Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix llama and test llama2 #515

Merged
merged 5 commits into from
Aug 9, 2023
Merged

fix llama and test llama2 #515

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
df28c7a
fix llama
xiezipeng-ML Aug 7, 2023
db57cde
Merge branch 'main' of https://github.com/Oneflow-Inc/libai into dev_…
xiezipeng-ML Aug 8, 2023
f0474b0
fix gpt bloom llama
xiezipeng-ML Aug 8, 2023
befc66b
refine
xiezipeng-ML Aug 8, 2023
a57092b
reformat
xiezipeng-ML Aug 8, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 10 additions & 5 deletions projects/mock_transformers/dist_infer_bloom.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -90,8 +90,17 @@ def __init__(self, config):
)
dist.setup_dist_util(parallel_config)

placement_sbp_dict = dict(
placement=flow.env.all_device_placement("cuda"),
sbp=flow.sbp.broadcast,
)

# initial and load model
model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", torch_dtype=flow.float16)
with global_mode(True, **placement_sbp_dict):
model = AutoModelForCausalLM.from_pretrained(
"bigscience/bloom-560m", torch_dtype=flow.float16
)

# set model to cuda
dist.set_device_type("cuda")
model._apply(dist.convert_to_distributed_default_setting)
Expand All @@ -108,10 +117,6 @@ def __init__(self, config):
)

# 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)
Expand Down
13 changes: 8 additions & 5 deletions projects/mock_transformers/dist_infer_gpt.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -126,8 +126,15 @@ def __init__(self, intermediate_size, config):
)
dist.setup_dist_util(parallel_config)

placement_sbp_dict = dict(
placement=flow.env.all_device_placement("cuda"),
sbp=flow.sbp.broadcast,
)

# initial and load model
model = AutoModelForCausalLM.from_pretrained("gpt2", torch_dtype=flow.float16)
with global_mode(True, **placement_sbp_dict):
model = AutoModelForCausalLM.from_pretrained("gpt2", torch_dtype=flow.float16)

# set model to cuda
dist.set_device_type("cuda")
model._apply(dist.convert_to_distributed_default_setting)
Expand All @@ -144,10 +151,6 @@ def __init__(self, intermediate_size, config):
)

# 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)
Expand Down
29 changes: 16 additions & 13 deletions projects/mock_transformers/dist_infer_llama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,10 +68,10 @@ def __init__(self, config):


class LiBaiLlamaMLP(temp_class):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
hidden_size = kwargs["hidden_size"]
intermediate_size = kwargs["intermediate_size"]
def __init__(self, config):
super().__init__(config)
hidden_size = config.hidden_size
intermediate_size = config.intermediate_size
self.gate_proj = Linear(
hidden_size, intermediate_size, bias=False, parallel="col", dtype=flow.float16
)
Expand All @@ -90,23 +90,30 @@ def __init__(self, *args, **kwargs):
parallel_config = DictConfig(
dict(
data_parallel_size=1,
tensor_parallel_size=4,
tensor_parallel_size=2,
pipeline_parallel_size=1, # set to 1, unsupport pipeline parallel now
pipeline_num_layers=None,
device_type="cpu",
)
)
dist.setup_dist_util(parallel_config)

# initial and load model
model = AutoModelForCausalLM.from_pretrained(
"decapoda-research/llama-13b-hf", torch_dtype=flow.float16
placement_sbp_dict = dict(
placement=flow.env.all_device_placement("cuda"),
sbp=flow.sbp.broadcast,
)

# initial and load model
with global_mode(True, **placement_sbp_dict):
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b", 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("decapoda-research/llama-13b-hf", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b", use_fast=False)

# get input_ids
prompt = "Hello, I'm am conscious and"
Expand All @@ -118,10 +125,6 @@ def __init__(self, *args, **kwargs):
)

# 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)
Expand Down
Loading