[shardformer] support ep for deepseek v3 (#6185)

* [feature] support ep for deepseek v3

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

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

* fix test

* [shardformer] fix deepseek v3 init

* [lazy] fit lora for lazy init

* [example] support npu for deepseek v3

---------

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

tests/test_shardformer/test_model/_utils.py

@@ -223,7 +223,6 @@ def run_forward_backward_with_hybrid_plugin(
     for k, v in data.items():
         unshard_test_data[k] = data[k].clone()
 
-    sharded_model.train()
     if booster.plugin.stage_manager is not None:
         for k, v in shard_test_data.items():
             if torch.is_tensor(v) or "Tensor" in v.__class__.__name__:
@@ -248,7 +247,6 @@ def run_forward_backward_with_hybrid_plugin(
         sharded_loss = criterion(sharded_output)
         sharded_optimizer.backward(sharded_loss)
 
-    org_model.train()
     if booster.plugin.stage_manager is not None:
         for k, v in unshard_test_data.items():
             if torch.is_tensor(v) or "Tensor" in v.__class__.__name__:

tests/test_shardformer/test_model/test_shard_deepseek_v3.py

@@ -0,0 +1,102 @@
+from typing import Tuple
+
+import pytest
+import torch
+import torch.distributed
+import torch.distributed as dist
+from torch.testing import assert_close
+
+import colossalai
+from colossalai.booster.plugin import MoeHybridParallelPlugin
+from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing.random import seed_all
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    run_forward_backward_with_hybrid_plugin,
+)
+
+
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+    enable_gradient_checkpointing = test_config.pop("enable_gradient_checkpointing", False)
+    seed_all(42)
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
+        model_fn, loss_fn, test_config, pluggin_cls=MoeHybridParallelPlugin
+    )
+    if enable_gradient_checkpointing:
+        # org_model.gradient_checkpointing_enable()
+        sharded_model.unwrap().gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
+
+    org_model = org_model.to(torch.bfloat16)
+    org_model.eval()
+    sharded_model.eval()
+
+    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
+        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
+    )
+
+    assert_close(org_loss, sharded_loss)
+
+    param_dict = {n: p for n, p in org_model.named_parameters()}
+    for n, p in sharded_model.unwrap().named_parameters():
+        if n in param_dict:
+            if booster.plugin.zero_stage == 0:
+                grad = p.grad
+                target_grad = param_dict[n].grad
+            else:
+                grad = sharded_optimizer.get_working_grad_by_param_id(id(p))
+                pg = sharded_optimizer.param_to_pg[p]
+                target_grad = param_dict[n].grad
+                if target_grad is None:
+                    continue
+                target_grad = target_grad.view(-1).chunk(dist.get_world_size(pg))[dist.get_rank(pg)]
+            assert_close(grad, target_grad, atol=3e-1, rtol=0)
+
+
+@parameterize(
+    "config",
+    [
+        # zero 1
+        (1, 4),
+        (1, 2),
+    ],
+)
+def run_deepseek_v3_test(config: Tuple[int, ...]):
+    zero_stage, ep_size = config
+    plugin_config = dict(
+        pp_size=1,
+        tp_size=1,
+        ep_size=ep_size,
+        zero_stage=zero_stage,
+        overlap_communication=False,
+        precision="bf16",
+        find_unused_parameters=True,
+    )
+
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_deepseek_v3")
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+
+        check_forward_backward(
+            model_fn,
+            data_gen_fn,
+            output_transform_fn,
+            loss_fn,
+            plugin_config,
+        )
+
+
+def check_deepseek_v3(rank, world_size, port):
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_deepseek_v3_test()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [4])
+@rerun_if_address_is_in_use()
+def test_deepseek_v3(world_size):
+    spawn(check_deepseek_v3, world_size)
+
+
+if __name__ == "__main__":
+    test_deepseek_v3(world_size=4)