mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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2d234357c6
* Update TensorRT-LLM --------- Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
281 lines
11 KiB
Python
281 lines
11 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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from ..._utils import pad_vocab_size
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from ...functional import (Tensor, is_gated_activation, non_gated_version, recv,
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send)
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from ...layers import (MLP, MOE, Attention, AttentionMaskType, ColumnLinear,
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Embedding, GatedMLP, LayerNorm, MoeConfig,
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PositionEmbeddingType)
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from ...lora_manager import LoraConfig, use_lora
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from ...mapping import Mapping
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from ...module import Module
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from ...quantization import QuantMode
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from ..modeling_utils import DecoderLayerList, DecoderModelForCausalLM
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from .config import GPTConfig
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def MLPFactory(hidden_size,
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ffn_hidden_size,
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hidden_act,
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bias=True,
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dtype=None,
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moe_config: MoeConfig = MoeConfig(),
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tp_group=None,
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tp_size=1,
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mapping=Mapping(),
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quant_mode=QuantMode(0),
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inner_layernorm=False,
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eps=1e-05):
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if moe_config.has_moe():
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return MOE(moe_config,
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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mapping=mapping,
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bias=bias,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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quant_mode=quant_mode)
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MLPClass = GatedMLP if is_gated_activation(hidden_act) else MLP
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hidden_act = non_gated_version(hidden_act)
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return MLPClass(
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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bias,
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dtype,
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tp_group,
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tp_size,
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quant_mode,
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inner_layernorm=inner_layernorm,
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eps=eps,
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)
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class GPTDecoderLayer(Module):
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def __init__(self, config: GPTConfig, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.config = config
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tp_group = config.mapping.tp_group
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tp_size = config.mapping.tp_size
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tp_rank = config.mapping.tp_rank
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self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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layers_range = config.mapping.pp_layers(config.num_hidden_layers)
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local_layer_idx = layer_idx - layers_range[0]
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inner_layernorm = config.inner_layernorm if hasattr(
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config, "inner_layernorm") else False
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attention_head_size = config.head_size if hasattr(config,
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"head_size") else None
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self.attention = Attention(
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local_layer_idx=local_layer_idx,
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hidden_size=config.hidden_size,
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num_attention_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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max_position_embeddings=config.max_position_embeddings,
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num_layers=config.num_hidden_layers,
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q_scaling=config.q_scaling,
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apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
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dtype=config.dtype,
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attention_mask_type=AttentionMaskType.causal,
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attention_head_size=attention_head_size,
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position_embedding_type=config.position_embedding_type,
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rotary_embedding_percentage=config.rotary_pct,
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rotary_embedding_base=config.rotary_base,
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rotary_embedding_scaling=config.rotary_scaling,
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bias=config.bias,
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tp_group=tp_group,
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tp_size=tp_size,
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tp_rank=tp_rank,
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quant_mode=config.quant_mode,
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qk_layernorm=config.qk_layernorm,
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inner_layernorm=inner_layernorm,
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eps=config.norm_epsilon)
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mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
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self.norm_before_bmm1 = config.norm_before_bmm1 if hasattr(
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config, "norm_before_bmm1") else False
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self.mlp = MLPFactory(hidden_size=config.hidden_size,
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ffn_hidden_size=mlp_hidden_size,
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hidden_act=config.hidden_act,
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dtype=config.dtype,
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bias=config.bias,
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moe_config=config.moe,
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tp_group=tp_group,
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tp_size=tp_size,
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mapping=config.mapping,
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quant_mode=config.quant_mode,
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inner_layernorm=inner_layernorm,
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eps=config.norm_epsilon)
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self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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def forward(self,
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hidden_states: Tensor,
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attention_mask=None,
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use_cache=False,
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kv_cache_params=None,
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attention_params=None,
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lora_layer_params=None,
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spec_decoding_params=None):
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assert isinstance(hidden_states, Tensor)
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attention_output = self.attention(
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hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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spec_decoding_params=spec_decoding_params,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_layer_params=lora_layer_params,
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norm_before_bmm1=self.norm_before_bmm1)
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if use_cache:
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attention_output, presents = attention_output
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hidden_states = residual + attention_output
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residual = hidden_states
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hidden_states = self.post_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states,
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lora_layer_params=lora_layer_params)
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hidden_states = residual + hidden_states
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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class GPTModel(Module):
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def __init__(self, config: GPTConfig):
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super().__init__()
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self.mapping = config.mapping
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self.position_embedding_type = config.position_embedding_type
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if config.mapping.is_first_pp_rank():
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self.vocab_embedding = Embedding(config.vocab_size,
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config.hidden_size,
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dtype=config.dtype)
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self.embedding_scale = config.embedding_scale
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if config.position_embedding_type == PositionEmbeddingType.learned_absolute:
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self.position_embedding = Embedding(
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num_embeddings=config.max_position_embeddings,
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embedding_dim=config.hidden_size,
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dtype=config.dtype)
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self.layers = DecoderLayerList(GPTDecoderLayer, config)
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if config.mapping.is_last_pp_rank():
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self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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def forward(self,
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input_ids,
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position_ids,
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use_cache=False,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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hidden_states=None,
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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None,
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lora_params=None,
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spec_decoding_params=None):
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if self.mapping.is_first_pp_rank():
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ptuning_args = [
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prompt_embedding_table, prompt_tasks, prompt_vocab_size
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] if prompt_embedding_table is not None else []
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hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
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if self.embedding_scale is not None:
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hidden_states *= self.embedding_scale
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if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
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hidden_states = hidden_states + self.position_embedding(
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position_ids)
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else:
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hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
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hidden_states = self.layers(hidden_states,
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use_cache=use_cache,
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attention_mask=attention_mask,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_params=lora_params,
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spec_decoding_params=spec_decoding_params)
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if use_cache:
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hidden_states, presents = hidden_states
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if self.mapping.is_last_pp_rank():
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hidden_states = self.ln_f(hidden_states)
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else:
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hidden_states = send(hidden_states, self.mapping.next_pp_rank())
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if use_cache:
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return (hidden_states, tuple(presents))
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return hidden_states
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class GPTForCausalLM(DecoderModelForCausalLM):
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config_class = GPTConfig
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def __init__(self, config: GPTConfig):
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transformer = GPTModel(config)
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if config.mapping.is_last_pp_rank():
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vocab_size_padded = pad_vocab_size(config.vocab_size,
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config.mapping.tp_size)
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lm_head = ColumnLinear(config.hidden_size,
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vocab_size_padded,
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bias=False,
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dtype=config.dtype,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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gather_output=True)
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else:
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lm_head = None
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self.trtllm_modules_to_hf_modules = {
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"attn_q": "q_proj",
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"attn_k": "k_proj",
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"attn_v": "v_proj",
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"attn_dense": "o_proj",
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"mlp_h_to_4h": "c_fc",
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"mlp_4h_to_h": "c_proj",
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}
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super().__init__(config, transformer, lm_head)
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def use_lora(self, lora_config: LoraConfig):
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use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)
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