Feature/chatglm (#4240)

* [shardformer] added tests

* [shardformer] vit test finish and support

* [shardformer] chatglm ready

* import chatglm

* [shardformer] add test kit in model zoo for chatglm

* [sharformer] add first version of policy of chatglm

* [shardformer] polish chatglm code

* [shardformer] polish code

* [shardformer] support chatglm without layernorm

* [shardformer] chatglm shard without mlp sharding

* [shardformer] delete some file

* [shardformer] ChatGLM support layernorm sharding

* [shardformer] register without auto policy

* [shardformer] pre-commit check files

* [shardformer] fix chatglm configuration with pre-commit

colossalai/shardformer/policies/chatglm.py

+from typing import Dict, Union
+
+import torch.nn as nn
+
+import colossalai.shardformer.layer as col_nn
+
+from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+
+__all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']
+
+
+class ChatGLMModelPolicy(Policy):
+
+    def config_sanity_check(self):
+        pass
+
+    def preprocess(self):
+        # Resize embedding
+        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)
+
+        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
+
+        policy = {}
+
+        if self.shard_config.enable_tensor_parallelism:
+
+            policy[ChatGLMModel] = ModulePolicyDescription(attribute_replacement={},
+                                                           sub_module_replacement=[
+                                                               SubModuleReplacementDescription(
+                                                                   suffix="embedding.word_embeddings",
+                                                                   target_module=col_nn.VocabParallelEmbedding1D,
+                                                               )
+                                                           ])
+
+            policy[GLMBlock] = ModulePolicyDescription(attribute_replacement={
+                "self_attention.num_attention_heads_per_partition":
+                    self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
+                "self_attention.projection_size":
+                    (self.model.config.kv_channels * self.model.config.num_attention_heads) //
+                    self.shard_config.tensor_parallel_size,
+                "self_attention.qkv_hidden_size":
+                    (self.model.config.kv_channels * self.model.config.num_attention_heads * 3) //
+                    self.shard_config.tensor_parallel_size,
+                "self_attention.core_attention.num_attention_heads_per_partition":
+                    self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
+                "self_attention.core_attention.hidden_size_per_partition":
+                    self.model.config.kv_channels * self.model.config.num_attention_heads //
+                    self.shard_config.tensor_parallel_size,
+            },
+                                                       param_replacement=[],
+                                                       sub_module_replacement=[
+                                                           SubModuleReplacementDescription(
+                                                               suffix="self_attention.query_key_value",
+                                                               target_module=col_nn.Linear1D_Col,
+                                                           ),
+                                                           SubModuleReplacementDescription(
+                                                               suffix="self_attention.dense",
+                                                               target_module=col_nn.Linear1D_Row,
+                                                           ),
+                                                           SubModuleReplacementDescription(
+                                                               suffix="self_attention.core_attention.attention_dropout",
+                                                               target_module=col_nn.DropoutForParallelInput,
+                                                           ),
+                                                       ])
+        # optimization configuration
+        if self.shard_config.enable_fused_normalization:
+            if not self.model.config.rmsnorm:
+
+                self.append_or_create_submodule_replacement(description=[
+                    SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedLayerNorm),
+                    SubModuleReplacementDescription(suffix="post_attention_layernorm",
+                                                    target_module=col_nn.FusedLayerNorm)
+                ],
+                                                            policy=policy,
+                                                            target_key=GLMBlock)
+
+                if self.model.config.post_layer_norm:
+                    self.append_or_create_submodule_replacement(description=[
+                        SubModuleReplacementDescription(suffix="encoder.final_layernorm",
+                                                        target_module=col_nn.FusedLayerNorm)
+                    ],
+                                                                policy=policy,
+                                                                target_key=ChatGLMModel)
+
+        return policy
+
+    def postprocess(self):
+        return self.model

tests/kit/model_zoo/transformers/__init__.py

 from .albert import *
 from .bert import *
 from .bloom import *
+from .chatglm import *
 from .gpt import *
 from .llama import *
 from .opt import *

tests/kit/model_zoo/transformers/chatglm.py

+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+from .chatglm2_6b.configuration_chatglm import ChatGLMConfig
+from .chatglm2_6b.modeling_chatglm import ChatGLMModel
+
+# ================================
+# Register single-sentence ChatGLM
+# ================================
+
+
+def data_gen():
+    input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075]], dtype=torch.int64)
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]])
+    return dict(input_ids=input_ids, attention_mask=attention_mask)
+
+
+# define output transform function
+output_transform_fn = lambda x: x
+
+# define loss function
+loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean()
+loss_fn = lambda x: x.loss
+config = ChatGLMConfig(num_layers=1,
+                       padded_vocab_size=65024,
+                       hidden_size=64,
+                       num_attention_heads=8,
+                       rmsnorm=False,
+                       original_rope=True,
+                       use_cache=True)
+
+model_zoo.register(name='transformers_chatglm',
+                   model_fn=lambda: ChatGLMModel(config, empty_init=False),
+                   data_gen_fn=data_gen,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_chatglm_model,
+                   model_attribute=ModelAttribute(has_control_flow=True))

tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py

+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)

tests/test_shardformer/test_model/test_shard_chatglm.py

+import copy
+import os
+
+import pytest
+import torch
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.shardformer.policies.chatglm import 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, run_forward
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
+                                                                 output_transform_fn, loss_fn)
+    assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'])
+    # do backward
+    org_loss.backward()
+    shard_loss.backward()
+
+    assert torch.allclose(org_loss, shard_loss,
+                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
+
+    # unwrap model
+    if org_model.__class__.__name__ == 'ChatGLMModel':
+        chatglm_model = org_model
+        shard_chatglm_model = sharded_model
+    else:
+        chatglm_model = org_model.transformer
+        shard_chatglm_model = sharded_model.transformer
+
+    # check attention grad
+    org_grad = chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad
+    shard_grad = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad
+    shard_weight = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.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 = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
+    assert torch.allclose(org_grad, all_shard_grad,
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
+
+    # check embedding weights
+    org_grad = chatglm_model.embedding.word_embeddings.weight.grad
+    shard_grad = shard_chatglm_model.embedding.word_embeddings.weight.grad
+    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)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
+
+    assert torch.allclose(org_grad, all_shard_grad,
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
+
+
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism):
+    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
+        org_model = model_fn().cuda()
+
+        # shard model
+        shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization,
+                                   enable_tensor_parallelism=enable_tensor_parallelism)
+        model_copy = copy.deepcopy(org_model)
+        shard_former = ShardFormer(shard_config=shard_config)
+        if name == "transformers_chatglm":
+            sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda()
+
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    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, 2)
+
+
+if __name__ == "__main__":
+    test_chatglm()