[MoE/ZeRO] Moe refactor with zero refactor (#5821)

* [moe] removed openmoe-coupled code and rectify mixstral code (#5471)

* [Feauture] MoE refractor; Intergration with Mixtral  (#5682)

* cherry pick from refractor-moe branch

* tests passed

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

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* support ep + zero

---------

Co-authored-by: Edenzzzz <wtan45@wisc.edu>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* add mixtral auto policy & move pipeline forward code to modeling folder

* [moe refactor] modify kernel test without Route Class

* [moe refactor] add moe tensor test path environment variable to github workflow

* fix typos

* fix moe test bug due to the code rebase

* [moe refactor] fix moe zero test, and little bug in low level zero

* fix typo

* add moe tensor path to github workflow

* remove some useless code

* fix typo & unify global variable XX_AXIS logic without using -1

* fix typo & prettifier the code

* remove print code & support zero 2 test

* remove useless code

* reanme function

* fix typo

* fix typo

* Further improve the test code

* remove print code

* [moe refactor] change test model from fake moe model to mixtral moe layer and remove useless test

* [moe refactor] skip some unit test which will be refactored later

* [moe refactor] fix unit import error

* [moe refactor] fix circular import issues

* [moe refactor] remove debug code

* [moe refactor] update github workflow

* [moe/zero] refactor low level optimizer (#5767)

* [zero] refactor low level optimizer

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* [Feature] MoE refactor with newest version of ZeRO (#5801)

* [zero] remove redundant members in BucketStore (#5802)

* [zero] align api with previous version

* [Moe/Zero] Update MoeHybridParallelPlugin with refactored ZeRO and Fix Zero bug (#5819)

* [moe refactor] update unit test with the refactored ZeRO and remove useless test

* move moe checkpoint to checkpoint folder and exchange global axis to class member

* update moe hybrid parallel plugin with newest version of zero & fix zero working/master params bug

* fix zero unit test

* Add an assertion to prevent users from using it incorrectly

* [hotfix]Solve the compatibility issue of zero refactor (#5823)

* [moe refactor] update unit test with the refactored ZeRO and remove useless test

* move moe checkpoint to checkpoint folder and exchange global axis to class member

* update moe hybrid parallel plugin with newest version of zero & fix zero working/master params bug

* fix zero unit test

* Add an assertion to prevent users from using it incorrectly

* Modify function parameter names to resolve compatibility issues

* [zero] fix missing hook removal (#5824)

* [MoE] Resolve .github conflict (#5829)

* [Fix/Example] Fix Llama Inference Loading Data Type (#5763)

* [fix/example] fix llama inference loading dtype

* revise loading dtype of benchmark llama3

* [release] update version (#5752)

* [release] update version

* [devops] update compatibility test

* [devops] update compatibility test

* [devops] update compatibility test

* [devops] update compatibility test

* [test] fix ddp plugin test

* [test] fix gptj and rpc test

* [devops] fix cuda ext compatibility

* [inference] fix flash decoding test

* [inference] fix flash decoding test

* fix (#5765)

* [test] Fix/fix testcase (#5770)

* [fix] branch for fix testcase;

* [fix] fix test_analyzer & test_auto_parallel;

* [fix] remove local change about moe;

* [fix] rm local change moe;

* [Hotfix] Add missing init file in inference.executor (#5774)

* [CI/tests] simplify some test case to reduce testing time (#5755)

* [ci/tests] simplify some test case to reduce testing time

* [ci/tests] continue to remove test case to reduce ci time cost

* restore some test config

* [ci/tests] continue to reduce ci time cost

* [misc] update dockerfile (#5776)

* [misc] update dockerfile

* [misc] update dockerfile

* [devops] fix docker ci (#5780)

* [Inference]Add Streaming LLM (#5745)

* Add Streaming LLM

* add some parameters to llama_generation.py

* verify streamingllm config

* add test_streamingllm.py

* modified according to the opinions of review

* add Citation

* change _block_tables tolist

* [hotfix] fix llama flash attention forward (#5777)

* [misc] Accelerate CI for zero and dist optim (#5758)

* remove fp16 from lamb

* remove d2h copy in checking states

---------

Co-authored-by: Edenzzzz <wtan45@wisc.edu>

* [Test/CI] remove test cases to reduce CI duration (#5753)

* [test] smaller gpt2 test case

* [test] reduce test cases: tests/test_zero/test_gemini/test_zeroddp_state_dict.py

* [test] reduce test cases: tests/test_zero/test_gemini/test_grad_accum.py

* [test] reduce test cases tests/test_zero/test_gemini/test_optim.py

* Revert "[test] smaller gpt2 test case"

Some tests might depend on the size of model (num of chunks)

This reverts commit df705a5210.

* [test] reduce test cases: tests/test_checkpoint_io/test_gemini_checkpoint_io.py

* [CI] smaller test model for two mwo the two modifid cases

* [CI] hardcode gpt model for tests/test_zero/test_gemini/test_search.py since we need a fixed answer there

* [hotfix] fix testcase in test_fx/test_tracer (#5779)

* [fix] branch for fix testcase;

* [fix] fix test_analyzer & test_auto_parallel;

* [fix] remove local change about moe;

* [fix] rm local change moe;

* [fix] fix test_deepfm_model & test_dlrf_model；

* [fix] fix test_hf_albert & test_hf_gpt;

* [gemini] optimize reduce scatter d2h copy (#5760)

* [gemini] optimize reduce scatter d2h copy

* [fix] fix missing reduce variable

* [refactor] remove legacy async reduce scatter code

* [gemini] missing sync

* Revert "[refactor] remove legacy async reduce scatter code"

This reverts commit 58ad76d466.

* [gemini] further optimize with async all reduce

* [fix] pass flag from manager to chunk

* Allow building cuda extension without a device. (#5535)

Added FORCE_CUDA environment variable support, to enable building extensions where a GPU device is not present but cuda libraries are.

* [misc] fix dist logger (#5782)

* [install]fix setup (#5786)

* fix

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

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---------

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* [misc] update requirements (#5787)

* [shardformer] fix import (#5788)

* upgrade colossal-chat support tp_group>1, add sp for sft

* upgrade ppo dpo rm script

* run pre-commit

* moupdate ci tests, st ci test cases passed, tp failed in generation for ppo, sp is buggy

* fix training script

* fix ci

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

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* fix transformers version

* remove duplicated test

* fix datasets version

* remove models that require huggingface auth from ci

* remove local data path

* update ci

* remove baichuan from template test due to transformer version conflict

* merge

* Refactor modeling by adding attention backend

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Fix tests and naming

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Pass inference model shard configs for module init

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Clean up

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* replace the customized dataloader setup with the build-in one

* replace the customized dataloader setup with the build-in one

* Remove flash attention backend

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* fix readme

* Fix test import

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* update sft trainning script

* [Inference]refactor baichuan (#5791)

* refactor baichuan

* remove unused code and add TODO for lazyinit

* [test] fix chatglm test kit (#5793)

* [shardformer] fix modeling of bloom and falcon (#5796)

* [test] fix qwen2 pytest distLarge (#5797)

* [Inference] Fix flash-attn import and add model test (#5794)

* Fix torch int32 dtype

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Fix flash-attn import

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Add generalized model test

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Remove exposed path to model

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Add default value for use_flash_attn

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* Rename model test

Signed-off-by: char-1ee <xingjianli59@gmail.com>

---------

Signed-off-by: char-1ee <xingjianli59@gmail.com>

* [Gemini] Use async stream to prefetch and h2d data moving (#5781)

* use async stream to prefetch and h2d data moving

* Remove redundant code

* [gemini] quick fix on possible async operation (#5803)

* [gemini] quick fix on possible async operation

* [gemini] quick fix on possible async operation

* [shardformer] upgrade transformers to 4.39.3 (#5815)

* [shardformer]upgrade transformers for gpt2/gptj/whisper (#5807)

* [shardformer] fix modeling of gpt2 and gptj

* [shardformer] fix whisper modeling

* [misc] update requirements

---------

Co-authored-by: ver217 <lhx0217@gmail.com>

* [shardformer]upgrade transformers for mistral (#5808)

* upgrade transformers for mistral

* fix

* fix

* [shardformer]upgrade transformers for llama (#5809)

* update transformers

fix

* fix

* fix

* [inference] upgrade transformers (#5810)

* update transformers

fix

* fix

* fix

* fix

* fix

* [gemini] update transformers for gemini (#5814)

---------

Co-authored-by: ver217 <lhx0217@gmail.com>

* Support 4d parallel + flash attention (#5789)

* support tp + sp + pp

* remove comments

---------

Co-authored-by: Edenzzzz <wtan45@wisc.edu>

---------

Signed-off-by: char-1ee <xingjianli59@gmail.com>
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* [zero] fix hook bug

* [zero] add low level optimizer back (#5839)

* [zero] fix param & refactor

* [zero] add back original low level opt

* [zero] remove moe related

* [zero] pass zero tests

* [zero] refactor

* [chore] add del func back

* [zero] comments and naming (#5840)

* [zero] modify api (#5843)

* [zero] modify api

* [test] remove _grad_store access in tests

* [test] fix (#5857)

* [CI] skip openmoe CI check

* [CI] fox pre-commit

* [zero] remove redundant memebr init (#5862)

* [misc] remove useless code, modify the pg mesh implementation

* [misc] remove useless code, modify the pg mesh implementation

* [misc] use tempfile

* resolve conflict with main branch

* [misc] use tempfile in test_moe_checkpoint.py

* [misc] remove useless code, add assertion about sequence parallel, move logger into function

* [misc] remove useless code

---------

Signed-off-by: char-1ee <xingjianli59@gmail.com>
Co-authored-by: Frank Lee <somerlee.9@gmail.com>
Co-authored-by: Edenzzzz <wenxuan.tan@wisc.edu>
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tests/test_zero/test_low_level/test_zero1_2.py

@@ -91,10 +91,13 @@ def exam_zero_1_2():
     zero2_optimizer.backward(zero2_output.mean().float())
 
     # check grad
-    z1g_list = zero1_optimizer._grad_store.get_working_grads_by_group_id(0)
-    z2g_list = zero2_optimizer._grad_store.get_working_grads_by_group_id(0)
-    for z1g, z2g in zip(z1g_list, z2g_list):
-        assert torch.equal(z1g, z2g)
+    for p1, p2 in zip(zero1_model.parameters(), zero2_model.parameters()):
+        g1 = zero1_optimizer.get_param_grad(p1)
+        g2 = zero2_optimizer.get_param_grad(p2)
+        if g1 is None or g2 is None:
+            assert g1 is None and g2 is None
+            continue
+        assert torch.allclose(g1, g2)
 
     # step
     zero1_optimizer.step()
@@ -102,7 +105,7 @@ def exam_zero_1_2():
 
     # check updated param
     for z1p, z2p in zip(zero1_model.parameters(), zero2_model.parameters()):
-        assert torch.equal(z1p.data, z2p.data)
+        assert torch.allclose(z1p, z2p)
 
 
 @parameterize("dtype", [torch.float16, torch.bfloat16])
@@ -120,7 +123,7 @@ def exam_zero_1_torch_ddp(world_size, dtype: torch.dtype, master_weights: bool):
     seed_all(1453)
 
     # create models
-    torch_model = MlpModel().cuda()
+    torch_model = MlpModel().cuda().to(dtype)
     zero_model = copy.deepcopy(torch_model).to(dtype)
 
     torch_model = DDP(torch_model.cuda(), static_graph=True).cuda()
@@ -142,39 +145,41 @@ def exam_zero_1_torch_ddp(world_size, dtype: torch.dtype, master_weights: bool):
     torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1)
 
     seed_all(1453 + local_rank)
-    # create
-    input_data = torch.rand(32, 123).cuda()
 
-    # zero-dp forward
-    zero_output = zero_model(input_data.to(dtype))
+    for _ in range(2):
+        # create
+        input_data = torch.rand(32, 123).cuda().to(dtype)
 
-    # torch-ddp forward
-    torch_output = torch_model(input_data)
-    loose_close(zero_output, torch_output, dtype=dtype)
+        # zero-dp forward
+        zero_output = zero_model(input_data)
 
-    # zero-dp backward
-    zero_optimizer.backward(zero_output.mean().float())
+        # torch-ddp forward
+        torch_output = torch_model(input_data)
+        loose_close(zero_output, torch_output, dtype=dtype)
 
-    # torch-ddp backward
-    torch_output.mean().backward()
+        # zero-dp backward
+        zero_optimizer.backward(zero_output.mean())
 
-    # check grad
-    for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()):
-        if p.grad is not None:
-            zero_grad_list = zero_optimizer._grad_store.get_partitioned_gradients_by_param_id(0, id(z1p))
-            torch_grad_list = split_ddp_grad(p.grad, world_size)
-            for zero_grad, torch_grad in zip(zero_grad_list, torch_grad_list):
-                loose_close(zero_grad, torch_grad, dtype=dtype)
+        # torch-ddp backward
+        torch_output.mean().backward()
 
-    # zero-dp step
-    zero_optimizer.step()
+        # check grad
+        for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()):
+            zero_grad = zero_optimizer.get_param_grad(z1p)
+            if p.grad is None:
+                assert zero_grad is None
+                continue
+            loose_close(p.grad, zero_grad, dtype=dtype)
 
-    # torch ddp step
-    torch_optimizer.step()
+        # zero-dp step
+        zero_optimizer.step()
 
-    # check updated param
-    for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()):
-        loose_close(p.data, z1p.data, dtype=dtype)
+        # torch ddp step
+        torch_optimizer.step()
+
+        # check updated param
+        for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()):
+            loose_close(p, z1p, dtype=dtype)
 
 
 def run_dist(rank, world_size, port):