wehub-resource-sync
83 lines
3.1 KiB
Python
83 lines
3.1 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 deepspeed.model_implementations.transformers.ds_bert import DeepSpeedBERTInference
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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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class DS_DistilBERTContainer(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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self.triangular_masking = False
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self.return_single_tuple = True
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self.use_triton = kwargs['config'].use_triton and deepspeed.HAS_TRITON
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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 = DeepSpeedBERTInference(_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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class HFDistilBertLayerPolicy(TransformerPolicy):
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_orig_layer_class = None
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def __init__(self, client_module, inference=False, preln=False):
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super().__init__(inference)
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self.client_module = client_module
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self.preln = preln
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self.cuda_graph_supported = True
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if HFDistilBertLayerPolicy._orig_layer_class is None:
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try:
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import transformers
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HFDistilBertLayerPolicy._orig_layer_class = [
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transformers.models.distilbert.modeling_distilbert.TransformerBlock,
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]
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except Exception:
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HFDistilBertLayerPolicy._orig_layer_class = None
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def get_hidden_heads(self):
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return self.client_module.attention.q_lin.weight.shape[1], \
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self.client_module.attention.n_heads, \
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self.client_module.sa_layer_norm.eps, \
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DEFAULT_INTERMEDIATE_SIZE
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def attention(self, enable_training=False):
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qw = self.client_module.attention.q_lin.weight
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qb = self.client_module.attention.q_lin.bias
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kw = self.client_module.attention.k_lin.weight
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kb = self.client_module.attention.k_lin.bias
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vw = self.client_module.attention.v_lin.weight
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vb = self.client_module.attention.v_lin.bias
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qkvw = Parameter(torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training)
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qkvb = Parameter(torch.cat((qb, kb, vb), dim=0), requires_grad=enable_training)
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return qkvw, \
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qkvb, \
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self.client_module.attention.out_lin.weight, \
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self.client_module.attention.out_lin.bias
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def mlp(self, enable_training=False):
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intermediate_ff = self.client_module.ffn.lin1
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return intermediate_ff.weight, intermediate_ff.bias, \
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self.client_module.ffn.lin2.weight, \
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self.client_module.ffn.lin2.bias
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def layernorm(self):
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attention_layernorm = self.client_module.sa_layer_norm
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transformer_layernorm = self.client_module.output_layer_norm
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return attention_layernorm.weight, \
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attention_layernorm.bias, \
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transformer_layernorm.weight, \
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transformer_layernorm.bias
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