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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

config.json

@@ -0,0 +1,37 @@
+{
+    "_name_or_path": "bigscience/bloomz-3b",
+    "apply_residual_connection_post_layernorm": false,
+    "architectures": [
+      "BloomForCausalLM"
+    ],
+    "auto_map": {
+      "AutoModel": "modeling_chatsakura.SakuraForCausalLM",
+      "AutoModelForCausalLM": "modeling_chatsakura.SakuraForCausalLM"
+    },
+    "attention_dropout": 0.0,
+    "attention_softmax_in_fp32": true,
+    "bias_dropout_fusion": true,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_dropout": 0.0,
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "layer_norm_epsilon": 1e-05,
+    "masked_softmax_fusion": true,
+    "model_type": "bloom",
+    "n_head": 32,
+    "n_inner": null,
+    "n_layer": 30,
+    "offset_alibi": 100,
+    "pad_token_id": 3,
+    "pretraining_tp": 4,
+    "seq_length": 2048,
+    "skip_bias_add": true,
+    "skip_bias_add_qkv": false,
+    "slow_but_exact": false,
+    "torch_dtype": "float16",
+    "transformers_version": "4.27.1",
+    "unk_token_id": 0,
+    "use_cache": true,
+    "vocab_size": 250880
+  }

gptq.py

@@ -0,0 +1,163 @@
+import math
+import time
+
+import torch
+import torch.nn as nn
+import transformers
+
+from quant import *
+
+
+DEBUG = False
+
+torch.backends.cuda.matmul.allow_tf32 = False
+torch.backends.cudnn.allow_tf32 = False
+
+
+class GPTQ:
+    def __init__(self, layer):
+        self.layer = layer
+        self.dev = self.layer.weight.device
+        W = layer.weight.data.clone()
+        if isinstance(self.layer, nn.Conv2d):
+            W = W.flatten(1)
+        if isinstance(self.layer, transformers.Conv1D):
+            W = W.t()
+        self.rows = W.shape[0]
+        self.columns = W.shape[1]
+        self.H = torch.zeros((self.columns, self.columns), device=self.dev)
+        self.nsamples = 0
+
+    def add_batch(self, inp, out):
+        if DEBUG:
+            self.inp1 = inp
+            self.out1 = out
+        if len(inp.shape) == 2:
+            inp = inp.unsqueeze(0)
+        tmp = inp.shape[0]
+        if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
+            if len(inp.shape) == 3:
+                inp = inp.reshape((-1, inp.shape[-1]))
+            inp = inp.t()
+        if isinstance(self.layer, nn.Conv2d):
+            unfold = nn.Unfold(
+                self.layer.kernel_size,
+                dilation=self.layer.dilation,
+                padding=self.layer.padding,
+                stride=self.layer.stride
+            )
+            inp = unfold(inp)
+            inp = inp.permute([1, 0, 2])
+            inp = inp.flatten(1)
+        self.H *= self.nsamples / (self.nsamples + tmp)
+        self.nsamples += tmp
+        # inp = inp.float()
+        inp = math.sqrt(2 / self.nsamples) * inp.float()
+        # self.H += 2 / self.nsamples * inp.matmul(inp.t())
+        self.H += inp.matmul(inp.t())
+
+    def fasterquant(
+        self, blocksize=128, percdamp=.01, groupsize=-1
+    ):
+        W = self.layer.weight.data.clone()
+        if isinstance(self.layer, nn.Conv2d):
+            W = W.flatten(1)
+        if isinstance(self.layer, transformers.Conv1D):
+            W = W.t()
+        W = W.float()
+
+        tick = time.time()
+
+        if not self.quantizer.ready():
+            self.quantizer.find_params(W, weight=True)
+
+        H = self.H
+        del self.H
+        dead = torch.diag(H) == 0
+        H[dead, dead] = 1
+        W[:, dead] = 0
+
+        Losses = torch.zeros_like(W)
+        Q = torch.zeros_like(W)
+
+        damp = percdamp * torch.mean(torch.diag(H))
+        diag = torch.arange(self.columns, device=self.dev)
+        H[diag, diag] += damp
+        H = torch.linalg.cholesky(H)
+        H = torch.cholesky_inverse(H)
+        H = torch.linalg.cholesky(H, upper=True)
+        Hinv = H
+
+        scale = []
+        zero = []
+        now_idx = 1
+
+        for i1 in range(0, self.columns, blocksize):
+            i2 = min(i1 + blocksize, self.columns)
+            count = i2 - i1
+
+            W1 = W[:, i1:i2].clone()
+            Q1 = torch.zeros_like(W1)
+            Err1 = torch.zeros_like(W1)
+            Losses1 = torch.zeros_like(W1)
+            Hinv1 = Hinv[i1:i2, i1:i2]
+
+            for i in range(count):
+                w = W1[:, i]
+                d = Hinv1[i, i]
+
+                if groupsize != -1:
+                    if (i1 + i) % groupsize == 0:
+                        self.quantizer.find_params(W[:, (i1 + i):(i1 + i + groupsize)], weight=True)
+
+                    if ((i1 + i) // groupsize) - now_idx == -1:
+                        scale.append(self.quantizer.scale)
+                        zero.append(self.quantizer.zero)
+                        now_idx += 1
+
+                q = quantize(
+                    w.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq
+                ).flatten()
+                Q1[:, i] = q
+                Losses1[:, i] = (w - q) ** 2 / d ** 2
+
+                err1 = (w - q) / d
+                W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
+                Err1[:, i] = err1
+
+            Q[:, i1:i2] = Q1
+            Losses[:, i1:i2] = Losses1 / 2
+
+            W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
+
+            if DEBUG:
+                self.layer.weight.data[:, :i2] = Q[:, :i2]
+                self.layer.weight.data[:, i2:] = W[:, i2:]
+                print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
+                print(torch.sum(Losses))
+
+        torch.cuda.synchronize()
+        print('time %.2f' % (time.time() - tick))
+        print('error', torch.sum(Losses).item())
+
+        if isinstance(self.layer, transformers.Conv1D):
+            Q = Q.t()
+        self.layer.weight.data = Q.reshape(self.layer.weight.shape).to(self.layer.weight.data.dtype)
+        if DEBUG:
+            print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
+
+        if scale == []:
+            scale.append(self.quantizer.scale)
+            zero.append(self.quantizer.zero)
+        scale = torch.cat(scale,dim=1)
+        zero = torch.cat(zero,dim=1)
+        return scale,zero
+
+    def free(self):
+        if DEBUG:
+            self.inp1 = None
+            self.out1 = None
+        self.H = None
+        self.Losses = None
+        self.Trace = None
+        torch.cuda.empty_cache()

modeling_chatsakura.py

@@ -0,0 +1,26 @@
+import torch
+import torch.nn as nn
+
+from gptq import *
+from modelutils import *
+from quant import *
+from transformers import BloomForCausalLM as LM
+
+class SakuraForCausalLM(LM):
+    def __init__(self,*args,**kwargs):
+        def noop(*args, **kwargs):
+            pass
+        torch.nn.init.kaiming_uniform_ = noop
+        torch.nn.init.uniform_ = noop
+        torch.nn.init.normal_ = noop
+        torch.set_default_dtype(torch.half)
+        transformers.modeling_utils._init_weights = False
+        torch.set_default_dtype(torch.half)
+        super().__init__(*args,**kwargs)
+        torch.set_default_dtype(torch.float)
+        self.eval()
+        layers = find_layers(self)
+        for name in ['lm_head']:
+            if name in layers:
+                del layers[name]
+        make_quant(self, layers, 8, 128)

modelutils.py

@@ -0,0 +1,11 @@
+import torch.nn as nn
+
+def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
+    if type(module) in layers:
+        return {name: module}
+    res = {}
+    for name1, child in module.named_children():
+        res.update(find_layers(
+            child, layers=layers, name=name + '.' + name1 if name != '' else name1
+        ))
+    return res

quant.py

@@ -0,0 +1,305 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import math
+
+def quantize(x, scale, zero, maxq):
+    q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
+    return scale * (q - zero)
+
+class Quantizer(nn.Module):
+
+    def __init__(self, shape=1):
+        super(Quantizer, self).__init__()
+        self.register_buffer('maxq', torch.tensor(0))
+        self.register_buffer('scale', torch.zeros(shape))
+        self.register_buffer('zero', torch.zeros(shape))
+
+    def configure(
+            self,
+            bits, perchannel=False, sym=True,
+            mse=False, norm=2.4, grid=100, maxshrink=.8
+        ):
+        self.maxq = torch.tensor(2 ** bits - 1)
+        self.perchannel = perchannel
+        self.sym = sym
+        self.mse = mse
+        self.norm = norm
+        self.grid = grid
+        self.maxshrink = maxshrink
+
+    def find_params(self, x, weight=False):
+        dev = x.device
+        self.maxq = self.maxq.to(dev)
+
+        shape = x.shape
+        if self.perchannel:
+            if weight:
+                x = x.flatten(1)
+            else:
+                if len(shape) == 4:
+                    x = x.permute([1, 0, 2, 3])
+                    x = x.flatten(1)
+                if len(shape) == 3:
+                    x = x.reshape((-1, shape[-1])).t()
+                if len(shape) == 2:
+                    x = x.t()
+        else:
+            x = x.flatten().unsqueeze(0)
+
+        tmp = torch.zeros(x.shape[0], device=dev)
+        xmin = torch.minimum(x.min(1)[0], tmp)
+        xmax = torch.maximum(x.max(1)[0], tmp)
+
+        if self.sym:
+            xmax = torch.maximum(torch.abs(xmin), xmax)
+            tmp = xmin < 0
+            if torch.any(tmp):
+                xmin[tmp] = -xmax[tmp]
+        tmp = (xmin == 0) & (xmax == 0)
+        xmin[tmp] = -1
+        xmax[tmp] = +1
+
+        self.scale = (xmax - xmin) / self.maxq
+        if self.sym:
+            self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
+        else:
+            self.zero = torch.round(-xmin / self.scale)
+
+        if self.mse:
+            best = torch.full([x.shape[0]], float('inf'), device=dev)
+            for i in range(int(self.maxshrink * self.grid)):
+                p = 1 - i / self.grid
+                xmin1 = p * xmin
+                xmax1 = p * xmax
+                scale1 = (xmax1 - xmin1) / self.maxq
+                zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
+                q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
+                q -= x
+                q.abs_()
+                q.pow_(self.norm)
+                err = torch.sum(q, 1)
+                tmp = err < best
+                if torch.any(tmp):
+                    best[tmp] = err[tmp]
+                    self.scale[tmp] = scale1[tmp]
+                    self.zero[tmp] = zero1[tmp]
+        if not self.perchannel:
+            if weight:
+                tmp = shape[0]
+            else:
+                tmp = shape[1] if len(shape) != 3 else shape[2]
+            self.scale = self.scale.repeat(tmp)
+            self.zero = self.zero.repeat(tmp)
+
+        if weight:
+            shape = [-1] + [1] * (len(shape) - 1)
+            self.scale = self.scale.reshape(shape)
+            self.zero = self.zero.reshape(shape)
+            return
+        if len(shape) == 4:
+            self.scale = self.scale.reshape((1, -1, 1, 1))
+            self.zero = self.zero.reshape((1, -1, 1, 1))
+        if len(shape) == 3:
+            self.scale = self.scale.reshape((1, 1, -1))
+            self.zero = self.zero.reshape((1, 1, -1))
+        if len(shape) == 2:
+            self.scale = self.scale.unsqueeze(0)
+            self.zero = self.zero.unsqueeze(0)
+
+    def quantize(self, x):
+        if self.ready():
+            return quantize(x, self.scale, self.zero, self.maxq)
+        return x
+
+    def enabled(self):
+        return self.maxq > 0
+
+    def ready(self):
+        return torch.all(self.scale != 0)
+
+
+try:
+    import quant_cuda
+except:
+    import os
+    import sys
+    argv = sys.argv
+    sys.argv = ['quant.py','install']
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    from setuptools import setup, Extension
+    from torch.utils import cpp_extension
+    os.chdir(dir_path)
+    setup(
+        name='quant_cuda',
+        ext_modules=[cpp_extension.CUDAExtension(
+            'quant_cuda', ['quant_cuda.cpp', 'quant_cuda_kernel.cu']
+        )],
+        cmdclass={'build_ext': cpp_extension.BuildExtension}
+    )
+    os.chdir(os.getcwd())
+    sys.argv = argv
+    for i in sys.path:
+        if i.endswith("site-packages"):
+            for j in os.listdir(i):
+                if j.find("quant_cuda") != -1:
+                    sys.path.append(os.path.join(i,j))
+                    break
+            break
+    import quant_cuda
+
+
+# Assumes layer is perfectly divisible into 256 * 256 blocks
+class QuantLinear(nn.Module):
+    def __init__(self, bits, groupsize, infeatures, outfeatures):
+        super().__init__()
+        if bits not in [2,3,4,8]:
+            raise NotImplementedError("Only 2,3,4,8 bits are supported.")
+        self.infeatures = infeatures
+        self.outfeatures = outfeatures
+        self.bits = bits
+        if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2,int(math.log2(groupsize)))):
+            raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)")
+        groupsize = groupsize if groupsize != -1 else infeatures
+        self.groupsize = groupsize
+        self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures/groupsize),outfeatures // 256 * (bits * 8)), dtype=torch.int))
+        self.register_buffer('scales', torch.zeros((math.ceil(infeatures/groupsize),outfeatures)))
+        self.register_buffer('bias', torch.zeros(outfeatures))
+        self.register_buffer(
+            'qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
+        )
+        self._initialized_quant_state = False
+
+    def pack(self, linear, scales, zeros):
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        self.scales = scales.clone()
+        if linear.bias is not None:
+            self.bias = linear.bias.clone()
+
+        intweight = []
+        for idx in range(self.infeatures):
+            g_idx = idx // self.groupsize
+            intweight.append(torch.round((linear.weight.data[:,idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:,None])
+        intweight = torch.cat(intweight,dim=1)
+        intweight = intweight.t().contiguous()
+        intweight = intweight.numpy().astype(np.uint32)
+        qweight = np.zeros(
+            (intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32
+        )
+        i = 0
+        row = 0
+        while row < qweight.shape[0]:
+            if self.bits in [2,4,8]:
+                for j in range(i, i + (32//self.bits)):
+                    qweight[row] |= intweight[j] << (self.bits * (j - i))
+                i += 32//self.bits
+                row += 1
+            elif self.bits == 3:
+                for j in range(i, i + 10):
+                    qweight[row] |= intweight[j] << (3 * (j - i))
+                i += 10
+                qweight[row] |= intweight[i] << 30
+                row += 1
+                qweight[row] |= (intweight[i] >> 2) & 1
+                i += 1
+                for j in range(i, i + 10):
+                    qweight[row] |= intweight[j] << (3 * (j - i) + 1)
+                i += 10
+                qweight[row] |= intweight[i] << 31
+                row += 1
+                qweight[row] |= (intweight[i] >> 1) & 0x3
+                i += 1
+                for j in range(i, i + 10):
+                    qweight[row] |= intweight[j] << (3 * (j - i) + 2)
+                i += 10
+                row += 1
+            else:
+                raise NotImplementedError("Only 2,3,4,8 bits are supported.")
+
+        qweight = qweight.astype(np.int32)
+        self.qweight = torch.from_numpy(qweight)
+
+        zeros -= 1;
+        zeros = zeros.numpy().astype(np.uint32)
+        qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 256 * (self.bits * 8)), dtype=np.uint32)
+        i = 0
+        col = 0
+        while col < qzeros.shape[1]:
+            if self.bits in [2,4,8]:
+                for j in range(i, i + (32//self.bits)):
+                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
+                i += 32//self.bits
+                col += 1
+            elif self.bits == 3:
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
+                i += 10
+                qzeros[:, col] |= zeros[:, i] << 30
+                col += 1
+                qzeros[:, col] |= (zeros[:, i] >> 2) & 1
+                i += 1
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
+                i += 10
+                qzeros[:, col] |= zeros[:, i] << 31
+                col += 1
+                qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
+                i += 1
+                for j in range(i, i + 10):
+                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
+                i += 10
+                col += 1
+            else:
+                raise NotImplementedError("Only 2,3,4,8 bits are supported.")
+
+        qzeros = qzeros.astype(np.int32)
+        self.qzeros = torch.from_numpy(qzeros)
+
+    def forward(self, x):
+        intermediate_dtype = torch.float32
+
+        if not self._initialized_quant_state:
+            # Do we even have a bias? Check for at least one non-zero element.
+            if self.bias is not None and bool(torch.any(self.bias != 0)):
+                # Then make sure it's the right type.
+                self.bias.data = self.bias.data.to(intermediate_dtype)
+            else:
+                self.bias = None
+
+        outshape = list(x.shape)
+        outshape[-1] = self.outfeatures
+        x = x.reshape(-1, x.shape[-1])
+        if self.bias is None:
+            y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device)
+        else:
+            y = self.bias.clone().repeat(x.shape[0], 1)
+
+        output_dtype = x.dtype
+        x = x.to(intermediate_dtype)
+        if self.bits == 2:
+            quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
+        elif self.bits == 3:
+            quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
+        elif self.bits == 4:
+            quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
+        elif self.bits == 8:
+            quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
+        else:
+            raise NotImplementedError("Only 2,3,4,8 bits are supported.")
+        y = y.to(output_dtype)
+        return y.reshape(outshape)
+
+def make_quant(module, names, bits, groupsize, name=''):
+    if isinstance(module, QuantLinear):
+        return
+    for attr in dir(module):
+        tmp = getattr(module, attr)
+        name1 = name + '.' + attr if name != '' else attr
+        if name1 in names:
+            setattr(
+                module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features)
+            )
+    for name1, child in module.named_children():
+        make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)

quant_cuda.cpp

@@ -0,0 +1,70 @@
+#include <torch/all.h>
+#include <torch/python.h>
+#include <c10/cuda/CUDAGuard.h>
+
+void vecquant2matmul_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+);
+
+void vecquant2matmul(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant2matmul_cuda(vec, mat, mul, scales, zeros,groupsize);
+}
+
+void vecquant3matmul_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+);
+
+void vecquant3matmul(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant3matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
+}
+
+void vecquant4matmul_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+);
+
+void vecquant4matmul(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant4matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
+}
+
+void vecquant8matmul_cuda(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+);
+
+void vecquant8matmul(
+  torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
+  torch::Tensor scales, torch::Tensor zeros,
+  int groupsize
+) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
+  vecquant8matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("vecquant2matmul", &vecquant2matmul, "Vector 2-bit Quantized Matrix Multiplication (CUDA)");
+  m.def("vecquant3matmul", &vecquant3matmul, "Vector 3-bit Quantized Matrix Multiplication (CUDA)");
+  m.def("vecquant4matmul", &vecquant4matmul, "Vector 4-bit Quantized Matrix Multiplication (CUDA)");
+  m.def("vecquant8matmul", &vecquant8matmul, "Vector 8-bit Quantized Matrix Multiplication (CUDA)");
+}

quant_cuda_kernel.cu

@@ -0,0 +1,524 @@
+#include <torch/all.h>
+#include <torch/python.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+// atomicAdd for double-precision floating-point numbers on hardware with
+// compute capability < 6.0 from:
+// https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#atomic-functions
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600
+__device__ double atomicAdd(
+    double* address,
+    double val
+) {
+  unsigned long long int* address_as_ull = (unsigned long long int*)address;
+  unsigned long long int old = *address_as_ull, assumed;
+
+  do {
+    assumed = old;
+    old = atomicCAS(
+      address_as_ull,
+      assumed,
+      __double_as_longlong(val + __longlong_as_double(assumed))
+    );
+
+  // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
+  } while (assumed != old);
+
+  return __longlong_as_double(old);
+}
+#endif
+
+template <typename scalar_t>
+__global__ void VecQuant2MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const               int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+);
+
+template <typename scalar_t>
+__global__ void VecQuant3MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+);
+
+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       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+);
+
+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       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+);
+
+const int BLOCKWIDTH  = 256;
+const int BLOCKHEIGHT2 =  16;
+const int BLOCKHEIGHT3 =  24;
+const int BLOCKHEIGHT4 =  32;
+const int BLOCKHEIGHT8 =  64;
+
+__device__ inline unsigned int as_unsigned(int i) {
+  return *reinterpret_cast<unsigned int*>(&i);
+}
+
+void vecquant2matmul_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros,
+  int groupsize
+) {
+  int batch = vec.size(0);
+  int vec_height = vec.size(1);
+  int height = mat.size(0);
+  int width = mat.size(1);
+  int zero_width = zeros.size(1);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT2 - 1) / BLOCKHEIGHT2,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    batch
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant2matmul_cuda", ([&] {
+      VecQuant2MatMulKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, vec_height, height, width, zero_width, groupsize
+      );
+    })
+  );
+}
+
+template <typename scalar_t>
+__global__ void VecQuant2MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+) {
+  int b = blockIdx.z;
+  int h = BLOCKHEIGHT2 * blockIdx.x;
+  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
+  __syncthreads();
+
+  scalar_t res = 0;
+  int i = width * h + w;
+  int g_h = h * 16;
+  int k = 0;
+
+  int z_w = w / 16;
+  int z_mod = (w % 16) * 2;
+
+  unsigned int tmp;
+
+  while (k < BLOCKWIDTH) {
+    tmp = as_unsigned(mat[i]);
+
+    int g = (g_h + k) / groupsize;
+    scalar_t scale = scales[g * width + w];
+    scalar_t zero = scale * scalar_t((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod & 0x3) + 1);
+
+    res += (scale * scalar_t((tmp >> 0) & 0x3) - zero) * blockvec[k + 0];
+    res += (scale * scalar_t((tmp >> 2) & 0x3) - zero) * blockvec[k + 1];
+    res += (scale * scalar_t((tmp >> 4) & 0x3) - zero) * blockvec[k + 2];
+    res += (scale * scalar_t((tmp >> 6) & 0x3) - zero) * blockvec[k + 3];
+    res += (scale * scalar_t((tmp >> 8) & 0x3) - zero) * blockvec[k + 4];
+    res += (scale * scalar_t((tmp >> 10) & 0x3) - zero) * blockvec[k + 5];
+    res += (scale * scalar_t((tmp >> 12) & 0x3) - zero) * blockvec[k + 6];
+    res += (scale * scalar_t((tmp >> 14) & 0x3) - zero) * blockvec[k + 7];
+    res += (scale * scalar_t((tmp >> 16) & 0x3) - zero) * blockvec[k + 8];
+    res += (scale * scalar_t((tmp >> 18) & 0x3) - zero) * blockvec[k + 9];
+    res += (scale * scalar_t((tmp >> 20) & 0x3) - zero) * blockvec[k + 10];
+    res += (scale * scalar_t((tmp >> 22) & 0x3) - zero) * blockvec[k + 11];
+    res += (scale * scalar_t((tmp >> 24) & 0x3) - zero) * blockvec[k + 12];
+    res += (scale * scalar_t((tmp >> 26) & 0x3) - zero) * blockvec[k + 13];
+    res += (scale * scalar_t((tmp >> 28) & 0x3) - zero) * blockvec[k + 14];
+    res += (scale * scalar_t((tmp >> 30) & 0x3) - zero) * blockvec[k + 15];
+
+    i += width;
+    k += 16;
+  }
+
+  atomicAdd(&mul[b * width + w], res);
+}
+
+void vecquant3matmul_cuda(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor scales,
+  torch::Tensor zeros,
+  int groupsize
+) {
+  int batch = vec.size(0);
+  int vec_height = vec.size(1);
+  int height = mat.size(0);
+  int width = mat.size(1);
+  int zero_width = zeros.size(1);
+
+  dim3 blocks(
+    (height + BLOCKHEIGHT3 - 1) / BLOCKHEIGHT3,
+    (width + BLOCKWIDTH - 1) / BLOCKWIDTH,
+    batch
+  );
+  dim3 threads(BLOCKWIDTH);
+
+  AT_DISPATCH_FLOATING_TYPES(
+    vec.type(), "vecquant3matmul_cuda", ([&] {
+      VecQuant3MatMulKernel<<<blocks, threads>>>(
+        vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
+        scales.data<scalar_t>(), zeros.data<int>(),
+        batch, vec_height, height, width, zero_width, groupsize
+      );
+    })
+  );
+}
+
+template <typename scalar_t>
+__global__ void VecQuant3MatMulKernel(
+    const  scalar_t* __restrict__ vec,
+    const       int* __restrict__ mat,
+           scalar_t* __restrict__ mul,
+    const  scalar_t* __restrict__ scales,
+    const       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+) {
+  int b = blockIdx.z;
+  int h = BLOCKHEIGHT3 * blockIdx.x;
+  int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
+
+  __shared__ scalar_t blockvec[BLOCKWIDTH];
+  blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
+  __syncthreads();
+
+  scalar_t res = 0;
+  int i = width * h + w;
+  int g_h = (h / 3) * 32;
+  int k = 0;
+
+  int z_w = (w / 32) * 3; // ((w / 256) * 24) / 3
+  int z_mod = w % 32;
+  int z_bit;
+
+  if (z_mod != 10){
+    if (z_mod != 21){
+      z_bit = z_mod;
+      if (z_bit > 21){
+        z_bit -= 22;
+        z_bit *= 3;
+        z_bit += 2;
+        z_w += 2;
+      } else if (z_bit > 10){
+        z_bit -= 11;
+        z_bit *= 3;
+        z_bit += 1;
+        z_w += 1;
+      } else {
+        z_bit *= 3;
+      }
+    } else {
+      z_w += 1;
+    }
+  }
+
+  unsigned int tmp1;
+  unsigned int tmp2;
+  unsigned int tmp;
+  unsigned int z_tmp;
+
+  while (k < BLOCKWIDTH) {
+    tmp1 = as_unsigned(mat[i]);
+
+    int g = (g_h + k) / groupsize;
+    scalar_t scale = scales[g * width + w];
+    scalar_t zero;
+    if (z_mod == 10) {
+      z_tmp = (as_unsigned(zeros[g * zero_width + z_w]) >> 30) | ((as_unsigned(zeros[g * zero_width + (z_w + 1)]) << 2) & 0x4);
+      zero = scale * scalar_t((z_tmp) + 1);
+    } else if (z_mod == 21){
+      z_tmp = (as_unsigned(zeros[g * zero_width + z_w]) >> 31) | ((as_unsigned(zeros[g * zero_width + (z_w + 1)]) << 1) & 0x6);
+      zero = scale * scalar_t((z_tmp) + 1);
+    } else {
+      zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_bit) & 0x7) + 1);
+    }
+
+    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;
+    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 groupsize
+) {
+  int batch = vec.size(0);
+  int vec_height = vec.size(1);
+  int height = mat.size(0);
+  int width = mat.size(1);
+  int zero_width = zeros.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<int>(),
+        batch, vec_height, height, width, zero_width, groupsize
+      );
+    })
+  );
+}
+
+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       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+) {
+  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 + blockIdx.x * BLOCKWIDTH + threadIdx.x];
+  __syncthreads();
+
+  scalar_t res = 0;
+  int i = width * h + w;
+  int g_h = h * 8;
+  int k = 0;
+
+  int z_w = w / 8;
+  int z_mod = (w % 8) * 4;
+
+  unsigned int tmp;
+
+  while (k < BLOCKWIDTH) {
+    tmp = as_unsigned(mat[i]);
+
+    int g = (g_h + k) / groupsize;
+    scalar_t scale = scales[g * width + w];
+    scalar_t zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod) & 0xF) + 1);
+
+    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 groupsize
+) {
+  int batch = vec.size(0);
+  int vec_height = vec.size(1);
+  int height = mat.size(0);
+  int width = mat.size(1);
+  int zero_width = zeros.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<int>(),
+        batch, vec_height, height, width, zero_width, groupsize
+      );
+    })
+  );
+}
+
+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       int* __restrict__ zeros,
+    int batch,
+    int vec_height,
+    int height,
+    int width,
+    int zero_width,
+    int groupsize
+) {
+  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 + blockIdx.x * BLOCKWIDTH + threadIdx.x];
+  __syncthreads();
+
+  scalar_t res = 0;
+  int i = width * h + w;
+  int g_h = h * 4;
+  int k = 0;
+
+  int z_w = w / 4;
+  int z_mod = (w % 4) * 8;
+
+  unsigned int tmp;
+
+  while (k < BLOCKWIDTH) {
+    tmp = as_unsigned(mat[i]);
+
+    int g = (g_h + k) / groupsize;
+    scalar_t scale = scales[g * width + w];
+    scalar_t zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod) & 0xFF) + 1);
+
+    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);
+}

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+    "bos_token": "<s>",
+    "eos_token": "</s>",
+    "pad_token": "<pad>",
+    "unk_token": "<unk>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fa39cd4b1500feb205bcce3b9703a4373414cafe4970e0657b413f7ddd2a9d3
+size 14500438

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+    "add_prefix_space": false,
+    "bos_token": "<s>",
+    "eos_token": "</s>",
+    "model_max_length": 2048,
+    "pad_token": "<pad>",
+    "padding_side": "right",
+    "special_tokens_map_file": null,
+    "tokenizer_class": "BloomTokenizer",
+    "unk_token": "<unk>"
+}