wehub-resource-sync
147 lines
5.8 KiB
Python
147 lines
5.8 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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from .base import *
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from .features.meta_tensor import MetaTensorContainer
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from .features.hybrid_megatron import HybridMegatronContainer
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from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
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import torch
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from ..policy import TransformerPolicy
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from ..policy import transformer_param_names
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from ..policy import maybe_copy
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from packaging import version as pkg_version
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from ..policy import maybe_get_lora
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class DS_GPTNEOXContainer(MetaTensorContainer, HybridMegatronContainer, BaseTransformerContainer):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# All model specific things should be defined here instead of the base class.
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def create_module(self, config=None):
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_config = config if config is not None else self.ds_model_config
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self.module = DeepSpeedGPTInference(_config, mp_group=self.mp_group)
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self.module.config.scale_attention = self.scale_attention
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if self.megatron_v2:
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self.module.config.rotate_half = True
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self.module.config.rotate_every_two = False
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return self.module
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def get_lora_matched_pair(self):
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"""
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Necessary to implement for `HybridEngineContainer`
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"""
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fc1_lora, fc2_lora, qkv_lora, out_lora = self.get_lora_params()
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ret = [(fc1_lora, self._h4h_w), (fc2_lora, self._4hh_w), (qkv_lora, self.qkvw), (out_lora, self.dense_w)]
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return ret
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def set_lora_params(self):
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"""
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Necessary to implement for `HybridEngineContainer`
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"""
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if GPTNEOXLayerPolicy.version == 0:
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attention = self.policy.client_module.attention
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else:
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attention = self.policy.client_module.self_attention
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self.lora_params = [
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maybe_get_lora(p) for p in [
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self.policy.client_module.mlp.dense_h_to_4h, self.policy.client_module.mlp.dense_4h_to_h,
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attention.query_key_value, attention.dense
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]
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]
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def load_params(self, module, sd, weight_quantizer, mp_replace, prefix):
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param_names = (
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'attention.query_key_value.weight', \
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'attention.query_key_value.bias', \
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'attention.dense.weight', \
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'attention.dense.bias', \
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'mlp.dense_h_to_4h.weight', \
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'mlp.dense_h_to_4h.bias', \
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'mlp.dense_4h_to_h.weight', \
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'mlp.dense_4h_to_h.bias', \
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'post_attention_layernorm.weight', \
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'post_attention_layernorm.bias', \
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'input_layernorm.weight', \
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'input_layernorm.bias'
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)
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for i in range(0, 2):
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maybe_copy(module.attention,
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sd,
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weight_quantizer,
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mp_replace,
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transformer_param_names[i],
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prefix + param_names[i],
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qkv=True,
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megatron_v2=self.policy.is_megatron_v2,
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split_qkv=self.policy.split_qkv,
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heads=self.policy.client_module.attention.num_attention_heads)
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for i in range(2, 4):
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[i],
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prefix + param_names[i])
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for i in range(4, 10):
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[i],
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prefix + param_names[i])
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for i in range(10, 12):
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maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[i], prefix + param_names[i])
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class GPTNEOXLayerPolicy(TransformerPolicy):
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_orig_layer_class = None
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version = 0
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def __init__(self, client_module, inference=True, megatron_v2=True, split_qkv=False):
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super().__init__(inference, megatron_v2=megatron_v2, split_qkv=split_qkv)
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self.client_module = client_module
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if GPTNEOXLayerPolicy._orig_layer_class is None:
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if pkg_version.parse(torch.__version__) <= pkg_version.parse("1.2"):
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GPTNEOXLayerPolicy._orig_layer_class = None
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else:
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try:
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from transformers import GPTNeoXLayer
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GPTNEOXLayerPolicy._orig_layer_class = GPTNeoXLayer
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except ImportError:
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GPTNEOXLayerPolicy._orig_layer_class = None
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def get_hidden_heads(self):
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if GPTNEOXLayerPolicy.version == 0:
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attention = self.client_module.attention
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else:
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attention = self.client_module.self_attention
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return self.client_module.attention.hidden_size, \
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self.client_module.attention.num_attention_heads, \
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self.client_module.input_layernorm.eps, \
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DEFAULT_INTERMEDIATE_SIZE
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def attention(self, enable_training=False):
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if GPTNEOXLayerPolicy.version == 0:
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attention = self.client_module.attention
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else:
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attention = self.client_module.self_attention
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return attention.query_key_value.weight, \
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attention.query_key_value.bias, \
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attention.dense.weight, \
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attention.dense.bias
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def mlp(self, enable_training=False):
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return self.client_module.mlp.dense_h_to_4h.weight, \
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self.client_module.mlp.dense_h_to_4h.bias, \
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self.client_module.mlp.dense_4h_to_h.weight, \
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self.client_module.mlp.dense_4h_to_h.bias
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def layernorm(self):
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return self.client_module.post_attention_layernorm.weight, \
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self.client_module.post_attention_layernorm.bias, \
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self.client_module.input_layernorm.weight, \
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self.client_module.input_layernorm.bias
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