462 lines
15 KiB
Python
462 lines
15 KiB
Python
import math
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from typing import Iterable, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import Phi3Config
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from transformers.configuration_utils import PretrainedConfig
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix, make_layers
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@torch.jit.script
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def quick_gelu(x):
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return x * torch.sigmoid(1.702 * x)
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@torch.jit.script
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def gegelu(input, limit: Optional[float] = None):
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a_gelu, a_linear = input[..., ::2], input[..., 1::2]
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if limit is not None:
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a_gelu = torch.where(
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torch.isinf(a_gelu), a_gelu, a_gelu.clamp(min=None, max=limit)
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)
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a_linear = torch.where(
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torch.isinf(a_linear),
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a_linear,
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a_linear.clamp(min=-limit, max=limit),
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)
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out_gelu = quick_gelu(a_gelu)
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return out_gelu * (a_linear + 1)
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class Phi3SmallMLP(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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assert (
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self.config.hidden_act == "gegelu"
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), "Only `gegelu` is supported for the 4.7 series of models .."
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self.hidden_size = config.hidden_size
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self.gegelu_limit = config.gegelu_limit
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self.intermediate_size = config.intermediate_size
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self.up_proj = MergedColumnParallelLinear(
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self.hidden_size,
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2 * [self.intermediate_size],
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bias=True,
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quant_config=quant_config,
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prefix=add_prefix("up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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self.intermediate_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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)
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def forward(self, x):
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gate_up, _ = self.up_proj(x)
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x = gegelu(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Phi3SmallSelfAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.config = config
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self.sparse_block_size = config.blocksparse_block_size
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self.homo_heads = config.blocksparse_homo_head_pattern
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self.local_blocks = config.blocksparse_num_local_blocks
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self.vert_stride = config.blocksparse_vert_stride
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assert (
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config.blocksparse_block_size == config.blocksparse_triton_kernel_block_size
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)
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self.hidden_size = config.hidden_size
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# Number of Query Heads
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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.tp_size = get_tensor_model_parallel_world_size()
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# Number of total Key Value Heads before tensor parallel
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self.num_key_value_heads = config.num_key_value_heads
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self.num_q_per_kv = self.num_heads // self.num_key_value_heads
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if self.tp_size > 1:
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assert self.num_key_value_heads % self.tp_size == 0
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self.num_kv_heads_per_partion = max(1, self.num_key_value_heads // self.tp_size)
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self.num_heads_per_partition = self.num_heads // self.tp_size
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_embedding_base = config.rope_embedding_base
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self.rope_position_scale = config.rope_position_scale
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self.is_causal = True
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norm_factor = None
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if config.mup_use_scaling:
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norm_factor = self.head_dim / config.mup_attn_multiplier
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else:
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norm_factor = math.sqrt(self.head_dim)
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self.scale = 1 / norm_factor
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.num_heads,
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self.num_key_value_heads,
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bias=True,
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quant_config=quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.dense = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=add_prefix("o_proj", prefix),
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)
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if getattr(self.config, "rope_scaling", None) is not None:
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rope_scaling = self.config.rope_scaling
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for key in rope_scaling:
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if isinstance(rope_scaling[key], list):
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rope_scaling[key] = tuple(rope_scaling[key])
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if "factor" not in rope_scaling:
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rope_scaling["factor"] = self.rope_position_scale
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else:
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rope_scaling = {
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"rope_type": "linear",
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"factor": self.rope_position_scale,
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}
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_embedding_base,
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rope_scaling=rope_scaling,
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)
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# blocksparse params
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self.blocksparse_block_size = config.blocksparse_block_size
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self.blocksparse_num_local_blocks = config.blocksparse_num_local_blocks
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self.blocksparse_vert_stride = config.blocksparse_vert_stride
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use_dense_attn = (
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getattr(self.config, "dense_attention_every_n_layers", None)
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and (self.layer_id + 1) % self.config.dense_attention_every_n_layers == 0
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)
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bs_params = None
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if not use_dense_attn:
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bs_params = {
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"max_seqlen": self.max_position_embeddings,
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"num_heads": self.num_heads_per_partition,
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"num_kv_heads": self.num_kv_heads_per_partion,
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"block_size": self.sparse_block_size,
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"local_blocks": self.local_blocks,
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"vert_stride": self.vert_stride,
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"homo_head": self.homo_heads,
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}
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self.attn = RadixAttention(
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self.num_heads_per_partition,
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self.head_dim,
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self.scale,
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num_kv_heads=self.num_kv_heads_per_partion,
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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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qkv, _ = self.query_key_value(hidden_states)
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qkv = qkv.view(qkv.shape[:-1] + (-1, (self.num_q_per_kv + 2), self.head_dim))
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q, k, v = qkv.split([self.num_q_per_kv, 1, 1], dim=-2)
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# NOTE: this is required by RotaryEmbed, which indeed does not have to
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# TODO: allow 3D QK for rotary forward
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q = q.reshape(-1, self.head_dim * self.num_heads_per_partition)
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k = k.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
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v = v.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch=forward_batch)
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output, _ = self.dense(attn_output)
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return output
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class Phi3SmallDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Phi3SmallSelfAttention(
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config,
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layer_id,
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quant_config=quant_config,
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prefix=add_prefix("self_attn", prefix),
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)
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self.mlp = Phi3SmallMLP(
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config,
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quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_epsilon
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)
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_epsilon
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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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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forward_batch=forward_batch,
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)
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hidden_states = residual + hidden_states
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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 Phi3SmallModel(nn.Module):
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def __init__(
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self,
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config: Phi3Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.mup_embedding_multiplier = config.mup_embedding_multiplier
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: Phi3SmallDecoderLayer(
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config,
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int(prefix.split(".")[-1]),
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quant_config,
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prefix=prefix,
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),
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prefix=add_prefix("layers", prefix),
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)
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self.final_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_epsilon
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: Optional[torch.LongTensor],
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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor],
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) -> Union[torch.Tensor]:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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if (
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self.mup_embedding_multiplier is not None
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and self.mup_embedding_multiplier > 0.0
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):
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hidden_states = hidden_states * self.mup_embedding_multiplier
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states = layer(positions, hidden_states, forward_batch=forward_batch)
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class Phi3SmallForCausalLM(nn.Module):
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(
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self,
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config: Phi3Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = Phi3SmallModel(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("model", prefix),
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)
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self.vocab_size = config.vocab_size
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self.mup_width_multiplier = config.mup_width_multiplier
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self.lm_head = ParallelLMHead(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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# tokens in tiktoken but not used
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if hasattr(config, "dummy_token_indices"):
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device = self.lm_head.weight.device
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self.register_buffer(
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"dummy_token_indices",
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torch.LongTensor(config.dummy_token_indices).to(device),
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persistent=False,
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)
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else:
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self.dummy_token_indices = None
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def set_input_embeddings(self, value):
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self.model.embed_tokens = value
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def get_output_embeddings(self):
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return self.lm_head
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def set_output_embeddings(self, value):
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self.lm_head = value
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def set_decoder(self, decoder):
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self.model = decoder
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def get_decoder(self):
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return self.model
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def compute_logits(
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self,
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input_ids: torch.LongTensor,
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hidden_states: torch.Tensor,
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sampling_metadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(
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input_ids, self.lm_head, hidden_states, sampling_metadata
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)
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if self.dummy_token_indices is not None and logits is not None:
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logits.index_fill_(-1, self.dummy_token_indices, -torch.inf)
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return logits
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: Optional[torch.LongTensor],
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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor] = None,
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get_embedding: bool = False,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(
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input_ids=input_ids,
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positions=positions,
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forward_batch=forward_batch,
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inputs_embeds=inputs_embeds,
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)
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if not get_embedding:
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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else:
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return self.pooler(hidden_states, forward_batch)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if name.endswith(".bias") and name not in params_dict:
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continue
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = Phi3SmallForCausalLM
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