# Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team import torch from ..module_inject.replace_policy import HFBertLayerPolicy, replace_policies from deepspeed.accelerator import get_accelerator class WeightQuantization(object): def __init__(self, mlp_extra_grouping=True, mp_size=1): self.dense_scales = [] self.qkv_scales = [] self.mlp4hh_scales = [] self.mlph4h_scales = [] self.mlp_extra_grouping = mlp_extra_grouping self.mp_size = mp_size def quantize_data(self, data, quantize_bits, groups, key=None): data_groups = torch.split(data.float().view(-1), data.numel() // groups) max_d = [max(g.max(), g.min().abs()) for g in data_groups] data_scale = [float(1 << quantize_bits) / (2 * mx + 1e-5) for mx in max_d] data_int = [(g * s) for g, s in zip(data_groups, data_scale)] data_int = [ di.round().clamp(-(1 << (quantize_bits - 1)), (((1 << (quantize_bits - 1)) - 1))) for di in data_int ] data_int = torch.cat(data_int).reshape(data.shape) data_int = data_int.to(torch.int8) data_scale = torch.cat([s.unsqueeze(0).unsqueeze(0) for s in data_scale]) return data_int, data_scale def is_mlp(self, data, merge_count=1): return ((self.mp_size *data.shape[0] * merge_count) / data.shape[1] == 4 or \ (self.mp_size *data.shape[1] * merge_count) / data.shape[0] == 4) def is_qkv(self, data): return ((self.mp_size * data.shape[0]) / data.shape[1] == 3 or \ (self.mp_size * data.shape[1]) / data.shape[0] == 3) def Quantize(self, value_list, quantize_bits, groups, key, merge_dim=0): if self.mlp_extra_grouping and self.is_mlp(value_list[0], merge_count=len(value_list)): groups *= 2 q_scale = [] index = 0 for data in value_list: data_int, data_scale = self.quantize_data(data, quantize_bits, groups, key) q_scale.append(data_scale) value_list[index] = data_int index += 1 q_scale = (1 / torch.cat(q_scale, dim=merge_dim).to(get_accelerator().current_device_name()).view(-1).unsqueeze(0)) if "mlp.dense_4h_to_h.weight" in key: self.mlp4hh_scales.append(q_scale) elif "mlp.dense_h_to_4h.weight" in key: self.mlph4h_scales.append(q_scale) elif "attention.query_key_value.weight" in key: self.qkv_scales.append(q_scale) else: self.dense_scales.append(q_scale) return value_list def merge_layer_scales(self, layer_scales): max_dim = max([s.shape[-1] for s in layer_scales]) layer_scales = [ torch.cat((s, torch.zeros((1, max_dim - s.shape[-1]), device=get_accelerator().current_device_name())), dim=-1) if s.shape[-1] < max_dim else s for s in layer_scales ] return torch.cat(layer_scales).unsqueeze(0) def merge_scales(self): all_scales = [] for dense_scale, qkv_scale, m4hh_scale, mh4h_scale in \ zip(self.dense_scales, self.qkv_scales, self.mlp4hh_scales, self.mlph4h_scales): all_scales.append(self.merge_layer_scales([qkv_scale, dense_scale, mh4h_scale, m4hh_scale])) return torch.cat(all_scales) def merge_scales_split(self, split_count): all_scales = [[] for _ in range(split_count)] for dense_scale, qkv_scale, m4hh_scale, mh4h_scale in \ zip(self.dense_scales, self.qkv_scales, self.mlp4hh_scales, self.mlph4h_scales): dense_scale = torch.split(dense_scale, dense_scale.numel() // split_count) qkv_scale = torch.split(qkv_scale, qkv_scale.numel() // split_count) m4hh_scale = torch.split(m4hh_scale, m4hh_scale.numel() // split_count) mh4h_scale = torch.split(mh4h_scale, mh4h_scale.numel() // split_count) for s in range(split_count): all_scales[s].append( torch.cat([ torch.cat((qkv_scale[s], torch.zeros_like(qkv_scale[s])), dim=1), torch.cat((dense_scale[s], torch.zeros_like(dense_scale[s])), dim=1), mh4h_scale[s], m4hh_scale[s] ]).unsqueeze(0)) for scales_a in all_scales: torch.cat(scales_a) return all_scales def sd_quantize_megatron(self, sd, quantize_bits, groups): keys = sd.keys() for key in keys: value_list = [sd[key]] if "attention.dense.weight" in key or "mlp.dense_4h_to_h.weight" in key or \ "mlp.dense_h_to_4h.weight" in key or "attention.query_key_value.weight" in key: value_list = self.Quantize(value_list, quantize_bits, groups, key=key) sd[key] = value_list[0] all_scales = self.merge_scales() return sd, all_scales def model_quantize(self, model, quantize_policy, quantize_bits, groups): all_scales = [] def quantize_fn(layer, policy_cls): policy = policy_cls(layer) _, qkvw, _, dense_w, _, _ = policy.attention() _, _h4h_w, _, _4hh_w, _ = policy.mlp() keys = [qkvw, dense_w, _h4h_w, _4hh_w] layer_scales = [] for key in range(len(keys)): if self.mlp_extra_grouping and self.is_mlp(keys[key]): data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups * 2) elif policy_cls is HFBertLayerPolicy and self.is_qkv(keys[key]): data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups * 3) else: data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups) keys[key].copy_(data_quantized) layer_scales.append((1 / data_scale.to(get_accelerator().current_device_name()).view(-1).unsqueeze(0))) all_scales.append(self.merge_layer_scales(layer_scales)) return layer def _quantize_module(model, policies): for name, child in model.named_children(): if child.__class__ in policies: quantize_fn, replace_policy = policies[child.__class__] setattr(model, name, quantize_fn(child, replace_policy)) else: _quantize_module(child, policies) return model policy = {} if quantize_policy is not None: for layer_name, replace_policy in quantize_policy.items(): policy.update({layer_name: (quantize_fn, replace_policy)}) else: for plcy in replace_policies: policy.update({plcy._orig_layer_class: (quantize_fn, plcy)}) quantized_module = _quantize_module(model, policy) return quantized_module, torch.cat(all_scales)