304 lines
11 KiB
Python
304 lines
11 KiB
Python
"""1D LLaMA 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 transformers import LlamaConfig
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from cacheflow.models import InputMetadata
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from cacheflow.models.attention import LlamaCacheFlowAttention
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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 LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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return self.weight * hidden_states
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class LlamaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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):
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super().__init__()
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# TODO: Merge the gate and down linear layers.
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self.gate_proj = ColumnParallelLinear(hidden_size, intermediate_size,
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bias=False, gather_output=False,
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perform_initialization=False)
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self.down_proj = RowParallelLinear(intermediate_size, hidden_size,
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bias=False, input_is_parallel=True,
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perform_initialization=False)
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self.up_proj = ColumnParallelLinear(hidden_size, intermediate_size,
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bias=False, gather_output=False,
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perform_initialization=False)
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assert hidden_act == 'silu'
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self.act_fn = nn.SiLU()
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def forward(self, x):
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gate, _ = self.gate_proj(x)
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up, _ = self.up_proj(x)
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x = self.act_fn(gate) * up
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x, _ = self.down_proj(x)
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return x
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class LlamaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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):
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super().__init__()
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self.hidden_size = hidden_size
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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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.head_dim = hidden_size // self.total_num_heads
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self.scaling = self.head_dim ** -0.5
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# TODO: Merge the QKV linear layers.
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self.q_proj = ColumnParallelLinear(
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hidden_size,
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self.total_num_heads * self.head_dim,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.k_proj = ColumnParallelLinear(
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hidden_size,
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self.total_num_heads * self.head_dim,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.v_proj = ColumnParallelLinear(
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hidden_size,
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self.total_num_heads * self.head_dim,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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input_is_parallel=True,
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perform_initialization=False,
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)
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self.attn = LlamaCacheFlowAttention(self.scaling, self.head_dim)
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def forward(
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self,
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positions: 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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q, _ = self.q_proj(hidden_states)
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k, _ = self.k_proj(hidden_states)
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v, _ = self.v_proj(hidden_states)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(
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positions, q, k, v, k_cache, v_cache, input_metadata, cache_event)
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output, _ = self.o_proj(attn_output)
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return output
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class LlamaDecoderLayer(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = LlamaAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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)
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self.mlp = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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)
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self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: 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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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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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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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class LlamaModel(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
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perform_initialization=False)
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self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_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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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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) -> torch.Tensor:
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hidden_states = self.embed_tokens(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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positions,
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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.norm(hidden_states)
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return hidden_states
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class LlamaForCausalLM(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.model = LlamaModel(config)
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self.lm_head = ColumnParallelLinear(config.hidden_size,
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config.vocab_size,
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bias=False,
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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.model(
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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.lm_head.weight, hidden_states, input_metadata)
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return next_tokens
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_column_parallel_weights = ["embed_tokens.weight", "lm_head.weight",
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"q_proj.weight", "k_proj.weight",
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"v_proj.weight", "gate_proj.weight",
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"up_proj.weight"]
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_row_parallel_weights = ["o_proj.weight", "down_proj.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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loaded_weight = torch.from_numpy(np.load(os.path.join(weights_path,
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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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if not os.path.isfile(os.path.join(model_name, "config.json")):
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raise ValueError("LLaMA model's model_name has to be a path"
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"to the huggingface model's directory.")
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path = os.path.join(model_name, f"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(path, "model.embed_tokens.weight")
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if os.path.exists(test_weight_path):
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return path
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bin_files = glob.glob(os.path.join(model_name, "*.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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