feat(sanity-check): implem version of gptq now added

quantize/gptq/quant.py

@@ -147,6 +147,188 @@ def make_quant(module, names, bits, groupsize, name=''):
     for name1, child in module.named_children():
         make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
 
+def make_quant_custom(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:
+
+            bias_name = attr.replace('w', 'b')
+            layer_name = attr.replace('w', 'quant')
+            setattr(module, layer_name, QuantLinear_custom(bits, groupsize, tmp.shape[0], tmp.shape[1], module.w[bias_name] is not None))
+
+
+class QuantLinear_custom(nn.Module):
+    def __init__(self, bits, groupsize, infeatures, outfeatures, bias, kernel_switch_threshold=128, is_cuda=is_cuda):
+        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
+        self.groupsize = groupsize if groupsize != -1 else infeatures
+        self.maxq = 2 ** self.bits - 1
+
+        self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
+        self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
+        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
+
+        # is performed by unpacking the weights and using torch.matmul
+        if self.bits in [2,4,8]: 
+            self.register_buffer('wf',torch.tensor(list(range(0,32,self.bits)), dtype=torch.int32).unsqueeze(0),persistent=False)
+        elif self.bits == 3:
+            self.register_buffer('wf', torch.tensor([[0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
+                                                     [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
+                                                     [0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],], dtype=torch.int32).reshape(1,3,12), persistent=False)
+
+        self.kernel_switch_threshold = kernel_switch_threshold
+        self.is_cuda = is_cuda
+
+    def pack(self, weight, bias, scales, zeros, g_idx = None):
+        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
+
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        self.scales = scales.clone().half()
+        if bias is not None:
+            self.bias = bias.clone().half()
+
+        intweight = []
+        for idx in range(self.infeatures):
+            intweight.append(torch.round((weight[:,idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[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] // 32 * self.bits, 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] // 32 * self.bits), 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):
+        out_shape = x.shape[:-1] + (self.outfeatures, )
+        x = x.reshape(-1,x.shape[-1])     
+        if  self.is_cuda is True and (self.kernel_switch_threshold is False or x.shape[0] < self.kernel_switch_threshold):
+            out = torch.zeros((x.shape[0], self.outfeatures), device=x.device, dtype=torch.float32)
+            if self.bits == 2:
+                quant_cuda.vecquant2matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
+            elif self.bits == 3:
+                quant_cuda.vecquant3matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
+            elif self.bits == 4:
+                quant_cuda.vecquant4matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
+            elif self.bits == 8:
+                quant_cuda.vecquant8matmul(x.float(), self.qweight, out, self.scales.float(), self.qzeros, self.g_idx)
+            out = out.half()
+        else:
+             if self.bits in [2,4,8]:
+                zeros = torch.bitwise_right_shift(torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits), self.wf.unsqueeze(0)).to(torch.int16 if self.bits == 8 else torch.int8)
+                torch.bitwise_and(zeros, (2 ** self.bits) - 1, out=zeros)
+
+                zeros = zeros + 1
+                zeros = zeros.reshape(self.scales.shape)   
+
+                weight = torch.bitwise_right_shift(torch.unsqueeze(self.qweight, 1).expand(-1, 32 // self.bits, -1), self.wf.unsqueeze(-1)).to(torch.int16 if self.bits == 8 else torch.int8)
+                torch.bitwise_and(weight,(2 ** self.bits) - 1, out=weight)
+             elif self.bits == 3:
+                zeros = self.qzeros.reshape(self.qzeros.shape[0], self.qzeros.shape[1]//3, 3, 1).expand(-1, -1, -1, 12)
+                zeros = (zeros >> self.wf.unsqueeze(0))
+                zeros[:,:,0,10] = (zeros[:,:,0,10]&0x3) | ((zeros[:,:,1,0] << 2)&0x4)
+                zeros[:,:,1,11] = (zeros[:,:,1,11]&0x1) | ((zeros[:,:,2,0] << 1)&0x6)
+                zeros = zeros & 0x7
+                zeros = torch.cat([zeros[:,:,0,:11], zeros[:,:,1,1:12], zeros[:,:,2,1:11]], dim=2)
+
+                zeros = zeros + 1
+                zeros = zeros.reshape(self.scales.shape)  
+
+                weight = self.qweight.reshape(self.qweight.shape[0]//3, 3, 1, self.qweight.shape[1]).expand(-1, -1, 12, -1)
+                weight = (weight >> self.wf.unsqueeze(-1))&0x7
+                weight[:,0,10] = (weight[:,0,10]&0x3) | ((weight[:,1,0] << 2)&0x4)
+                weight[:,1,11] = (weight[:,1,11]&0x1) | ((weight[:,2,0] << 1)&0x6)
+                weight = weight & 0x7
+                weight = torch.cat([weight[:,0,:11], weight[:,1,1:12], weight[:,2,1:11]], dim=1)
+
+             weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
+
+             weights = (self.scales[self.g_idx] * (weight - zeros[self.g_idx]))
+             out = torch.matmul(x.half(), weights)
+        out = out.reshape(out_shape)
+        out = out + self.bias if self.bias is not None else out
+        return out
+
 class QuantLinear(nn.Module): 
     def __init__(self, bits, groupsize, infeatures, outfeatures, bias, kernel_switch_threshold=128, is_cuda=is_cuda):
         super().__init__()

quantize/gptq/sanity_check_main.py

@@ -4,6 +4,8 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from collections import OrderedDict
+import torch.nn.functional as F
 
 from sanity_check_utils import seed_everything, MNISTloader, SimpleNet, train, evaluate, SimpleNet_V2
 from gptq import *
@@ -34,9 +36,8 @@ def load_quant(model, checkpoint, wbits, groupsize):
 
     # Don't quantize the last layer because qzeros is empty (I don't know why they create qzeros that way)
     # (gptq.py:L235, second dimension of qzeros is 0 because last layer is 10 for classification)
-    for name in ["linear4"]:
-        if name in layers:
-            del layers[name]
+    if "linear4" in layers:
+        del layers["linear4"]
 
     make_quant(model, layers, wbits, groupsize)    
     model.load_state_dict(torch.load(checkpoint))
@@ -258,8 +259,8 @@ def fasterquant(self, blocksize=128, percdamp=.01, groupsize=-1, actorder=False)
     ### begin GPTQ_CUSTOM
     def __init__(self, checkpoint_path):
         super().__init__()
-        self.load_state_dict(torch.load(checkpoint_path,  map_location="cpu"))        
-    
+        self.load_state_dict(torch.load(checkpoint_path,  map_location="cpu"))
+
     def _fill_subset(self, layer_id):
         is_last_layer = (layer_id == self.nb_layers - 1)
         if is_last_layer:
@@ -292,7 +293,7 @@ def fasterquant(self, layer_id, quantizers):
             print(layer_id, name)
             print('Quantizing ...')
             scale,zero,g_idx = self.gptq[name].fasterquant(percdamp=0.01, groupsize=GROUPSIZE, actorder=False)
-            quantizers[f"linear{layer_id + 1}"] = (self.gptq[name].quantizer.cpu(), scale.cpu(), zero.cpu(), g_idx.cpu())
+            quantizers[f"linear{layer_id}_w"] = (self.gptq[name].quantizer.cpu(), scale.cpu(), zero.cpu(), g_idx.cpu())
 
     ## end GPTQ_CUSTOM
 
@@ -301,6 +302,19 @@ def my_linear(self, x, weight, bias):
         out = x @ weight.weight + bias
         weight.add_batch(x)
         return out
+
+    def forward(self, x):
+        if len(x.shape) == 4:
+            x = x.view(x.size(0), -1)
+
+        residual = x
+        x = F.relu(self.linear0_quant(x))
+        x = self.linear1_quant(x)
+        x = F.relu(x) + residual
+        x = self.linear2_quant(x)
+        x = F.relu(x) + residual
+        x = super().my_linear(x, self.linear3_w, self.linear3_b)
+        return x
     ## End SimpleNet_V2
 
 
@@ -321,9 +335,11 @@ def quantize_gptq_custom(model, train_loader):
     quantizers = {}
 
     for layer_id in range(nb_layers):
-
+        
         if not is_last_layer(layer_id):
-
+
+            print(f"Quantizing layer {layer_id} ...")
+
             model.alloc_gptq(layer_id)
 
             for i in range(nsamples):
@@ -342,12 +358,56 @@ def quantize_gptq_custom(model, train_loader):
 
     return quantizers
 
-
 def model_pack_custom(model, quantizers, wbits, groupsize):
-    pass
+    # Extract weights and bias from model
+    is_weight = re.compile(r'^linear\d+_w$')
+    weights, bias = OrderedDict(), OrderedDict()
+    for name, param in model.w.items():
+        if is_weight.match(name):
+            weights[name] = param
+        else:
+            bias[name] = param
+
+    make_quant_custom(model, quantizers, wbits, groupsize)
+    qlayers = find_layers(model, [QuantLinear_custom])
+
+    print('Packing ...')
+    for i in range(len(qlayers)):
+        name_w, name_b, layer_quant_name = f'linear{i}_w', f'linear{i}_b', f'linear{i}_quant'
+        quantizers[name_w],scale,zero,g_idx = quantizers[name_w]
+        qlayers[layer_quant_name].pack(weights[name_w], bias[name_b], scale, zero, g_idx)
+    print('Done.')
+    return model
+
+def load_quant_custom(model, checkpoint, wbits, groupsize):
+    print('Loading model ...')
+    model = model.eval()
+    # Extract weights and bias from model
+    is_weight = re.compile(r'^linear\d+_w$')
+    weights, bias = OrderedDict(), OrderedDict()
+    for name, param in model.w.items():
+        if is_weight.match(name):
+            weights[name] = param
+        else:
+            bias[name] = param
+
+    # Create linear layer out of weights and bias
+    layers = {}
+    for (w_name, w_param), (_, b_param) in zip(weights.items(), bias.items()):
+        layers[w_name] = nn.Linear(w_param.shape[1], w_param.shape[0], bias=True)
+        layers[w_name].weight.data = w_param
+        layers[w_name].bias.data = b_param
+
+    # Don't quantize the last layer because qzeros is empty (I don't know why they create qzeros that way)
+    # (gptq.py:L235, second dimension of qzeros is 0 because last layer is 10 for classification)
+    if "linear3_w" in layers:
+        del layers["linear3_w"]
+
+    make_quant_custom(model, layers, wbits, groupsize)    
+    model.load_state_dict(torch.load(checkpoint))
+    print('Done.')
+    return model
 
-def load_quant_custom(model, quantizers, wbits, groupsize):
-    pass
 
 def assert_parameters(model, model_custom):
     is_weight = re.compile(r'^linear\d+.weight$')
@@ -371,6 +431,7 @@ def assert_parameters(model, model_custom):
     parser.add_argument("--eval_gptq", action="store_true")
     parser.add_argument("--train_custom", action="store_true")
     parser.add_argument("--gptq_custom", action="store_true")
+    parser.add_argument("--eval_gptq_custom", action="store_true")
     parser.add_argument("--pyquant", action="store_true")
 
     args = parser.parse_args()
@@ -381,7 +442,9 @@ def assert_parameters(model, model_custom):
     criterion = nn.CrossEntropyLoss()
     train_loader, _, _ = MNISTloader(train_val_split=0.95).load()
 
-    #TODO: Do Custom packing
+    #TODO: Do custom eval gptq
+    #TODO: Is reference GPTQ quantizing bias as well ?
+    #TODO: Add seed everywhere in GPT for reproducibility
 
     ## ================== REFERENCE ==================
     if args.train:
@@ -430,6 +493,19 @@ def assert_parameters(model, model_custom):
         model_pack_custom(model, quantizers, WBITS, GROUPSIZE)
         torch.save(model.state_dict(), "model_quantized_custom.pt")
         print("Done Custom GPTQ")
+    elif args.eval_gptq_custom:
+        model = GPTQ_CUSTOM("./model_custom.pt")
+        device = torch.device("cuda:0")
+        model = load_quant_custom(model, "model_quantized_custom.pt", WBITS, GROUPSIZE)
+        model = model.to(device)
+
+        start = time.time()
+        val_loss, val_acc = evaluate(device, model, criterion, train_loader)
+        end = time.time()
+
+        print(f"wbits = {WBITS} using {device}")
+        print(f"val_loss: {val_loss:.3f} \t val_acc: {val_acc:.3f}")
+        print(f"Latency: {end - start}")
     ## ================== MISC ==================
     elif args.pyquant:
         # Baseline post-training quantization from Pytorch