[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

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)
-    attention_mask = 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.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
+    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)