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397
model_executor/models/jais.py
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397
model_executor/models/jais.py
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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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# Adapted from
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# https://huggingface.co/inceptionai/jais-30b-chat-v3/blob/main/modeling_jais.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 the Jais authors and HuggingFace Inc. team. All rights
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# reserved.
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# Copyright 2023 Cerebras Systems.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only Jais model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.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 vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs import JAISConfig
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from .interfaces import SupportsPP
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from .utils import (
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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class SwiGLUActivation(nn.Module):
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def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
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return x1 * nn.functional.silu(x2)
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def _get_alibi_slopes(n):
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def get_slopes_power_of_2(n):
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start = 2 ** (-(2 ** -(math.log2(n) - 3)))
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ratio = start
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return [start * ratio**i for i in range(n)]
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if math.log2(n).is_integer():
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return get_slopes_power_of_2(n)
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else:
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closest_power_of_2 = 2 ** math.floor(math.log2(n))
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return (
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get_slopes_power_of_2(closest_power_of_2)
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+ _get_alibi_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
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)
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class JAISAttention(nn.Module):
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def __init__(
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self,
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config: JAISConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = 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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total_num_heads = config.num_attention_heads
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = total_num_heads // tensor_model_parallel_world_size
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self.head_dim = self.hidden_size // total_num_heads
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if hasattr(config, "scale_qk_dot_by_d"):
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config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
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self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
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self.scale = self.head_dim**-self.attn_scale_power
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self.c_attn = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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total_num_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_attn",
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)
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self.c_proj = 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=f"{prefix}.c_proj",
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)
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = _get_alibi_slopes(total_num_heads)
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alibi_slopes = alibi_slopes[head_start:head_end]
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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scale=self.scale,
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alibi_slopes=alibi_slopes,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.c_attn(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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attn_output = self.attn(q, k, v)
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attn_output, _ = self.c_proj(attn_output)
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return attn_output
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class JAISMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: JAISConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.swiglu = config.activation_function == "swiglu"
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self.c_fc = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_fc",
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)
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self.c_fc2 = (
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ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_fc2",
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)
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if self.swiglu
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else None
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)
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self.c_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.c_proj",
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)
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self.act = SwiGLUActivation()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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if self.swiglu:
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hidden_states2, _ = self.c_fc2(hidden_states)
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = (
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self.act(hidden_states, hidden_states2)
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if self.swiglu
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else self.act(hidden_states)
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)
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hidden_states, _ = self.c_proj(hidden_states)
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return hidden_states
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class JAISBlock(nn.Module):
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def __init__(
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self,
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config: JAISConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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hidden_size = config.hidden_size
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inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
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self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.attn = JAISAttention(
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config, cache_config, quant_config, prefix=f"{prefix}.attn"
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)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = JAISMLP(inner_dim, config, quant_config, prefix=f"{prefix}.mlp")
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def forward(
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self,
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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.ln_1(hidden_states)
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attn_output = self.attn(
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hidden_states=hidden_states,
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)
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# residual connection
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hidden_states = attn_output + residual
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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feed_forward_hidden_states = self.mlp(hidden_states)
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# residual connection
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hidden_states = residual + feed_forward_hidden_states
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return hidden_states
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@support_torch_compile
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class JAISModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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assert not config.add_cross_attention
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assert not config.scale_attn_by_inverse_layer_idx
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assert not config.reorder_and_upcast_attn
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self.embed_dim = config.hidden_size
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self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
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self.wpe = (
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nn.Embedding(config.max_position_embeddings, self.embed_dim)
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if config.position_embedding_type != "alibi"
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else None
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)
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if hasattr(config, "embeddings_scale"):
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self.embeddings_scale = config.embeddings_scale
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else:
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self.embeddings_scale = config.mup_embeddings_scale
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self.start_layer, self.end_layer, self.h = make_layers(
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config.num_hidden_layers,
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lambda prefix: JAISBlock(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.h",
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)
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states"], config.n_embd
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(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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position_ids: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> IntermediateTensors | torch.Tensor:
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if get_pp_group().is_first_rank:
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if inputs_embeds is None:
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inputs_embeds = self.embed_input_ids(input_ids)
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if self.wpe is not None:
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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else:
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hidden_states = inputs_embeds
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hidden_states *= torch.tensor(
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float(self.embeddings_scale), dtype=hidden_states.dtype
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)
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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for layer in islice(self.h, self.start_layer, self.end_layer):
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hidden_states = layer(hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class JAISLMHeadModel(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.transformer = JAISModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
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)
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if self.config.tie_word_embeddings:
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self.lm_head = self.transformer.wte
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else:
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self.lm_head = ParallelLMHead(
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self.config.vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if hasattr(config, "width_scale"):
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self.output_logits_scale = config.width_scale
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else:
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self.output_logits_scale = config.mup_output_alpha * config.mup_width_scale
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self.logits_processor = LogitsProcessor(
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vocab_size=config.vocab_size, scale=self.output_logits_scale
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)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.transformer.embed_input_ids(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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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> IntermediateTensors | torch.Tensor:
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hidden_states = self.transformer(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "lm_head.weight" in name:
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# GPT-2 ties the weights of the embedding layer and the final
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# linear layer.
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continue
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if ".attn.bias" in name or ".attn.masked_bias" in name:
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# Skip attention mask.
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# NOTE: "c_attn.bias" should not be skipped.
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continue
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if "relative_pe" in name:
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continue
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if not name.startswith("transformer."):
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name = "transformer." + name
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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# The HF's GPT-2 implementation uses Conv1D instead of Linear.
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# Because of this, we need to transpose the weights.
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# Note(zhuohan): the logic below might break quantized models.
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for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
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if conv1d_weight_name not in name:
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continue
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if not name.endswith(".weight"):
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continue
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loaded_weight = loaded_weight.t()
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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(name)
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return loaded_params
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