Support Sharding Overlap (PaddlePaddle#8473)

* update * update is_casual_mask to use_casual_mask * update by environment

Author: iosmers

paddlenlp/trainer/trainer.py

@@ -1892,7 +1892,6 @@ def get_expected_keys(inputs, keys):
  optimizer._set_broadcast_overlap(True, model)
 
  self.optimizer = optimizer
-
  # pure tesnor parallel mode, no pipeline_parallel, no sharding.
  if (
  not in_pipeline_parallel_mode
@@ -1908,6 +1907,21 @@ def get_expected_keys(inputs, keys):
  self.optimizer = mix_precision_utils.MixPrecisionOptimizer(self.optimizer)
  self.optimizer = fleet.distributed_optimizer(self.optimizer)
 
+ # stage1 has v1 and v2 version
+ if in_sharding_parallel_mode and ShardingOption.SHARD_OP in self.args.sharding:
+ if "split_param" in self.args.sharding_parallel_config:
+ if (
+ hasattr(self.optimizer, "_set_all_gather_overlap_forward")
+ and "enable_stage1_allgather_overlap" in self.args.sharding_parallel_config
+ ):
+ self.optimizer._set_all_gather_overlap_forward(True, model)
+ else:
+ if (
+ hasattr(self.optimizer, "_set_broadcast_overlap")
+ and "enable_stage1_broadcast_overlap" in self.args.sharding_parallel_config
+ ):
+ self.optimizer._set_broadcast_overlap(True, model)
+
  return model
 
  def _prepare_input(self, data: Union[paddle.Tensor, Any]) -> Union[paddle.Tensor, Any]:

paddlenlp/transformers/llama/modeling.py

@@ -115,11 +115,15 @@ def _get_interleave_power_of_2(n):
  )
 
 
+def get_use_casual_mask():
+ """Get the value of the 'USE_CASUAL_MASK' environment variable."""
+ return os.getenv("USE_CASUAL_MASK", "False")
+
+
 def build_alibi_tensor(
  bool_attention_mask: Tensor, num_heads: int, dtype: paddle.dtype, tensor_parallel_degree=1
 ) -> Tensor:
- attention_mask = bool_attention_mask.astype("float32")
- batch_size, seq_length = attention_mask.shape[0], attention_mask.shape[-1]
+ batch_size, seq_length = bool_attention_mask.shape[0], bool_attention_mask.shape[-1]
  slopes = paddle.to_tensor(_get_interleave(num_heads), dtype="float32")
  alibi = slopes.unsqueeze(axis=[1, 2]) * paddle.arange(seq_length, dtype="float32").unsqueeze(axis=[0, 1]).expand(
  [num_heads, -1, -1]
@@ -307,7 +311,7 @@ def is_casual_mask(attention_mask):
 
 def _make_causal_mask(input_ids_shape, past_key_values_length):
  """
- Make causal mask used for self-attention
+ Make casual mask used for self-attention
  """
  batch_size, target_length = input_ids_shape # target_length: seq_len
 
@@ -1543,12 +1547,22 @@ def forward(
  if position_ids is None:
  position_ids = paddle.arange(seq_length, dtype="int64").expand((batch_size, seq_length))
 
- attention_mask = self._prepare_decoder_attention_mask(
- attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
- ) # [bs, 1, seq_len, seq_len]
+ use_casual_mask = get_use_casual_mask()
+
+ if use_casual_mask:
+ attention_mask = None
+ else:
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
+ ) # [bs, 1, seq_len, seq_len]
+
  is_casual = False
+
  if self.config.use_flash_attention and get_env_device() != "gcu":
- is_casual = is_casual_mask(attention_mask)
+ if use_casual_mask:
+ is_casual = True
+ else:
+ is_casual = is_casual_mask(attention_mask)
  if get_env_device() != "npu":
  if is_casual and alibi is None:
  attention_mask = None