[Model Support]: Magihuman support (#2301)
Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: Didan Deng <33117903+wtomin@users.noreply.github.com>
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from __future__ import annotations
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.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 vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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class T5GemmaRMSNorm(nn.Module):
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def __init__(self, hidden_size: int, eps: float = 1e-6):
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super().__init__()
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# Normal RMSNorm but T5Gemma requires (1 + weight)
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self.weight = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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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 (hidden_states * (1.0 + self.weight.float())).to(input_dtype)
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class T5GemmaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size, intermediate_size],
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bias=False,
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gather_output=False,
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=False,
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input_is_parallel=True,
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)
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self.act_fn = get_act_fn(hidden_act)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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gate, up = gate_up.chunk(2, dim=-1)
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x = self.act_fn(gate) * up
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x, _ = self.down_proj(x)
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return x
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class T5GemmaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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max_position_embeddings: int,
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rope_theta: float,
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cache_config: VllmConfig | None = None,
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quant_config: dict | None = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=False,
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)
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=False,
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input_is_parallel=True,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=max_position_embeddings,
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is_neox_style=True,
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rope_parameters={"base": rope_theta},
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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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attention_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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# Scale Q appropriately. T5Gemma uses query_pre_attn_scalar=256 => 256**-0.5 = 1/16
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# The standard scaling is head_dim**-0.5. For T5Gemma, head_dim=256.
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# So we don't need to manually scale if F.scaled_dot_product_attention scales by head_dim.
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# But we must reshape.
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batch_size, seq_len, _ = hidden_states.shape
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q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
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k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
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v = v.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
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# GQA repeat KV
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if self.num_kv_heads != self.num_heads:
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num_repeat = self.num_heads // self.num_kv_heads
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k = k.repeat_interleave(num_repeat, dim=1)
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v = v.repeat_interleave(num_repeat, dim=1)
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attn_output = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attention_mask, dropout_p=0.0)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.view(batch_size, seq_len, self.q_size)
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output, _ = self.o_proj(attn_output)
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return output
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class T5GemmaEncoderLayer(nn.Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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self.self_attn = T5GemmaAttention(
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hidden_size=config.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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max_position_embeddings=config.max_position_embeddings,
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rope_theta=config.rope_theta,
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)
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self.mlp = T5GemmaMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_activation,
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)
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self.pre_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pre_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None,
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) -> torch.Tensor:
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# Self Attention
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residual = hidden_states
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hidden_states = self.pre_self_attn_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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attention_mask=attention_mask,
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)
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hidden_states = self.post_self_attn_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.pre_feedforward_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_feedforward_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class T5GemmaEncoderModelTP(nn.Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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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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)
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self.layers = nn.ModuleList([T5GemmaEncoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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@property
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def dtype(self) -> torch.dtype:
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return next(self.parameters()).dtype
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@property
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def device(self) -> torch.device:
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return next(self.parameters()).device
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def forward(
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self,
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input_ids: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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# Scaling inputs
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normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype, device=hidden_states.device)
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hidden_states = hidden_states * normalizer
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# Simple position ids for RoPE
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batch_size, seq_len = input_ids.shape
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positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.long).unsqueeze(0).expand(batch_size, -1)
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# Build attention mask: (batch, seq) -> (batch, 1, 1, seq)
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# Assuming typical bidirectional causal mask handling in HF: T5Gemma uses non-causal encoder.
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if attention_mask is not None:
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# HuggingFace expects boolean mask for scaled_dot_product_attention
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# or additive mask (0 and -inf). Let's use boolean matching FA patterns.
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# SDPA expects attention_mask to be boolean (True = keep, False = masking)
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bool_mask = attention_mask.to(torch.bool)
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extended_mask = bool_mask.unsqueeze(1).unsqueeze(2) # (B, 1, 1, S)
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else:
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extended_mask = None
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for idx, layer in enumerate(self.layers):
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# T5Gemma has layer_types switching between "sliding_attention" and "full_attention"
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# However, for text encoder inference, the sequences are typically < max sequence length
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# and local sliding window only affects very long contexts. For simplicity we use full.
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hidden_states = layer(
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positions=positions,
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hidden_states=hidden_states,
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attention_mask=extended_mask,
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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# HF checkpoint keys may carry a "model." prefix (e.g.
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# "model.encoder.layers.0..."). Strip it so the rest of the
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# logic only needs to handle the "encoder.*" namespace.
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if name.startswith("model."):
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name = name[len("model.") :]
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if not name.startswith("encoder."):
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continue
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# Strip "encoder." prefix as this model only wraps the encoder
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name = name[len("encoder.") :]
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# Map self_attn to self_attn and correct normalization names
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# HF: layers.0.pre_self_attn_layernorm.weight -> Ours: layers.0.pre_self_attn_layernorm.weight
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lookup_name = name
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if f".{weight_name}." not in name:
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continue
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lookup_name = name.replace(f".{weight_name}.", f".{param_name}.")
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if lookup_name not in params_dict:
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continue
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param = params_dict[lookup_name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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if name not in params_dict:
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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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loaded_params.add("encoder." + name)
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loaded_params.add("encoder." + lookup_name)
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return loaded_params
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