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from .base import * |
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from .features.meta_tensor import MetaTensorContainer |
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from .features.hybrid_engine import HybridEngineContainer |
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from deepspeed.model_implementations.transformers.ds_bloom import DeepSpeedBloomInference |
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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_get_lora |
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supported_models = {None} |
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class DS_BloomContainer(MetaTensorContainer, HybridEngineContainer, BaseTransformerContainer): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.bigscience_bloom = True |
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self.triangular_masking = False |
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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 = DeepSpeedBloomInference(_config, mp_group=self.mp_group) |
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self.module.config.scale_attention = self.scale_attention |
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self.module.config.invert_mask = False |
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return self.module |
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def attention_qkv_mp(self, mp_replace, reversed_dim=False): |
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self.module.attention.attn_qkvw = mp_replace.copy(self.module.attention.attn_qkvw, self.qkvw) |
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self.module.attention.attn_qkvb = mp_replace.copy(self.module.attention.attn_qkvb, self.qkvb) |
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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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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, self.policy. |
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client_module.self_attention.query_key_value, self.policy.client_module.self_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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'self_attention.query_key_value.weight', \ |
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'self_attention.query_key_value.bias', \ |
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'self_attention.dense.weight', \ |
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'self_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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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 BLOOMLayerPolicy(TransformerPolicy): |
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_orig_layer_class = None |
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def __init__(self, client_module, inference=True, use_load_prefix=True, split_qkv=False): |
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super().__init__(inference, linear_layer=True, use_load_prefix=use_load_prefix, split_qkv=split_qkv) |
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self.client_module = client_module |
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try: |
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import transformers |
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BLOOMLayerPolicy._orig_layer_class = transformers.models.bloom.modeling_bloom.BloomBlock |
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global supported_models |
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supported_models.update({transformers.models.bloom.modeling_bloom.BloomModel}) |
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except Exception as e: |
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print(f"WARNING! Setting BLOOMLayerPolicy._orig_layer_class to None due to Exception: {e}") |
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BLOOMLayerPolicy._orig_layer_class = None |
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def get_hidden_heads(self): |
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return self.client_module.self_attention.hidden_size, \ |
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self.client_module.self_attention.num_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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return self.client_module.self_attention.query_key_value.weight, \ |
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self.client_module.self_attention.query_key_value.bias, \ |
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self.client_module.self_attention.dense.weight, \ |
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self.client_module.self_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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