|
from torch.nn import Linear, Embedding |
|
from torch.nn.parameter import Parameter |
|
import torch.nn.functional as F |
|
|
|
import os |
|
import bz2 |
|
import torch |
|
import base64 |
|
import ctypes |
|
from transformers.utils import logging |
|
|
|
from typing import List |
|
from functools import partial |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
try: |
|
from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up |
|
|
|
class Kernel: |
|
def __init__(self, code: bytes, function_names: List[str]): |
|
self.code = code |
|
self._function_names = function_names |
|
self._cmodule = LazyKernelCModule(self.code) |
|
|
|
for name in self._function_names: |
|
setattr(self, name, KernelFunction(self._cmodule, name)) |
|
|
|
quantization_code = "$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" |
|
|
|
kernels = Kernel( |
|
bz2.decompress(base64.b64decode(quantization_code)), |
|
[ |
|
"int4WeightCompression", |
|
"int4WeightExtractionFloat", |
|
"int4WeightExtractionHalf", |
|
"int8WeightExtractionFloat", |
|
"int8WeightExtractionHalf", |
|
], |
|
) |
|
except Exception as exception: |
|
kernels = None |
|
logger.warning("Failed to load cpm_kernels:", exception) |
|
|
|
|
|
class W8A16Linear(torch.autograd.Function): |
|
@staticmethod |
|
def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): |
|
ctx.inp_shape = inp.size() |
|
ctx.weight_bit_width = weight_bit_width |
|
out_features = quant_w.size(0) |
|
inp = inp.contiguous().view(-1, inp.size(-1)) |
|
weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) |
|
ctx.weight_shape = weight.size() |
|
output = inp.mm(weight.t()) |
|
ctx.save_for_backward(inp, quant_w, scale_w) |
|
return output.view(*(ctx.inp_shape[:-1] + (out_features,))) |
|
|
|
@staticmethod |
|
def backward(ctx, grad_output: torch.Tensor): |
|
inp, quant_w, scale_w = ctx.saved_tensors |
|
weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) |
|
grad_output = grad_output.contiguous().view(-1, weight.size(0)) |
|
grad_input = grad_output.mm(weight) |
|
grad_weight = grad_output.t().mm(inp) |
|
return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None |
|
|
|
|
|
class W8A16LinearCPU(torch.autograd.Function): |
|
@staticmethod |
|
def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width, quantization_cache=None): |
|
ctx.inp_shape = inp.size() |
|
ctx.weight_bit_width = weight_bit_width |
|
out_features = quant_w.size(0) |
|
inp = inp.contiguous().view(-1, inp.size(-1)) |
|
weight = extract_weight_to_float(quant_w, scale_w, weight_bit_width, quantization_cache=quantization_cache) |
|
ctx.weight_shape = weight.size() |
|
output = inp.mm(weight.t()) |
|
ctx.save_for_backward(inp, quant_w, scale_w) |
|
return output.view(*(ctx.inp_shape[:-1] + (out_features,))) |
|
|
|
@staticmethod |
|
def backward(ctx, grad_output: torch.Tensor): |
|
inp, quant_w, scale_w = ctx.saved_tensors |
|
weight = extract_weight_to_float(quant_w, scale_w, ctx.weight_bit_width) |
|
grad_output = grad_output.contiguous().view(-1, weight.size(0)) |
|
grad_input = grad_output.mm(weight) |
|
grad_weight = grad_output.t().mm(inp) |
|
return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None |
|
|
|
|
|
default_cpu_kernel_code_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "quantization_kernels.c") |
|
default_cpu_kernel_code = "QlpoOTFBWSZTWXLbSoQAAgzbgERwQXxmTwAAr/ff3kABt0Q2oRVT0hpo9RtEAAAAyBEiSQ9EGjQGQAAAwANGhowjJoNGmgMEUplMTNSMJ5TQaDJpsoMyRMj8P4mZzFSVVwqSXG8GG7MlVwiToYEQwVD7noBxMhNfkeZYtYFtbgOBUSIGtIQjhNHCEnPJsadhb3yBmRIOD3TeAtNLSaU5GgvKUBWSNuuOIHmVt0YhW6rsmDMDUjeUJGJ64R1Jm5lrh0Aa0tKjhFwPdWcGogxLDSXPWQUWTM8Sd3Qz1HMYNxx3HMeiNqNo4jeRDEfZ3gUSHIcU/heomq0vEzL1Msz5KKGxH8FrNOYw3KaxdqaEmNHYMxJFgQbR0DyRknL2L4kwUSxKRdhjRpEtUqilVfggFL1klaMS3PPRDfNqbBOPWO7m4JTVGhS9QTBDDJaEbLbrUQNB+IpJSKQbG5SZZ5gkwJEhJ3aYKJipZ/i7kinChIOW2lQg" |
|
default_cpu_parallel_kernel_code_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "quantization_kernels_parallel.c") |
|
default_cpu_parallel_kernel_code = "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" |
|
|
|
cpu_kernels = None |
|
|
|
|
|
class CPUKernel: |
|
def __init__(self, kernel_file="", source_code=default_cpu_kernel_code_path, compile_parallel_kernel=None, parallel_num=None): |
|
self.load =False |
|
self.int8WeightExtractionFloat = None |
|
self.int4WeightExtractionFloat = None |
|
self.int4WeightCompression = None |
|
self.SetNumThreads = None |
|
|
|
try: |
|
if not os.path.exists(default_cpu_kernel_code_path): |
|
with open(default_cpu_kernel_code_path, "w", encoding="utf-8") as file: |
|
code = default_cpu_kernel_code |
|
cpu_quantization_code = bz2.decompress(base64.b64decode(code)).decode() |
|
file.write(cpu_quantization_code) |
|
|
|
if not os.path.exists(default_cpu_parallel_kernel_code_path): |
|
with open(default_cpu_parallel_kernel_code_path, "w", encoding="utf-8") as file: |
|
code = default_cpu_parallel_kernel_code |
|
cpu_quantization_code = bz2.decompress(base64.b64decode(code)).decode() |
|
file.write(cpu_quantization_code) |
|
|
|
except Exception as ex: |
|
print("Error when generating default cpu kernel code(can be ignored when using custom kernels).") |
|
|
|
if compile_parallel_kernel is None: |
|
compile_parallel_kernel = bool(int(os.cpu_count()) >= 4) |
|
|
|
if compile_parallel_kernel and source_code == default_cpu_kernel_code_path: |
|
source_code = default_cpu_parallel_kernel_code_path |
|
|
|
if (not kernel_file) or (not os.path.exists(kernel_file)): |
|
print("No compiled kernel found.") |
|
try: |
|
if os.path.exists(source_code): |
|
print("Compiling kernels :", source_code) |
|
kernel_file = source_code[:-2] + ".so" |
|
if compile_parallel_kernel: |
|
compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(source_code, kernel_file) |
|
print("Compiling", compile_command) |
|
exit_state = os.system(compile_command) |
|
if exit_state: |
|
print("Compile failed, using default cpu kernel code.") |
|
compile_parallel_kernel = False |
|
source_code = default_cpu_kernel_code_path |
|
kernel_file = source_code[:-2] + ".so" |
|
compile_command = "gcc -O3 -fPIC -std=c99 {} -shared -o {}".format(source_code, kernel_file) |
|
print("Compiling", compile_command) |
|
else: |
|
compile_command = "gcc -O3 -fPIC -std=c99 {} -shared -o {}".format(source_code, kernel_file) |
|
print("Compiling", compile_command) |
|
exit_state = os.system(compile_command) |
|
|
|
print("Kernels compiled :", kernel_file) |
|
else: |
|
print("Kernel source code not found.") |
|
return |
|
except: |
|
print("Failed to build kernel.") |
|
return |
|
if kernel_file: |
|
kernels = ctypes.cdll.LoadLibrary(kernel_file) |
|
self.int8WeightExtractionFloat = kernels.extract_int8_weight_to_float |
|
self.int4WeightExtractionFloat = kernels.extract_int4_weight_to_float |
|
self.int4WeightCompression = kernels.compress_int4_weight |
|
if compile_parallel_kernel: |
|
try: |
|
self.SetNumThreads = kernels.set_num_threads |
|
except: |
|
print("No set_num_threads() found in kernel.") |
|
self.SetNumThreads = lambda x: x |
|
self.load = True |
|
print("Load kernel :", kernel_file) |
|
else: |
|
print("Failed to load kernel.") |
|
|
|
if compile_parallel_kernel: |
|
if parallel_num is None: |
|
parallel_num = max(os.cpu_count() // 2, 1) |
|
print("Setting CPU quantization kernel threads to", parallel_num) |
|
if parallel_num < 4: |
|
print("Parallel kernel is not recommended when parallel num < 4.") |
|
self.SetNumThreads(parallel_num) |
|
|
|
self.parallel_num = parallel_num |
|
|
|
|
|
def compress_int4_weight(weight: torch.Tensor): |
|
"""compress weight on cpu or cuda to int4""" |
|
if weight.device == torch.device("cpu"): |
|
assert isinstance(cpu_kernels, CPUKernel) |
|
n, m = weight.size(0), weight.size(1) |
|
assert m % 2 == 0 |
|
m = m // 2 |
|
out = torch.empty(n, m, dtype=torch.int8, device="cpu") |
|
cpu_kernels.int4WeightCompression( |
|
ctypes.c_void_p(weight.data_ptr()), |
|
ctypes.c_void_p(out.data_ptr()), |
|
ctypes.c_int32(n), |
|
ctypes.c_int32(m) |
|
) |
|
return out |
|
else: |
|
with torch.cuda.device(weight.device): |
|
n, m = weight.size(0), weight.size(1) |
|
assert m % 2 == 0 |
|
m = m // 2 |
|
out = torch.empty(n, m, dtype=torch.int8, device="cuda") |
|
stream = torch.cuda.current_stream() |
|
|
|
gridDim = (n, 1, 1) |
|
blockDim = (min(round_up(m, 32), 1024), 1, 1) |
|
|
|
kernels.int4WeightCompression( |
|
gridDim, |
|
blockDim, |
|
0, |
|
stream, |
|
[ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], |
|
) |
|
return out |
|
|
|
|
|
def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): |
|
if source_bit_width == 8: |
|
func = kernels.int8WeightExtractionHalf |
|
elif source_bit_width == 4: |
|
func = kernels.int4WeightExtractionHalf |
|
else: |
|
assert False, "Unsupported bit-width" |
|
|
|
with torch.cuda.device(weight.device): |
|
n, m = weight.size(0), weight.size(1) |
|
out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.half, device="cuda") |
|
stream = torch.cuda.current_stream() |
|
|
|
gridDim = (n, 1, 1) |
|
blockDim = (min(round_up(m, 32), 1024), 1, 1) |
|
|
|
func( |
|
gridDim, |
|
blockDim, |
|
0, |
|
stream, |
|
[ |
|
ctypes.c_void_p(weight.data_ptr()), |
|
ctypes.c_void_p(scale_list.data_ptr()), |
|
ctypes.c_void_p(out.data_ptr()), |
|
ctypes.c_int32(n), |
|
ctypes.c_int32(m), |
|
], |
|
) |
|
return out |
|
|
|
|
|
def extract_weight_to_float(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int, quantization_cache=None): |
|
"""extract weight on cpu to float32""" |
|
if source_bit_width == 8: |
|
func = cpu_kernels.int8WeightExtractionFloat |
|
elif source_bit_width == 4: |
|
func = cpu_kernels.int4WeightExtractionFloat |
|
else: |
|
assert False, "Unsupported bit-width" |
|
|
|
n, m = weight.size(0), weight.size(1) |
|
|
|
if quantization_cache is not None: |
|
out = quantization_cache |
|
func( |
|
ctypes.c_void_p(weight.data_ptr()), |
|
ctypes.c_void_p(scale_list.data_ptr()), |
|
ctypes.c_void_p(out.data_ptr()), |
|
ctypes.c_int32(n), |
|
ctypes.c_int32(m) |
|
) |
|
return out.tensor |
|
else: |
|
out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.float, device="cpu") |
|
func( |
|
ctypes.c_void_p(weight.data_ptr()), |
|
ctypes.c_void_p(scale_list.data_ptr()), |
|
ctypes.c_void_p(out.data_ptr()), |
|
ctypes.c_int32(n), |
|
ctypes.c_int32(m) |
|
) |
|
return out |
|
|
|
|
|
class CacheTensor(): |
|
def __init__(self, *args, **kwargs): |
|
self.tensor = torch.empty(*args, **kwargs) |
|
|
|
def to(self, *args, **kwargs): |
|
self.tensor = self.tensor.to(*args, **kwargs) |
|
|
|
def data_ptr(self): |
|
return self.tensor.data_ptr() |
|
|
|
|
|
class QuantizedLinear(Linear): |
|
def __init__(self, weight_bit_width: int, weight_tensor=None, bias_tensor=None, quantized_weight=None, quantized_weight_scale=None, quantization_cache=None, empty_init=False, *args, **kwargs): |
|
super(QuantizedLinear, self).__init__(*args, **kwargs) |
|
self.weight_bit_width = weight_bit_width |
|
self.quantization_cache = quantization_cache |
|
|
|
if (quantized_weight is not None) and (quantized_weight_scale is not None): |
|
del self.weight |
|
self.weight = Parameter(quantized_weight.to(kwargs["device"]), requires_grad=False) |
|
self.weight_scale = Parameter(quantized_weight_scale.to(kwargs["device"]), requires_grad=False) |
|
else: |
|
shape = self.weight.shape |
|
del self.weight |
|
|
|
if weight_tensor is None or empty_init: |
|
self.weight = torch.empty( |
|
shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"] |
|
) |
|
self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"]) |
|
else: |
|
self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).to(kwargs["dtype"]) |
|
self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8) |
|
if weight_bit_width == 4: |
|
self.weight = compress_int4_weight(self.weight) |
|
|
|
self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False) |
|
self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False) |
|
|
|
if bias_tensor is not None: |
|
self.bias = Parameter(bias_tensor.to(kwargs["device"]), requires_grad=False) |
|
else: |
|
self.bias = None |
|
|
|
def reset_parameters(self): |
|
"""To accelerate initialization""" |
|
pass |
|
|
|
def forward(self, input): |
|
if self.weight.device == torch.device("cpu"): |
|
output = W8A16LinearCPU.apply(input, self.weight, self.weight_scale, self.weight_bit_width, self.quantization_cache) |
|
else: |
|
output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) |
|
if self.bias is not None: |
|
output = output + self.bias |
|
return output |
|
|
|
def _apply(self, fn): |
|
self_obj = super()._apply(fn) |
|
if self.quantization_cache is not None: |
|
self.quantization_cache.to(self_obj.weight.device) |
|
self.quantization_cache.to(self_obj.weight_scale.dtype) |
|
return self_obj |
|
|
|
|
|
class QuantizedEmbedding(Embedding): |
|
def __init__(self, weight_bit_width: int, weight_tensor=None, quantized_weight=None, quantized_weight_scale=None, empty_init=False, *args, **kwargs): |
|
super(QuantizedEmbedding, self).__init__(*args, **kwargs) |
|
self.weight_bit_width = weight_bit_width |
|
|
|
if (quantized_weight is not None) and (quantized_weight_scale is not None): |
|
del self.weight |
|
self.weight = Parameter(quantized_weight.to(kwargs["device"]), requires_grad=False) |
|
self.weight_scale = Parameter(quantized_weight_scale.to(kwargs["device"]), requires_grad=False) |
|
else: |
|
shape = self.weight.shape |
|
del self.weight |
|
|
|
if weight_tensor is None or empty_init: |
|
self.weight = torch.empty( |
|
shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"] |
|
) |
|
self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"]) |
|
else: |
|
self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).half() |
|
self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8) |
|
if weight_bit_width == 4: |
|
self.weight = compress_int4_weight(self.weight) |
|
|
|
self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False) |
|
self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False) |
|
|
|
def forward(self, input): |
|
if self.weight.device == torch.device("cpu"): |
|
original_weight = extract_weight_to_float(weight=self.weight, scale_list=self.weight_scale, source_bit_width=self.weight_bit_width) |
|
else: |
|
original_weight = extract_weight_to_half(weight=self.weight, scale_list=self.weight_scale, source_bit_width=self.weight_bit_width) |
|
output = F.embedding( |
|
input, original_weight, self.padding_idx, self.max_norm, |
|
self.norm_type, self.scale_grad_by_freq, self.sparse |
|
) |
|
return output |
|
|
|
|
|
def load_cpu_kernel(**kwargs): |
|
global cpu_kernels |
|
cpu_kernels = CPUKernel(**kwargs) |
|
assert cpu_kernels.load |
|
|
|
|
|
def quantize(model, weight_bit_width, use_quantization_cache=False, empty_init=False, **kwargs): |
|
"""Replace fp16 linear with quantized linear""" |
|
|
|
query_key_value_quantization_cache = None |
|
dense_quantization_cache = None |
|
dense_h_to_4h_quantization_cache = None |
|
dense_4h_to_h_quantization_cache = None |
|
|
|
try: |
|
load_cpu_kernel(**kwargs) |
|
except: |
|
print("Cannot load cpu kernel, don't use quantized model on cpu.") |
|
if kernels is None: |
|
print("Cannot load cuda kernel, quantization failed.") |
|
return model |
|
|
|
current_device = model.device |
|
|
|
if model.device == torch.device("cpu"): |
|
dtype=torch.float32 |
|
else: |
|
dtype = torch.half |
|
|
|
QuantizedLinearWithPara = partial( |
|
QuantizedLinear, |
|
weight_bit_width=weight_bit_width, |
|
bias=True, |
|
dtype=dtype, |
|
empty_init=empty_init |
|
) |
|
|
|
if use_quantization_cache: |
|
print("Using quantization cache") |
|
layer = model.layers[0] |
|
weight = layer.attention.query_key_value.weight |
|
n, m = weight.size(0), weight.size(1) |
|
query_key_value_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False) |
|
weight = layer.attention.dense.weight |
|
n, m = weight.size(0), weight.size(1) |
|
dense_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False) |
|
weight = layer.mlp.dense_h_to_4h.weight |
|
n, m = weight.size(0), weight.size(1) |
|
dense_h_to_4h_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False) |
|
weight = layer.mlp.dense_4h_to_h.weight |
|
n, m = weight.size(0), weight.size(1) |
|
dense_4h_to_h_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False) |
|
|
|
print("Applying quantization to glm layers") |
|
|
|
for layer in model.layers: |
|
layer.attention.query_key_value = QuantizedLinearWithPara( |
|
weight_tensor=layer.attention.query_key_value.weight.to(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, |
|
device=layer.attention.query_key_value.weight.device, |
|
quantization_cache=query_key_value_quantization_cache |
|
) |
|
layer.attention.dense = QuantizedLinearWithPara( |
|
weight_tensor=layer.attention.dense.weight.to(current_device), |
|
bias_tensor=layer.attention.dense.bias, |
|
in_features=layer.attention.dense.in_features, |
|
out_features=layer.attention.dense.out_features, |
|
device=layer.attention.dense.weight.device, |
|
quantization_cache=dense_quantization_cache |
|
) |
|
layer.mlp.dense_h_to_4h = QuantizedLinearWithPara( |
|
weight_tensor=layer.mlp.dense_h_to_4h.weight.to(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, |
|
device=layer.mlp.dense_h_to_4h.weight.device, |
|
quantization_cache=dense_h_to_4h_quantization_cache |
|
) |
|
layer.mlp.dense_4h_to_h = QuantizedLinearWithPara( |
|
weight_tensor=layer.mlp.dense_4h_to_h.weight.to(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, |
|
device=layer.mlp.dense_4h_to_h.weight.device, |
|
quantization_cache=dense_4h_to_h_quantization_cache |
|
) |
|
return model |
|
|
