add patch for gptq and peft
This commit is contained in:
John Smith
GitHub
parent
326bc9214a
commit
551f62a0e8
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@ -0,0 +1,144 @@
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import quant
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import torch
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import numpy as np
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import torch.nn as nn
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def matmul4bit(x, qweight, scales, zeros):
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"""
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input x: (n, m)
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qweight: (j, k)
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where m == j*8
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perform x @ qweight
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return y:
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"""
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assert qweight.shape[0] * 8 == x.shape[-1]
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outshape = tuple(list(x.shape[:-1]) + [qweight.shape[1]])
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x = x.reshape(-1, x.shape[-1])
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assert x.shape[0] % 256 == 0
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y = torch.zeros((x.shape[0], qweight.shape[-1]), dtype=torch.float32, device=x.device)
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dtype = x.dtype
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x = x.float()
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quant.quant_cuda.vecquant4matmul(x, qweight, y, scales, zeros)
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y = y.to(dtype)
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return y.reshape(outshape)
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def matmul4bit_transpose(x, qweight, scales, zeros):
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"""
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input x: (n, m)
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qweight: (j, k)
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where m == k
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perform qweight @ x.T
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return y:
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"""
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assert qweight.shape[1] == x.shape[-1]
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outshape = tuple(list(x.shape[:-1]) + [qweight.shape[0] * 8])
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x = x.reshape(-1, x.shape[-1])
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assert x.shape[0] % 256 == 0
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y = torch.zeros((qweight.shape[0] * 8, x.shape[0]), dtype=torch.float32, device=x.device)
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dtype = x.dtype
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x = x.float()
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quant.quant_cuda.vecquant4transposematmul(x, qweight, y, scales, zeros)
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y = y.to(dtype)
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return y.reshape(outshape)
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class AutogradMatmul4bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, qweight, scales, zeros):
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ctx.save_for_backward(x, qweight, scales, zeros)
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output = matmul4bit(x, qweight, scales, zeros).clone()
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return output # equals to torch.matmul(x, qweight)
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@staticmethod
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def backward(ctx, grad_output):
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x, qweight, scales, zeros = ctx.saved_tensors
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# print(grad_output.shape, A.shape, B.shape)
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# compute x @ qweight.T = (qweight @ x.T).T = f(x, qweight).T
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grad1 = matmul4bit_transpose(grad_output, qweight, scales, zeros)
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grad2 = torch.matmul(x.transpose(-1, -2), grad_output)
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return grad1, grad2, None, None
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# Assumes layer is perfectly divisible into 256 * 256 blocks
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class Autograd4bitQuantLinear(nn.Module):
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def __init__(self, infeatures, outfeatures):
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super().__init__()
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bits = 4
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self.in_features = infeatures
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self.out_features = outfeatures
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self.bits = bits
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self.register_buffer('zeros', torch.empty((outfeatures, 1)))
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self.register_buffer('scales', torch.empty((outfeatures, 1)))
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self.register_buffer('bias', torch.empty(outfeatures))
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self.register_buffer(
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'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
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)
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def forward(self, x):
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out = AutogradMatmul4bit.apply(x, self.qweight, self.scales, self.zeros)
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out += self.bias
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return out
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def make_quant_for_4bit_autograd(module, names, name=''):
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if isinstance(module, Autograd4bitQuantLinear):
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return
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for attr in dir(module):
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tmp = getattr(module, attr)
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name1 = name + '.' + attr if name != '' else attr
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if name1 in names:
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setattr(
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module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features)
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)
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for name1, child in module.named_children():
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make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1)
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def load_llama_model_4bit_low_ram(config_path, model_path):
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import transformers
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import accelerate
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from transformers import LLaMAConfig, LLaMAForCausalLM, LLaMATokenizer
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from modelutils import find_layers
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print("Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LLaMAConfig.from_pretrained(config_path)
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def noop(*args, **kwargs):
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pass
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torch.nn.init.kaiming_uniform_ = noop
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torch.nn.init.uniform_ = noop
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torch.nn.init.normal_ = noop
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torch.set_default_dtype(torch.half)
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transformers.modeling_utils._init_weights = False
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torch.set_default_dtype(torch.half)
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model = LLaMAForCausalLM(config)
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torch.set_default_dtype(torch.float)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers)
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model = accelerate.load_checkpoint_and_dispatch(model=model, checkpoint=model_path, device_map='auto')
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model.cuda()
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model.seqlen = 2048
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tokenizer = LLaMATokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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@ -0,0 +1,76 @@
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#include <torch/all.h>
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#include <torch/python.h>
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#include <c10/cuda/CUDAGuard.h>
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void vecquant2matmul_cuda(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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);
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void vecquant2matmul(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
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vecquant2matmul_cuda(vec, mat, mul, scales, zeros);
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}
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void vecquant3matmul_cuda(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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);
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void vecquant3matmul(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
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vecquant3matmul_cuda(vec, mat, mul, scales, zeros);
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}
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void vecquant4matmul_cuda(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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);
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void vecquant4matmul(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
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vecquant4matmul_cuda(vec, mat, mul, scales, zeros);
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}
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void vecquant8matmul_cuda(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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);
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void vecquant8matmul(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
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vecquant8matmul_cuda(vec, mat, mul, scales, zeros);
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}
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void vecquant4transposematmul_cuda(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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);
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void vecquant4transposematmul(
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torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
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torch::Tensor scales, torch::Tensor zeros
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
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vecquant4transposematmul_cuda(vec, mat, mul, scales, zeros);
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("vecquant2matmul", &vecquant2matmul, "Vector 2-bit Quantized Matrix Multiplication (CUDA)");
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m.def("vecquant3matmul", &vecquant3matmul, "Vector 3-bit Quantized Matrix Multiplication (CUDA)");
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m.def("vecquant4matmul", &vecquant4matmul, "Vector 4-bit Quantized Matrix Multiplication (CUDA)");
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m.def("vecquant8matmul", &vecquant8matmul, "Vector 8-bit Quantized Matrix Multiplication (CUDA)");
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m.def("vecquant4transposematmul", &vecquant4transposematmul, "Vector 4-bit Transpose Quantized Matrix Multiplication (CUDA)");
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}
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@ -0,0 +1,480 @@
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#include <torch/all.h>
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#include <torch/python.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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template <typename scalar_t>
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__global__ void VecQuant2MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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);
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template <typename scalar_t>
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__global__ void VecQuant3MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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);
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template <typename scalar_t>
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__global__ void VecQuant4MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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);
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template <typename scalar_t>
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__global__ void VecQuant8MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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);
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const int BLOCKWIDTH = 256;
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const int BLOCKHEIGHT2 = 16;
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const int BLOCKHEIGHT3 = 24;
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const int BLOCKHEIGHT4 = 32;
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const int BLOCKHEIGHT8 = 64;
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__device__ inline unsigned int as_unsigned(int i) {
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return *reinterpret_cast<unsigned int*>(&i);
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}
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void vecquant2matmul_cuda(
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torch::Tensor vec,
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torch::Tensor mat,
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torch::Tensor mul,
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torch::Tensor scales,
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torch::Tensor zeros
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) {
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int batch = vec.size(0);
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int vec_height = vec.size(1);
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int height = mat.size(0);
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int width = mat.size(1);
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dim3 blocks(
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(height + BLOCKHEIGHT2 - 1) / BLOCKHEIGHT2,
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(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
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batch
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);
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dim3 threads(BLOCKWIDTH);
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AT_DISPATCH_FLOATING_TYPES(
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vec.type(), "vecquant2matmul_cuda", ([&] {
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VecQuant2MatMulKernel<<<blocks, threads>>>(
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vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
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scales.data<scalar_t>(), zeros.data<scalar_t>(),
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batch, vec_height, height, width
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);
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})
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);
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}
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template <typename scalar_t>
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__global__ void VecQuant2MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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) {
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int b = blockIdx.z;
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int h = BLOCKHEIGHT2 * blockIdx.x;
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int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
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__shared__ scalar_t blockvec[BLOCKWIDTH];
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blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT2) * BLOCKWIDTH + threadIdx.x];
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__syncthreads();
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scalar_t scale = scales[w];
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scalar_t zero = zeros[w];
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scalar_t res = 0;
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int i = width * h + w;
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int k = 0;
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unsigned int tmp;
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while (k < BLOCKWIDTH) {
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tmp = as_unsigned(mat[i]);
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res += (scale * scalar_t((tmp >> 0) & 0x3) - zero) * blockvec[k + 0];
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res += (scale * scalar_t((tmp >> 2) & 0x3) - zero) * blockvec[k + 1];
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res += (scale * scalar_t((tmp >> 4) & 0x3) - zero) * blockvec[k + 2];
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res += (scale * scalar_t((tmp >> 6) & 0x3) - zero) * blockvec[k + 3];
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res += (scale * scalar_t((tmp >> 8) & 0x3) - zero) * blockvec[k + 4];
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res += (scale * scalar_t((tmp >> 10) & 0x3) - zero) * blockvec[k + 5];
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res += (scale * scalar_t((tmp >> 12) & 0x3) - zero) * blockvec[k + 6];
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res += (scale * scalar_t((tmp >> 14) & 0x3) - zero) * blockvec[k + 7];
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res += (scale * scalar_t((tmp >> 16) & 0x3) - zero) * blockvec[k + 8];
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res += (scale * scalar_t((tmp >> 18) & 0x3) - zero) * blockvec[k + 9];
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res += (scale * scalar_t((tmp >> 20) & 0x3) - zero) * blockvec[k + 10];
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res += (scale * scalar_t((tmp >> 22) & 0x3) - zero) * blockvec[k + 11];
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res += (scale * scalar_t((tmp >> 24) & 0x3) - zero) * blockvec[k + 12];
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res += (scale * scalar_t((tmp >> 26) & 0x3) - zero) * blockvec[k + 13];
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res += (scale * scalar_t((tmp >> 28) & 0x3) - zero) * blockvec[k + 14];
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res += (scale * scalar_t((tmp >> 30) & 0x3) - zero) * blockvec[k + 15];
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i += width;
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k += 16;
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}
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atomicAdd(&mul[b * width + w], res);
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}
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void vecquant3matmul_cuda(
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torch::Tensor vec,
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torch::Tensor mat,
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torch::Tensor mul,
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torch::Tensor scales,
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torch::Tensor zeros
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) {
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int batch = vec.size(0);
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int vec_height = vec.size(1);
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int height = mat.size(0);
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int width = mat.size(1);
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dim3 blocks(
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(height + BLOCKHEIGHT3 - 1) / BLOCKHEIGHT3,
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(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
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batch
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);
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dim3 threads(BLOCKWIDTH);
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AT_DISPATCH_FLOATING_TYPES(
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vec.type(), "vecquant3matmul_cuda", ([&] {
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VecQuant3MatMulKernel<<<blocks, threads>>>(
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vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
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scales.data<scalar_t>(), zeros.data<scalar_t>(),
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batch, vec_height, height, width
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);
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})
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);
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}
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template <typename scalar_t>
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__global__ void VecQuant3MatMulKernel(
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const scalar_t* __restrict__ vec,
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const int* __restrict__ mat,
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scalar_t* __restrict__ mul,
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const scalar_t* __restrict__ scales,
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const scalar_t* __restrict__ zeros,
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int batch,
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int vec_height,
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int height,
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int width
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) {
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int b = blockIdx.z;
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int h = BLOCKHEIGHT3 * blockIdx.x;
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int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
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__shared__ scalar_t blockvec[BLOCKWIDTH];
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blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT3) * BLOCKWIDTH + threadIdx.x];
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__syncthreads();
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scalar_t scale = scales[w];
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scalar_t zero = zeros[w];
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scalar_t res = 0;
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int i = width * h + w;
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int k = 0;
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unsigned int tmp1;
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unsigned int tmp2;
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unsigned int tmp;
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while (k < BLOCKWIDTH) {
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tmp1 = as_unsigned(mat[i]);
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res += (scale * scalar_t((tmp1 >> 0) & 0x7) - zero) * blockvec[k + 0];
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res += (scale * scalar_t((tmp1 >> 3) & 0x7) - zero) * blockvec[k + 1];
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res += (scale * scalar_t((tmp1 >> 6) & 0x7) - zero) * blockvec[k + 2];
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res += (scale * scalar_t((tmp1 >> 9) & 0x7) - zero) * blockvec[k + 3];
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res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
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res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
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res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
|
||||
res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
|
||||
res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
|
||||
res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
|
||||
i += width;
|
||||
tmp2 = as_unsigned(mat[i]);
|
||||
tmp = (tmp1 >> 30) | ((tmp2 << 2) & 0x4);
|
||||
tmp2 >>= 1;
|
||||
res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
|
||||
k += 11;
|
||||
res += (scale * scalar_t((tmp2 >> 0) & 0x7) - zero) * blockvec[k + 0];
|
||||
res += (scale * scalar_t((tmp2 >> 3) & 0x7) - zero) * blockvec[k + 1];
|
||||
res += (scale * scalar_t((tmp2 >> 6) & 0x7) - zero) * blockvec[k + 2];
|
||||
res += (scale * scalar_t((tmp2 >> 9) & 0x7) - zero) * blockvec[k + 3];
|
||||
res += (scale * scalar_t((tmp2 >> 12) & 0x7) - zero) * blockvec[k + 4];
|
||||
res += (scale * scalar_t((tmp2 >> 15) & 0x7) - zero) * blockvec[k + 5];
|
||||
res += (scale * scalar_t((tmp2 >> 18) & 0x7) - zero) * blockvec[k + 6];
|
||||
res += (scale * scalar_t((tmp2 >> 21) & 0x7) - zero) * blockvec[k + 7];
|
||||
res += (scale * scalar_t((tmp2 >> 24) & 0x7) - zero) * blockvec[k + 8];
|
||||
res += (scale * scalar_t((tmp2 >> 27) & 0x7) - zero) * blockvec[k + 9];
|
||||
i += width;
|
||||
tmp1 = as_unsigned(mat[i]);
|
||||
tmp = (tmp2 >> 30) | ((tmp1 << 1) & 0x6);
|
||||
tmp1 >>= 2;
|
||||
res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
|
||||
k += 11;
|
||||
res += (scale * scalar_t((tmp1 >> 0) & 0x7) - zero) * blockvec[k + 0];
|
||||
res += (scale * scalar_t((tmp1 >> 3) & 0x7) - zero) * blockvec[k + 1];
|
||||
res += (scale * scalar_t((tmp1 >> 6) & 0x7) - zero) * blockvec[k + 2];
|
||||
res += (scale * scalar_t((tmp1 >> 9) & 0x7) - zero) * blockvec[k + 3];
|
||||
res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
|
||||
res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
|
||||
res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
|
||||
res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
|
||||
res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
|
||||
res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
|
||||
i += width;
|
||||
k += 10;
|
||||
}
|
||||
|
||||
atomicAdd(&mul[b * width + w], res);
|
||||
}
|
||||
|
||||
void vecquant4matmul_cuda(
|
||||
torch::Tensor vec,
|
||||
torch::Tensor mat,
|
||||
torch::Tensor mul,
|
||||
torch::Tensor scales,
|
||||
torch::Tensor zeros
|
||||
) {
|
||||
int batch = vec.size(0);
|
||||
int vec_height = vec.size(1);
|
||||
int height = mat.size(0);
|
||||
int width = mat.size(1);
|
||||
|
||||
dim3 blocks(
|
||||
(height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
|
||||
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
|
||||
batch
|
||||
);
|
||||
dim3 threads(BLOCKWIDTH);
|
||||
|
||||
AT_DISPATCH_FLOATING_TYPES(
|
||||
vec.type(), "vecquant4matmul_cuda", ([&] {
|
||||
VecQuant4MatMulKernel<<<blocks, threads>>>(
|
||||
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
|
||||
scales.data<scalar_t>(), zeros.data<scalar_t>(),
|
||||
batch, vec_height, height, width
|
||||
);
|
||||
})
|
||||
);
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
__global__ void VecQuant4MatMulKernel(
|
||||
const scalar_t* __restrict__ vec,
|
||||
const int* __restrict__ mat,
|
||||
scalar_t* __restrict__ mul,
|
||||
const scalar_t* __restrict__ scales,
|
||||
const scalar_t* __restrict__ zeros,
|
||||
int batch,
|
||||
int vec_height,
|
||||
int height,
|
||||
int width
|
||||
) {
|
||||
int b = blockIdx.z;
|
||||
int h = BLOCKHEIGHT4 * blockIdx.x;
|
||||
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
|
||||
|
||||
__shared__ scalar_t blockvec[BLOCKWIDTH];
|
||||
blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT4) * BLOCKWIDTH + threadIdx.x];
|
||||
__syncthreads();
|
||||
|
||||
scalar_t scale = scales[w];
|
||||
scalar_t zero = zeros[w];
|
||||
|
||||
scalar_t res = 0;
|
||||
int i = width * h + w;
|
||||
int k = 0;
|
||||
|
||||
unsigned int tmp;
|
||||
|
||||
while (k < BLOCKWIDTH) {
|
||||
tmp = as_unsigned(mat[i]);
|
||||
res += (scale * scalar_t((tmp >> 0) & 0xF) - zero) * blockvec[k + 0];
|
||||
res += (scale * scalar_t((tmp >> 4) & 0xF) - zero) * blockvec[k + 1];
|
||||
res += (scale * scalar_t((tmp >> 8) & 0xF) - zero) * blockvec[k + 2];
|
||||
res += (scale * scalar_t((tmp >> 12) & 0xF) - zero) * blockvec[k + 3];
|
||||
res += (scale * scalar_t((tmp >> 16) & 0xF) - zero) * blockvec[k + 4];
|
||||
res += (scale * scalar_t((tmp >> 20) & 0xF) - zero) * blockvec[k + 5];
|
||||
res += (scale * scalar_t((tmp >> 24) & 0xF) - zero) * blockvec[k + 6];
|
||||
res += (scale * scalar_t((tmp >> 28) & 0xF) - zero) * blockvec[k + 7];
|
||||
i += width;
|
||||
k += 8;
|
||||
}
|
||||
|
||||
atomicAdd(&mul[b * width + w], res);
|
||||
}
|
||||
|
||||
void vecquant8matmul_cuda(
|
||||
torch::Tensor vec,
|
||||
torch::Tensor mat,
|
||||
torch::Tensor mul,
|
||||
torch::Tensor scales,
|
||||
torch::Tensor zeros
|
||||
) {
|
||||
int batch = vec.size(0);
|
||||
int vec_height = vec.size(1);
|
||||
int height = mat.size(0);
|
||||
int width = mat.size(1);
|
||||
|
||||
dim3 blocks(
|
||||
(height + BLOCKHEIGHT8 - 1) / BLOCKHEIGHT8,
|
||||
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
|
||||
batch
|
||||
);
|
||||
dim3 threads(BLOCKWIDTH);
|
||||
|
||||
AT_DISPATCH_FLOATING_TYPES(
|
||||
vec.type(), "vecquant8matmul_cuda", ([&] {
|
||||
VecQuant8MatMulKernel<<<blocks, threads>>>(
|
||||
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
|
||||
scales.data<scalar_t>(), zeros.data<scalar_t>(),
|
||||
batch, vec_height, height, width
|
||||
);
|
||||
})
|
||||
);
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
__global__ void VecQuant8MatMulKernel(
|
||||
const scalar_t* __restrict__ vec,
|
||||
const int* __restrict__ mat,
|
||||
scalar_t* __restrict__ mul,
|
||||
const scalar_t* __restrict__ scales,
|
||||
const scalar_t* __restrict__ zeros,
|
||||
int batch,
|
||||
int vec_height,
|
||||
int height,
|
||||
int width
|
||||
) {
|
||||
int b = blockIdx.z;
|
||||
int h = BLOCKHEIGHT8 * blockIdx.x;
|
||||
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
|
||||
|
||||
__shared__ scalar_t blockvec[BLOCKWIDTH];
|
||||
blockvec[threadIdx.x] = vec[b * vec_height + (h / BLOCKHEIGHT8) * BLOCKWIDTH + threadIdx.x];
|
||||
__syncthreads();
|
||||
|
||||
scalar_t scale = scales[w];
|
||||
scalar_t zero = zeros[w];
|
||||
|
||||
scalar_t res = 0;
|
||||
int i = width * h + w;
|
||||
int k = 0;
|
||||
|
||||
unsigned int tmp;
|
||||
|
||||
while (k < BLOCKWIDTH) {
|
||||
tmp = as_unsigned(mat[i]);
|
||||
res += (scale * scalar_t((tmp >> 0) & 0xFF) - zero) * blockvec[k + 0];
|
||||
res += (scale * scalar_t((tmp >> 8) & 0xFF) - zero) * blockvec[k + 1];
|
||||
res += (scale * scalar_t((tmp >> 16) & 0xFF) - zero) * blockvec[k + 2];
|
||||
res += (scale * scalar_t((tmp >> 24) & 0xFF) - zero) * blockvec[k + 3];
|
||||
i += width;
|
||||
k += 4;
|
||||
}
|
||||
|
||||
atomicAdd(&mul[b * width + w], res);
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
__global__ void VecQuant4TransposeMatMulKernel(
|
||||
const scalar_t* __restrict__ vec,
|
||||
const int* __restrict__ mat,
|
||||
scalar_t* __restrict__ mul,
|
||||
const scalar_t* __restrict__ scales,
|
||||
const scalar_t* __restrict__ zeros,
|
||||
int batch,
|
||||
int vec_height,
|
||||
int height,
|
||||
int width
|
||||
) {
|
||||
int b = blockIdx.z;
|
||||
int h = BLOCKHEIGHT4 * blockIdx.x + threadIdx.x / 8;
|
||||
unsigned int shift = (unsigned int)((threadIdx.x % 8) * 4);
|
||||
int w = BLOCKWIDTH * blockIdx.y;
|
||||
|
||||
int n_rows = 8 * BLOCKHEIGHT4 * blockIdx.x + threadIdx.x;
|
||||
int n_cols = b;
|
||||
|
||||
__shared__ scalar_t blockvec[BLOCKWIDTH];
|
||||
blockvec[threadIdx.x] = vec[n_cols * vec_height + w + threadIdx.x];
|
||||
__syncthreads();
|
||||
|
||||
scalar_t res = 0;
|
||||
int i = width * h + w;
|
||||
int k = 0;
|
||||
int j = w;
|
||||
unsigned int tmp;
|
||||
while (k < BLOCKWIDTH) {
|
||||
tmp = as_unsigned(mat[i]);
|
||||
res += (scales[j] * scalar_t((tmp >> shift) & 0xF) - zeros[j]) * blockvec[k];
|
||||
i += 1;
|
||||
j += 1;
|
||||
k += 1;
|
||||
}
|
||||
|
||||
atomicAdd(&mul[n_cols * height * 8 + n_rows], res);
|
||||
}
|
||||
|
||||
void vecquant4transposematmul_cuda(
|
||||
torch::Tensor vec,
|
||||
torch::Tensor mat,
|
||||
torch::Tensor mul,
|
||||
torch::Tensor scales,
|
||||
torch::Tensor zeros
|
||||
) {
|
||||
int batch = vec.size(0);
|
||||
int vec_height = vec.size(1);
|
||||
int height = mat.size(0);
|
||||
int width = mat.size(1);
|
||||
|
||||
dim3 blocks(
|
||||
(height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
|
||||
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
|
||||
batch
|
||||
);
|
||||
dim3 threads(BLOCKWIDTH);
|
||||
|
||||
AT_DISPATCH_FLOATING_TYPES(
|
||||
vec.type(), "vecquant4transposematmul_cuda", ([&] {
|
||||
VecQuant4TransposeMatMulKernel<<<blocks, threads>>>(
|
||||
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
|
||||
scales.data<scalar_t>(), zeros.data<scalar_t>(),
|
||||
batch, vec_height, height, width
|
||||
);
|
||||
})
|
||||
);
|
||||
}
|
||||
|
|
@ -0,0 +1,697 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2023-present the HuggingFace Inc. team.
|
||||
#
|
||||
# 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.
|
||||
import importlib
|
||||
import math
|
||||
import re
|
||||
import warnings
|
||||
from dataclasses import asdict, dataclass, field
|
||||
from enum import Enum
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from transformers.pytorch_utils import Conv1D
|
||||
|
||||
from ..utils import PeftConfig, PeftType, transpose
|
||||
|
||||
|
||||
def is_bnb_available():
|
||||
return importlib.util.find_spec("bitsandbytes") is not None
|
||||
|
||||
|
||||
def is_gptq_available():
|
||||
return importlib.util.find_spec("quant") is not None
|
||||
|
||||
|
||||
if is_bnb_available():
|
||||
import bitsandbytes as bnb
|
||||
|
||||
|
||||
if is_gptq_available():
|
||||
import quant
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoraConfig(PeftConfig):
|
||||
"""
|
||||
This is the configuration class to store the configuration of a [`~peft.Lora`].
|
||||
|
||||
Args:
|
||||
r (`int`): Lora attention dimension
|
||||
target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
|
||||
lora_alpha (`float`): The alpha parameter for Lora scaling.
|
||||
lora_dropout (`float`): The dropout probability for Lora layers.
|
||||
merge_weights (`bool`):
|
||||
Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode.
|
||||
fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out)
|
||||
enable_lora ( `List[bool]`): Used with `lora.MergedLinear`.
|
||||
bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
|
||||
modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
|
||||
and saved in the final checkpoint.
|
||||
"""
|
||||
|
||||
r: int = field(default=8, metadata={"help": "Lora attention dimension"})
|
||||
target_modules: Optional[Union[List[str], str]] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "List of module names or regex expression of the module names to replace with Lora."
|
||||
"For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
|
||||
},
|
||||
)
|
||||
lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"})
|
||||
lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"})
|
||||
merge_weights: bool = field(
|
||||
default=False, metadata={"help": "Merge weights of the original model and the Lora model"}
|
||||
)
|
||||
fan_in_fan_out: bool = field(
|
||||
default=False,
|
||||
metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"},
|
||||
)
|
||||
enable_lora: Optional[List[bool]] = field(default=None, metadata={"help": "Used with `lora.MergedLinear`."})
|
||||
bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"})
|
||||
modules_to_save: Optional[List[str]] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
|
||||
"For example, in Sequence Classification or Token Classification tasks, "
|
||||
"the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
|
||||
},
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
self.peft_type = PeftType.LORA
|
||||
|
||||
|
||||
class LoraModel(torch.nn.Module):
|
||||
"""
|
||||
Creates Low Rank Adapter (Lora) model from a pretrained transformers model.
|
||||
|
||||
Args:
|
||||
model ([`transformers.PreTrainedModel`]): The model to be adapted.
|
||||
config ([`LoraConfig`]): The configuration of the Lora model.
|
||||
|
||||
Returns:
|
||||
`torch.nn.Module`: The Lora model.
|
||||
|
||||
Example::
|
||||
|
||||
>>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import LoraModel, LoraConfig >>>
|
||||
config = LoraConfig(
|
||||
peft_type="LORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"],
|
||||
lora_dropout=0.01, )
|
||||
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> lora_model = LoraModel(config, model)
|
||||
|
||||
**Attributes**:
|
||||
- **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
|
||||
- **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
|
||||
"""
|
||||
|
||||
def __init__(self, config, model):
|
||||
super().__init__()
|
||||
self.peft_config = config
|
||||
self.model = model
|
||||
self._find_and_replace()
|
||||
mark_only_lora_as_trainable(self.model, self.peft_config.bias)
|
||||
self.forward = self.model.forward
|
||||
|
||||
def _find_and_replace(self):
|
||||
loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
|
||||
if loaded_in_8bit and not is_bnb_available():
|
||||
raise ImportError(
|
||||
"To use Lora with 8-bit quantization, please install the `bitsandbytes` package. "
|
||||
"You can install it with `pip install bitsandbytes`."
|
||||
)
|
||||
is_target_modules_in_base_model = False
|
||||
is_hf_device_map_available = hasattr(self.model, "hf_device_map")
|
||||
kwargs = {
|
||||
"r": self.peft_config.r,
|
||||
"lora_alpha": self.peft_config.lora_alpha,
|
||||
"lora_dropout": self.peft_config.lora_dropout,
|
||||
"fan_in_fan_out": self.peft_config.fan_in_fan_out,
|
||||
"merge_weights": (self.peft_config.merge_weights or self.peft_config.inference_mode)
|
||||
and not is_hf_device_map_available,
|
||||
}
|
||||
key_list = [key for key, _ in self.model.named_modules()]
|
||||
for key in key_list:
|
||||
if isinstance(self.peft_config.target_modules, str):
|
||||
target_module_found = re.fullmatch(self.peft_config.target_modules, key)
|
||||
else:
|
||||
target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules)
|
||||
if target_module_found:
|
||||
if not is_target_modules_in_base_model:
|
||||
is_target_modules_in_base_model = True
|
||||
parent, target, target_name = self._get_submodules(key)
|
||||
bias = target.bias is not None
|
||||
if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
|
||||
kwargs.update(
|
||||
{
|
||||
"has_fp16_weights": target.state.has_fp16_weights,
|
||||
"memory_efficient_backward": target.state.memory_efficient_backward,
|
||||
"threshold": target.state.threshold,
|
||||
"index": target.index,
|
||||
}
|
||||
)
|
||||
if self.peft_config.enable_lora is None:
|
||||
new_module = Linear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs)
|
||||
else:
|
||||
kwargs.update({"enable_lora": self.peft_config.enable_lora})
|
||||
new_module = MergedLinear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs)
|
||||
elif isinstance(target, torch.nn.Linear) and self.peft_config.enable_lora is None:
|
||||
new_module = Linear(target.in_features, target.out_features, bias=bias, **kwargs)
|
||||
elif isinstance(target, Autograd4bitQuantLinear) and self.peft_config.enable_lora is None:
|
||||
new_module = Linear4bitLt(target.in_features, target.out_features, bias=bias, **kwargs)
|
||||
elif self.peft_config.enable_lora is not None:
|
||||
kwargs.update({"enable_lora": self.peft_config.enable_lora})
|
||||
if isinstance(target, Conv1D):
|
||||
in_features, out_features = (
|
||||
target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
|
||||
)
|
||||
else:
|
||||
in_features, out_features = target.in_features, target.out_features
|
||||
if kwargs["fan_in_fan_out"]:
|
||||
warnings.warn(
|
||||
"fan_in_fan_out is set to True but the target module is not a Conv1D. "
|
||||
"Setting fan_in_fan_out to False."
|
||||
)
|
||||
kwargs["fan_in_fan_out"] = self.peft_config.fan_in_fan_out = False
|
||||
new_module = MergedLinear(in_features, out_features, bias=bias, **kwargs)
|
||||
self._replace_module(parent, target_name, new_module, target)
|
||||
if not is_target_modules_in_base_model:
|
||||
raise ValueError(
|
||||
f"Target modules {self.peft_config.target_modules} not found in the base model. "
|
||||
f"Please check the target modules and try again."
|
||||
)
|
||||
|
||||
def _get_submodules(self, key):
|
||||
parent = self.model.get_submodule(".".join(key.split(".")[:-1]))
|
||||
target_name = key.split(".")[-1]
|
||||
target = self.model.get_submodule(key)
|
||||
return parent, target, target_name
|
||||
|
||||
def _replace_module(self, parent_module, child_name, new_module, old_module):
|
||||
setattr(parent_module, child_name, new_module)
|
||||
if isinstance(old_module, Autograd4bitQuantLinear) and isinstance(new_module, Linear4bitLt):
|
||||
new_module.qweight = old_module.qweight
|
||||
new_module.scales = old_module.scales
|
||||
new_module.zeros = old_module.zeros
|
||||
new_module.bias = old_module.bias
|
||||
if getattr(old_module, "state", None) is not None:
|
||||
new_module.state = old_module.state
|
||||
new_module.to(old_module.qweight.device)
|
||||
|
||||
# dispatch to correct device
|
||||
for name, module in new_module.named_modules():
|
||||
if "lora_" in name:
|
||||
module.to(old_module.qweight.device)
|
||||
else:
|
||||
new_module.weight = old_module.weight
|
||||
if old_module.bias is not None:
|
||||
new_module.bias = old_module.bias
|
||||
if getattr(old_module, "state", None) is not None:
|
||||
new_module.state = old_module.state
|
||||
new_module.to(old_module.weight.device)
|
||||
|
||||
# dispatch to correct device
|
||||
for name, module in new_module.named_modules():
|
||||
if "lora_" in name:
|
||||
module.to(old_module.weight.device)
|
||||
|
||||
def __getattr__(self, name: str):
|
||||
"""Forward missing attributes to the wrapped module."""
|
||||
try:
|
||||
return super().__getattr__(name) # defer to nn.Module's logic
|
||||
except AttributeError:
|
||||
return getattr(self.model, name)
|
||||
|
||||
@property
|
||||
def modules_to_save(self):
|
||||
return None
|
||||
|
||||
def get_peft_config_as_dict(self, inference: bool = False):
|
||||
config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(self.peft_config).items()}
|
||||
if inference:
|
||||
config["inference_mode"] = True
|
||||
return config
|
||||
|
||||
def _set_adapter_layers(self, enabled=True):
|
||||
for module in self.model.modules():
|
||||
if isinstance(module, LoraLayer):
|
||||
module.disable_adapters = False if enabled else True
|
||||
|
||||
def enable_adapter_layers(self):
|
||||
self._set_adapter_layers(enabled=True)
|
||||
|
||||
def disable_adapter_layers(self):
|
||||
self._set_adapter_layers(enabled=False)
|
||||
|
||||
|
||||
# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
|
||||
# and modified to work with PyTorch FSDP
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
# had to adapt it for `lora_only` to work
|
||||
def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None:
|
||||
for n, p in model.named_parameters():
|
||||
if "lora_" not in n:
|
||||
p.requires_grad = False
|
||||
if bias == "none":
|
||||
return
|
||||
elif bias == "all":
|
||||
for n, p in model.named_parameters():
|
||||
if "bias" in n:
|
||||
p.requires_grad = True
|
||||
elif bias == "lora_only":
|
||||
for m in model.modules():
|
||||
if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None:
|
||||
m.bias.requires_grad = True
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class LoraLayer:
|
||||
def __init__(
|
||||
self,
|
||||
r: int,
|
||||
lora_alpha: int,
|
||||
lora_dropout: float,
|
||||
merge_weights: bool,
|
||||
):
|
||||
self.r = r
|
||||
self.lora_alpha = lora_alpha
|
||||
# Optional dropout
|
||||
if lora_dropout > 0.0:
|
||||
self.lora_dropout = nn.Dropout(p=lora_dropout)
|
||||
else:
|
||||
self.lora_dropout = lambda x: x
|
||||
# Mark the weight as unmerged
|
||||
self.merged = False
|
||||
self.merge_weights = merge_weights
|
||||
self.disable_adapters = False
|
||||
|
||||
|
||||
class Linear(nn.Linear, LoraLayer):
|
||||
# Lora implemented in a dense layer
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
out_features: int,
|
||||
r: int = 0,
|
||||
lora_alpha: int = 1,
|
||||
lora_dropout: float = 0.0,
|
||||
fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
|
||||
merge_weights: bool = True,
|
||||
**kwargs,
|
||||
):
|
||||
nn.Linear.__init__(self, in_features, out_features, **kwargs)
|
||||
LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
|
||||
|
||||
self.fan_in_fan_out = fan_in_fan_out
|
||||
# Actual trainable parameters
|
||||
if r > 0:
|
||||
self.lora_A = nn.Linear(in_features, r, bias=False)
|
||||
self.lora_B = nn.Linear(r, out_features, bias=False)
|
||||
self.scaling = self.lora_alpha / self.r
|
||||
# Freezing the pre-trained weight matrix
|
||||
self.weight.requires_grad = False
|
||||
self.reset_parameters()
|
||||
if fan_in_fan_out:
|
||||
self.weight.data = self.weight.data.T
|
||||
|
||||
def reset_parameters(self):
|
||||
nn.Linear.reset_parameters(self)
|
||||
if hasattr(self, "lora_A"):
|
||||
# initialize A the same way as the default for nn.Linear and B to zero
|
||||
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
|
||||
nn.init.zeros_(self.lora_B.weight)
|
||||
|
||||
def train(self, mode: bool = True):
|
||||
nn.Linear.train(self, mode)
|
||||
self.lora_A.train(mode)
|
||||
self.lora_B.train(mode)
|
||||
if not mode and self.merge_weights and not self.merged:
|
||||
# Merge the weights and mark it
|
||||
if self.r > 0:
|
||||
self.weight.data += (
|
||||
transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling
|
||||
)
|
||||
self.merged = True
|
||||
elif self.merge_weights and self.merged:
|
||||
# Make sure that the weights are not merged
|
||||
if self.r > 0:
|
||||
self.weight.data -= (
|
||||
transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling
|
||||
)
|
||||
self.merged = False
|
||||
|
||||
def eval(self):
|
||||
nn.Linear.eval(self)
|
||||
self.lora_A.eval()
|
||||
self.lora_B.eval()
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
if self.disable_adapters:
|
||||
if self.r > 0 and self.merged:
|
||||
self.weight.data -= (
|
||||
transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling
|
||||
)
|
||||
self.merged = False
|
||||
|
||||
return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
elif self.r > 0 and not self.merged:
|
||||
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
if self.r > 0:
|
||||
result += self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling
|
||||
return result
|
||||
else:
|
||||
return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
|
||||
|
||||
class MergedLinear(nn.Linear, LoraLayer):
|
||||
# Lora implemented in a dense layer
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
out_features: int,
|
||||
r: int = 0,
|
||||
lora_alpha: int = 1,
|
||||
lora_dropout: float = 0.0,
|
||||
enable_lora: List[bool] = [False],
|
||||
fan_in_fan_out: bool = False,
|
||||
merge_weights: bool = True,
|
||||
**kwargs,
|
||||
):
|
||||
nn.Linear.__init__(self, in_features, out_features, **kwargs)
|
||||
LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
|
||||
if out_features % len(enable_lora) != 0:
|
||||
raise ValueError("The length of enable_lora must divide out_features")
|
||||
self.enable_lora = enable_lora
|
||||
self.fan_in_fan_out = fan_in_fan_out
|
||||
# Actual trainable parameters
|
||||
if r > 0 and any(enable_lora):
|
||||
self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False)
|
||||
self.lora_B = nn.Conv1d(
|
||||
r * sum(enable_lora),
|
||||
out_features // len(enable_lora) * sum(enable_lora),
|
||||
kernel_size=1,
|
||||
groups=2,
|
||||
bias=False,
|
||||
)
|
||||
self.scaling = self.lora_alpha / self.r
|
||||
# Freezing the pre-trained weight matrix
|
||||
self.weight.requires_grad = False
|
||||
# Compute the indices
|
||||
self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1)
|
||||
self.lora_ind[enable_lora, :] = True
|
||||
self.lora_ind = self.lora_ind.view(-1)
|
||||
self.reset_parameters()
|
||||
if fan_in_fan_out:
|
||||
self.weight.data = self.weight.data.T
|
||||
|
||||
def reset_parameters(self):
|
||||
nn.Linear.reset_parameters(self)
|
||||
if hasattr(self, "lora_A"):
|
||||
# initialize A the same way as the default for nn.Linear and B to zero
|
||||
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
|
||||
nn.init.zeros_(self.lora_B.weight)
|
||||
|
||||
def zero_pad(self, x):
|
||||
result = x.new_zeros((*x.shape[:-1], self.out_features))
|
||||
result = result.view(-1, self.out_features)
|
||||
result[:, self.lora_ind] = x.reshape(-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora))
|
||||
return result.view((*x.shape[:-1], self.out_features))
|
||||
|
||||
def train(self, mode: bool = True):
|
||||
nn.Linear.train(self, mode)
|
||||
self.lora_A.train(mode)
|
||||
self.lora_B.train(mode)
|
||||
if not mode and self.merge_weights and not self.merged:
|
||||
# Merge the weights and mark it
|
||||
if self.r > 0 and any(self.enable_lora):
|
||||
delta_w = (
|
||||
F.conv1d(
|
||||
self.lora_A.weight.data.unsqueeze(0),
|
||||
self.lora_B.weight.data,
|
||||
groups=sum(self.enable_lora),
|
||||
)
|
||||
.squeeze(0)
|
||||
.transpose(-2, -1)
|
||||
)
|
||||
self.weight.data += transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out)
|
||||
self.merged = True
|
||||
elif self.merge_weights and self.merged:
|
||||
# Make sure that the weights are not merged
|
||||
if self.r > 0 and any(self.enable_lora):
|
||||
delta_w = (
|
||||
F.conv1d(
|
||||
self.lora_A.weight.data.unsqueeze(0),
|
||||
self.lora_B.weight.data,
|
||||
groups=sum(self.enable_lora),
|
||||
)
|
||||
.squeeze(0)
|
||||
.transpose(-2, -1)
|
||||
)
|
||||
self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out)
|
||||
self.merged = False
|
||||
|
||||
def eval(self):
|
||||
nn.Linear.eval(self)
|
||||
self.lora_A.eval()
|
||||
self.lora_B.eval()
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
if self.disable_adapters:
|
||||
if self.r > 0 and self.merged and any(self.enable_lora):
|
||||
delta_w = (
|
||||
F.conv1d(
|
||||
self.lora_A.weight.data.unsqueeze(0),
|
||||
self.lora_B.weight.data,
|
||||
groups=sum(self.enable_lora),
|
||||
)
|
||||
.squeeze(0)
|
||||
.transpose(-2, -1)
|
||||
)
|
||||
self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out)
|
||||
self.merged = False
|
||||
return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
elif self.merged:
|
||||
return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
else:
|
||||
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
|
||||
if self.r > 0:
|
||||
after_A = self.lora_A(self.lora_dropout(x))
|
||||
after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1)
|
||||
result += self.zero_pad(after_B) * self.scaling
|
||||
return result
|
||||
|
||||
|
||||
if is_bnb_available():
|
||||
|
||||
class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
|
||||
# Lora implemented in a dense layer
|
||||
def __init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features,
|
||||
r: int = 0,
|
||||
lora_alpha: int = 1,
|
||||
lora_dropout: float = 0.0,
|
||||
**kwargs,
|
||||
):
|
||||
bnb.nn.Linear8bitLt.__init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features,
|
||||
bias=kwargs.get("bias", True),
|
||||
has_fp16_weights=kwargs.get("has_fp16_weights", True),
|
||||
memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
|
||||
threshold=kwargs.get("threshold", 0.0),
|
||||
index=kwargs.get("index", None),
|
||||
)
|
||||
LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False)
|
||||
# Actual trainable parameters
|
||||
if r > 0:
|
||||
self.lora_A = nn.Linear(in_features, r, bias=False)
|
||||
self.lora_B = nn.Linear(r, out_features, bias=False)
|
||||
self.scaling = self.lora_alpha / self.r
|
||||
# Freezing the pre-trained weight matrix
|
||||
self.weight.requires_grad = False
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
if hasattr(self, "lora_A"):
|
||||
# initialize A the same way as the default for nn.Linear and B to zero
|
||||
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
|
||||
nn.init.zeros_(self.lora_B.weight)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
result = super().forward(x)
|
||||
|
||||
if self.disable_adapters:
|
||||
return result
|
||||
elif self.r > 0:
|
||||
if not torch.is_autocast_enabled():
|
||||
expected_dtype = result.dtype
|
||||
|
||||
if x.dtype != torch.float32:
|
||||
x = x.float()
|
||||
output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling
|
||||
result += output
|
||||
else:
|
||||
output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling
|
||||
result += output
|
||||
return result
|
||||
|
||||
class MergedLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
|
||||
# Lora implemented in a dense layer
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
out_features: int,
|
||||
r: int = 0,
|
||||
lora_alpha: int = 1,
|
||||
lora_dropout: float = 0.0,
|
||||
enable_lora: List[bool] = [False],
|
||||
**kwargs,
|
||||
):
|
||||
bnb.nn.Linear8bitLt.__init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features,
|
||||
bias=kwargs.get("bias", True),
|
||||
has_fp16_weights=kwargs.get("has_fp16_weights", True),
|
||||
memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
|
||||
threshold=kwargs.get("threshold", 0.0),
|
||||
index=kwargs.get("index", None),
|
||||
)
|
||||
LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False)
|
||||
if out_features % len(enable_lora) != 0:
|
||||
raise ValueError("The length of enable_lora must divide out_features")
|
||||
self.enable_lora = enable_lora
|
||||
# Actual trainable parameters
|
||||
if r > 0 and any(enable_lora):
|
||||
self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False)
|
||||
self.lora_B = nn.Conv1d(
|
||||
r * sum(enable_lora),
|
||||
out_features // len(enable_lora) * sum(enable_lora),
|
||||
kernel_size=1,
|
||||
groups=2,
|
||||
bias=False,
|
||||
)
|
||||
self.scaling = self.lora_alpha / self.r
|
||||
# Freezing the pre-trained weight matrix
|
||||
self.weight.requires_grad = False
|
||||
# Compute the indices
|
||||
self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1)
|
||||
self.lora_ind[enable_lora, :] = True
|
||||
self.lora_ind = self.lora_ind.view(-1)
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
if hasattr(self, "lora_A"):
|
||||
# initialize A the same way as the default for nn.Linear and B to zero
|
||||
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
|
||||
nn.init.zeros_(self.lora_B.weight)
|
||||
|
||||
def zero_pad(self, x):
|
||||
result = x.new_zeros((*x.shape[:-1], self.out_features))
|
||||
result = result.view(-1, self.out_features)
|
||||
result[:, self.lora_ind] = x.reshape(
|
||||
-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)
|
||||
)
|
||||
return result.view((*x.shape[:-1], self.out_features))
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
result = super().forward(x)
|
||||
if self.disable_adapters:
|
||||
return result
|
||||
elif self.r > 0:
|
||||
if not torch.is_autocast_enabled():
|
||||
expected_dtype = result.dtype
|
||||
if x.dtype != torch.float32:
|
||||
x = x.float()
|
||||
after_A = self.lora_A(self.lora_dropout(x))
|
||||
after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1)
|
||||
output = self.zero_pad(after_B).to(expected_dtype) * self.scaling
|
||||
result += output
|
||||
else:
|
||||
after_A = self.lora_A(self.lora_dropout(x))
|
||||
after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1)
|
||||
output = self.zero_pad(after_B) * self.scaling
|
||||
result += output
|
||||
return result
|
||||
|
||||
if is_gptq_available():
|
||||
|
||||
from autograd_4bit import Autograd4bitQuantLinear
|
||||
|
||||
class Linear4bitLt(Autograd4bitQuantLinear, LoraLayer):
|
||||
# Lora implemented in a dense layer
|
||||
def __init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features,
|
||||
r: int = 0,
|
||||
lora_alpha: int = 1,
|
||||
lora_dropout: float = 0.0,
|
||||
**kwargs,
|
||||
):
|
||||
Autograd4bitQuantLinear.__init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features
|
||||
)
|
||||
LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False)
|
||||
# Actual trainable parameters
|
||||
if r > 0:
|
||||
self.lora_A = nn.Linear(in_features, r, bias=False)
|
||||
self.lora_B = nn.Linear(r, out_features, bias=False)
|
||||
self.scaling = self.lora_alpha / self.r
|
||||
# Freezing the pre-trained weight matrix
|
||||
self.qweight.requires_grad = False
|
||||
self.scales.requires_grad = False
|
||||
self.zeros.requires_grad = False
|
||||
self.bias.requires_grad = False
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
if hasattr(self, "lora_A"):
|
||||
# initialize A the same way as the default for nn.Linear and B to zero
|
||||
nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
|
||||
nn.init.zeros_(self.lora_B.weight)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
result = super().forward(x)
|
||||
|
||||
if self.disable_adapters:
|
||||
return result
|
||||
elif self.r > 0:
|
||||
if not torch.is_autocast_enabled():
|
||||
expected_dtype = result.dtype
|
||||
|
||||
if x.dtype != torch.float32:
|
||||
x = x.float()
|
||||
output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling
|
||||
result += output
|
||||
else:
|
||||
output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling
|
||||
result += output
|
||||
return result
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
torch
|
||||
accelerate
|
||||
bitsandbytes
|
||||
git+https://github.com/zphang/transformers@llama_push
|
||||
git+https://github.com/qwopqwop200/GPTQ-for-LLaMa.git
|
||||
git+https://github.com/huggingface/peft.git
|
||||
Loading…
Reference in New Issue