[shardformer] Sequence Parallelism Optimization (#5533)

* sequence parallel optimization

* validate sequence parallel in llama (code to be polished)

* shardformer api writing

* integrate sequence parallel in ShardFormer

* fix pp bugs and sp bugs for LlaMa model

* integrating ring-based sequence parallelism into ShardFormer

* [sequence parallelism]: Add fused megatron function

* integrating ring-based sequence parallelism into ShardFormer

---------

Co-authored-by: linsj20 <linsj20@mails.tsinghua.edu.cn>

* fix bugs when useing sp and flashattention together

* fix operation function name

* support flash attention for ulysses-style sp

* clarify sp process group

* fix compatibility bugs in moe plugin

* fix fused linear bugs

* fix linear layer test

* support gpt model all-to-all sp

* modify shard data dimension (meant to be dim=-1)

* support megtron-style sp and distributed attn for llama model

* [shardformer] add megatron sp to llama

* support llama7B 128k with distributed attention

* [shardformer] robustness enhancement

* add block attn

* sp mode 1: keep input as a complete sequence

* fix sp compatability

* finish sp mode 3 support for gpt

* using all_to_all_single when batch size is 1

* support mode 2 sp in gpt2 (#5)

* [shardformer] add megatron sp to llama

* support llama7B 128k with distributed attention

* [shardformer] robustness enhancement

* add block attn

* sp mode 1: keep input as a complete sequence

* fix sp compatability

* refactor ring implementation

* support mode 2 sp in gpt2

* polish code

* enable distributed attn mask when using sp mode 2 and 3 in llama

* automatically enable flash attn when using sp mode 2 and 3 in llama

* inplace attn mask

* add zero2 support for sequence parallel

* polish code

* fix bugs

* fix gemini checkpoint io

* loose tensor checking atol and rtol

* add comment

* fix llama layernorm grad

* fix zero grad

* fix zero grad

* fix conflict

* update split and gather auto grad func

* sequence parallel: inside text split (#6)

* polish code (part 1)

* polish code (part 2)

* polish code (part 2.5)

* polish code (part 3)

* sequence parallel: inside text split

* miscellaneous minor fixes

* polish code

* fix ulysses style ZeRO

* sequence parallel: inside text split

* miscellaneous minor fixes

* disaggregate sp group and dp group for  sp

* fix llama and gpt sp

* polish code

* move ulysses grad sync to ddp (#9)

* remove zero_stage and unbind the grad sync for alltoall sp

* add 2d group creation test

* move ulysses grad sync to ddp

* add 2d group creation test

* remove useless code

* change shard config not to enable sp when enable_all_optimizations

* add sp warnings for several model

* remove useless code

---------

Co-authored-by: linsj20 <linsj20@mails.tsinghua.edu.cn>

colossalai/shardformer/modeling/chatglm2.py

@@ -191,12 +191,11 @@ class ChatGLMPipelineForwards:
         all_hidden_states = () if output_hidden_states else None
         start_idx, end_idx = stage_index[0], stage_index[1]
 
-        if shard_config.enable_sequence_parallelism:
-            hidden_states = split_forward_gather_backward(
-                hidden_states,
-                dim=0,
-                process_group=shard_config.tensor_parallel_process_group,
-            )
+        if shard_config and shard_config.enable_sequence_parallelism:
+            if shard_config.sequence_parallelism_mode == "split_gather":
+                hidden_states = split_forward_gather_backward(
+                    hidden_states, dim=0, process_group=shard_config.tensor_parallel_process_group
+                )
         for idx in range(start_idx, end_idx):
             layer = self.encoder._get_layer(idx)
             if output_hidden_states:
@@ -222,12 +221,11 @@ class ChatGLMPipelineForwards:
             if use_cache:
                 presents = presents + (kv_cache,)
 
-        if shard_config.enable_sequence_parallelism:
-            hidden_states = gather_forward_split_backward(
-                hidden_states,
-                dim=0,
-                process_group=shard_config.tensor_parallel_process_group,
-            )
+        if shard_config and shard_config.enable_sequence_parallelism:
+            if shard_config.sequence_parallelism_mode == "split_gather":
+                hidden_states = gather_forward_split_backward(
+                    hidden_states, dim=0, process_group=shard_config.tensor_parallel_process_group
+                )
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
         if stage_manager.is_last_stage():