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import copy
import torch
from scipy.stats import ortho_group
def permute_model(model, tmp_model, mlp_permutation, emb_permutation, n_blocks=32):
permute_embedding_layer(model, tmp_model, emb_permutation)
for i in range(n_blocks):
permute_transformer_block(tmp_model, i, tmp_model, mlp_permutation, emb_permutation)
permute_output_layer(tmp_model, tmp_model, emb_permutation)
def permute_transformer_block(model, i, tmp_model, mlp_permutation, emb_permutation):
weights = model.state_dict()
weights["model.layers." + str(i) + ".self_attn.q_proj.weight"] = weights[
"model.layers." + str(i) + ".self_attn.q_proj.weight"
][:, emb_permutation]
weights["model.layers." + str(i) + ".self_attn.k_proj.weight"] = weights[
"model.layers." + str(i) + ".self_attn.k_proj.weight"
][:, emb_permutation]
weights["model.layers." + str(i) + ".self_attn.v_proj.weight"] = weights[
"model.layers." + str(i) + ".self_attn.v_proj.weight"
][:, emb_permutation]
weights["model.layers." + str(i) + ".self_attn.o_proj.weight"] = weights[
"model.layers." + str(i) + ".self_attn.o_proj.weight"
][emb_permutation]
weights["model.layers." + str(i) + ".mlp.gate_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.gate_proj.weight"
][mlp_permutation]
weights["model.layers." + str(i) + ".mlp.up_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.up_proj.weight"
][mlp_permutation]
weights["model.layers." + str(i) + ".mlp.down_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.down_proj.weight"
][:, mlp_permutation]
weights["model.layers." + str(i) + ".mlp.gate_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.gate_proj.weight"
][:, emb_permutation]
weights["model.layers." + str(i) + ".mlp.up_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.up_proj.weight"
][:, emb_permutation]
weights["model.layers." + str(i) + ".mlp.down_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.down_proj.weight"
][emb_permutation]
weights["model.layers." + str(i) + ".input_layernorm.weight"] = weights[
"model.layers." + str(i) + ".input_layernorm.weight"
][
emb_permutation
] # 1d
weights["model.layers." + str(i) + ".post_attention_layernorm.weight"] = weights[
"model.layers." + str(i) + ".post_attention_layernorm.weight"
][emb_permutation]
tmp_model.load_state_dict(weights)
def permute_embedding_layer(model, tmp_model, emb_permutation):
weights = model.state_dict()
weights["model.embed_tokens.weight"] = weights["model.embed_tokens.weight"][:, emb_permutation]
tmp_model.load_state_dict(weights)
def permute_output_layer(model, tmp_model, emb_permutation):
weights = model.state_dict()
weights["lm_head.weight"] = weights["lm_head.weight"][:, emb_permutation]
weights["model.norm.weight"] = weights["model.norm.weight"][emb_permutation]
tmp_model.load_state_dict(weights)
def permute_mlp_block(model, i, tmp_model, mlp_permutation):
weights = model.state_dict()
weights["model.layers." + str(i) + ".mlp.gate_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.gate_proj.weight"
][mlp_permutation]
weights["model.layers." + str(i) + ".mlp.up_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.up_proj.weight"
][mlp_permutation]
weights["model.layers." + str(i) + ".mlp.down_proj.weight"] = weights[
"model.layers." + str(i) + ".mlp.down_proj.weight"
][:, mlp_permutation]
tmp_model.load_state_dict(weights)
def avg_mlp_block(model0, model1, i, tmp_model, alpha=0.5):
weights0 = model0.state_dict()
weights1 = model1.state_dict()
weights0["model.layers." + str(i) + ".mlp.gate_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.gate_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.gate_proj.weight"]
)
weights0["model.layers." + str(i) + ".mlp.up_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.up_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.up_proj.weight"]
)
weights0["model.layers." + str(i) + ".mlp.down_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.down_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.down_proj.weight"]
)
tmp_model.load_state_dict(weights0)
def avg_transformer_block(model0, model1, i, tmp_model, alpha=0.5, attn=True):
weights0 = model0.state_dict()
weights1 = model1.state_dict()
if attn is True:
weights0["model.layers." + str(i) + ".self_attn.q_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".self_attn.q_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".self_attn.q_proj.weight"]
)
weights0["model.layers." + str(i) + ".self_attn.k_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".self_attn.k_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".self_attn.k_proj.weight"]
)
weights0["model.layers." + str(i) + ".self_attn.v_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".self_attn.v_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".self_attn.v_proj.weight"]
)
weights0["model.layers." + str(i) + ".self_attn.o_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".self_attn.o_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".self_attn.o_proj.weight"]
)
weights0["model.layers." + str(i) + ".mlp.gate_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.gate_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.gate_proj.weight"]
)
weights0["model.layers." + str(i) + ".mlp.up_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.up_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.up_proj.weight"]
)
weights0["model.layers." + str(i) + ".mlp.down_proj.weight"] = (
alpha * weights0["model.layers." + str(i) + ".mlp.down_proj.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".mlp.down_proj.weight"]
)
weights0["model.layers." + str(i) + ".input_layernorm.weight"] = (
alpha * weights0["model.layers." + str(i) + ".input_layernorm.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".input_layernorm.weight"]
)
weights0["model.layers." + str(i) + ".post_attention_layernorm.weight"] = (
alpha * weights0["model.layers." + str(i) + ".post_attention_layernorm.weight"]
+ (1 - alpha) * weights1["model.layers." + str(i) + ".post_attention_layernorm.weight"]
)
tmp_model.load_state_dict(weights0)
def avg_embedding_layer(model0, model1, tmp_model, alpha=0.5):
weights0 = model0.state_dict()
weights1 = model1.state_dict()
weights0["model.embed_tokens.weight"] = (
alpha * weights0["model.embed_tokens.weight"]
+ (1 - alpha) * weights1["model.embed_tokens.weight"]
)
tmp_model.load_state_dict(weights0)
def avg_output_layer(model0, model1, tmp_model, alpha=0.5):
weights0 = model0.state_dict()
weights1 = model1.state_dict()
weights0["lm_head.weight"] = (
alpha * weights0["lm_head.weight"] + (1 - alpha) * weights1["lm_head.weight"]
)
weights0["model.norm.weight"] = (
alpha * weights0["model.norm.weight"] + (1 - alpha) * weights1["model.norm.weight"]
)
tmp_model.load_state_dict(weights0)
def avg_model(model0, model1, tmp_model, alpha=0.5, n_blocks=32, attn=True, emb=True):
model1 = copy.deepcopy(model1)
if emb is True:
avg_embedding_layer(model0, model1, tmp_model, alpha=alpha)
else:
tmp_model.load_state_dict(model0.state_dict())
for i in range(n_blocks):
avg_transformer_block(tmp_model, model1, i, tmp_model, alpha=alpha, attn=attn)
if emb is True:
avg_output_layer(tmp_model, model1, tmp_model, alpha=alpha)
def get_mlp_weights(model, i):
return model.state_dict()["model.layers." + str(i) + ".mlp.gate_proj.weight"]
def get_emb_weights(model):
return model.state_dict()["model.embed_tokens.weight"]
def rotate_model(model, num_layers=32, hidden_dim=4096):
model.to("cuda")
rotation = ortho_group.rvs(dim=hidden_dim)
rotation = torch.tensor(rotation, dtype=torch.bfloat16).to("cuda")
weights = model.state_dict()
weights_rotated = model.state_dict()
weights_rotated["model.embed_tokens.weight"] = weights["model.embed_tokens.weight"] @ rotation
for i in range(num_layers):
weights_rotated[f"model.layers.{i}.input_layernorm.weight"] = torch.ones(hidden_dim)
weights_rotated[f"model.layers.{i}.post_attention_layernorm.weight"] = torch.ones(
hidden_dim
)
weights_rotated[f"model.layers.{i}.self_attn.q_proj.weight"] = (
weights[f"model.layers.{i}.self_attn.q_proj.weight"]
@ torch.diag(weights[f"model.layers.{i}.input_layernorm.weight"])
@ rotation
)
weights_rotated[f"model.layers.{i}.self_attn.k_proj.weight"] = (
weights[f"model.layers.{i}.self_attn.k_proj.weight"]
@ torch.diag(weights[f"model.layers.{i}.input_layernorm.weight"])
@ rotation
)
weights_rotated[f"model.layers.{i}.self_attn.v_proj.weight"] = (
weights[f"model.layers.{i}.self_attn.v_proj.weight"]
@ torch.diag(weights[f"model.layers.{i}.input_layernorm.weight"])
@ rotation
)
weights_rotated[f"model.layers.{i}.self_attn.o_proj.weight"] = (
rotation.T @ weights[f"model.layers.{i}.self_attn.o_proj.weight"]
)
weights_rotated[f"model.layers.{i}.mlp.gate_proj.weight"] = (
weights[f"model.layers.{i}.mlp.gate_proj.weight"]
@ torch.diag(weights[f"model.layers.{i}.post_attention_layernorm.weight"])
@ rotation
)
weights_rotated[f"model.layers.{i}.mlp.up_proj.weight"] = (
weights[f"model.layers.{i}.mlp.up_proj.weight"]
@ torch.diag(weights[f"model.layers.{i}.post_attention_layernorm.weight"])
@ rotation
)
weights_rotated[f"model.layers.{i}.mlp.down_proj.weight"] = (
rotation.T @ weights[f"model.layers.{i}.mlp.down_proj.weight"]
)
weights_rotated["model.norm.weight"] = torch.ones(hidden_dim)
weights_rotated["lm_head.weight"] = (
weights["lm_head.weight"] @ torch.diag(weights["model.norm.weight"]) @ rotation
)
model.load_state_dict(weights_rotated)
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