[shardformer] shardformer support opt models (#4091)

* [shardformer] shardformer support opt models

* [shardformer] shardformer support opt models, fix

* [shardformer] shardformer support opt models, fix

* [shardformer] shardformer support opt models, fix

colossalai/shardformer/policies/autopolicy.py

@@ -68,6 +68,16 @@ _POLICY_LIST = {
         PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"),
     "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification":
         PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"),
+
+    # OPT
+    "transformers.models.opt.modeling_opt.OPTModel":
+        PolicyLocation(file_name="opt", class_name="OPTModelPolicy"),
+    "transformers.models.opt.modeling_opt.OPTForCausalLM":
+        PolicyLocation(file_name="opt", class_name="OPTForCausalLMPolicy"),
+    "transformers.models.opt.modeling_opt.OPTForSequenceClassification":
+        PolicyLocation(file_name="opt", class_name="OPTForSequenceClassificationPolicy"),
+    "transformers.models.opt.modeling_opt.OPTForQuestionAnswering":
+        PolicyLocation(file_name="opt", class_name="OPTForQuestionAnsweringPolicy"),
 }
 
 

colossalai/shardformer/policies/opt.py

@@ -0,0 +1,133 @@
+from transformers.models.opt.modeling_opt import (
+    OPTAttention,
+    OPTDecoder,
+    OPTDecoderLayer,
+    OPTForCausalLM,
+    OPTForSequenceClassification,
+)
+
+from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row
+
+from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+
+
+class OPTPolicy(Policy):
+
+    def preprocess(self):
+        # reshape the embedding layer
+        r"""
+        Reshape the Embedding layer to make the embedding dimension divisible by world_size
+        """
+        vocab_size = self.model.config.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):
+        base_policy = {
+            OPTDecoder:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="embed_tokens",
+                                                target_module=Embedding1D,
+                                            )
+                                        ]),
+            OPTDecoderLayer:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="fc1",
+                                                target_module=Linear1D_Col,
+                                            ),
+                                            SubModuleReplacementDescription(
+                                                suffix="fc2",
+                                                target_module=Linear1D_Row,
+                                            )
+                                        ]),
+            OPTAttention:
+                ModulePolicyDescription(attribute_replacement={
+                    "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
+                    "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size
+                },
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="q_proj",
+                                                target_module=Linear1D_Col,
+                                            ),
+                                            SubModuleReplacementDescription(
+                                                suffix="k_proj",
+                                                target_module=Linear1D_Col,
+                                            ),
+                                            SubModuleReplacementDescription(
+                                                suffix="v_proj",
+                                                target_module=Linear1D_Col,
+                                            ),
+                                            SubModuleReplacementDescription(
+                                                suffix="out_proj",
+                                                target_module=Linear1D_Row,
+                                            ),
+                                        ]),
+        }
+        if self.shard_config.fused_layernorm:
+            base_policy[OPTDecoder].sub_module_replacement.append(
+                SubModuleReplacementDescription(suffix="final_layer_norm",
+                                                target_module=FusedLayerNorm,
+                                                ignore_if_not_exist=True))
+            base_policy[OPTDecoderLayer].sub_module_replacement.extend([
+                SubModuleReplacementDescription(suffix="self_attn_layer_norm",
+                                                target_module=FusedLayerNorm,
+                                                ignore_if_not_exist=True),
+                SubModuleReplacementDescription(suffix="final_layer_norm",
+                                                target_module=FusedLayerNorm,
+                                                ignore_if_not_exist=True)
+            ])
+        return base_policy
+
+    def new_model_class(self):
+        return None
+
+    def postprocess(self):
+        return self.model
+
+
+class OPTModelPolicy(OPTPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+class OPTForCausalLMPolicy(OPTPolicy):
+
+    def module_policy(self):
+        policy = super().module_policy()
+        new_item = {
+            OPTForCausalLM:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(suffix="lm_head",
+                                                                            target_module=Linear1D_Col,
+                                                                            kwargs=dict(gather_output=True))
+                                        ])
+        }
+
+        policy.update(new_item)
+        return policy
+
+
+class OPTForSequenceClassificationPolicy(OPTPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+class OPTForQuestionAnsweringPolicy(OPTPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()

tests/kit/model_zoo/transformers/opt.py

@@ -11,14 +11,47 @@ SEQ_LENGTH = 16
 
 
 def data_gen():
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+    input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long()
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+    attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long()
     return dict(input_ids=input_ids, attention_mask=attention_mask)
 
 
-output_transform_fn = lambda x: x
+def data_gen_for_causal_lm():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    labels = data['input_ids'].clone()
+    data['labels'] = labels
+    return data
 
-config = transformers.OPTConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4)
+
+def data_gen_for_sequence_classification():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    labels = data['input_ids'].clone()
+    data['labels'] = torch.tensor([1])
+    return data
+
+
+def data_gen_for_question_answering():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    data['start_positions'] = torch.tensor([0])
+    data['end_positions'] = torch.tensor([1])
+    return data
+
+
+output_transform_fn = lambda x: x
+loss_fn_for_opt_model = lambda x: x.last_hidden_state.mean()
+loss_fn_for_lm = lambda x: x.loss
+config = transformers.OPTConfig(
+    hidden_size=128,
+    num_hidden_layers=2,
+    num_attention_heads=4,
+    dropout=0,
+)
 
 # register the following models
 # transformers.OPTModel,
@@ -27,9 +60,23 @@ model_zoo.register(name='transformers_opt',
                    model_fn=lambda: transformers.OPTModel(config),
                    data_gen_fn=data_gen,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_opt_model,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_opt_for_causal_lm',
                    model_fn=lambda: transformers.OPTForCausalLM(config),
-                   data_gen_fn=data_gen,
+                   data_gen_fn=data_gen_for_causal_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_lm,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_opt_for_question_answering',
+                   model_fn=lambda: transformers.OPTForQuestionAnswering(config),
+                   data_gen_fn=data_gen_for_question_answering,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_lm,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='transformers_opt_for_sequence_classification',
+                   model_fn=lambda: transformers.OPTForSequenceClassification(config),
+                   data_gen_fn=data_gen_for_sequence_classification,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_lm,
                    model_attribute=ModelAttribute(has_control_flow=True))

tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py

@@ -11,10 +11,9 @@ from tests.kit.model_zoo import model_zoo
 @clear_cache_before_run()
 def test_opt():
     sub_registry = model_zoo.get_sub_registry('transformers_opt')
-
     for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items():
         model = model_fn()
-        trace_model_and_compare_output(model, data_gen_fn)
+        trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels', 'start_positions', 'end_positions'])
 
 
 if __name__ == '__main__':

tests/test_shardformer/test_model/_utils.py

@@ -25,7 +25,6 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn,
     # switch to train mode
     original_model.train()
     sharded_model.train()
-
     # run forward
     org_output = original_model(**data)
     org_output = output_transform_fn(org_output)
@@ -34,5 +33,4 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn,
     shard_output = sharded_model(**data)
     shard_output = output_transform_fn(shard_output)
     shard_loss = loss_fn(shard_output)
-
     return org_output, org_loss, shard_output, shard_loss

tests/test_shardformer/test_model/test_shard_opt.py

@@ -0,0 +1,67 @@
+import copy
+import os
+
+import pytest
+import torch
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import (
+    assert_hf_output_close,
+    check_state_dict_equal,
+    clear_cache_before_run,
+    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
+
+os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    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'], rtol=1e-4)
+
+    # run backward
+    org_loss.backward()
+    shard_loss.backward()
+
+    # check grad
+    if hasattr(org_model, 'model'):
+        opt_model = org_model.model
+        shard_opt_model = sharded_model.model
+    else:
+        opt_model = org_model
+        shard_opt_model = sharded_model
+
+    org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
+    shard_grad = shard_opt_model.decoder.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_OPTModel(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_opt')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(world_size, model_fn)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_OPTModel():
+    spawn(check_OPTModel, 4)