mirror of https://github.com/THUDM/ChatGLM-6B
354 lines
20 KiB
Python
354 lines
20 KiB
Python
import torch
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from typing import Optional, Tuple
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# 来自quantization.py
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def quantize(layer, weight_bit_width, empty_init=False):
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"""Replace fp16 linear with quantized linear"""
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from torch.nn import Linear
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from torch.nn.parameter import Parameter
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import bz2
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import torch
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import base64
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import ctypes
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from transformers.utils import logging
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from typing import List
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from functools import partial
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logger = logging.get_logger(__name__)
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try:
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from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
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class Kernel:
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def __init__(self, code: bytes, function_names: List[str]):
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self.code = code
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self._function_names = function_names
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self._cmodule = LazyKernelCModule(self.code)
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for name in self._function_names:
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setattr(self, name, KernelFunction(self._cmodule, name))
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quantization_code = "$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"
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kernels = Kernel(
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bz2.decompress(base64.b64decode(quantization_code)),
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[
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"int4WeightCompression",
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"int4WeightExtractionFloat",
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"int4WeightExtractionHalf",
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"int8WeightExtractionFloat",
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"int8WeightExtractionHalf",
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],
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)
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except Exception as exception:
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kernels = None
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logger.warning("Failed to load cpm_kernels:" + str(exception))
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class W8A16Linear(torch.autograd.Function):
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@staticmethod
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def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
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ctx.inp_shape = inp.size()
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ctx.weight_bit_width = weight_bit_width
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out_features = quant_w.size(0)
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inp = inp.contiguous().view(-1, inp.size(-1))
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weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
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ctx.weight_shape = weight.size()
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output = inp.mm(weight.t())
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ctx.save_for_backward(inp, quant_w, scale_w)
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return output.view(*(ctx.inp_shape[:-1] + (out_features,)))
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@staticmethod
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def backward(ctx, grad_output: torch.Tensor):
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inp, quant_w, scale_w = ctx.saved_tensors
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weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
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grad_output = grad_output.contiguous().view(-1, weight.size(0))
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grad_input = grad_output.mm(weight)
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grad_weight = grad_output.t().mm(inp)
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return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None
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def compress_int4_weight(weight: torch.Tensor): # (n, m)
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with torch.cuda.device(weight.device):
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n, m = weight.size(0), weight.size(1)
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assert m % 2 == 0
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m = m // 2
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out = torch.empty(n, m, dtype=torch.int8, device="cuda")
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stream = torch.cuda.current_stream()
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gridDim = (n, 1, 1)
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blockDim = (min(round_up(m, 32), 1024), 1, 1)
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kernels.int4WeightCompression(
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gridDim,
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blockDim,
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0,
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stream,
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[ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)],
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)
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return out
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def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
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if source_bit_width == 8:
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func = kernels.int8WeightExtractionHalf
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elif source_bit_width == 4:
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func = kernels.int4WeightExtractionHalf
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else:
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assert False, "Unsupported bit-width"
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with torch.cuda.device(weight.device):
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n, m = weight.size(0), weight.size(1)
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out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.half, device="cuda")
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stream = torch.cuda.current_stream()
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gridDim = (n, 1, 1)
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blockDim = (min(round_up(m, 32), 1024), 1, 1)
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func(
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gridDim,
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blockDim,
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0,
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stream,
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[
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ctypes.c_void_p(weight.data_ptr()),
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ctypes.c_void_p(scale_list.data_ptr()),
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ctypes.c_void_p(out.data_ptr()),
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ctypes.c_int32(n),
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ctypes.c_int32(m),
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],
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)
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return out
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class QuantizedLinear(Linear):
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def __init__(self, weight_bit_width: int, weight_tensor=None, bias_tensor=None, empty_init=False, *args, **kwargs):
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super(QuantizedLinear, self).__init__(*args, **kwargs)
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self.weight_bit_width = weight_bit_width
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shape = self.weight.shape
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del self.weight
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if weight_tensor is None or empty_init:
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self.weight = torch.empty(
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shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"]
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)
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self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"])
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else:
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self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).half()
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self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8)
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if weight_bit_width == 4:
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self.weight = compress_int4_weight(self.weight)
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self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False)
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self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False)
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if bias_tensor is not None:
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self.bias = Parameter(bias_tensor.to(kwargs["device"]), requires_grad=False)
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else:
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self.bias = None
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def forward(self, input):
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output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
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|
if self.bias is not None:
|
|
output = output + self.bias
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|
return output
|
|
|
|
# 量化开始
|
|
layer.attention.query_key_value = QuantizedLinear(
|
|
weight_bit_width=weight_bit_width,
|
|
weight_tensor=layer.attention.query_key_value.weight.to(torch.cuda.current_device()),
|
|
bias_tensor=layer.attention.query_key_value.bias,
|
|
in_features=layer.attention.query_key_value.in_features,
|
|
out_features=layer.attention.query_key_value.out_features,
|
|
bias=True,
|
|
dtype=torch.half,
|
|
device=layer.attention.query_key_value.weight.device,
|
|
empty_init=empty_init
|
|
)
|
|
layer.attention.dense = QuantizedLinear(
|
|
weight_bit_width=weight_bit_width,
|
|
weight_tensor=layer.attention.dense.weight.to(torch.cuda.current_device()),
|
|
bias_tensor=layer.attention.dense.bias,
|
|
in_features=layer.attention.dense.in_features,
|
|
out_features=layer.attention.dense.out_features,
|
|
bias=True,
|
|
dtype=torch.half,
|
|
device=layer.attention.dense.weight.device,
|
|
empty_init=empty_init
|
|
)
|
|
layer.mlp.dense_h_to_4h = QuantizedLinear(
|
|
weight_bit_width=weight_bit_width,
|
|
weight_tensor=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()),
|
|
bias_tensor=layer.mlp.dense_h_to_4h.bias,
|
|
in_features=layer.mlp.dense_h_to_4h.in_features,
|
|
out_features=layer.mlp.dense_h_to_4h.out_features,
|
|
bias=True,
|
|
dtype=torch.half,
|
|
device=layer.mlp.dense_h_to_4h.weight.device,
|
|
empty_init=empty_init
|
|
)
|
|
layer.mlp.dense_4h_to_h = QuantizedLinear(
|
|
weight_bit_width=weight_bit_width,
|
|
weight_tensor=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()),
|
|
bias_tensor=layer.mlp.dense_4h_to_h.bias,
|
|
in_features=layer.mlp.dense_4h_to_h.in_features,
|
|
out_features=layer.mlp.dense_4h_to_h.out_features,
|
|
bias=True,
|
|
dtype=torch.half,
|
|
device=layer.mlp.dense_4h_to_h.weight.device,
|
|
empty_init=empty_init
|
|
)
|
|
return layer
|
|
|
|
CPU_precision = 'fp32'
|
|
GPU_precision = 'fp16'
|
|
embeddings = 'cuda:1'
|
|
layers = {'cuda:1': '1-28'}
|
|
final_layernorm = 'cuda:1'
|
|
new_layers = [None for i in range(28)] # ['cuda:1','cuda:0'...]
|
|
|
|
def CPU_weight_type(_nn):
|
|
global CPU_precision
|
|
if(CPU_precision == 'fp32'):
|
|
return _nn.float()
|
|
elif(CPU_precision == "bf16"):
|
|
return _nn.bfloat16()
|
|
|
|
|
|
def quantize_func(_nn):
|
|
global GPU_precision
|
|
if(GPU_precision == 'fp16'):
|
|
return _nn
|
|
elif(GPU_precision == 'int8'):
|
|
print('int8', end=' -> ')
|
|
return quantize(_nn, 8)
|
|
elif(GPU_precision == 'int4'):
|
|
# print('建议使用已量化的模型')
|
|
print('int4', end=' -> ')
|
|
return quantize(_nn, 4)
|
|
|
|
class layers_data_temp():
|
|
def __init__(self) -> None:
|
|
self.position_ids = None
|
|
self.attention_mask = None
|
|
|
|
|
|
class hook_layer(torch.nn.Module):
|
|
def __init__(self, layer, device, data_temp, tag) -> None:
|
|
super().__init__()
|
|
self.layer = CPU_weight_type(layer).to(device) if device == 'cpu' else quantize_func(layer).to(device)
|
|
print(device)
|
|
self.device = device
|
|
self.device_index = None if device == 'cpu' else int(device.split(':')[1])
|
|
self.data_temp = data_temp
|
|
self.tag = tag
|
|
|
|
def ToDevice_hidden_states(self, _nn):
|
|
# print(self.tag, _nn.device, '->', self.device)
|
|
if(self.device == 'cpu'):
|
|
return CPU_weight_type(_nn).to(self.device)
|
|
else:
|
|
return _nn.half().to(self.device)
|
|
|
|
def ToDevice(self, _nn):
|
|
# print(self.tag, _nn.device, '->', self.device)
|
|
return _nn.to(self.device)
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
position_ids,
|
|
attention_mask: torch.Tensor,
|
|
layer_id,
|
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
use_cache: bool = False,
|
|
output_attentions: bool = False,):
|
|
if(layer_id == 0 or hidden_states.device.index != self.device_index):
|
|
hidden_states = self.ToDevice_hidden_states(hidden_states)
|
|
self.data_temp.position_ids = self.ToDevice(position_ids)
|
|
self.data_temp.attention_mask = self.ToDevice(attention_mask)
|
|
output = self.layer(hidden_states,
|
|
self.data_temp.position_ids,
|
|
self.data_temp.attention_mask,
|
|
layer_id,
|
|
layer_past,
|
|
use_cache,
|
|
output_attentions)
|
|
return output
|
|
|
|
|
|
class hook_easy(torch.nn.Module):
|
|
def __init__(self, nn, device, tag) -> None:
|
|
super().__init__()
|
|
self.nn = CPU_weight_type(nn).to(device) if device == 'cpu' else nn.to(device)
|
|
print(device)
|
|
self.device = device
|
|
self.device_index = None if device == 'cpu' else int(device.split(':')[1])
|
|
self.tag = tag
|
|
|
|
def ToDevice(self, _nn):
|
|
# print(self.tag, _nn.device, '->', self.device)
|
|
if(self.tag == 'final_layernorm'):
|
|
if(self.device == 'cpu'):
|
|
return CPU_weight_type(_nn).to(self.device)
|
|
else:
|
|
return _nn.half().to(self.device)
|
|
else:
|
|
if(self.device == 'cpu'):
|
|
return CPU_weight_type(_nn).to(self.device)
|
|
else:
|
|
return _nn.to(self.device)
|
|
|
|
def forward(self, input):
|
|
if(input.device.index != self.device_index):
|
|
input = self.ToDevice(input)
|
|
output = self.nn(input)
|
|
return output
|
|
|
|
|
|
def hook(model):
|
|
global embeddings,new_layers,final_layernorm
|
|
print('word_embeddings', end=' -> ')
|
|
model.transformer.word_embeddings = hook_easy(model.transformer.word_embeddings, embeddings, 'word_embeddings')
|
|
data_temp = layers_data_temp()# 创建layers临时数据实例
|
|
for index, _ in enumerate(model.transformer.layers):
|
|
print('layer', index, end=' -> ')
|
|
model.transformer.layers[index] = hook_layer(model.transformer.layers[index], new_layers[index], data_temp, 'layer:' + str(index))
|
|
print('final_layernorm', end=' -> ')
|
|
model.transformer.final_layernorm = hook_easy(model.transformer.final_layernorm, final_layernorm, 'final_layernorm')
|
|
print('lm_head', end=' -> ')
|
|
model.lm_head = hook_easy(model.lm_head, embeddings, 'lm_head')
|
|
print('hooked.')
|
|
return model
|
|
|
|
|
|
def PickupLayersParameter():
|
|
global layers
|
|
# 处理layers参数
|
|
if(layers is None or len(layers) < 1):
|
|
raise 'bad layer parameter'
|
|
check_id = set(range(1, 28 + 1))
|
|
layers_num = 0
|
|
for i in layers:
|
|
layer_id = layers[i].split('-')
|
|
layers[i] = set(range(int(layer_id[0]), int(layer_id[1]) + 1))
|
|
if(not(layers[i] <= check_id)):
|
|
raise 'found bad layer id.'
|
|
layers_num += len(layers[i])
|
|
if(layers_num != 28):
|
|
raise 'the layer num is not 28.'
|
|
for i in layers:
|
|
for ii in layers[i]:
|
|
new_layers[ii - 1] = i
|
|
|
|
|
|
def ConfigMultiDevices(model):
|
|
global embeddings,layers,final_layernorm
|
|
|
|
PickupLayersParameter()
|
|
|
|
model = hook(model)
|
|
|
|
return model
|