wehub-resource-sync
146 lines
5.7 KiB
Python
146 lines
5.7 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.split_qkv import HybridSplitQKVContainer
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from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
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import torch
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from torch.nn.parameter import Parameter
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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 ..policy import maybe_copy_qkv
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from ..policy import maybe_get_lora
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class DS_GPTNEOContainer(MetaTensorContainer, HybridSplitQKVContainer, 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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return self.module
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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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self.lora_params = [
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maybe_get_lora(p) for p in [
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self.policy.client_module.mlp.c_fc, self.policy.client_module.mlp.c_proj,
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self.policy.client_module.attn.attention.q_proj, self.policy.client_module.attn.attention.k_proj,
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self.policy.client_module.attn.attention.v_proj, self.policy.client_module.attn.attention.out_proj
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]
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]
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def set_q_k_v(self):
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"""
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Necessary to implement for `HybridSplitQKVContainer`
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"""
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self.qw = self.policy.client_module.attn.attention.q_proj.weight
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self.qb = None
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self.kw = self.policy.client_module.attn.attention.k_proj.weight
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self.kb = None
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self.vw = self.policy.client_module.attn.attention.v_proj.weight
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self.vb = None
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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, q_lora, k_lora, v_lora, out_lora = self.get_lora_params()
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ret = [(fc1_lora, self._h4h_w), (fc2_lora, self._4hh_w), (out_lora, self.dense_w), (q_lora, self.qw),
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(k_lora, self.kw), (v_lora, self.vw)]
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return ret
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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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'attn.attention.q_proj.weight', \
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'attn.attention.k_proj.weight', \
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'attn.attention.v_proj.weight', \
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'attn.attention.out_proj.weight', \
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'attn.attention.out_proj.bias', \
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'mlp.c_fc.weight', \
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'mlp.c_fc.bias', \
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'mlp.c_proj.weight', \
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'mlp.c_proj.bias', \
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'ln_2.weight', \
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'ln_2.bias', \
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'ln_1.weight', \
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'ln_1.bias'
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)
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maybe_copy_qkv(module.attention,
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sd,
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weight_quantizer,
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mp_replace,
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'attn_qkvw', [prefix + param_names[0], prefix + param_names[1], prefix + param_names[2]],
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split_qkv=self.policy.split_qkv)
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for i in range(3, 5):
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maybe_copy(module.attention, sd, weight_quantizer, mp_replace, transformer_param_names[i - 1],
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prefix + param_names[i])
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for i in range(5, 11):
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maybe_copy(module.mlp, sd, weight_quantizer, mp_replace, transformer_param_names[i - 1],
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prefix + param_names[i])
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for i in range(11, 13):
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maybe_copy(module, sd, weight_quantizer, mp_replace, transformer_param_names[i - 1],
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prefix + param_names[i])
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class HFGPTNEOLayerPolicy(TransformerPolicy):
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def __init__(self, client_module, inference=True):
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super().__init__(inference, scale_attention=False)
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self.client_module = client_module
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try:
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import transformers
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HFGPTNEOLayerPolicy._orig_layer_class = transformers.models.gpt_neo.modeling_gpt_neo.GPTNeoBlock
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except Exception:
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HFGPTNEOLayerPolicy._orig_layer_class = None
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def get_hidden_heads(self):
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return self.client_module.attn.attention.embed_dim, \
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self.client_module.attn.attention.num_heads, \
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self.client_module.ln_1.eps, \
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DEFAULT_INTERMEDIATE_SIZE
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def get_q_k_v(self):
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return self.client_module.attn.attention.q_proj.weight, \
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None, \
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self.client_module.attn.attention.k_proj.weight, \
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None, \
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self.client_module.attn.attention.v_proj.weight, \
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None
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def attention(self, enable_training=False):
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qw = self.client_module.attn.attention.q_proj.weight
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kw = self.client_module.attn.attention.k_proj.weight
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vw = self.client_module.attn.attention.v_proj.weight
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qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training)
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return qkvw, \
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None, \
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self.client_module.attn.attention.out_proj.weight, \
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self.client_module.attn.attention.out_proj.bias
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def mlp(self, enable_training=False):
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return self.client_module.mlp.c_fc.weight, \
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self.client_module.mlp.c_fc.bias, \
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self.client_module.mlp.c_proj.weight, \
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self.client_module.mlp.c_proj.bias
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def layernorm(self):
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return self.client_module.ln_2.weight, \
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self.client_module.ln_2.bias, \
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self.client_module.ln_1.weight, \
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self.client_module.ln_1.bias
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