[gptq] add gptq kernel

colossalai/gptq/__init__.py

@@ -0,0 +1,7 @@
+from .cai_gptq import HAS_AUTO_GPTQ
+
+if HAS_AUTO_GPTQ: 
+    from .cai_gptq import (gptq_fused_linear_triton, make_cai_quant_linear, 
+                            CaiQuantLinear, CaiGPTQLinearOp)
+
+

colossalai/gptq/cai_gptq/__init__.py

@@ -0,0 +1,14 @@
+import warnings
+
+HAS_AUTO_GPTQ = False
+try:
+    import auto_gptq
+    HAS_AUTO_GPTQ = True
+except ImportError:
+    warnings.warn('please install auto-gptq from https://github.com/PanQiWei/AutoGPTQ')
+    HAS_AUTO_GPTQ = False
+
+if HAS_AUTO_GPTQ:
+    from .gptq_triton import gptq_fused_linear_triton
+    from .cai_quant_linear import make_cai_quant_linear, CaiQuantLinear
+    from .gptq_op import CaiGPTQLinearOp

colossalai/gptq/cai_gptq/cai_quant_linear.py

@@ -0,0 +1,131 @@
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from .gptq_op import CaiGPTQLinearOp
+import triton
+
+class CaiQuantLinear(nn.Module):
+
+    def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
+        super().__init__()
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+        self.infeatures = infeatures
+        self.outfeatures = outfeatures
+        self.bits = bits
+        self.maxq = 2**self.bits - 1
+        self.groupsize = groupsize if groupsize != -1 else infeatures
+
+        self.register_buffer('qweight', torch.zeros((infeatures // 64 * self.bits, outfeatures), dtype=torch.int64))
+        self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 64 * self.bits), dtype=torch.int64))
+        self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
+        self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
+
+        if bias:
+            self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
+        else:
+            self.bias = None
+
+        self.gptq_linear = CaiGPTQLinearOp(groupsize, bits)
+
+
+    def pack(self, linear, scales, zeros, g_idx=None):
+
+        g_idx = g_idx.clone() if g_idx is not None else torch.tensor([i // self.groupsize for i in range(self.infeatures)], dtype=torch.int32)
+
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        half_scales = scales.clone().half()
+        # print("scale shape ", scales.shape, scale_zeros.shape, linear.weight.shape)
+        self.scales = scales.clone().half()
+        if linear.bias is not None:
+            self.bias = linear.bias.clone().half()
+
+        wn = 16
+        pbits = 64
+        ptype = torch.int64
+        unsign_type = np.uint64
+        sign_type = np.int64
+
+        # wn = 8
+        # pbits = 32
+        # ptype = torch.int32
+        # unsign_type = np.uint32
+        # sign_type = np.int32
+
+        intweight = []
+        for idx in range(self.infeatures):
+            intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx[idx]]) / half_scales[g_idx[idx]]).to(ptype)[:, None])
+        intweight = torch.cat(intweight, dim=1)
+        intweight = intweight.t().contiguous()
+        intweight = intweight.numpy().astype(unsign_type)
+        qweight = np.zeros((intweight.shape[0] // pbits * self.bits, intweight.shape[1]), dtype=unsign_type)
+
+        i = 0
+        row = 0
+        # print("weight shape ", intweight.shape, qweight.shape, out_qweight.shape, bits)
+        # print("weight shape ", intweight[0].shape, qweight[0].shape, out_qweight[0].shape)
+        # print("weight value ", intweight[0], qweight[0])
+
+        while row < qweight.shape[0]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (pbits //  self.bits)):
+                    qweight[row] |= intweight[j] << ( self.bits * (j - i))
+                i += pbits //  self.bits
+                row += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+        qweight = qweight.astype(sign_type)
+        qweight1 = torch.from_numpy(qweight)
+        qweight1 = qweight1.contiguous() #.to("cuda")
+        self.qweight.data.copy_(qweight1)
+
+        qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // pbits *  self.bits), dtype=unsign_type)
+        zeros -= 1
+        zeros = zeros.numpy().astype(unsign_type)
+        i = 0
+        col = 0
+        while col < qzeros.shape[1]:
+            if  self.bits in [2, 4, 8]:
+                for j in range(i, i + (pbits //  self.bits)):
+                    qzeros[:, col] |= zeros[:, j] << ( self.bits * (j - i))
+                i += pbits //  self.bits
+                col += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+        qzeros = qzeros.astype(sign_type)
+        qzeros = torch.from_numpy(qzeros)
+        qzeros = qzeros
+        self.qzeros.data.copy_(qzeros)
+
+        if torch.equal(self.g_idx, g_idx):
+            self.g_idx = None
+        else:
+            self.g_idx = g_idx
+
+
+    def forward(self, x):
+
+        cai_out = self.gptq_linear(x,
+                            self.qweight,
+                            self.scales,
+                            self.qzeros,
+                            g_idx = self.g_idx,
+                            bias = self.bias,)
+        return cai_out
+
+def make_cai_quant_linear(module, names, bits, groupsize, name=''):
+    if isinstance(module, CaiQuantLinear):
+        return
+    for attr in dir(module):
+        tmp = getattr(module, attr)
+        name1 = name + '.' + attr if name != '' else attr
+        if name1 in names:
+            delattr(module, attr)
+            setattr(module, attr, CaiQuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
+    for name1, child in module.named_children():
+        make_cai_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
+

colossalai/gptq/cai_gptq/gptq_op.py

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+from .gptq_triton import gptq_fused_linear_triton
+import torch
+
+
+class CaiGPTQLinearOp(torch.nn.Module):
+
+    def __init__(self, gptq_group_size, gptq_quant_bits):
+        super(CaiGPTQLinearOp, self).__init__()
+        self.group_size = gptq_group_size
+        self.bits = gptq_quant_bits
+        self.maxq = 2**self.bits - 1
+        self.empty_tensor = torch.zeros(4, device=torch.cuda.current_device())
+
+    def forward(self,
+                input: torch.Tensor,
+                weight: torch.Tensor,
+                weight_scales: torch.Tensor,
+                weight_zeros: torch.Tensor,
+                g_idx: torch.Tensor = None,
+                act_type = 0,
+                bias: torch.Tensor = None,
+                residual: torch.Tensor=None,
+                qkv_fused = False):
+
+        add_bias = True
+        if bias is None:
+            bias = self.empty_tensor
+            add_bias = False
+
+        add_residual = True
+        if residual is None:
+            residual = self.empty_tensor
+            add_residual = False
+        x = input.view(-1, input.shape[-1])
+
+        out = gptq_fused_linear_triton(x, weight, weight_scales, weight_zeros, bias, residual,
+                    self.bits, self.maxq, self.group_size, qkv_fused, add_bias, add_residual, 
+                    act_type=act_type, g_idx=g_idx)
+        if qkv_fused:
+            out = out.view(3, input.shape[0], input.shape[1], weight.shape[-1])
+        else:
+            out = out.view(input.shape[0], input.shape[1], weight.shape[-1])
+
+        return out