File size: 5,876 Bytes
72268ee |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
from typing import Dict
from .base import BaseAWQForCausalLM
from transformers.models.mistral.modeling_mistral import MistralDecoderLayer, MistralForCausalLM
class MistralAWQForCausalLM(BaseAWQForCausalLM):
layer_type = "MistralDecoderLayer"
max_new_tokens_key = "max_position_embeddings"
@staticmethod
def fuse_layers(model: MistralForCausalLM, quant_config: Dict):
fuser = MistralFuser(model, quant_config)
fuser.fuse_attention()
fuser.fuse_rmsnorm()
fuser.fuse_mlp()
@staticmethod
def get_model_layers(model: MistralForCausalLM):
return model.model.layers
@staticmethod
def get_act_for_scaling(module: MistralDecoderLayer):
return dict(
is_scalable=False
)
@staticmethod
def move_embed(model: MistralForCausalLM, device: str):
model.model.embed_tokens = model.model.embed_tokens.to(device)
@staticmethod
def get_layers_for_scaling(module: MistralDecoderLayer, input_feat, module_kwargs):
layers = []
# attention input
layers.append(dict(
prev_op=module.input_layernorm,
layers=[module.self_attn.q_proj,
module.self_attn.k_proj, module.self_attn.v_proj],
inp=input_feat['self_attn.q_proj'],
module2inspect=module.self_attn, kwargs=module_kwargs,
))
# attention out
# Please refer to https://github.com/mit-han-lab/llm-awq/pull/67#issue-1850622696
if module.self_attn.v_proj.weight.shape == module.self_attn.o_proj.weight.shape:
layers.append(dict(
prev_op=module.self_attn.v_proj,
layers=[module.self_attn.o_proj],
inp=input_feat['self_attn.o_proj'],
))
# linear 1
layers.append(dict(
prev_op=module.post_attention_layernorm,
layers=[module.mlp.gate_proj, module.mlp.up_proj],
inp=input_feat['mlp.gate_proj'],
module2inspect=module.mlp,
))
# linear 2
layers.append(dict(
prev_op=module.mlp.up_proj,
layers=[module.mlp.down_proj],
inp=input_feat['mlp.down_proj'],
))
return layers
import torch
from typing import List, Tuple, Union
from awq.utils.utils import set_module_name
from awq.modules.fused.mlp import QuantLlamaMLP
from awq.modules.fused.attn import QuantAttentionFused
from awq.modules.fused.norm import FasterTransformerRMSNorm
from awq.modules.linear import WQLinear_GEMM, WQLinear_GEMV
from transformers.models.mistral.modeling_mistral import MistralAttention, MistralRMSNorm, MistralMLP
class MistralFuser:
def __init__(self, model, quant_config):
self.model = model
self.quant_config = quant_config
self.attention_modules: List[Tuple[str, MistralAttention]] = [
(name, module) for name, module in self.model.named_modules()
if isinstance(module, MistralAttention)
]
self.rmsnorm_modules: List[Tuple[str, MistralRMSNorm]] = [
(name, module) for name, module in self.model.named_modules()
if isinstance(module, MistralRMSNorm)
]
self.mlp_modules: List[Tuple[str, MistralMLP]] = [
(name, module) for name, module in self.model.named_modules()
if isinstance(module, MistralMLP)
]
def fuse_attention(self):
for name, module in self.attention_modules:
qkv_layer: Union[WQLinear_GEMM, WQLinear_GEMV] = self._fuse_qkv(module)
attn = QuantAttentionFused(
module.hidden_size,
module.num_heads,
module.num_key_value_heads,
qkv_layer,
module.o_proj,
next(iter(qkv_layer.state_dict().values())).device,
self.model.config.max_new_tokens
)
set_module_name(self.model, name, attn)
def _fuse_qkv(self, module: MistralAttention):
q_proj, k_proj, v_proj = module.q_proj, module.k_proj, module.v_proj
bias = torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0) if q_proj.bias is not None else None
if isinstance(q_proj, WQLinear_GEMV):
q_linear = WQLinear_GEMV
else:
q_linear = WQLinear_GEMM
qkv_layer = q_linear(
q_proj.w_bit,
q_proj.group_size,
q_proj.in_features,
q_proj.out_features + k_proj.out_features + v_proj.out_features,
q_proj.bias is not None,
next(iter(module.state_dict().values())).device
)
if isinstance(qkv_layer, WQLinear_GEMV):
qkv_layer.qweight = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=0)
qkv_layer.qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=0)
qkv_layer.scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=0)
qkv_layer.split_k_iters = q_proj.split_k_iters
else:
qkv_layer.qweight = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
qkv_layer.qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
qkv_layer.scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
qkv_layer.bias = bias
return qkv_layer
def fuse_rmsnorm(self):
for name, module in self.rmsnorm_modules:
norm = FasterTransformerRMSNorm(module.weight, module.variance_epsilon)
set_module_name(self.model, name, norm)
def fuse_mlp(self):
for name, module in self.mlp_modules:
mlp = QuantLlamaMLP(module.gate_proj, module.down_proj, module.up_proj)
set_module_name(self.model, name, mlp)
|