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