mirror of
https://github.com/deepspeedai/DeepSpeed.git
synced 2025-10-20 23:46:02 +08:00
47f9f13bd3
Co-authored-by: Reza Yazdani <44502768+RezaYazdaniAminabadi@users.noreply.github.com> Co-authored-by: yaozhewei <zheweiy@berkeley.edu> Co-authored-by: Ammar Ahmad Awan <ammar.awan@microsoft.com> Co-authored-by: Jeff Rasley <jerasley@microsoft.com> Co-authored-by: Connor Holmes <connorholmes@microsoft.com> Co-authored-by: Lok Chand Koppaka <lokoppak@microsoft.com> Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# DeepSpeed Team
|
|
|
|
from .base import *
|
|
from .features.meta_tensor import MetaTensorContainer
|
|
from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
|
|
import torch
|
|
from torch.nn.parameter import Parameter
|
|
from ..policy import TransformerPolicy
|
|
from ..policy import transformer_param_names
|
|
from ..policy import maybe_copy
|
|
from ..policy import maybe_copy_qkv
|
|
|
|
|
|
class DS_GPTJContainer(MetaTensorContainer, BaseTransformerContainer):
|
|
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
# All model specific things should be defined here instead of the base class.
|
|
|
|
def create_module(self, config=None):
|
|
_config = config if config is not None else self.ds_model_config
|
|
self.module = DeepSpeedGPTInference(_config, mp_group=self.mp_group)
|
|
self.module.config.scale_attention = self.scale_attention
|
|
return self.module
|
|
|
|
def load_params(self, module, sd, weight_quantizer, mp_replace, prefix):
|
|
param_names = (
|
|
'attn.q_proj.weight', \
|
|
'attn.k_proj.weight', \
|
|
'attn.v_proj.weight', \
|
|
'attn.out_proj.weight', \
|
|
'mlp.fc_in.weight', \
|
|
'mlp.fc_in.bias', \
|
|
'mlp.fc_out.weight', \
|
|
'mlp.fc_out.bias', \
|
|
'ln_1.weight', \
|
|
'ln_1.bias'
|
|
)
|
|
maybe_copy_qkv(module.attention,
|
|
sd,
|
|
weight_quantizer,
|
|
mp_replace,
|
|
'attn_qkvw', [prefix + param_names[0], prefix + param_names[1], prefix + param_names[2]],
|
|
split_qkv=self.policy.split_qkv)
|
|
for i in range(3, 4):
|
|
maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[i - 1],
|
|
prefix + param_names[i])
|
|
for i in range(4, 8):
|
|
maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[i],
|
|
prefix + param_names[i])
|
|
for i in range(8, 10):
|
|
maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[i + 2],
|
|
prefix + param_names[i])
|
|
|
|
|
|
class HFGPTJLayerPolicy(TransformerPolicy):
|
|
_orig_layer_class = None
|
|
|
|
def __init__(self, client_module, inference=True):
|
|
super().__init__(inference, scale_attention=True)
|
|
self.client_module = client_module
|
|
try:
|
|
import transformers
|
|
HFGPTJLayerPolicy._orig_layer_class = transformers.models.gptj.modeling_gptj.GPTJBlock
|
|
except:
|
|
HFGPTJLayerPolicy._orig_layer_class = None
|
|
|
|
def get_hidden_heads(self):
|
|
return self.client_module.attn.q_proj.weight.shape[1], \
|
|
self.client_module.attn.num_attention_heads, \
|
|
self.client_module.ln_1.eps
|
|
|
|
def get_q_k_v(self):
|
|
return None
|
|
|
|
def attention(self, enable_training=False):
|
|
qw = self.client_module.attn.q_proj.weight
|
|
kw = self.client_module.attn.k_proj.weight
|
|
vw = self.client_module.attn.v_proj.weight
|
|
|
|
qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training)
|
|
|
|
return qkvw, \
|
|
None, \
|
|
self.client_module.attn.out_proj.weight, \
|
|
None,
|
|
|
|
def mlp(self):
|
|
return self.client_module.mlp.fc_in.weight, \
|
|
self.client_module.mlp.fc_in.bias, \
|
|
self.client_module.mlp.fc_out.weight, \
|
|
self.client_module.mlp.fc_out.bias
|
|
|
|
def layernorm(self):
|
|
return None, \
|
|
None, \
|
|
self.client_module.ln_1.weight, \
|
|
self.client_module.ln_1.bias
|
|
|
|
def get_lora_params(self):
|
|
return []
|