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https://github.com/NVIDIA/TensorRT-LLM.git
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9dbc5b38ba
* Update TensorRT-LLM --------- Co-authored-by: Marks101 <markus.schnoes@gmx.de> Co-authored-by: lkm2835 <lkm2835@gmail.com>
148 lines
5.8 KiB
Python
148 lines
5.8 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 PositionEmbeddingType, Tensor
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from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear,
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Embedding, LayerNorm)
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from ...module import Module
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from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
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PretrainedConfig)
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class GPTNeoXDecoderLayer(Module):
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def __init__(self, config: PretrainedConfig, 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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hidden_size = config.hidden_size
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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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self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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self.post_attention_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=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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self.attention = Attention(
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local_layer_idx=local_layer_idx,
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hidden_size=hidden_size,
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num_attention_heads=config.num_attention_heads,
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rotary_embedding_percentage=config.rotary_pct,
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rotary_embedding_base=config.rotary_emb_base,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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max_position_embeddings=config.max_position_embeddings,
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dtype=dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=True,
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tp_group=tp_group,
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tp_size=tp_size,
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quant_mode=config.quant_mode)
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self.mlp = MLP(hidden_size=hidden_size,
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ffn_hidden_size=hidden_size * 4,
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hidden_act=config.hidden_act,
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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=config.quant_mode)
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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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residual = hidden_states
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input_layernorm_output = self.input_layernorm(hidden_states)
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post_attention_layernorm_output = self.post_attention_layernorm(
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hidden_states)
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attention_output = self.attention(input_layernorm_output,
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attention_mask=attention_mask,
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use_cache=use_cache,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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norm_before_bmm1=True)
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if use_cache:
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attention_output, presents = attention_output
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feed_forward_hidden_states = self.mlp(post_attention_layernorm_output)
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hidden_states = attention_output + feed_forward_hidden_states + residual
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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 GPTNeoXModel(Module):
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def __init__(self, config: PretrainedConfig):
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super().__init__()
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self.vocab_embedding = Embedding(num_embeddings=config.vocab_size,
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embedding_dim=config.hidden_size,
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dtype=config.dtype)
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self.layers = DecoderLayerList(GPTNeoXDecoderLayer, config)
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self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
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dtype=config.dtype)
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def forward(self,
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input_ids: Tensor,
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position_ids=None,
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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 = self.vocab_embedding(input_ids)
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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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if use_cache:
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hidden_states, presents = hidden_states
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hidden_states = self.ln_f(hidden_states)
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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 GPTNeoXForCausalLM(DecoderModelForCausalLM):
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def __init__(self, config: PretrainedConfig):
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transformer = GPTNeoXModel(config)
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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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super().__init__(config, transformer, lm_head)
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