254 lines
11 KiB
Python
254 lines
11 KiB
Python
# coding=utf-8
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# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
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# Copyright 2023 The CacheFlow team.
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# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only GPT-NeoX model compatible with HuggingFace weights.
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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import GPTNeoXConfig
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from cacheflow.model_executor.input_metadata import InputMetadata
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from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
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from cacheflow.model_executor.layers.sampler import Sampler
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from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
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load_tensor_parallel_weights)
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from cacheflow.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from cacheflow.model_executor.parallel_utils.tensor_parallel import (
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VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
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from cacheflow.sequence import SequenceOutputs
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class GPTNeoXAttention(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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super().__init__()
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self.total_num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.total_num_heads
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
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self.query_key_value = ColumnParallelLinear(config.hidden_size,
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3 * config.hidden_size,
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gather_output=False,
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perform_initialization=False)
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self.dense = RowParallelLinear(config.hidden_size, config.hidden_size,
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input_is_parallel=True,
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perform_initialization=False)
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scaling = self.head_size ** -0.5
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rotary_dim = int(self.head_size * config.rotary_pct)
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assert rotary_dim % 2 == 0
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self.attn = GPTNeoXCacheFlowAttention(self.num_heads, self.head_size,
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scaling, rotary_dim)
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def forward(
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self,
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position_ids: torch.LongTensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(
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position_ids, q, k, v, k_cache, v_cache, input_metadata, cache_event)
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output, _ = self.dense(attn_output)
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return output
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class GPTNeoXMLP(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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super().__init__()
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self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
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config.intermediate_size,
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gather_output=False,
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perform_initialization=False)
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self.dense_4h_to_h = RowParallelLinear(config.intermediate_size, config.hidden_size,
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input_is_parallel=True,
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perform_initialization=False)
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if config.hidden_act != 'gelu':
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raise ValueError(f'Unsupported activation: {config.hidden_act}. '
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'Only gelu is supported for now.')
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self.act = torch.nn.GELU()
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def forward(self, hidden_states):
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hidden_states, _ = self.dense_h_to_4h(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.dense_4h_to_h(hidden_states)
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return hidden_states
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class GPTNeoXLayer(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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super().__init__()
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self.use_parallel_residual = config.use_parallel_residual
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.attention = GPTNeoXAttention(config)
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self.mlp = GPTNeoXMLP(config)
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def forward(
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self,
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position_ids: torch.LongTensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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attn_input = self.input_layernorm(hidden_states)
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attn_output = self.attention(
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position_ids=position_ids,
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hidden_states=attn_input,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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if self.use_parallel_residual:
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# pseudocode:
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# x = x + attn(ln1(x)) + mlp(ln2(x))
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mlp_input = self.post_attention_layernorm(hidden_states)
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mlp_output = self.mlp(mlp_input)
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hidden_states = mlp_output + attn_output + hidden_states
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else:
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# pseudocode:
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# x = x + attn(ln1(x))
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# x = x + mlp(ln2(x))
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attn_output = attn_output + hidden_states
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mlp_input = self.post_attention_layernorm(attn_output)
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mlp_output = self.mlp(mlp_input)
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hidden_states = mlp_output + attn_output
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return hidden_states
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class GPTNeoXModel(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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super().__init__()
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self.config = config
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self.embed_in = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
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perform_initialization=False)
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self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
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self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(
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self,
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input_ids: torch.LongTensor,
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position_ids: torch.LongTensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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hidden_states = self.embed_in(input_ids)
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for i in range(len(self.layers)):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.layers[i]
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hidden_states = layer(
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position_ids,
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hidden_states,
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kv_caches[i],
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input_metadata,
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cache_event,
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)
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hidden_states = self.final_layer_norm(hidden_states)
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return hidden_states
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class GPTNeoXForCausalLM(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.gpt_neox = GPTNeoXModel(config)
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self.embed_out = ColumnParallelLinear(config.hidden_size, config.vocab_size,
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bias=False, gather_output=False,
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perform_initialization=False)
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self.sampler = Sampler(config.vocab_size)
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.LongTensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> Dict[int, SequenceOutputs]:
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hidden_states = self.gpt_neox(
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input_ids, positions, kv_caches, input_metadata, cache_events)
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next_tokens = self.sampler(
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self.embed_out.weight, hidden_states, input_metadata)
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return next_tokens
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_column_parallel_weights = ["embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias"]
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_row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
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def load_weights(self, model_name_or_path: str,
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cache_dir: Optional[str] = None,
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use_np_cache: bool = False):
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tensor_model_parallel_rank = get_tensor_model_parallel_rank()
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state_dict = self.state_dict()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, use_np_cache):
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if ("attention.bias" in name or "attention.masked_bias" in name
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or "rotary_emb.inv_freq" in name):
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continue
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param = state_dict[name]
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if "query_key_value" in name:
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# NOTE(woosuk): GPT-NeoX's fused QKV has the shape of
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# [num_heads * 3 * head_size, hidden_size], while the
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# required shape is [3 * num_heads * head_size, hidden_size].
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# Thus, we need weight conversion.
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shard_size = param.shape[0]
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loaded_weight = loaded_weight[shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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num_heads = self.config.num_attention_heads
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hidden_size = self.config.hidden_size
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head_size = hidden_size // num_heads
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if 'query_key_value.weight' in name:
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loaded_weight = loaded_weight.view(-1, 3, head_size, hidden_size)
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loaded_weight = loaded_weight.transpose(0, 1)
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loaded_weight = loaded_weight.reshape(-1, hidden_size)
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elif 'query_key_value.bias' in name:
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loaded_weight = loaded_weight.view(-1, 3, head_size)
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loaded_weight = loaded_weight.transpose(0, 1)
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loaded_weight = loaded_weight.reshape(-1)
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else:
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raise ValueError(f"Unexpected weight name: {name}")
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load_tensor_parallel_weights(param, loaded_weight, name,
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self._column_parallel_weights,
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self._row_parallel_weights,
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tensor_model_parallel_rank)
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