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""" PyTorch DeciLM model.""" |
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import math |
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from typing import Optional, Tuple |
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import torch |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from torch import nn |
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from packaging import version |
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import transformers |
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if version.parse(transformers.__version__) < version.parse("4.31.0"): |
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raise ImportError( |
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f"You are using transformers=={transformers.__version__}, but transformers>=4.31.0 is required to use DeciLM. Please upgrade transformers." |
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) |
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from transformers.models.llama.modeling_llama import LlamaMLP, LlamaRMSNorm, LlamaAttention, apply_rotary_pos_emb, \ |
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repeat_kv, LlamaPreTrainedModel, LLAMA_START_DOCSTRING, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel |
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from transformers.utils import add_start_docstrings |
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from .configuration_decilm import DeciLMConfig |
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_CONFIG_FOR_DOC = "DeciLMConfig" |
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class DeciLMAttention(LlamaAttention): |
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"""Multi-headed attention from 'Attention Is All You Need' paper""" |
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def __init__(self, config: DeciLMConfig, layer_idx: int): |
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nn.Module.__init__(self) |
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self.config = config |
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self.hidden_size = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.hidden_size // self.num_heads |
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self.layer_idx = layer_idx |
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self.num_key_value_heads = config.num_key_value_heads_per_layer[layer_idx] |
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads |
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self.pretraining_tp = config.pretraining_tp |
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self.max_position_embeddings = config.max_position_embeddings |
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self.rope_theta = getattr(config, 'rope_theta', None) |
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if (self.head_dim * self.num_heads) != self.hidden_size: |
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raise ValueError( |
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" |
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f" and `num_heads`: {self.num_heads})." |
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) |
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) |
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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.naive_attention_prefill = config.naive_attention_prefill |
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self.naive_attention_decode_batched = config.naive_attention_decode_batched |
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self.naive_attention_decode_single = config.naive_attention_decode_single |
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self._init_rope() |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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attention_mask: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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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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padding_mask: Optional[torch.LongTensor] = None, |
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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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if past_key_value is None: |
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is_decode = False |
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else: |
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is_decode = True |
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if self.pretraining_tp > 1: |
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key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp |
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query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) |
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) |
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) |
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query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] |
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query_states = torch.cat(query_states, dim=-1) |
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key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] |
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key_states = torch.cat(key_states, dim=-1) |
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value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] |
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value_states = torch.cat(value_states, dim=-1) |
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else: |
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query_states = self.q_proj(hidden_states) |
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key_states = self.k_proj(hidden_states) |
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value_states = self.v_proj(hidden_states) |
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
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kv_seq_len = key_states.shape[-2] |
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if past_key_value is not None: |
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kv_seq_len += past_key_value[0].shape[-2] |
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) |
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if past_key_value is not None: |
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key_states = torch.cat([past_key_value[0], key_states], dim=2) |
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value_states = torch.cat([past_key_value[1], value_states], dim=2) |
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past_key_value = (key_states, value_states) if use_cache else None |
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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 is_decode: |
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if self.naive_attention_decode_batched and bsz > 1 or self.naive_attention_decode_single and bsz == 1: |
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attn_weights = (query_states @ key_states.transpose(-2, -1)) / math.sqrt(key_states.size(-1)) |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
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if attention_mask is not None: |
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
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raise ValueError( |
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
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) |
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attn_weights = attn_weights + attention_mask |
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attn_output = torch.matmul(attn_weights, value_states) |
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else: |
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=False, |
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dropout_p=0.0) |
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attn_output = attn_output.contiguous().view(bsz, q_len, self.hidden_size) |
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else: |
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if not self.naive_attention_prefill: |
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with torch.backends.cuda.sdp_kernel(enable_math=True, enable_flash=False, enable_mem_efficient=False): |
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=True, |
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dropout_p=0.0) |
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else: |
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) |
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
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raise ValueError( |
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
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f" {attn_weights.size()}" |
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) |
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if attention_mask is not None: |
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
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raise ValueError( |
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
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) |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) |
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attn_output = torch.matmul(attn_weights, value_states) |
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
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raise ValueError( |
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" |
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f" {attn_output.size()}" |
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) |
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attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size) |
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if self.pretraining_tp > 1: |
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attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2) |
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o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1) |
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attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)]) |
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else: |
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attn_output = self.o_proj(attn_output) |
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if not output_attentions: |
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attn_weights = None |
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return attn_output, attn_weights, past_key_value |
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class DeciLMDecoderLayer(LlamaDecoderLayer): |
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def __init__(self, config: DeciLMConfig, layer_idx: int): |
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nn.Module.__init__(self) |
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self.hidden_size = config.hidden_size |
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self.layer_idx = layer_idx |
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self.self_attn = DeciLMAttention(config=config, layer_idx=layer_idx) |
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self.mlp = LlamaMLP(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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@add_start_docstrings( |
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"The bare DeciLM Model outputting raw hidden-states without any specific head on top.", |
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LLAMA_START_DOCSTRING, |
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) |
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class DeciLMPreTrainedModel(LlamaPreTrainedModel): |
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config_class = DeciLMConfig |
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_no_split_modules = ["DeciLMDecoderLayer"] |
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_keys_to_ignore_on_load_missing = ["self_attn.rotary_emb.inv_freq"] |
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@add_start_docstrings( |
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"The bare DeciLM Model outputting raw hidden-states without any specific head on top.", |
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LLAMA_START_DOCSTRING, |
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) |
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class DeciLMModel(LlamaModel, DeciLMPreTrainedModel): |
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""" |
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Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciLMDecoderLayer`] |
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Args: |
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config: DeciLMConfig |
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""" |
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def __init__(self, config: DeciLMConfig): |
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DeciLMPreTrainedModel.__init__(self, 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 = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) |
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self.layers = nn.ModuleList([DeciLMDecoderLayer(config, layer_idx) for layer_idx |
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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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self.gradient_checkpointing = False |
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self.post_init() |
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def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): |
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self._validate_config_supports_attention_mask(attention_mask, input_shape, past_key_values_length) |
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return LlamaModel._prepare_decoder_attention_mask( |
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self, attention_mask, input_shape, inputs_embeds, past_key_values_length) |
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def _validate_config_supports_attention_mask(self, attention_mask, input_shape, past_key_values_length): |
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is_decode = past_key_values_length > 0 |
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if not torch.all(torch.eq(attention_mask, 1)).item(): |
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if is_decode: |
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if input_shape[0] == 1 and not self.config.naive_attention_decode_single: |
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raise ValueError( |
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"For support of custom attention masks please set naive_attention_decode_single to True in the " |
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"config") |
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elif input_shape[0] > 1 and not self.config.naive_attention_decode_batched: |
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raise ValueError( |
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"For support of custom attention masks please set naive_attention_decode_batched to True in the" |
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"config") |
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else: |
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if not self.config.naive_attention_prefill: |
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raise ValueError("For support of custom attention masks please set naive_attention_prefill to " |
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"True in the config") |
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class DeciLMForCausalLM(LlamaForCausalLM, DeciLMPreTrainedModel): |
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def __init__(self, config): |
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DeciLMPreTrainedModel.__init__(self, config) |
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self.model = DeciLMModel(config) |
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self.pretraining_tp = config.pretraining_tp |
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self.vocab_size = config.vocab_size |
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
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self.post_init() |
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