From 92f85c964d3cde865053f3262a78cfaf1cb0d134 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 9 Jun 2023 15:59:49 +0000 Subject: [PATCH] Removing dead code. --- .../utils/gptq/quant_linear.py | 119 ------------------ 1 file changed, 119 deletions(-) diff --git a/server/text_generation_server/utils/gptq/quant_linear.py b/server/text_generation_server/utils/gptq/quant_linear.py index fee1bb291..00f902ce8 100644 --- a/server/text_generation_server/utils/gptq/quant_linear.py +++ b/server/text_generation_server/utils/gptq/quant_linear.py @@ -318,127 +318,8 @@ class QuantLinear(nn.Module): self.outfeatures = qweight.shape[1] self.infeatures = qweight.shape[0] * 32 // 4 - # expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures // 32 * self.bits) - # assert tuple(self.qzeros.shape) == expected, f"{self.qzeros.shape} != {expected}" - # expected = (math.ceil(self.infeatures / self.groupsize), self.outfeatures) - # assert tuple(self.scales.shape) == expected, f"{self.scales.shape} != {expected}" - # assert self.g_idx.shape == (math.ceil(self.infeatures / self.group_size), outfeatures) - - # def new(cls, bits, groupsize, infeatures, outfeatures, bias): - # 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 // 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 - - def pack(self, linear, 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 linear.bias is not None: - self.bias = linear.bias.clone().half() - - intweight = [] - for idx in range(self.infeatures): - intweight.append(torch.round((linear.weight.data[:, 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 - else: - raise NotImplementedError("Only 2,4,8 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 - else: - raise NotImplementedError("Only 2,4,8 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, ) out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq) out = out + self.bias if self.bias is not None else out return out.reshape(out_shape) - - -def make_quant_linear(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: - delattr(module, attr) - setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None)) - for name1, child in module.named_children(): - make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) - - -def autotune_warmup_linear(model, transpose=False): - """ - Pre-tunes the quantized kernel - """ - from tqdm import tqdm - - kn_values = {} - - for _, m in model.named_modules(): - if not isinstance(m, QuantLinear): - continue - - k = m.infeatures - n = m.outfeatures - - if (k, n) not in kn_values: - kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq) - - print(f'Found {len(kn_values)} unique KN Linear values.') - - print('Warming up autotune cache ...') - with torch.no_grad(): - for m in tqdm(range(0, 12)): - m = 2**m # [1, 2048] - for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq) in kn_values.items(): - a = torch.randn(m, k, dtype=torch.float16, device='cuda') - matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq) - if transpose: - a = torch.randn(m, n, dtype=torch.float16, device='cuda') - transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq) - del kn_values