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[LLM] Support dynamic batching for chatglm #2251

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Oct 20, 2023
Merged

[LLM] Support dynamic batching for chatglm #2251

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848451b
[LLM] Support dynamic batching for chatglm
jiangjiajun Oct 19, 2023
89501a4
fix bug in triton model
jiangjiajun Oct 19, 2023
0e38ae9
fix bug
jiangjiajun Oct 19, 2023
040562d
fix bug
jiangjiajun Oct 19, 2023
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91 changes: 45 additions & 46 deletions llm/fastdeploy_llm/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -372,7 +372,7 @@ def get_alibi_slopes(num_heads):
alibi_decoder + (1 - inputs["tgt_generation_mask"]) *
paddle.finfo(inputs["tgt_generation_mask"].dtype).min)
attention_mask = inputs["attention_mask"]
tgt_generation_mask = inputs["tgt_generation_mask"]
tgt_generation_mask = inputs["tgt_generation_mask"]


def dy_input_preprocess(inputs):
Expand All @@ -394,70 +394,70 @@ def dy_input_preprocess(inputs):
if args.is_ptuning:
model_id = inputs['model_id'][i]
if "chatglm" in args.architecture:
attention_mask[i, 0, :length, : length+max_prefix_len] = 1
attention_mask[i, 0, :length - 1, length+max_prefix_len - 1] = 0
attention_mask[i, 0, :length, :length + max_prefix_len] = 1
attention_mask[i, 0, :length - 1, length + max_prefix_len -
1] = 0
tgt_pos[i, 0, 0] = paddle.to_tensor(
[length], dtype="int64")

if not model_id:
tgt_generation_mask[i, 0, 0, max_prefix_len : length + max_prefix_len] = paddle.ones(
shape=[1, length], dtype=args.dtype
)
tgt_generation_mask[i, 0, 0, max_prefix_len:length +
max_prefix_len] = paddle.ones(
shape=[1, length],
dtype=args.dtype)
else:
tgt_generation_mask[i, 0, 0, : length + max_prefix_len] = paddle.ones(
shape=[1, length + max_prefix_len], dtype=args.dtype
)
tgt_generation_mask[
i, 0, 0, :length + max_prefix_len] = paddle.ones(
shape=[1, length + max_prefix_len],
dtype=args.dtype)
else:
if "bloom" in args.architecture:
attention_mask[i, :, :length, :length] = paddle.tril(
attention_mask[i, :, :length, :length] = paddle.tril(
paddle.ones(
shape=[length, length], dtype=args.dtype))
shape=[length, length], dtype=args.dtype))
if not model_id:
attention_mask[i, :, :length, :
max_prefix_len] = paddle.zeros(
[1, length, max_prefix_len],
dtype=args.dtype)
tgt_generation_mask[i, 0, 0, max_prefix_len : length + max_prefix_len] = paddle.ones(
shape=[1, length], dtype=args.dtype
)
max_prefix_len] = paddle.zeros(
[1, length, max_prefix_len],
dtype=args.dtype)
tgt_generation_mask[i, 0, 0, max_prefix_len:length +
max_prefix_len] = paddle.ones(
shape=[1, length],
dtype=args.dtype)
else:
attention_mask[i, :, :length, :
max_prefix_len] = paddle.ones(
[1, length, max_prefix_len],
dtype=args.dtype)
tgt_generation_mask[i, 0, 0, :max_prefix_len +
length] = paddle.ones(
shape=[1, max_prefix_len + length],
dtype=args.dtype)
attention_mask[i, :, :length, max_prefix_len:max_prefix_len +
length] = paddle.tril(
paddle.ones(
shape=[length, length],
dtype=args.dtype))
max_prefix_len] = paddle.ones(
[1, length, max_prefix_len],
dtype=args.dtype)
tgt_generation_mask[
i, 0, 0, :max_prefix_len + length] = paddle.ones(
shape=[1, max_prefix_len + length],
dtype=args.dtype)

attention_mask[i, :, :length, max_prefix_len:max_prefix_len
+ length] = paddle.tril(
paddle.ones(
shape=[length, length],
dtype=args.dtype))
position_ids[i, :max_prefix_len] = 0
position_ids[i, max_prefix_len:max_prefix_len + inputs[
"input_ids"].shape[1]] = paddle.arange(inputs["input_ids"]
.shape[1])
"input_ids"].shape[1]] = paddle.arange(inputs[
"input_ids"].shape[1])
if "bloom" in args.architecture:
tgt_generation_mask[i, :, 0, :max_prefix_len +
length] = paddle.ones(
shape=[1, max_prefix_len + length],
dtype=args.dtype)
arange_tensor_encoder[i, :, :length + max_prefix_len] = paddle.arange(
length + max_prefix_len).astype(args.dtype)


tgt_generation_mask[
i, :, 0, :max_prefix_len + length] = paddle.ones(
shape=[1, max_prefix_len + length],
dtype=args.dtype)
arange_tensor_encoder[
i, :, :length + max_prefix_len] = paddle.arange(
length + max_prefix_len).astype(args.dtype)
else:
if "chatglm" in args.architecture:
attention_mask[i, 0, :length, :length] = 1
attention_mask[i, 0, :length, :length] = 1
attention_mask[i, 0, :length - 1, length - 1] = 0
tgt_pos[i, 0, 0] = paddle.to_tensor(
[length], dtype="int64")
tgt_generation_mask[i, 0, 0, :length] = paddle.ones(
shape=[1, length], dtype=args.dtype)
else:

position_ids[i, :length] = paddle.arange(length)
attention_mask[i, 0, :length, :length] = paddle.tril(
paddle.ones(
Expand All @@ -475,7 +475,6 @@ def dy_input_preprocess(inputs):
inputs["position_ids"] = position_ids
inputs["tgt_generation_mask"] = tgt_generation_mask
if "chatglm" in args.architecture:
inputs["tgt_pos"] = tgt_pos
inputs["position_ids"] = generate_position_ids_for_chatglm(enc_length)
if args.is_ptuning:
prefix_caches = []
Expand Down
13 changes: 9 additions & 4 deletions llm/fastdeploy_llm/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -298,9 +298,9 @@ def _update_task_results(self, tasks):
for i in range(len(tasks)):
tasks[i].decode_status["finished_id"] = int(info["finished_ids"][
i])
# TODO JiangJiajun
if self.config.is_arch("chatglm"):
tasks[i].decode_status["tgt_pos"] = int(info["tgt_pos"][i][0])
tasks[i].decode_status["tgt_pos"] = info["tgt_pos"][i].flatten(
).tolist()
else:
tasks[i].decode_status["tgt_pos"] = int(info["tgt_pos"][i])

Expand Down Expand Up @@ -398,9 +398,14 @@ def _add_dynamic_batching_inputs(self, inputs, tasks, stop_nums):
inputs["model_id"].append(tasks[i].model_id)
length = inputs["num_input_tokens"][i]
if tasks[i].status == TaskStatus.DECODING:
tgt_pos.append(tasks[i].decode_status["tgt_pos"])
if self.config.is_arch("chatglm"):
tgt_pos.append(1)
tgt_pos += [
tasks[i].decode_status["seq_lens_decoder"] -
tasks[i].decode_status["step_idx"] + 1,
tasks[i].decode_status["step_idx"]
]
else:
tgt_pos.append(tasks[i].decode_status["tgt_pos"])
sequence_lengths_encoder[i] = 0
sequence_lengths_decoder[i] = tasks[i].decode_status[
"seq_lens_decoder"]
Expand Down
6 changes: 0 additions & 6 deletions llm/fastdeploy_llm/serving/serving_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,12 +23,6 @@ class ServingModel:
def __init__(self, config):
self.config = config

if self.config.is_arch("chatglm") or self.config.is_arch("bloom"):
logger.warning(
"Dynamic batching will be disabled for model ChatGLM/BLOOM now!"
)
self.config.disable_dynamic_batching = 1

logger.info("=============== Debug Information ===============")
for k, v in self.config.__dict__.items():
logger.info("{:<20}:{:<6}{}".format(k, "", v))
Expand Down
4 changes: 2 additions & 2 deletions llm/fastdeploy_llm/serving/triton_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,11 +122,12 @@ def execute(self, requests):
continue

# 2. validate the deserializing process
task = Task()
try:
task.from_dict(data)
except Exception as e:
error_res = pb_utils.InferenceResponse(error=pb_utils.TritonError(
"There's error while deserializing data from reqeust, error={}".
"There's error while deserializing data from request, error={}".
format(e)))
res_sender = request.get_response_sender()
res_sender.send(
Expand All @@ -135,7 +136,6 @@ def execute(self, requests):
continue

# 3. check if exists task id conflict
task = Task()
if task.task_id is None:
task.task_id = str(uuid.uuid4())
if task.task_id in self.response_handler:
Expand Down