[chat] support quant

applications/Chat/coati/quant/__init__.py

@@ -0,0 +1,7 @@
+from .llama_gptq import load_quant as llama_load_quant
+from .utils import low_resource_init
+
+__all__ = [
+    'llama_load_quant',
+    'low_resource_init',
+]

applications/Chat/coati/quant/llama_gptq/__init__.py

@@ -0,0 +1,5 @@
+from .loader import load_quant
+
+__all__ = [
+    'load_quant',
+]

applications/Chat/coati/quant/llama_gptq/loader.py

@@ -0,0 +1,26 @@
+import torch
+import torch.nn as nn
+
+from .model_utils import find_layers
+from .quant import make_quant
+
+
+def load_quant(model: nn.Module, checkpoint: str, wbits: int, groupsize: int):
+    model = model.eval()
+    layers = find_layers(model)
+
+    # ignore lm head
+    layers = find_layers(model)
+    for name in ['lm_head']:
+        if name in layers:
+            del layers[name]
+
+    make_quant(model, layers, wbits, groupsize)
+
+    if checkpoint.endswith('.safetensors'):
+        from safetensors.torch import load_file as safe_load
+        model.load_state_dict(safe_load(checkpoint))
+    else:
+        model.load_state_dict(torch.load(checkpoint))
+
+    return model

applications/Chat/coati/quant/llama_gptq/model_utils.py

@@ -0,0 +1,13 @@
+# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/modelutils.py
+
+import torch
+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

applications/Chat/coati/quant/llama_gptq/quant.py

@@ -0,0 +1,283 @@
+# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/quant.py
+
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+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:
+    print('CUDA extension not installed.')
+
+# 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)

applications/Chat/coati/quant/utils.py

@@ -0,0 +1,28 @@
+from contextlib import contextmanager
+
+import torch
+
+
+def _noop(*args, **kwargs):
+    pass
+
+
+@contextmanager
+def low_resource_init():
+    """This context manager disables weight initialization and sets the default float dtype to half.
+    """
+    old_kaiming_uniform_ = torch.nn.init.kaiming_uniform_
+    old_uniform_ = torch.nn.init.uniform_
+    old_normal_ = torch.nn.init.normal_
+    dtype = torch.get_default_dtype()
+    try:
+        torch.nn.init.kaiming_uniform_ = _noop
+        torch.nn.init.uniform_ = _noop
+        torch.nn.init.normal_ = _noop
+        torch.set_default_dtype(torch.half)
+        yield
+    finally:
+        torch.nn.init.kaiming_uniform_ = old_kaiming_uniform_
+        torch.nn.init.uniform_ = old_uniform_
+        torch.nn.init.normal_ = old_normal_
+        torch.set_default_dtype(dtype)

applications/Chat/coati/ray/utils.py

@@ -75,6 +75,10 @@ def get_strategy_from_args(strategy: str):
         strategy_ = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5)
     elif strategy == 'colossalai_zero2':
         strategy_ = ColossalAIStrategy(stage=2, placement_policy='cuda')
+    elif strategy == 'colossalai_gemini_cpu':
+        strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5)
+    elif strategy == 'colossalai_zero2_cpu':
+        strategy = ColossalAIStrategy(stage=2, placement_policy='cpu')
     else:
         raise ValueError(f'Unsupported strategy "{strategy}"')
     return strategy_