279 lines
12 KiB
Python
279 lines
12 KiB
Python
"""1D GPT-NeoX model compatible with HuggingFace weights."""
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import os
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import glob
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import filelock
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from tqdm import tqdm
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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import torch
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from torch import nn
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from huggingface_hub import snapshot_download
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from cacheflow.models import InputMetadata
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from cacheflow.models.attention import GPTNeoXCacheFlowAttention
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from cacheflow.models.sample import Sampler
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from cacheflow.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.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
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ColumnParallelLinear,
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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):
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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(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):
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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):
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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):
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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()
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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, weights_path: str):
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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, param in state_dict.items():
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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, num_heads * head_size], while the
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# required shape is [3 * num_heads * head_size, num_heads * head_size].
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# Thus, we need weight conversion.
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loaded_weight = torch.from_numpy(
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np.load(os.path.join(weights_path, name)))
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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).contiguous()
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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).contiguous()
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else:
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assert False
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else:
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loaded_weight = torch.from_numpy(
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np.load(os.path.join(weights_path, name)))
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for p in self._column_parallel_weights:
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if p in name:
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shard_size = param.shape[0]
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loaded_weight = loaded_weight[
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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break
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for p in self._row_parallel_weights:
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if p in name:
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shard_size = param.shape[1]
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loaded_weight = loaded_weight[
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:,
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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break
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assert param.shape == loaded_weight.shape
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param.data.copy_(loaded_weight)
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@staticmethod
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def get_weights(model_name: str, path: str):
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path = os.path.join(path, f"{model_name}-np")
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path = os.path.abspath(os.path.expanduser(path))
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os.makedirs(path, exist_ok=True)
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lock_path = os.path.join(path, "file_lock")
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lock = filelock.FileLock(lock_path)
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with lock:
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test_weight_path = os.path.join(
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path, "gpt_neox.embed_in.weight")
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if os.path.exists(test_weight_path):
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return path
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folder = snapshot_download(model_name, allow_patterns="*.bin",
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cache_dir=os.path.join(path, "cache"))
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bin_files = glob.glob(os.path.join(folder, "*.bin"))
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for bin_file in tqdm(bin_files, desc="Convert format"):
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state = torch.load(bin_file, map_location="cpu")
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for name, param in tqdm(state.items(), leave=False):
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param_path = os.path.join(path, name)
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with open(param_path, "wb") as f:
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np.save(f, param.cpu().detach().numpy())
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return path
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def initialize_dummy_weights(self) -> None:
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for param in self.state_dict().values():
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param.data.uniform_(-1e-3, 1e-3)
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