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| 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 | |
| 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 | |
| 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): | |
| 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 | |
| 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 | |