import numpy as np import torch import torch.nn as nn from torch.cuda.amp import custom_bwd, custom_fwd import math 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 try: import triton import triton.language as tl from .custom_autotune import * except: print('triton not installed. Run `pip install triton` to load quantized version of MOSS.') # code based https://github.com/fpgaminer/GPTQ-triton @autotune( configs=[ triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), # These provided a benefit on a 3090 triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), ], key=['M', 'N'], nearest_power_of_two=True, ) @triton.jit def matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr): """ Compute the matrix multiplication C = A x B. A is of shape (M, K) float16 B is of shape (K//8, N) int32 C is of shape (M, N) float16 scales is of shape (G, N) float16 zeros is of shape (G, N) float16 g_ptr is of shape (K) int32 """ infearure_per_bits = 32 // bits pid = tl.program_id(axis=0) num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) num_pid_in_group = GROUP_SIZE_M * num_pid_n group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + (pid % group_size_m) pid_n = (pid % num_pid_in_group) // group_size_m offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K) a_mask = (offs_am[:, None] < M) # b_ptrs is set up such that it repeats elements along the K axis 8 times b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N) g_ptrs = g_ptr + offs_k # shifter is used to extract the N bits of each element in the 32-bit word from B scales_ptrs = scales_ptr + offs_bn[None, :] zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) shifter = (offs_k % infearure_per_bits) * bits zeros_shifter = (offs_bn % infearure_per_bits) * bits accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, num_pid_k): g_idx = tl.load(g_ptrs) # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = (zeros + 1) a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K) b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated # Now we need to unpack b (which is N-bit values) into 32-bit values b = (b >> shifter[:, None]) & maxq # Extract the N-bit values b = (b - zeros) * scales # Scale and shift accumulator += tl.dot(a, b) a_ptrs += BLOCK_SIZE_K b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk g_ptrs += BLOCK_SIZE_K c = accumulator.to(tl.float16) c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) tl.store(c_ptrs, accumulator, mask=c_mask) # code based https://github.com/fpgaminer/GPTQ-triton @autotune( configs=[ triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), # These provided a benefit on a 3090 triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), ], key=['M', 'K'], nearest_power_of_two=True, ) @triton.jit def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr): """ Compute the matrix multiplication C = A x B. A is of shape (M, N) float16 B is of shape (K//8, N) int32 C is of shape (M, K) float16 scales is of shape (G, N) float16 zeros is of shape (G, N) float16 g_ptr is of shape (K) int32 """ infearure_per_bits = 32 // bits pid = tl.program_id(axis=0) num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_in_group = GROUP_SIZE_M * num_pid_k group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + (pid % group_size_m) pid_k = (pid % num_pid_in_group) // group_size_m offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) offs_n = tl.arange(0, BLOCK_SIZE_N) a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N) a_mask = (offs_am[:, None] < M) # b_ptrs is set up such that it repeats elements along the K axis 8 times b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N) g_ptrs = g_ptr + offs_bk g_idx = tl.load(g_ptrs) # shifter is used to extract the N bits of each element in the 32-bit word from B scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros shifter = (offs_bk % infearure_per_bits) * bits zeros_shifter = (offs_n % infearure_per_bits) * bits accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32) for k in range(0, num_pid_n): # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = (zeros + 1) a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N) b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated # Now we need to unpack b (which is N-bit values) into 32-bit values b = (b >> shifter[:, None]) & maxq # Extract the N-bit values b = (b - zeros) * scales # Scale and shift b = tl.trans(b) accumulator += tl.dot(a, b) a_ptrs += BLOCK_SIZE_N b_ptrs += BLOCK_SIZE_N scales_ptrs += BLOCK_SIZE_N zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits) c = accumulator.to(tl.float16) c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :] c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K) tl.store(c_ptrs, accumulator, mask=c_mask) def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16) grid = lambda META: ( triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),) matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, input.stride(0), input.stride(1), qweight.stride(0), qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0)) return output def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): output_dim = (qweight.shape[0] * 32) // bits output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16) grid = lambda META: ( triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),) transpose_matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], output_dim, bits, maxq, input.stride(0), input.stride(1), qweight.stride(0), qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0)) return output class QuantLinearFunction(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float16) def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq): output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq) ctx.save_for_backward(qweight, scales, qzeros, g_idx) ctx.bits, ctx.maxq = bits, maxq return output @staticmethod @custom_bwd def backward(ctx, grad_output): qweight, scales, qzeros, g_idx = ctx.saved_tensors bits, maxq = ctx.bits, ctx.maxq grad_input = None if ctx.needs_input_grad[0]: grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq) return grad_input, None, None, None, None, None, None class QuantLinear(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 // 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(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(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) def quantize_with_gptq(model, wbits, groupsize): model = model.eval() layers = find_layers(model) for name in ['lm_head']: if name in layers: del layers[name] make_quant(model, layers, wbits, groupsize) # model.load_state_dict(torch.load(checkpoint)) return model