324 lines
12 KiB
Python
324 lines
12 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 CTranslate2, and Michael Feil
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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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 GPTBigCode model compatible with HuggingFace weights."""
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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 transformers import GPTBigCodeConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig
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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 (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, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, 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
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from .interfaces import SupportsLoRA
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class GPTBigCodeAttention(nn.Module):
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def __init__(
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self,
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config: GPTBigCodeConfig,
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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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self.tensor_model_parallel_world_size = (
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get_tensor_model_parallel_world_size())
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assert total_num_heads % self.tensor_model_parallel_world_size == 0
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self.num_heads = (total_num_heads //
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self.tensor_model_parallel_world_size)
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self.head_dim = self.hidden_size // total_num_heads
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self.scale = self.head_dim**-0.5
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self.multi_query = config.multi_query
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if self.multi_query:
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total_num_kv_heads = 1
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self.num_kv_heads = 1
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else:
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total_num_kv_heads = total_num_heads
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self.num_kv_heads = self.num_heads
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self.kv_dim = self.head_dim * self.num_kv_heads
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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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total_num_kv_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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self.attn = Attention(self.num_heads,
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self.head_dim,
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scale=self.scale,
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num_kv_heads=self.num_kv_heads,
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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.split(
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[
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self.hidden_size // self.tensor_model_parallel_world_size,
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self.kv_dim, self.kv_dim
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],
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dim=-1,
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)
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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 GPTBigMLP(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: GPTBigCodeConfig,
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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.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_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 = get_act_fn(config.activation_function, quant_config,
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intermediate_size)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = 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 GPTBigCodeBlock(nn.Module):
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def __init__(
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self,
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config: GPTBigCodeConfig,
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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 = GPTBigCodeAttention(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 = GPTBigMLP(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 GPTBigCodeModel(nn.Module):
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def __init__(
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self,
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config: GPTBigCodeConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = 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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self.embed_dim = config.hidden_size
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lora_vocab = (lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.wte = VocabParallelEmbedding(self.vocab_size,
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self.embed_dim,
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org_num_embeddings=config.vocab_size)
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.h = nn.ModuleList([
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GPTBigCodeBlock(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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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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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 GPTBigCodeForCausalLM(nn.Module, SupportsLoRA):
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packed_modules_mapping = {"c_attn": ["c_attn"]}
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supported_lora_modules = ["c_fc", "c_proj", "wte", "c_attn"]
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embedding_modules = {
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"wte": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = []
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def __init__(
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self,
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config: GPTBigCodeConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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):
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super().__init__()
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.transformer = GPTBigCodeModel(config, cache_config, quant_config,
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lora_config)
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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.transformer.vocab_size,
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self.transformer.embed_dim,
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org_num_embeddings=self.config.vocab_size)
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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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(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[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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continue
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if ".attn.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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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
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if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
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weight_loader(param, loaded_weight, 'q')
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weight_loader(param, loaded_weight, 'k')
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weight_loader(param, loaded_weight, 'v')
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else:
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weight_loader(param, loaded_weight)
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