File size: 2,822 Bytes
a5dc865 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from .base import *
from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
import torch
from torch.nn.parameter import Parameter
from ..policy import TransformerPolicy
class DS_CLIPContainer(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
class HFCLIPLayerPolicy(TransformerPolicy):
def __init__(self, client_module, inference=False):
super().__init__(inference, pre_attn_norm=True, scale_attention=True)
self.client_module = client_module
self.cuda_graph_supported = True
if HFCLIPLayerPolicy._orig_layer_class is None:
try:
import transformers
HFCLIPLayerPolicy._orig_layer_class = transformers.models.clip.modeling_clip.CLIPEncoderLayer
except:
HFCLIPLayerPolicy._orig_layer_class = None
def get_hidden_heads(self):
return self.client_module.self_attn.q_proj.weight.shape[1], \
self.client_module.self_attn.num_heads, \
self.client_module.layer_norm1.eps, \
DEFAULT_INTERMEDIATE_SIZE
def attention(self, enable_training=False):
qw = self.client_module.self_attn.q_proj.weight
qb = self.client_module.self_attn.q_proj.bias
kw = self.client_module.self_attn.k_proj.weight
kb = self.client_module.self_attn.k_proj.bias
vw = self.client_module.self_attn.v_proj.weight
vb = self.client_module.self_attn.v_proj.bias
qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training)
qkvb = Parameter(torch.cat((qb, kb, vb), dim=0), requires_grad=enable_training)
return qkvw, \
qkvb, \
self.client_module.self_attn.out_proj.weight, \
self.client_module.self_attn.out_proj.bias
def mlp(self, enable_training=False):
return self.client_module.mlp.fc1.weight, \
self.client_module.mlp.fc1.bias, \
self.client_module.mlp.fc2.weight, \
self.client_module.mlp.fc2.bias
def layernorm(self):
return self.client_module.layer_norm2.weight, \
self.client_module.layer_norm2.bias, \
self.client_module.layer_norm1.weight, \
self.client_module.layer_norm1.bias
|