mirror of https://github.com/hpcaitech/ColossalAI
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from functools import partial
|
|
|
|
import torch
|
|
from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaForCausalLM, LlamaModel, LlamaRMSNorm
|
|
|
|
from colossalai.inference.modeling.models.padding_llama import (
|
|
PadLlamaAttention,
|
|
llama_causal_lm_forward,
|
|
llama_decoder_layer_forward,
|
|
llama_model_forward,
|
|
)
|
|
from colossalai.inference.utils import init_to_get_rotary
|
|
from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, SubModuleReplacementDescription
|
|
|
|
# import colossalai
|
|
from colossalai.shardformer.policies.llama import LlamaForCausalLMPolicy
|
|
|
|
try:
|
|
from colossalai.kernel.triton import rms_layernorm
|
|
|
|
HAS_TRITON_RMSNORM = True
|
|
except:
|
|
print("you should install triton from https://github.com/openai/triton")
|
|
HAS_TRITON_RMSNORM = False
|
|
|
|
|
|
def get_triton_rmsnorm_forward():
|
|
if HAS_TRITON_RMSNORM:
|
|
|
|
def _triton_rmsnorm_forward(self: LlamaRMSNorm, hidden_states: torch.Tensor):
|
|
return rms_layernorm(hidden_states, self.weight.data, self.variance_epsilon)
|
|
|
|
return _triton_rmsnorm_forward
|
|
else:
|
|
return None
|
|
|
|
|
|
class PaddingLlamaModelInferPolicy(LlamaForCausalLMPolicy):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
def module_policy(self):
|
|
policy = super().module_policy()
|
|
|
|
policy[LlamaDecoderLayer] = ModulePolicyDescription(
|
|
sub_module_replacement=[
|
|
SubModuleReplacementDescription(
|
|
suffix="self_attn",
|
|
target_module=PadLlamaAttention,
|
|
),
|
|
]
|
|
)
|
|
|
|
self.shard_config._infer()
|
|
|
|
infer_forward = llama_causal_lm_forward
|
|
method_replacement = {"forward": partial(infer_forward)}
|
|
self.append_or_create_method_replacement(
|
|
description=method_replacement, policy=policy, target_key=LlamaForCausalLM
|
|
)
|
|
|
|
infer_forward = llama_model_forward
|
|
method_replacement = {"forward": partial(infer_forward)}
|
|
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=LlamaModel)
|
|
|
|
infer_forward = llama_decoder_layer_forward
|
|
method_replacement = {"forward": partial(infer_forward)}
|
|
self.append_or_create_method_replacement(
|
|
description=method_replacement, policy=policy, target_key=LlamaDecoderLayer
|
|
)
|
|
|
|
infer_forward = None
|
|
if HAS_TRITON_RMSNORM:
|
|
infer_forward = get_triton_rmsnorm_forward()
|
|
|
|
if infer_forward is not None:
|
|
method_replacement = {"forward": partial(infer_forward)}
|
|
self.append_or_create_method_replacement(
|
|
description=method_replacement, policy=policy, target_key=LlamaRMSNorm
|
|
)
|
|
|
|
return policy
|
|
|
|
def postprocess(self):
|
|
init_to_get_rotary(self.model.model)
|
|
return self.model
|