[shardformer] support llama model using shardformer (#3969)

adjust layer attr

colossalai/shardformer/layer/dist_crossentropy.py

@@ -21,7 +21,7 @@ class DistCrossEntropy(Function):
         and can be rewrite as:
         loss = log(sum(exp(x[i])) - x[class]
 
-        To avoid the `nan` of log(sim(exp(x[i]))), we minus the max of x[i]
+        To avoid the `nan` of log(sum(exp(x[i]))), we minus the max of x[i]
 
         Args:
             vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is

colossalai/shardformer/policies/autopolicy.py

@@ -19,6 +19,20 @@ def build_policies():
 
     from .bert import BertForSequenceClassificationPolicy
     auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy
+    from transformers.models.llama.modeling_llama import LlamaModel
+
+    from .llama import LlamaPolicy
+    auto_policy_dict[LlamaModel] = LlamaPolicy
+
+    from transformers import LlamaForSequenceClassification
+
+    from .llama import LlamaForSequenceClassificationPolicy
+    auto_policy_dict[LlamaForSequenceClassification] = LlamaForSequenceClassificationPolicy
+
+    from transformers import LlamaForCausalLM
+
+    from .llama import LlamaForCausalLMPolicy
+    auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy
 
     from transformers import GPT2Model
 

colossalai/shardformer/policies/llama.py

@@ -0,0 +1,122 @@
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Tuple, Type
+
+import torch.nn as nn
+from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
+
+import colossalai.shardformer.layer.layers as col_nn
+
+from .basepolicy import Argument, Col_Layer, Policy, Row_Layer
+
+
+class LlamaPolicy(Policy):
+
+    @staticmethod
+    def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]:
+        return {
+            LlamaDecoderLayer:
+                Argument(attr_dict={
+                    "self_attn.hidden_size": config.hidden_size // world_size,
+                    "self_attn.num_heads": config.num_attention_heads // world_size,
+                },
+                         param_funcs=[LlamaPolicy.attn_layer, LlamaPolicy.mlp_layer]),
+            LlamaModel:
+                Argument(attr_dict={}, param_funcs=[LlamaPolicy.embeddings])
+        }
+
+    @staticmethod
+    def attn_layer() -> List:
+        return [
+            Col_Layer(
+                suffix="self_attn.q_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Col,
+            ),
+            Col_Layer(
+                suffix="self_attn.k_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Col,
+            ),
+            Col_Layer(
+                suffix="self_attn.v_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Col,
+            ),
+            Row_Layer(
+                suffix="self_attn.o_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Row,
+            )
+        ]
+
+    @staticmethod
+    def mlp_layer() -> List:
+        return [
+            Col_Layer(
+                suffix="mlp.gate_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Col,
+                gather_output=True,
+            ),
+            Col_Layer(
+                suffix="mlp.up_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Row,
+                gather_output=True,
+            ),
+            Col_Layer(
+                suffix="mlp.down_proj",
+                weight="weight",
+                bias="bias",
+                replace_layer=col_nn.Linear1D_Col,
+                gather_output=True,
+            ),
+        ]
+
+    @staticmethod
+    def embeddings() -> List:
+        return [Col_Layer(
+            suffix="embed_tokens",
+            weight="weight",
+            replace_layer=col_nn.VocabParallelEmbedding1D,
+        )]
+
+from transformers import LlamaForCausalLM
+
+
+class LlamaForCausalLMPolicy(LlamaPolicy):
+
+    @staticmethod
+    def argument(config, world_size):
+        llamapolicy = LlamaPolicy.argument_policy(config, world_size)
+        argument = {LlamaForCausalLM: Argument(attr_dict={}, param_funcs=[LlamaForCausalLMPolicy.lm_head])}
+        argument.update(llamapolicy)
+
+    @staticmethod
+    def lm_head() -> List:
+        return [Col_Layer(suffix="lm_head", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)]
+
+
+from transformers import LlamaForSequenceClassification
+
+
+class LlamaForSequenceClassificationPolicy(LlamaPolicy):
+
+    @staticmethod
+    def argument(config, world_size):
+        llamapolicy = LlamaPolicy.argument_policy(config, world_size)
+        argument = {
+            LlamaForSequenceClassification:
+                Argument(attr_dict={}, param_funcs=[LlamaForSequenceClassificationPolicy.score])
+        }
+        argument.update(llamapolicy)
+
+    @staticmethod
+    def score() -> List:
+        return [Col_Layer(suffix="score", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)]

tests/test_shardformer/test_model/test_shard_llama.py

@@ -0,0 +1,106 @@
+import copy
+import os
+import random
+
+import pytest
+import torch
+from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.shard import ShardConfig, shard_model
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+
+os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
+CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=4, mode='1d')),)
+tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
+
+
+def build_model(rank, world_size):
+    cfg = LlamaConfig(num_hidden_layers=16)
+    org_model = LlamaForCausalLM(cfg)
+
+    shardconfig = ShardConfig(
+        rank=rank,
+        world_size=world_size,
+        gather_output=True,
+    )
+    org_model = org_model.to('cuda')
+
+    org_model_forshard = copy.deepcopy(org_model)
+    sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda')
+
+    return org_model, sharded_model
+
+
+def check_forward(org_model, sharded_model):
+    input = 'Hello, my dog is cute'
+    inputs = tokenizer(input, return_tensors='pt').to('cuda')
+    del inputs["token_type_ids"]
+    del inputs["attention_mask"]
+    #orgin model
+    org_model.eval()
+    org_out = org_model(**inputs)
+
+    #shard model
+    sharded_model.eval()
+    shard_out = sharded_model(**inputs)
+
+    assert torch.allclose(
+        org_out[0], shard_out[0],
+        atol=1e-4), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}"
+
+
+def check_backward(org_model, sharded_model):
+    # prepare input
+    input = 'Hello, my dog is cute'
+    tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
+    del tokenized_input["token_type_ids"]
+    del tokenized_input["attention_mask"]
+    labels = tokenized_input['input_ids'].clone()
+    labels[labels == tokenizer.pad_token_id] = -100
+    tokenized_input['labels'] = labels
+
+    #orgin model
+    org_model.train()
+    org_out = org_model(**tokenized_input)
+    org_loss = org_out.loss
+    org_loss.backward()
+    org_grad = org_model.model.layers[0].self_attn.q_proj.weight.grad
+
+    torch.cuda.empty_cache()
+    #shard model
+    sharded_model.train()
+    shard_out = sharded_model(**tokenized_input)
+    shard_loss = shard_out.loss
+    shard_loss.backward()
+    shard_grad = sharded_model.model.layers[0].self_attn.q_proj.weight.grad
+    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
+    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+
+    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}"
+    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}"
+
+
+def check_llama(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    org_model, sharded_model = build_model(rank, world_size)
+    check_forward(org_model, sharded_model)
+    check_backward(org_model, sharded_model)
+
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_llama():
+    spawn(check_llama, 4)
+
+
+if __name__ == "__main__":
+    test_llama()