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upgrad transformers to 4.31.0 #516

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Aug 8, 2023
Merged

upgrad transformers to 4.31.0 #516

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890b2cf
upgrad transformers to 4.31.0
Ldpe2G Aug 8, 2023
3c4de5a
remove useless codes
Ldpe2G Aug 8, 2023
9822842
format code
Ldpe2G Aug 8, 2023
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18 changes: 10 additions & 8 deletions projects/mock_transformers/dist_infer_opt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,13 +81,20 @@ def __init__(self, *args, **kwargs):
)
dist.setup_dist_util(parallel_config)

# initial and load model
model = AutoModelForCausalLM.from_pretrained("facebook/opt-2.7b", torch_dtype=flow.float16)
placement_sbp_dict = dict(
placement=flow.env.all_device_placement("cuda"),
sbp=flow.sbp.broadcast,
)

with global_mode(True, **placement_sbp_dict):
# initial and load model
model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m", torch_dtype=flow.float16)

# set model to cuda
dist.set_device_type("cuda")
model._apply(dist.convert_to_distributed_default_setting)
# initial tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-2.7b", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m", use_fast=False)

# get input_ids
prompt = "Hello, I'm am conscious and"
Expand All @@ -98,11 +105,6 @@ def __init__(self, *args, **kwargs):
placement=dist.get_layer_placement(0),
)

# generate id
placement_sbp_dict = dict(
placement=flow.env.all_device_placement("cuda"),
sbp=flow.sbp.broadcast,
)
with global_mode(True, **placement_sbp_dict):
generated_ids = model.generate(input_ids, max_length=30)
out_put_ids = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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14 changes: 7 additions & 7 deletions projects/mock_transformers/init_env.py
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Expand Up @@ -113,14 +113,14 @@ def flow_softmax(*args, **kwargs):
nn.functional.softmax = flow_softmax

# -----------------mock flow.tensor---------------
temp_tensor_func = flow.tensor
# temp_tensor_func = flow.tensor


def flow_tensor(input_x, **kwargs):
if isinstance(input_x, (int, float)):
return input_x
else:
return temp_tensor_func(input_x, **kwargs)
# def flow_tensor(input_x, **kwargs):
# if isinstance(input_x, (int, float)):
# return input_x
# else:
# return temp_tensor_func(input_x, **kwargs)


flow.tensor = flow_tensor
# flow.tensor = flow_tensor
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