Commit
•
b9db556
1
Parent(s):
43447fe
Upload 3 files
Browse files- modeling_openmoe.py +53 -37
- tokenization_openmoe.py +22 -0
- tokenizer_config.json +2445 -2
modeling_openmoe.py
CHANGED
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@@ -27,7 +27,9 @@ import torch.utils.checkpoint
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from torch import nn
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.models.llama.
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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@@ -41,6 +43,8 @@ from colossalai.moe.layers import SparseMLP
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.moe.utils import get_activation, set_moe_args
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if HAS_TRITON:
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from colossalai.kernel.triton.llama_act_combine_kernel import LlamaActCombine
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@@ -157,30 +161,6 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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def generate_fixed_pos_embedding(features, length, min_timescale=1.0, max_timescale=10000.0):
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"""Generate Sin/Cos for Rotary Embeddings.
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Args:
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features: an integer
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length: an integer
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min_timescale: an optional float
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max_timescale: an optional float
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Returns:
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output_sin: a float32 Tensor with shape [length, features]
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output_cos: a float32 Tensor with shape [length, features]
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"""
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fraction = torch.arange(0, features, 2, dtype=torch.float32) / features
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timescale = min_timescale * (max_timescale / min_timescale) ** fraction
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rotational_frequency = 1.0 / timescale
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sinusoid_inp = torch.einsum("i,j->ij", torch.arange(length, dtype=torch.float32), rotational_frequency)
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sinusoid_inp = torch.cat([sinusoid_inp, sinusoid_inp], dim=-1)
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return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)
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def apply_rotary_embedding(q, k, cos, sin, decode=False, rotary_index=None):
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# q: (bs, q_len, num_heads, head_dim)
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# k: (bs, q_len [+past_kv_len], num_heads, head_dim)
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@@ -229,6 +209,21 @@ def rotate_half(x):
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def SwiGLU(x):
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"""Gated linear unit activation function.
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@@ -307,13 +302,37 @@ class OpenMoeAttention(nn.Module):
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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@@ -322,7 +341,6 @@ class OpenMoeAttention(nn.Module):
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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use_kernel: bool = True,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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@@ -378,12 +396,9 @@ class OpenMoeAttention(nn.Module):
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if HAS_FLASH_ATTN and use_kernel:
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# Here is a workaround to avoid the error.
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exec("from flash_attn import flash_attn_func")
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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@@ -439,7 +454,8 @@ class OpenMoeDecoderLayer(nn.Module):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.moe = moe
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self.self_attn = OpenMoeAttention(config=config)
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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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if self.moe:
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@@ -553,7 +569,7 @@ class OpenMoePreTrainedModel(PreTrainedModel):
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config_class = LlamaConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["
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_skip_keys_device_placement = "past_key_values"
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def _init_weights(self, module):
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from torch import nn
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.models.llama.configuration_llama import LlamaConfig
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# from .llama_attn import LlamaAttention
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.moe.utils import get_activation, set_moe_args
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if HAS_TRITON:
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from colossalai.kernel.triton.llama_act_combine_kernel import LlamaActCombine
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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def apply_rotary_embedding(q, k, cos, sin, decode=False, rotary_index=None):
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# q: (bs, q_len, num_heads, head_dim)
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# k: (bs, q_len [+past_kv_len], num_heads, head_dim)
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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class LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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LlamaRMSNorm is equivalent to T5LayerNorm
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"""
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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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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def SwiGLU(x):
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"""Gated linear unit activation function.
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.generate_fixed_pos_embedding(self.head_dim, self.max_position_embeddings, 1.0, 1e4)
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self.use_kernel = config.enable_kernel
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def generate_fixed_pos_embedding(self, features, length, min_timescale=1.0, max_timescale=10000.0):
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"""Generate Sin/Cos for Rotary Embeddings.
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Args:
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features: an integer
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length: an integer
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min_timescale: an optional float
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max_timescale: an optional float
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Returns:
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output_sin: a float32 Tensor with shape [length, features]
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output_cos: a float32 Tensor with shape [length, features]
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"""
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fraction = torch.arange(0, features, 2, dtype=torch.float32) / features
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timescale = min_timescale * (max_timescale / min_timescale) ** fraction
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rotational_frequency = 1.0 / timescale
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sinusoid_inp = torch.einsum("i,j->ij", torch.arange(length, dtype=torch.float32), rotational_frequency)
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sinusoid_inp = torch.cat([sinusoid_inp, sinusoid_inp], dim=-1)
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self.register_buffer('sin', torch.sin(sinusoid_inp), persistent=False) # persistent=False --> buffer won't appear in the state_dict
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self.register_buffer('cos', torch.cos(sinusoid_inp), persistent=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if HAS_FLASH_ATTN and self.use_kernel:
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from flash_attn import flash_attn_func
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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super().__init__()
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self.hidden_size = config.hidden_size
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self.moe = moe
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self.self_attn = OpenMoeAttention(config=config)
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# self.self_attn = LlamaAttention(config=config) # TODO: introduce LLaMA Positional Encoding
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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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if self.moe:
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config_class = LlamaConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["OpenMoeDecoderLayer"]
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_skip_keys_device_placement = "past_key_values"
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def _init_weights(self, module):
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tokenization_openmoe.py
ADDED
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from transformers import T5Tokenizer
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from typing import List, Optional, Tuple, Union
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class OpenMoeTokenizer(T5Tokenizer):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.padding_side = 'left'
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self.add_bos_token = True
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self.add_eos_token = False
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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if self.add_eos_token:
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token_ids_0 = self._add_eos_if_not_present(token_ids_0)
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if self.add_bos_token:
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token_ids_0 = [self.pad_token_id] + token_ids_0
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if token_ids_1 is None:
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return token_ids_0
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else:
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token_ids_1 = self._add_eos_if_not_present(token_ids_1)
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return token_ids_0 + token_ids_1
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tokenizer_config.json
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|
| 2 |
"additional_special_tokens": [
|
| 3 |
"<extra_id_0>",
|
| 4 |
"<extra_id_1>",
|
|
@@ -305,10 +2739,19 @@
|
|
| 305 |
"clean_up_tokenization_spaces": true,
|
| 306 |
"eos_token": "</s>",
|
| 307 |
"extra_ids": 300,
|
|
|
|
| 308 |
"model_max_length": 1000000000000000019884624838656,
|
| 309 |
"pad_token": "<pad>",
|
| 310 |
"sp_model_kwargs": {},
|
| 311 |
"spaces_between_special_tokens": false,
|
| 312 |
-
"tokenizer_class": "
|
| 313 |
-
"
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
| 314 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<pad>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": false
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "</s>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": false
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "<s>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": false
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": false
|
| 34 |
+
},
|
| 35 |
+
"256000": {
|
| 36 |
+
"content": "<extra_id_299>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": false
|
| 42 |
+
},
|
| 43 |
+
"256001": {
|
| 44 |
+
"content": "<extra_id_298>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": false
|
| 50 |
+
},
|
| 51 |
+
"256002": {
|
| 52 |
+
"content": "<extra_id_297>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": false
|
| 58 |
+
},
|
| 59 |
+
"256003": {
|
| 60 |
+
"content": "<extra_id_296>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": false
|
| 66 |
+
},
|
| 67 |
+
"256004": {
|
| 68 |
+
"content": "<extra_id_295>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": false
|
| 74 |
+
},
|
| 75 |
+
"256005": {
|
| 76 |
+
"content": "<extra_id_294>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": false
|
| 82 |
+
},
|
| 83 |
+
"256006": {
|
| 84 |
+
"content": "<extra_id_293>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": false
|
| 90 |
+
},
|
| 91 |
+
"256007": {
|
| 92 |
+
"content": "<extra_id_292>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": false
|
| 98 |
+
},
|
| 99 |
+
"256008": {
|
| 100 |
+
"content": "<extra_id_291>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": false
|
| 106 |
+
},
|
| 107 |
+
"256009": {
|
| 108 |
+
"content": "<extra_id_290>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": false
|
| 114 |
+
},
|
| 115 |
+
"256010": {
|
| 116 |
+
"content": "<extra_id_289>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": false
|
| 122 |
+
},
|
| 123 |
+
"256011": {
|
| 124 |
+
"content": "<extra_id_288>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": false
|
| 130 |
+
},
|
| 131 |
+
"256012": {
|
| 132 |
+
"content": "<extra_id_287>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": false
|
| 138 |
+
},
|
| 139 |
+
"256013": {
|
| 140 |
+
"content": "<extra_id_286>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": false
|
| 146 |
+
},
|
| 147 |
+
"256014": {
|
| 148 |
+
"content": "<extra_id_285>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": false
|
| 154 |
+
},
|
| 155 |
+
"256015": {
|
| 156 |
+
"content": "<extra_id_284>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": false
|
| 162 |
+
},
|
| 163 |
+
"256016": {
|
| 164 |
+
"content": "<extra_id_283>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": false
|
| 170 |
+
},
|
| 171 |
+
"256017": {
|
| 172 |
+
"content": "<extra_id_282>",
|
| 173 |
+
"lstrip": false,
|
| 174 |
+
"normalized": false,
|
| 175 |
+
"rstrip": false,
|
| 176 |
+
"single_word": false,
|
| 177 |
+
"special": false
|
| 178 |
+
},
|
| 179 |
+
"256018": {
|
| 180 |
+
"content": "<extra_id_281>",
|
| 181 |
+
"lstrip": false,
|
| 182 |
+
"normalized": false,
|
| 183 |
+
"rstrip": false,
|
| 184 |
+
"single_word": false,
|
| 185 |
+
"special": false
|
| 186 |
+
},
|
| 187 |
+
"256019": {
|
| 188 |
+
"content": "<extra_id_280>",
|
| 189 |
+
"lstrip": false,
|
| 190 |
+
"normalized": false,
|
| 191 |
+
"rstrip": false,
|
| 192 |
+
"single_word": false,
|
| 193 |
+
"special": false
|
| 194 |
+
},
|
| 195 |
+
"256020": {
|
| 196 |
+
"content": "<extra_id_279>",
|
| 197 |
+
"lstrip": false,
|
| 198 |
+
"normalized": false,
|
| 199 |
+
"rstrip": false,
|
| 200 |
+
"single_word": false,
|
| 201 |
+
"special": false
|
| 202 |
+
},
|
| 203 |
+
"256021": {
|
| 204 |
+
"content": "<extra_id_278>",
|
| 205 |
+
"lstrip": false,
|
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| 2332 |
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| 2410 |
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| 2411 |
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| 2426 |
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},
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| 2427 |
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| 2433 |
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| 2434 |
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}
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| 2435 |
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},
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| 2436 |
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|
| 2437 |
"<extra_id_0>",
|
| 2438 |
"<extra_id_1>",
|
|
|
|
| 2739 |
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| 2740 |
"eos_token": "</s>",
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| 2741 |
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"legacy": false,
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| 2743 |
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| 2744 |
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| 2745 |
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| 2746 |
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|
| 2747 |
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"tokenizer_class": "OpenMoeTokenizer",
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| 2748 |
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"trust_remote_code": true,
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| 2749 |
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"unk_token": "<unk>",
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| 2750 |
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"verbose": false,
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| 2751 |
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"auto_map": {
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| 2752 |
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"AutoTokenizer": [
|
| 2753 |
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"tokenization_openmoe.OpenMoeTokenizer",
|
| 2754 |
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null
|
| 2755 |
+
]
|
| 2756 |
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}
|
| 2757 |
}
|