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# coding=utf-8
# Copyright 2023 WisdomShell Inc. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
try:
import bitsandbytes as bnb
from bitsandbytes.nn.modules import Params4bit, Int8Params
except ImportError:
pass
import torch
def Params4bitCuda(self, device):
self.data = self.data.cuda(device)
self.quant_state[0] = self.quant_state[0].cuda(device)
self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
self.quant_state[6] = self.quant_state[6].cuda(device)
return self
class Linear4bitOnline(torch.nn.Module):
def __init__(self, weight, bias, quant_type):
super().__init__()
self.weight = Params4bit(
weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
)
self.compute_dtype = None
#self.weight.cuda(weight.device)
self.bias = bias
def forward(self, x: torch.Tensor):
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
if getattr(self.weight, "quant_state", None) is None:
print(
"FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
)
inp_dtype = x.dtype
if self.compute_dtype is not None:
x = x.to(self.compute_dtype)
bias = None if self.bias is None else self.bias.to(self.compute_dtype)
out = bnb.matmul_4bit(
x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
)
out = out.to(inp_dtype)
return out
class Linear8bitLtOnline(torch.nn.Module):
def __init__(
self,
weight,
bias,
has_fp16_weights=True,
memory_efficient_backward=False,
threshold=0.0,
index=None,
):
super().__init__()
assert (
not memory_efficient_backward
), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
self.state = bnb.MatmulLtState()
self.index = index
# Necessary for stacked layers
self.state.threshold = threshold
self.state.has_fp16_weights = has_fp16_weights
self.state.memory_efficient_backward = memory_efficient_backward
if threshold > 0.0 and not has_fp16_weights:
self.state.use_pool = True
self.weight = Int8Params(
weight.data,
has_fp16_weights=has_fp16_weights,
requires_grad=has_fp16_weights,
)
self.bias = bias
def init_8bit_state(self):
self.state.CB = self.weight.CB
self.state.SCB = self.weight.SCB
self.weight.CB = None
self.weight.SCB = None
def forward(self, x: torch.Tensor):
self.state.is_training = self.training
if self.weight.CB is not None:
self.init_8bit_state()
# weights are cast automatically as Int8Params, but the bias has to be cast manually
if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype)
out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
if not self.state.has_fp16_weights:
if self.state.CB is not None and self.state.CxB is not None:
# we converted 8-bit row major to turing/ampere format in the first inference pass
# we no longer need the row-major weight
del self.state.CB
self.weight.data = self.state.CxB
return out
def quantize_online(model, bits: int):
def quant(weight, bias=None):
if bits == 8:
linear = Linear8bitLtOnline(
weight,
bias,
has_fp16_weights=False,
threshold=6.0,
)
if bias is not None:
linear.bias = torch.nn.Parameter(bias)
elif bits == 4:
linear = Linear4bitOnline(
weight,
bias,
quant_type="nf4", #fp4/nf4
)
else:
raise ValueError("quantize only support 4/8 bit")
return linear
def auto_quant(layer):
if hasattr(layer,"bias"):
linear = quant(layer.weight,bias=layer.bias)
else:
linear = quant(layer.weight)
return linear
for i,layer in enumerate(model.transformer.h):
layer.mlp.c_fc = auto_quant(layer.mlp.c_fc)
layer.mlp.c_proj = auto_quant(layer.mlp.c_proj)
layer.attn.c_attn=auto_quant(layer.attn.c_attn)
layer.attn.c_proj=auto_quant(layer.attn.c_proj)
return model |