[pipeline] add chatglm (#4363)

* add pipeline policy and bert forward to be done

* add bertmodel pipeline forward and make tests

* add Bert_Policy and test for policy

* update formatting

* update formatting

* update the code

* fix bugs

* fix name confilt

* add bloom model and policy ,revise the base class of policy

* revise

* revision

* add bert_for_pretraining

* add bert_for_pretraining forward and policy

* fix typos

* cancel warning

* change the imediate output to default dict

* change the default output of get_shared_params

* add chatglm

* add

* chatglm

* chatglm

* finish chatglm

* deletes

* fix rmsnorm

* chatglm

* fix chatglm shard

* init

colossalai/shardformer/modeling/chatglm.py

@@ -0,0 +1,189 @@
+""" PyTorch ChatGLM model. """
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss, LayerNorm
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.utils import logging
+
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
+from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
+    ChatGLMForConditionalGeneration,
+    ChatGLMModel,
+    GLMBlock,
+)
+
+
+class ChatGLMPipelineForwards:
+    '''
+    This class serves as a micro library for ChatGLM model forwards under pipeline parallelism.
+    '''
+
+    @staticmethod
+    def chatglm_model_forward(
+        self: ChatGLMModel,
+        input_ids,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        full_attention_mask: Optional[torch.BoolTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        stage_index: Optional[List[int]] = None,
+    ):
+        logger = logging.get_logger(__name__)
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else self.config.output_hidden_states)
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if past_key_values:
+            logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
+            past_key_values = None
+        if output_hidden_states:
+            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
+            output_hidden_states = False
+        if use_cache:
+            logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
+            use_cache = False
+        if stage_manager.is_first_stage():
+            batch_size, seq_length = input_ids.shape
+            if inputs_embeds is None:
+                inputs_embeds = self.embedding(input_ids)
+            hidden_states = inputs_embeds
+        else:
+            seq_length, batch_size = hidden_states.shape[:2]
+        if self.pre_seq_len is not None:
+            if past_key_values is None:
+                past_key_values = self.get_prompt(batch_size=batch_size,
+                                                  device=input_ids.device,
+                                                  dtype=inputs_embeds.dtype)
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), attention_mask],
+                                           dim=-1)
+        if full_attention_mask is None:
+            if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
+                full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
+        # Rotary positional embeddings
+        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+        if position_ids is not None:
+            rotary_pos_emb = rotary_pos_emb[position_ids]
+        else:
+            rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+        rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+        if not past_key_values:
+            past_key_values = [None for _ in range(self.num_layers)]
+        presents = () if use_cache else None
+        if self.encoder.gradient_checkpointing and self.encoder.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
+                use_cache = False
+        all_self_attentions = None
+        all_hidden_states = () if output_hidden_states else None
+        start_idx, end_idx = stage_index[0], stage_index[1]
+        for idx in range(start_idx, end_idx):
+            layer = self.encoder._get_layer(idx)
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            if self.encoder.gradient_checkpointing and self.encoder.training:
+                layer_ret = torch.utils.checkpoint.checkpoint(layer, hidden_states, attention_mask, rotary_pos_emb,
+                                                              past_key_values[idx], use_cache)
+            else:
+                layer_ret = layer(hidden_states,
+                                  full_attention_mask,
+                                  rotary_pos_emb,
+                                  kv_cache=past_key_values[idx],
+                                  use_cache=use_cache)
+            hidden_states, kv_cache = layer_ret
+            if use_cache:
+                presents = presents + (kv_cache,)
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+        if stage_manager.is_last_stage():
+            # final layer_norm
+            if self.encoder.post_layer_norm:
+                hidden_states = self.encoder.final_layernorm(hidden_states)
+            if not return_dict:
+                return tuple(
+                    v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+            return BaseModelOutputWithPast(
+                last_hidden_state=hidden_states,
+                past_key_values=presents,
+                hidden_states=all_hidden_states,
+                attentions=all_self_attentions,
+            )
+        else:
+            return {'hidden_states': hidden_states}
+
+    @staticmethod
+    def chatglm_for_conditional_generation_forward(
+        self: ChatGLMForConditionalGeneration,
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        return_last_logit: Optional[bool] = False,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        stage_index: Optional[List[int]] = None,
+    ):
+        logger = logging.get_logger(__name__)
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (return_dict if return_dict is not None else self.config.use_return_dict)
+        transformer_outputs = ChatGLMPipelineForwards.chatglm_model_forward(
+            self.transformer,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            stage_manager=stage_manager,
+            hidden_states=hidden_states,
+            stage_index=stage_index,
+        )
+        if stage_manager.is_last_stage():
+            hidden_states = transformer_outputs[0]
+            if return_last_logit:
+                hidden_states = hidden_states[-1:]
+            lm_logits = self.transformer.output_layer(hidden_states)
+            lm_logits = lm_logits.transpose(0, 1).contiguous()
+            loss = None
+            if labels is not None:
+                lm_logits = lm_logits.to(torch.float32)
+                # Shift so that tokens < n predict n
+                shift_logits = lm_logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+                # Flatten the tokens
+                loss_fct = CrossEntropyLoss(ignore_index=-100)
+                loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+                lm_logits = lm_logits.to(hidden_states.dtype)
+                loss = loss.to(hidden_states.dtype)
+            if not return_dict:
+                output = (lm_logits,) + transformer_outputs[1:]
+                return ((loss,) + output) if loss is not None else output
+            return CausalLMOutputWithPast(
+                loss=loss,
+                logits=lm_logits,
+                past_key_values=transformer_outputs.past_key_values,
+                hidden_states=transformer_outputs.hidden_states,
+                attentions=transformer_outputs.attentions,
+            )
+        else:
+            return transformer_outputs

colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py

@@ -0,0 +1,58 @@
+from transformers import PretrainedConfig
+
+
+class ChatGLMConfig(PretrainedConfig):
+    model_type = "chatglm"
+
+    def __init__(self,
+                 num_layers=28,
+                 padded_vocab_size=65024,
+                 hidden_size=4096,
+                 ffn_hidden_size=13696,
+                 kv_channels=128,
+                 num_attention_heads=32,
+                 seq_length=2048,
+                 hidden_dropout=0.0,
+                 attention_dropout=0.0,
+                 layernorm_epsilon=1e-5,
+                 rmsnorm=True,
+                 apply_residual_connection_post_layernorm=False,
+                 post_layer_norm=True,
+                 add_bias_linear=False,
+                 add_qkv_bias=False,
+                 bias_dropout_fusion=True,
+                 multi_query_attention=False,
+                 multi_query_group_num=1,
+                 apply_query_key_layer_scaling=True,
+                 attention_softmax_in_fp32=True,
+                 fp32_residual_connection=False,
+                 quantization_bit=0,
+                 pre_seq_len=None,
+                 prefix_projection=False,
+                 **kwargs):
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.rmsnorm = rmsnorm
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.pre_seq_len = pre_seq_len
+        self.prefix_projection = prefix_projection
+        super().__init__(**kwargs)

colossalai/shardformer/policies/chatglm.py

@@ -1,32 +1,46 @@
-from typing import Dict, Union
+from functools import partial
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch.nn as nn
+from torch import Tensor
+from transformers.modeling_outputs import BaseModelOutputWithPast
 
 import colossalai.shardformer.layer as col_nn
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.modeling.chatglm import ChatGLMPipelineForwards
+from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
+from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
+    ChatGLMForConditionalGeneration,
+    ChatGLMModel,
+    GLMBlock,
+)
 
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
-__all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']
+__all__ = ['ChatGLMPolicy', 'ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']
 
 
-class ChatGLMModelPolicy(Policy):
+class ChatGLMPolicy(Policy):
 
     def config_sanity_check(self):
         pass
 
     def preprocess(self):
         # Resize embedding
-        vocab_size = self.model.config.padded_vocab_size
+        if self.shard_config.enable_tensor_parallelism:
-        world_size = self.shard_config.tensor_parallel_size
+            vocab_size = self.model.config.padded_vocab_size
+            world_size = self.shard_config.tensor_parallel_size
+
+            if vocab_size % world_size != 0:
+                new_vocab_size = vocab_size + world_size - vocab_size % world_size
+                self.model.resize_token_embeddings(new_vocab_size)
 
-        if vocab_size % world_size != 0:
-            new_vocab_size = vocab_size + world_size - vocab_size % world_size
-            self.model.resize_token_embeddings(new_vocab_size)
 
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock
+        from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock
+
 
         policy = {}
 
@@ -112,9 +126,91 @@ class ChatGLMModelPolicy(Policy):
     def postprocess(self):
         return self.model
 
+    def get_held_layers(self) -> List[nn.Module]:
+        """Get pipeline layers for current stage."""
+        assert self.pipeline_stage_manager is not None
+
+        if self.model.__class__.__name__ == 'ChatGLMModel':
+            module = self.model
+        else:
+            module = self.model.transformer
+        stage_manager = self.pipeline_stage_manager
+
+        held_layers = []
+        layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages)
+        if stage_manager.is_first_stage():
+            held_layers.append(module.embedding)
+        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
+        held_layers.extend(module.encoder.layers[start_idx:end_idx])
+        if stage_manager.is_last_stage():
+            if module.encoder.post_layer_norm:
+                held_layers.append(module.encoder.final_layernorm)
+
+        # rotary_pos_emb is needed for all stages
+        held_layers.append(module.rotary_pos_emb)
+
+        return held_layers
+
+    def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
+        """If under pipeline parallel setting, replacing the original forward method of huggingface
+           to customized forward method, and add this changing to policy."""
+        if not self.pipeline_stage_manager:
+            raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
+        stage_manager = self.pipeline_stage_manager
+        if self.model.__class__.__name__ == 'ChatGLMModel':
+            module = self.model
+        else:
+            module = self.model.transformer
+
+        layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages)
+        stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+        method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
+        self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
+
+
+class ChatGLMModelPolicy(ChatGLMPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        from transformers.models.gpt2.modeling_gpt2 import GPT2Model
+
+        policy = super().module_policy()
+
+        if self.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=ChatGLMModel,
+                                      new_forward=ChatGLMPipelineForwards.chatglm_model_forward,
+                                      policy=policy)
+        return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        return super().get_held_layers()
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        """No shared params in ChatGLMModel."""
+        return []
+
+
 
 class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy):
 
     def module_policy(self):
         policy = super().module_policy()
+
+        if self.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=ChatGLMForConditionalGeneration,
+                                      new_forward=ChatGLMPipelineForwards.chatglm_for_conditional_generation_forward,
+                                      policy=policy)
         return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        held_layers = super().get_held_layers()
+        if self.pipeline_stage_manager.is_last_stage():
+            held_layers.append(self.model.transformer.output_layer)
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        """No shared params in ChatGLMForConditionalGenerationModel."""
+        return []
+

tests/kit/model_zoo/transformers/chatglm.py

@@ -1,9 +1,11 @@
 import torch
 import transformers
 
+from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
+from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
+
 from ..registry import ModelAttribute, model_zoo
-from .chatglm2_6b.configuration_chatglm import ChatGLMConfig
+
-from .chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
 
 # ================================
 # Register single-sentence ChatGLM
@@ -20,15 +22,18 @@ def data_gen():
 output_transform_fn = lambda x: x
 
 # define loss function
-loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean()
+loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.sum()
-loss_fn = lambda x: x.logits.mean()
+loss_fn = lambda x: x.logits.sum()
+
 config = ChatGLMConfig(num_layers=1,
                        padded_vocab_size=65024,
                        hidden_size=64,
                        num_attention_heads=8,
-                       rmsnorm=False,
+                       rmsnorm=True,
                        original_rope=True,
-                       use_cache=True)
+                       use_cache=True,
+                       torch_dtype=torch.float32)
+
 
 model_zoo.register(name='transformers_chatglm',
                    model_fn=lambda: ChatGLMModel(config, empty_init=False),

tests/test_pipeline/test_policy/test_t5_pipeline_utils.py

@@ -1,39 +0,0 @@
-from colossalai.shardformer.policies.t5 import T5BasePolicy
-
-
-def test_t5_pipeline_distribution():
-    num_test_cases = 8
-    test_dict = {
-        'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5],
-        'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22],
-        'num_stages': [2, 2, 2, 4, 4, 4, 8, 8],
-        'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2]
-    }
-
-    for i in range(num_test_cases):
-        _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(test_dict['num_encoder_layers'][i],
-                                                                      test_dict['num_decoder_layers'][i],
-                                                                      test_dict['num_stages'][i])
-        assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage
-
-
-def test_t5_pipeline_layers():
-    num_test_cases = 4
-    test_dict = {
-        'num_encoder_layers': [2, 3, 2, 4],
-        'num_decoder_layers': [2, 0, 2, 8],
-        'num_stages': [2, 2, 4, 4],
-        'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]],
-                             [[0, 4], [0, 3], [3, 6], [6, 8]]]
-    }
-
-    for i in range(num_test_cases):
-        layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
-            test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i])
-
-        for stage in range(test_dict['num_stages'][i]):
-            start_idx, end_idx = test_dict['layers_per_stage'][i][stage]
-            predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage,
-                                                                             decoder_starting_stage)
-            assert start_idx == predicted_start
-            assert end_idx == predicted_end

tests/test_shardformer/test_model/_utils.py

@@ -1,5 +1,6 @@
 import copy
 from contextlib import nullcontext
+from typing import Optional
 from typing import Any, Callable, Dict, List, Optional
 
 import torch
@@ -15,6 +16,7 @@ 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.policies.auto_policy import Policy
 from colossalai.shardformer._utils import getattr_
 from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
 
@@ -39,7 +41,8 @@ def build_pipeline_model(model_fn,
                          stage_manager=None,
                          enable_fused_normalization=False,
                          enable_tensor_parallelism=False,
-                         use_lazy_init: bool = False):
+                         use_lazy_init: bool = False,
+                         policy: Optional[Policy] = None):
     ctx = LazyInitContext() if use_lazy_init else nullcontext()
     with ctx:
         # create new model
@@ -54,7 +57,7 @@ def build_pipeline_model(model_fn,
                                pipeline_stage_manager=stage_manager)
 
     shard_former = ShardFormer(shard_config=shard_config)
-    sharded_model, shared_params = shard_former.optimize(model_copy)
+    sharded_model, shared_params = shard_former.optimize(model_copy, policy=policy)
     return org_model.cuda(), sharded_model.cuda()
 
 

tests/test_shardformer/test_model/test_shard_chatglm.py

@@ -60,7 +60,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     shard_weight = shard_chatglm_model.embedding.word_embeddings.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(2)]
+        shard_grad_list = [torch.zeros_like(shard_grad) for _ in range(2)]
         torch.distributed.all_gather(shard_grad_list, shard_grad)
         all_shard_grad = torch.cat(shard_grad_list, dim=0)
     else:

tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py

@@ -0,0 +1,86 @@
+import copy
+import os
+
+import pytest
+import torch
+
+import colossalai
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.logging import disable_existing_loggers
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    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, build_pipeline_model, run_forward
+
+
+@parameterize('enable_fused_normalization', [False])
+@parameterize('enable_tensor_parallelism', [False])
+@parameterize('use_lazy_init', [False])
+def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
+    DP_DIM, PP_DIM = 0, 1
+    DP_SIZE, PP_SIZE = 2, 2
+    pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
+    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        # create new model for test
+        inputs = data_gen_fn()
+        inputs = {k: v.cuda() for k, v in inputs.items()}
+        input_ids = inputs['input_ids']
+        hidden_size = 64
+        batch_size, seq_len = input_ids.shape
+        hidden_state_shape = (seq_len, batch_size, hidden_size)
+        if name == "transformers_chatglm":
+            _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
+                                                    enable_tensor_parallelism, use_lazy_init, ChatGLMModelPolicy())
+            if stage_manager.is_last_stage():
+                hidden_states = torch.randn(*hidden_state_shape).cuda()
+                inputs['input_ids'] = None
+                inputs['hidden_states'] = hidden_states
+            outputs = sharded_model(**inputs)
+            if stage_manager.is_last_stage():
+                assert outputs[0].shape == hidden_state_shape
+
+            else:
+                assert outputs['hidden_states'].shape == hidden_state_shape
+
+        if name == "transformers_chatglm_for_conditional_generation":
+            _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
+                                                    enable_tensor_parallelism, use_lazy_init,
+                                                    ChatGLMForConditionalGenerationPolicy())
+            if stage_manager.is_last_stage():
+                hidden_states = torch.randn(*hidden_state_shape).cuda()
+                inputs['input_ids'] = None
+                inputs['hidden_states'] = hidden_states
+            outputs = sharded_model(**inputs)
+            if stage_manager.is_last_stage():
+                assert outputs[0].shape == (batch_size, seq_len, 65024)
+            else:
+                assert outputs['hidden_states'].shape == hidden_state_shape
+
+    torch.cuda.empty_cache()
+
+
+def check_chatglm(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_chatglm_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_chatglm():
+    spawn(check_chatglm, 4)
+
+
+if __name__ == "__main__":
+    test_chatglm()