Q, _ = paddle.linalg.qr(A)
-#         return Q.astype(dtype), 1
-#     else:
-#         num_blocks = size
-#         while not (num_blocks % 2):
-#             num_blocks = num_blocks // 2
-#         block_size = size // num_blocks
-#         Q = paddle.diag(paddle.ones((block_size,), dtype="float32"))
-#         block = matmul_hadU(Q)
-#         large_matrix = paddle.zeros([size, size])
-
-#         for i in range(num_blocks):
-#             start_row = i * block_size
-#             start_col = i * block_size
-#             large_matrix[start_row : start_row + block_size, start_col : start_col + block_size] = block
-#     return large_matrix.cast(dtype), block_size
-
 import paddle
 
 from paddlenlp.utils import infohub