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[hotfix] Add layer norm gradients all-reduce for sequence parallel (#4926)

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* [hotfix] Add layer norm gradients all-reduce for sequence parallel. (#4915)

* Add layer norm gradients all-reduce for sequence parallel.

* skip pipeline inference test

* [hotfix] fixing polices of sequence parallel (#4922)

* Add layer norm gradients all-reduce for sequence parallel.

* fix parameter passing when calling get_autopolicy

---------

Co-authored-by: littsk <1214689160@qq.com>

* Hotfix/add grad all reduce for sequence parallel (#4927)

* Add layer norm gradients all-reduce for sequence parallel.


* fix parameter passing when calling get_autopolicy

* fix bug using wrong variables

---------

Co-authored-by: littsk <1214689160@qq.com>

* fix policy initialization

* fix bloom and chatglm policices

* polish code of handling layernorm

* fix moe module

* polish code of class initializing

---------

Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
This commit is contained in:
littsk
2023-11-03 13:32:43 +08:00
committed by GitHub
parent d99b2c961a
commit 1a3315e336
30 changed files with 1120 additions and 552 deletions
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14
tests/test_shardformer/test_model/test_shard_chatglm2.py
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@@ -35,6 +35,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
chatglm_model = unwrap_model(org_model, "ChatGLMModel", "transformer")
shard_chatglm_model = unwrap_model(sharded_model, "ChatGLMModel", "transformer")
norm_layer_for_check = ["encoder.layers[0].input_layernorm"]
row_layer_for_check = ["encoder.layers[0].self_attention.query_key_value", "embedding.word_embeddings"]
col_layer_for_check = ["encoder.layers[0].self_attention.dense"]
@@ -66,8 +67,21 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
dim=1,
verbose=False,
)
norm_layer_grads = get_grad_tensors_for_check(
chatglm_model,
shard_chatglm_model,
norm_layer_for_check,
tp_group,
atol=atol,
rtol=rtol,
dim=1,
verbose=False,
)
grads_to_check.update(col_layer_grads)
grads_to_check.update(row_layer_grads)
grads_to_check.update(norm_layer_grads)
# optimizer executes step
org_optimizer.step()