[shardformer] add util functions for shardformer tests/fix sync_shared_param

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -37,7 +37,8 @@ def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp
         self.shared_param_process_groups = []
         for shared_param in self.shared_params:
             if len(shared_param) > 0:
-                self.stage_manager.init_process_group_by_stages(list(shared_param.keys()))
+                self.shared_param_process_groups.append(
+                    self.stage_manager.init_process_group_by_stages(list(shared_param.keys())))
         if precision == 'fp16':
             module = module.half().cuda()
         elif precision == 'bf16':

tests/kit/model_zoo/transformers/gpt.py

@@ -72,7 +72,9 @@ def data_gen_for_sequence_classification():
                                  embd_pdrop=0,
                                  resid_pdrop=0,
                                  summary_first_dropout=0,
-                                 hidden_dropout=0)
+                                 hidden_dropout=0,
+                                 problem_type="single_label_classification",
+                                 pad_token_id=50256)
 
 config_for_token_classification = copy.deepcopy(config)
 config_for_token_classification.num_labels = 2

tests/test_shardformer/test_model/_utils.py

@@ -1,11 +1,19 @@
 import copy
 from contextlib import nullcontext
+from typing import Any, Callable, Dict, List, Optional
 
 import torch
 import torch.distributed as dist
+from torch import Tensor
+from torch import distributed as dist
+from torch.distributed import ProcessGroup
 from torch.nn import Module
+from torch.optim import Adam, Optimizer
 
+from colossalai.booster import Booster
+from colossalai.booster.plugin import HybridParallelPlugin
 from colossalai.lazy import LazyInitContext
+from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer import ShardConfig, ShardFormer
 from colossalai.shardformer._utils import getattr_
 from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
@@ -79,20 +87,151 @@ def check_state_dict(org_model: Module, sharded_model: Module, name: str = ''):
         assert torch.equal(v, shard_v), f'{name} {k} value mismatch'
 
 
-def check_grad(original_model, sharded_model, layer_suffix, atol=1e-5, rtol=1e-5, dim=0, verbose=False):
+def build_model_from_hybrid_plugin(model_fn: Callable, loss_fn: Callable, test_config: Dict[str, Any]):
+
+    use_lazy_init = False
+    if 'use_lazy_init' in test_config:
+        use_lazy_init = test_config.pop('use_lazy_init')
+
+    if use_lazy_init:
+        ctx = LazyInitContext()
+    else:
+        ctx = nullcontext()
+
+    plugin = HybridParallelPlugin(**test_config)
+    booster = Booster(plugin=plugin)
+
+    with ctx:
+        org_model = model_fn().cuda()
+        sharded_model = copy.deepcopy(org_model)
+
+    if use_lazy_init:
+        org_model = ctx.materialize(org_model)
+
+    org_optimizer = Adam(org_model.parameters(), lr=1e-3)
+    sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3)
+    criterion = loss_fn
+
+    sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion)
+
+    return org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster
+
+
+def run_forward_backward_with_hybrid_plugin(org_model: Module, sharded_model: Module, sharded_optimizer: Optimizer,
+                                            data_gen_fn: Callable, output_transform_fn: Callable, criterion: Callable,
+                                            booster: Booster):
+
+    def _criterion(outputs, inputs):
+        outputs = output_transform_fn(outputs)
+        loss = criterion(outputs)
+        return loss
+
+    data = data_gen_fn()
+    sharded_model.train()
+    if booster.plugin.stage_manager is not None:
+        data = {
+            k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v
+            for k, v in data.items()
+        }
+        data_iter = iter([data])
+        sharded_output = booster.execute_pipeline(data_iter,
+                                                  sharded_model,
+                                                  _criterion,
+                                                  sharded_optimizer,
+                                                  return_loss=True,
+                                                  return_outputs=True)
+        sharded_loss = sharded_output['loss']
+    else:
+        data = {k: v.cuda() for k, v in data.items()}
+        sharded_output = sharded_model(**data)
+        sharded_loss = criterion(sharded_output)
+        sharded_loss.backward()
+
+    org_model.train()
+    org_output = org_model(**data)
+    org_loss = criterion(org_output)
+    org_loss.backward()
+
+    return org_loss, org_output, sharded_loss, sharded_output
+
+
+def check_output_hidden_state(org_output: Tensor,
+                              sharded_output: Tensor,
+                              stage_manager: Optional[PipelineStageManager] = None,
+                              atol: float = 1e-5,
+                              rtol: float = 1e-3):
+
+    org_hidden_state = org_output.last_hidden_state
+
+    if stage_manager is None:
+        sharded_hidden_state = sharded_output.last_hidden_state
+
+    if stage_manager and stage_manager.is_last_stage():
+        sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], dim=0)
+
+    assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=atol, rtol=rtol), \
+        f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}"
+
+
+def check_loss(org_loss: Tensor, sharded_loss: Tensor, atol: float = 1e-5, rtol: float = 1e-3):
+    assert torch.allclose(org_loss, sharded_loss, atol=atol, rtol=rtol), \
+        f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}"
+
+
+def check_weight(org_model: Module,
+                 sharded_model: Module,
+                 layer_suffix: List[str],
+                 tp_group: Optional[ProcessGroup] = None,
+                 dim: int = 0,
+                 atol: float = 1e-5,
+                 rtol: float = 1e-3,
+                 verbose: bool = False):
+
+    for suffix in layer_suffix:
+        org_weight = getattr_(org_model, suffix).weight
+        sharded_weight = getattr_(sharded_model, suffix).weight
+
+        if is_distributed_tensor(sharded_weight) or is_customized_distributed_tensor(sharded_weight):
+            sharded_weight_list = [
+                torch.zeros([*sharded_weight.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group))
+            ]
+            dist.all_gather(sharded_weight_list, sharded_weight, tp_group)
+            sharded_weight = torch.cat(sharded_weight_list, dim=dim)
+
+        if verbose and dist.get_rank() == 0:
+            print(f"'{suffix}' weight: {org_weight}, {sharded_weight}")
+
+        assert torch.allclose(org_weight, sharded_weight, atol=atol, rtol=rtol), \
+            f"shard model weight is not equal to origin model weight\n{org_weight}\n{sharded_weight}"
+
+
+def check_grad(org_model: Module,
+               sharded_model: Module,
+               layer_suffix: List[str],
+               tp_group: ProcessGroup = None,
+               dim: int = 0,
+               atol: float = 1e-5,
+               rtol: float = 1e-3,
+               verbose: bool = False):
+
     for suffix in layer_suffix:
-        org_grad = getattr_(original_model, suffix).weight.grad
+        org_grad = getattr_(org_model, suffix).weight.grad
         shard_grad = getattr_(sharded_model, suffix).weight.grad
         shard_weight = getattr_(sharded_model, suffix).weight
 
         if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
-            shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size())]
-            shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-            all_shard_grad = torch.cat(shard_grad_list, dim=dim)
-        else:
-            all_shard_grad = shard_grad
+            shard_grad_list = [
+                torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group))
+            ]
+            dist.all_gather(shard_grad_list, shard_grad, tp_group)
+            shard_grad = torch.cat(shard_grad_list, dim=dim)
+
+        # embedding may be resized when using tensor parallel
+        if shard_grad.shape[0] > org_grad.shape[0]:
+            shard_grad = shard_grad[:org_grad.shape[0], :]
+
         if verbose and dist.get_rank() == 0:
-            print(f"'{suffix}' grad: {org_grad}, {all_shard_grad}")
+            print(f"'{suffix}' grad: {org_grad}, {shard_grad}")
         assert torch.allclose(
-            org_grad, all_shard_grad, rtol=rtol, atol=atol
-        ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{all_shard_grad}"
+            org_grad, shard_grad, rtol=rtol, atol=atol
+        ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{shard_grad}"

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -1,107 +1,48 @@
-import copy
-from contextlib import nullcontext
-
 import pytest
 import torch
 from torch import distributed as dist
-from torch.optim import Adam
 
 import colossalai
-from colossalai.booster import Booster
-from colossalai.booster.plugin import HybridParallelPlugin
-from colossalai.lazy.lazy_init import LazyInitContext
 from colossalai.logging import disable_existing_loggers
-from colossalai.tensor.d_tensor.api import (
-    clear_layout_converter,
-    is_customized_distributed_tensor,
-    is_distributed_tensor,
-)
+from colossalai.tensor.d_tensor.api import clear_layout_converter
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
-from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_grad,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    run_forward_backward_with_hybrid_plugin,
+)
 
 
 def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
 
-    use_lazy_init = False
-    if 'use_lazy_init' in test_config:
-        use_lazy_init = test_config.pop('use_lazy_init')
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
+        build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
 
-    if use_lazy_init:
-        ctx = LazyInitContext()
-    else:
-        ctx = nullcontext()
-
-    # prepare booster
-    plugin = HybridParallelPlugin(**test_config)
-    booster = Booster(plugin=plugin)
-    stage_manager = plugin.stage_manager
-
-    # prepare models and optimizers
-    with ctx:
-        org_model = model_fn().cuda()
-        sharded_model = copy.deepcopy(org_model)
-
-    if use_lazy_init:
-        org_model = ctx.materialize(org_model)
-
-    org_optimizer = Adam(org_model.parameters(), lr=1e-3)
-    sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3)
-    criterion = loss_fn
-
-    sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion)
-
-    def _criterion(outputs, inputs):
-        outputs = output_transform_fn(outputs)
-        loss = criterion(outputs)
-        return loss
-
-    # do forward and backward
-    data = data_gen_fn()
-    sharded_model.train()
-    if stage_manager:
-        data = {
-            k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v
-            for k, v in data.items()
-        }
-        data_iter = iter([data])
-        sharded_output = booster.execute_pipeline(data_iter,
-                                                  sharded_model,
-                                                  _criterion,
-                                                  sharded_optimizer,
-                                                  return_loss=True,
-                                                  return_outputs=True)
-        sharded_loss = sharded_output['loss']
-    else:
-        data = {k: v.cuda() for k, v in data.items()}
-        sharded_output = sharded_model(**data)
-        sharded_loss = criterion(sharded_output)
-        sharded_loss.backward()
 
-    org_model.train()
-    org_output = org_model(**data)
-    org_loss = criterion(org_output)
-    org_loss.backward()
+    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)
 
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+
+    # check last hidden state & loss
     if stage_manager is None or stage_manager.is_last_stage():
 
-        # check last hidden state
         if org_model.__class__.__name__ == 'GPT2Model':
-            org_hidden_state = org_output.last_hidden_state
-
-            if stage_manager is None:
-                sharded_hidden_state = sharded_output.last_hidden_state
-
-            if stage_manager and stage_manager.is_last_stage():
-                sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']],
-                                                 dim=0)
-
-            assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=1e-5, rtol=1e-3), \
-                f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}"
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
 
-        # check loss
-        assert torch.allclose(org_loss, sharded_loss, atol=1e-5, rtol=1e-3), \
-            f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}"
+        check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
 
     # unwrap model
     if org_model.__class__.__name__ == 'GPT2Model':
@@ -111,27 +52,19 @@ def _criterion(outputs, inputs):
         gpt2 = org_model.transformer
         sharded_gpt2 = sharded_model.unwrap().transformer
 
-    # check grad
     col_layer_for_check = ['h[0].mlp.c_fc']
-    row_layer_for_check = ['h[0].mlp.c_proj']
-    check_grad(gpt2, sharded_gpt2, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False)
-    check_grad(gpt2, sharded_gpt2, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False)
+    row_layer_for_check = ['wte', 'h[0].mlp.c_proj']
+
+    # check grad
+    if stage_manager is None or stage_manager.is_first_stage():
+        check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False)
+        check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False)
 
     # check weights after optimizer.step()
     org_optimizer.step()
     sharded_optimizer.step()
     if stage_manager is None or stage_manager.is_first_stage():
-
-        org_weight = org_model.h[0].mlp.c_fc.weight
-        shard_weight = sharded_model.h[0].mlp.c_fc.weight
-
-        if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
-            shard_weight_list = [torch.zeros([*shard_weight.shape]).to('cuda') for _ in range(plugin.tp_size)]
-            dist.all_gather(shard_weight_list, shard_weight, plugin.tp_group)
-            shard_weight = torch.cat(shard_weight_list, dim=1)
-
-        assert torch.allclose(org_weight, shard_weight, atol=5e-3, rtol=1e-3), \
-            f"shard model weight is not equal to origin model weight\n{org_weight}\n{shard_weight}"
+        check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False)
 
     torch.cuda.empty_cache()
 
@@ -156,9 +89,11 @@ def _criterion(outputs, inputs):
 @clear_cache_before_run()
 def run_gpt2_test(test_config):
 
-    # TODO: add plugin_config for TP+DP after supporting & debugging it
+    # TODO: add test_config for TP+DP after supporting & debugging it
     # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True}
 
+    # TODO: add test_config for flash attention & jit operator after supporting
+
     sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
     test_config['precision'] = 'float'    # Do not use fp16/bf16 in testing
 
@@ -175,7 +110,6 @@ def check_gpt2(rank, world_size, port):
     run_gpt2_test()
 
 
-@pytest.mark.skip('Have some bug caused by merge')
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()