Add PowerSGDCompressor #27

autodist/kernel/synchronization/compressor.py

@@ -13,13 +13,12 @@
 # limitations under the License.
 
 """Gradient Compressors for All-Reduce."""
+import copy
 from abc import ABC, abstractmethod
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework.ops import Tensor
-from tensorflow.python.ops import collective_ops, math_ops
-
-#from tensorflow.python.ops import array_ops, collective_ops, linalg_ops, math_ops, random_ops
-#from autodist.kernel.synchronization.collective_key import get_collective_keys
+from tensorflow.python.ops import collective_ops, math_ops, random_ops, array_ops, linalg_ops
+from autodist.kernel.synchronization.collective_key import get_collective_keys
 #from autodist.utils import logging
 
 
@@ -205,80 +204,101 @@ class HorovodCompressorEF(CompressorEF, HorovodCompressor):  # This works becaus
     """Horovod's Compression but with Error Feedback."""
 
 
-# class PowerSGDCompressor(CompressorEF):
-#     """An implementation of the PowerSGD compression algorithm (arxiv.org/abs/1905.13727)."""
-
-#     def __init__(self, var_op_name, rank=1):
-#         self.rank = rank
-#         self.og_shape, self.ndims, self.new_shape, self.compressor = None, None, None, None
-#         super().__init__(var_op_name)
-
-#     def reduce(self, tensor: Tensor, conf: CollectiveOpsConfig):
-#         """
-#         Compress, reduce, and decompress a given tensor.
-
-#         Args:
-#             tensor (Tensor): the Tensor to reduce.
-#             conf (CollectiveOpsConfig): the config for Collective Ops.
-
-#         Returns:
-#             Reduced Tensor
-#         """
-#         if self.og_shape is None:
-#             self.og_shape = tensor.shape
-#             self.ndims = len(self.og_shape)
-
-#         # Check if rank 1 tensor (this shouldn't be called with sparse tensors)
-#         # Just reduce it if it is, no need to compress
-#         if self._is_1d:
-#             return self._all_reduce(tensor, conf)
-
-#         logging.info(f"Compressing tensor {tensor.name} (var {self.var_op_name}) with shape {tensor.shape}")
-#         if self.ndims > 2:
-#             tensor = array_ops.reshape(tensor, [self.og_shape[0], -1])
-
-#         if self.compressor is None:
-#             self.new_shape = array_ops.shape_v2(tensor)
-#             self.compressor = random_ops.random_normal([self.new_shape[1], self.rank])
-
-#         if self.error is not None:
-#             tensor += self.error
-
-#         compressed_tensor = self._compress(tensor)
-#         self.error = tensor - self._decompress(compressed_tensor)
-
-#         # all reduce mean p
-#         reduced = self._all_reduce(compressed_tensor, conf)
-#         reduced = self._orthogonalize(reduced)
-
-#         # update compressor
-#         self.compressor = math_ops.matmul(tensor, reduced, transpose_a=True)
-#         conf.instance_key = get_collective_keys().get_instance_key(self.var_op_name + "/compressor")
-#         self.compressor = self._all_reduce(self.compressor, conf)
-#         return array_ops.reshape(self._decompress(reduced), self.og_shape) \
-#             if self.ndims > 2 else self._decompress(reduced)
-
-#     def _compress(self, tensor: Tensor):
-#         return math_ops.matmul(tensor, self.compressor)
-
-#     def _decompress(self, compressed_tensor: Tensor):
-#         return math_ops.matmul(compressed_tensor, self.compressor, transpose_b=True)
-
-#     @property
-#     def _is_1d(self):
-#         return self.ndims <= 1 or (
-#             self.ndims == 2 and any(d == 1 for d in self.og_shape)
-#         )
-
-#     @staticmethod
-#     def _orthogonalize(matrix):
-#         _, m = matrix.shape
-#         for i in range(m):
-#             v = matrix[:, i]
-#             v /= linalg_ops.norm_v2(v)
-#             v = array_ops.expand_dims_v2(v, 1)
-
-#             begin, rest = matrix[:, :i], matrix[:, (i + 1):]
-#             rest -= math_ops.matmul(v, rest, transpose_a=True) * v
-#             matrix = array_ops.concat([begin, v, rest], 1)
-#         return matrix
+class PowerSGDCompressor(CompressorEF):
+    """An implementation of the PowerSGD compression algorithm (arxiv.org/abs/1905.13727)."""
+
+    def __init__(self, var_op_name, rank=1):
+        self.rank = rank
+        self.og_shape, self.ndims = None, None
+        self.compressor, self.compressor_conf = None, None  # compressor is the Q in paper
+        self.var_op_name = var_op_name
+        super().__init__(var_op_name)
+
+    def reduce(self, tensor: Tensor, conf: CollectiveOpsConfig):
+        """
+        Compress, reduce, and decompress a given tensor.
+
+        Args:
+            tensor (Tensor): the Tensor to reduce.
+            conf (CollectiveOpsConfig): the config for Collective Ops.
+
+        Returns:
+            Reduced Tensor
+        """
+        if self.og_shape is None:
+            self.og_shape = tensor.shape
+            self.ndims = len(self.og_shape)
+
+        # rank <= 1
+        if self.ndims <= 1 or (self.ndims == 2 and any([d == 1 for d in self.og_shape])):
+            return self._all_reduce(tensor, conf)
+
+        # compressor init
+        if self.compressor is None:
+            self.compressor = random_ops.random_normal([array_ops.shape_v2(tensor)[1], self.rank], seed=1000)
+
+            self.compressor_conf = copy.copy(conf)
+            self.conf.instance_key = get_collective_keys().get_instance_key(self.var_op_name + '/compressor')
+
+        if self.error is not None:
+            tensor += self.error
+
+        compressed_tensor = self._compress(tensor)
+        self.error = tensor - self._decompress(compressed_tensor)
+
+        reduced_tensor = self._all_reduce(compressed_tensor, conf)
+
+        orthonormal_reduced_tensor = self._modified_gram_schmidt(reduced_tensor)
+
+        self.compressor = math_ops.matmul(tensor, orthonormal_reduced_tensor, transpose_a=True)  # mxn * nxr => mxr
+
+        # all reduce mean compressor
+        self.compressor = self._all_reduce(self.compressor, self.compressor_conf)
+
+        return self._decompress(orthonormal_reduced_tensor)
+
+    def _compress(self, tensor: Tensor):
+        """
+        Compress a given tensor.
+
+        Args:
+            tensor (Tensor): the Tensor to compress.
+
+        Returns:
+            Tensor
+        """
+        return math_ops.matmul(tensor, self.compressor)  # nxm * mxr => nxr
+
+    def _decompress(self, compressed_tensor: Tensor):
+        """
+        Decompress a given tensor.
+
+        Args:
+            compressed_tensor (Tensor): the Tensor to decompress.
+
+        Returns:
+            Tensor, Context
+        """
+        return math_ops.matmul(compressed_tensor, self.compressor, transpose_b=True)  # nxr * rxm => nxm
+
+    @staticmethod
+    def _modified_gram_schmidt(matrix):
+        """
+        Apply modified Gram-Schmidt procedure to orthogonalize a matrix in columns.
+
+        Args:
+            matrix (Tensor): the Tensor to orthogonalize.
+
+        Returns:
+            matrix (Tensor)
+        """
+        _, m = matrix.shape
+
+        for i in range(m):
+            v = matrix[:, i:(i + 1)]
+            v /= linalg_ops.norm_v2(v, axis=0)
+
+            rest = matrix[:, (i + 1):]
+            rest -= math_ops.reduce_sum_v1(v * rest, axis=0, keepdims=True) * v
+            matrix = array_ops.concat([matrix[:, :i], v, rest], axis=1)
+        return matrix