322 lines
10 KiB
Python
322 lines
10 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/phi.py
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from typing import Iterable, Optional, Union
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import torch
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from torch import nn
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from transformers import PhiConfig
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from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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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.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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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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class PhiAttention(nn.Module):
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def __init__(
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self,
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config: PhiConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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layer_id: int = 0,
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):
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super().__init__()
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self.total_num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.total_num_heads
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
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self.qkv_proj = QKVParallelLinear(
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self.hidden_size,
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self.head_size,
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self.total_num_heads,
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bias=True,
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quant_config=quant_config,
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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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quant_config=quant_config,
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)
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scaling = self.head_size**-0.5
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rotary_dim = int(
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config.partial_rotary_factor
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* (config.hidden_size // config.num_attention_heads)
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)
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assert rotary_dim % 2 == 0
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rope_theta = getattr(config, "rope_theta", 10000.0)
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max_position_embeddings = getattr(config, "max_position_embeddings", 2048)
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self.rotary_emb = get_rope(
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self.head_size,
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rotary_dim=rotary_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_size,
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scaling,
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num_kv_heads=self.num_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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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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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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q, k = self.rotary_emb(position_ids, 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 PhiMLP(nn.Module):
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def __init__(
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self, config: PhiConfig, quant_config: Optional[QuantizationConfig] = None
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):
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super().__init__()
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n_inner = getattr(config, "n_inner", None)
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n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
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self.fc1 = ColumnParallelLinear(
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config.hidden_size,
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n_inner,
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quant_config=quant_config,
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)
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self.fc2 = RowParallelLinear(
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n_inner,
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config.hidden_size,
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quant_config=quant_config,
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)
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self.act = get_act_fn(config.hidden_act)
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def forward(self, hidden_states):
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hidden_states, _ = self.fc1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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return hidden_states
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class PhiLayer(nn.Module):
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def __init__(
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self,
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config: PhiConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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idx: int = 0,
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):
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super().__init__()
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps
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)
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self.self_attn = PhiAttention(
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config,
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quant_config,
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prefix=add_prefix("self_attn", prefix),
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layer_id=idx,
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)
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self.mlp = PhiMLP(config, quant_config)
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def forward(
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self,
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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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hidden_states: torch.Tensor,
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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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attn_outputs = self.self_attn(
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position_ids=position_ids,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = attn_outputs + feed_forward_hidden_states + residual
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return hidden_states
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class PhiModel(nn.Module):
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def __init__(
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self,
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config: PhiConfig,
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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, config.hidden_size
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)
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pp_group = get_pp_group()
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pp_size = pp_group.world_size
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pp_rank = pp_group.rank
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self.start_layer = pp_rank * config.num_hidden_layers // pp_size
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self.end_layer = (pp_rank + 1) * config.num_hidden_layers // pp_size
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: PhiLayer(
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config, quant_config=quant_config, prefix=prefix, idx=idx
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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_eps
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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.Tensor,
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forward_batch: ForwardBatch,
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positions: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> 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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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(
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position_ids=positions,
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forward_batch=forward_batch,
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hidden_states=hidden_states,
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)
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class PhiForCausalLM(nn.Module):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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]
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}
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def __init__(
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self,
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config: PhiConfig,
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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 = PhiModel(
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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.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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bias=True,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config)
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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 forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor] = None,
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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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forward_batch=forward_batch,
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positions=positions,
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inputs_embeds=inputs_embeds,
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)
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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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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters())
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weights = dict(weights)
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loaded_keys = set()
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for name, param in params_dict.items():
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if name in loaded_keys:
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continue
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# Handle packed weights
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is_packed = False
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for packed_name, src_names in self.packed_modules_mapping.items():
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if packed_name not in name:
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continue
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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for src_name in src_names:
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full_src_name = name.replace(packed_name, src_name)
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if full_src_name in weights:
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loaded_weight = weights[full_src_name]
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# The shard_id for QKVParallelLinear is 'q', 'k', 'v'.
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shard_id = src_name.split("_")[0]
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weight_loader(param, loaded_weight, shard_id)
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loaded_keys.add(full_src_name)
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loaded_keys.add(name)
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is_packed = True
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break
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if is_packed:
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continue
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# Handle non-packed weights
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if name not in weights:
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# Redundant with the check in the loop, but good for safety
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continue
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loaded_weight = weights[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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loaded_keys.add(name)
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EntryClass = PhiForCausalLM
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