[Sharderformer] Support zbv in Sharderformer Policy (#6150)

* [feat] Sharderformer support zbv

* [feat] support chatglm2, command, deepseek for zbv

* [feat] support zbv in shardformer policy:
falcon,gptj,mistral,opt,qwen2,t5, vit, whisper

* [feat] support GPT2FusedLinearConv1D

* [feat] support GPT2FusedLinear (without tp)

* [fix] debug FusedConvLinear

* [shardfromer] support gpt2 policy for zbv, support GPT2FusedLinearConv
Col and Row.

* [Shardformer] support FusedLinear1D base for zbv

* [shardformer] support zbv in FusedLinear1D base, Col, Row

* [shardformer] support zbv in blip2 and sam policy

* [shardformer] fix bug incorrect number of gradients; add fusedLinear
base testcase;

* [fix] fix incorrect number of gradients ;

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* [Shardformer] add en doc for zbv;

* [fix] fix typo in Model compatibility table

* [fix] fix API Reference typo

* [Shardformer] add zh-Han doc for zbv

* [fix] fix Linear name; update en & zh doc

* [fix] fix shardformer doc import err

* [fix] fix shardconfig import in doc

* [fix] fix shardformer doc

* [fix] fix shardconfig doc

* [fix] fix config

* [fix] remove shardconfig

* [fix] fix doc

* [feat] add zbv doc string

* [fix] rm doc

* [fix] fix doc

* [fix] empty zbv doc

* [fix] ifx torch version

* [fix] fix torch version

* [fix] fix torch versions

* [fix] fix torch versions

* [fix] fix pyramid versions

* [fix] fix pyramid, zope version

* [fix] try fix workflow

* [fix] try import ShardConfig in yml

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix workflow

* [fix] fix ci

* [fix] fix zbv doc

* [fix] fix param for qkv linear, gpt2fused linear; fix requirments;

* [fix] fix policy use fused_linear

* [fix] fix weight grad none, err caused by  weight ptr change

* [fix] fix comm in WeightGradStore

* [fix] fix WeightGradStore pop param

* [fix] remove useless param in doc; fix gpt2 qkv test;

* [shardformer] simplify execute_w_pass_grad_accum;

* [fix] rm useless comments

* [shardformer] simplify execute_w_pass_grad_accum & execute_w_pass

* [shardformer] Run meaningful doc test

* [shadformer] fix doc test cmd;

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

colossalai/pipeline/schedule/zero_bubble_pp.py

@@ -38,6 +38,19 @@ def _wait_p2p(wait_handles: List[torch.cuda.Event]) -> None:
 
 
 class ZeroBubbleVPipeScheduler(PipelineSchedule):
+    r"""
+    ZeroBubbleVPipeScheduler
+
+    Args:
+        stage_manager (PipelineStageManager): If using pipeline parallelism, it's necessary to specify a pipeline stage manager for inter-process communication in pipeline parallelism. Defaults to None, which means not using pipeline parallelism.
+        schedule (List[ScheduledNode]): Schedule for ZeroBubbleVPipe.
+        num_model_chunks (int) : The number of model chunk in a device.
+        num_microbatch (Optional[int]): The number of microbatch.
+        microbatch_size (Optional[int]): The size per microbatch.
+        enable_metadata_cache (bool): whether to enable metadata cache to acclerate communication.
+        overlap_p2p (bool): whether to use overlap_p2p.
+    """
+
     def __init__(
         self,
         stage_manager: PipelineStageManager,

colossalai/pipeline/weight_grad_store.py

@@ -8,7 +8,6 @@ class WeightGradStore:
 
     @classmethod
     def put(cls, total_input, grad_output, weight, func):
-        # func(total_input, grad_output, weight.main_grad)
         cls.cache.append((total_input, grad_output, weight, func))
 
     @classmethod
@@ -18,15 +17,26 @@ class WeightGradStore:
 
     @classmethod
     def pop(cls, chunk=0):
-        # print(f"chunk id {chunk} queue size {cls.weight_grad_queue[chunk].qsize()}")
         if cls.weight_grad_queue[chunk].qsize() > 0:
             stored_grads = cls.weight_grad_queue[chunk].get()
             for total_input, grad_output, weight, func in stored_grads:
-                if weight.grad is not None:
-                    func(total_input, grad_output, weight.grad)
-                # for first bwd; weight.grad is None, assign grad_weight to weight.grad
+                if isinstance(weight, tuple):
+                    # In order to be hooked into Gemini's '__torch_function__', adding a view operation to weight and bias.
+                    # View will lead to weight ptr change
+                    # weight_cal & weight_origin in tuple, weight_cal use to cal dw, weight_origin use to update
+                    _, weight_origin = weight
+                    if weight_origin.grad is not None:
+                        func(total_input, grad_output, weight_origin.grad)
+                    # for first bwd; weight.grad is None, assign grad_weight to weight.grad
+                    else:
+                        grad_weight = func(total_input, grad_output)
+                        weight_origin.grad = grad_weight
                 else:
-                    grad_weight = func(total_input, grad_output)
-                    weight.grad = grad_weight
+                    if weight.grad is not None:
+                        func(total_input, grad_output, weight.grad)
+                    # for first bwd; weight.grad is None, assign grad_weight to weight.grad
+                    else:
+                        grad_weight = func(total_input, grad_output)
+                        weight.grad = grad_weight
         else:
             raise Exception("Pop empty queue.")