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340 lines
12 KiB
Python
340 lines
12 KiB
Python
# coding=utf-8
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# Adapted from
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# https://huggingface.co/core42/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 typing import Iterable, List, Optional, Tuple
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import torch
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors, SamplerOutput
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from vllm.transformers_utils.configs import JAISConfig
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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 (get_slopes_power_of_2(closest_power_of_2) + _get_alibi_slopes(
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2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
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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: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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 = (
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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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)
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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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)
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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(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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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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, kv_cache, attn_metadata)
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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: Optional[QuantizationConfig] = None,
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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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)
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self.c_fc2 = (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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) if self.swiglu else None)
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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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)
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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 = (self.act(hidden_states, hidden_states2)
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if self.swiglu else self.act(hidden_states))
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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: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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 *
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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(config, cache_config, quant_config)
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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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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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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class JAISModel(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: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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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 = (nn.Embedding(config.max_position_embeddings,
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self.embed_dim)
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if config.position_embedding_type != "alibi" else None)
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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.h = nn.ModuleList([
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JAISBlock(config, cache_config, quant_config)
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for _ in range(config.num_hidden_layers)
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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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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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inputs_embeds = self.wte(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(float(self.embeddings_scale),
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dtype=hidden_states.dtype)
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for i in range(len(self.h)):
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layer = self.h[i]
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hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)
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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):
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def __init__(
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self,
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config: JAISConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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.transformer = JAISModel(config, cache_config, quant_config)
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self.lm_head = self.transformer.wte
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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 *
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config.mup_width_scale)
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self.logits_processor = LogitsProcessor(vocab_size=config.vocab_size,
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scale=self.output_logits_scale)
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self.sampler = Sampler()
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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attn_metadata)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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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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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",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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