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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_BERTContainer(BaseTransformerContainer): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.return_tuple = True |
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self.triangular_masking = False |
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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 HFBertLayerPolicy(TransformerPolicy): |
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def __init__(self, client_module, inference=False): |
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super().__init__(inference, pre_attn_norm=False) |
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self.client_module = client_module |
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self.cuda_graph_supported = True |
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if HFBertLayerPolicy._orig_layer_class is None: |
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try: |
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import transformers |
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HFBertLayerPolicy._orig_layer_class = [ |
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transformers.models.bert.modeling_bert.BertLayer, |
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transformers.models.roberta.modeling_roberta.RobertaLayer |
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] |
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except: |
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HFBertLayerPolicy._orig_layer_class = None |
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def get_hidden_heads(self): |
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if self.pre_attn_norm: |
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attention_layernorm = self.client_module.PostAttentionLayerNorm |
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else: |
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attention_layernorm = self.client_module.attention.output.LayerNorm |
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return self.client_module.attention.self.query.weight.shape[1], \ |
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self.client_module.attention.self.num_attention_heads, \ |
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attention_layernorm.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.self.query.weight |
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qb = self.client_module.attention.self.query.bias |
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kw = self.client_module.attention.self.key.weight |
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kb = self.client_module.attention.self.key.bias |
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vw = self.client_module.attention.self.value.weight |
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vb = self.client_module.attention.self.value.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.output.dense.weight, \ |
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self.client_module.attention.output.dense.bias, \ |
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def mlp(self, enable_training=False): |
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if self.pre_attn_norm: |
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intermediate_ff = self.client_module.intermediate.dense_act |
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else: |
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intermediate_ff = self.client_module.intermediate.dense |
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return intermediate_ff.weight, intermediate_ff.bias, \ |
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self.client_module.output.dense.weight, \ |
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self.client_module.output.dense.bias |
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def layernorm(self): |
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if self.pre_attn_norm: |
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attention_layernorm = self.client_module.PostAttentionLayerNorm |
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transformer_layernorm = self.client_module.PreAttentionLayerNorm |
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else: |
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attention_layernorm = self.client_module.attention.output.LayerNorm |
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transformer_layernorm = self.client_module.output.LayerNorm |
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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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