mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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385 lines
15 KiB
Python
385 lines
15 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 collections import OrderedDict
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import tensorrt as trt
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from ..._common import default_net
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from ...functional import Tensor, gather_last_token_logits
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from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, KeyValueCacheParams, LayerNorm,
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PositionEmbeddingType)
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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from ..generation_mixin import GenerationMixin
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from ..gpt.model import GPTEmbedding
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class OPTDecoderLayer(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
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max_position_embeddings,
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dtype=None,
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hidden_act='relu',
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pre_norm=False,
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tp_group=None,
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tp_size=1):
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super().__init__()
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self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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self.attention = Attention(
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hidden_size,
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num_attention_heads,
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max_position_embeddings=max_position_embeddings,
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attention_mask_type=AttentionMaskType.causal,
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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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self.mlp = MLP(hidden_size=hidden_size,
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ffn_hidden_size=hidden_size * 4,
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hidden_act=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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self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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self.pre_norm = pre_norm
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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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attention_input = hidden_states
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if self.pre_norm:
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attention_input = self.input_layernorm(hidden_states)
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# At this point the hidden_states object must be a Tensor.
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assert isinstance(attention_input, Tensor)
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attention_output = self.attention(attention_input,
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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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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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if not self.pre_norm:
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hidden_states = self.input_layernorm(hidden_states)
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residual = hidden_states
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if self.pre_norm:
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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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hidden_states = residual + hidden_states
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if not self.pre_norm:
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hidden_states = self.post_layernorm(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 OPTModel(Module):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype=None,
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mapping=Mapping(),
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pre_norm=True,
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do_layer_norm_before=True,
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use_prompt_tuning=False,
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use_parallel_embedding=False,
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embedding_sharding_dim=0):
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super().__init__()
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self.do_layer_norm_before = do_layer_norm_before
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self.embedding = GPTEmbedding(
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vocab_size,
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hidden_size,
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max_position_embeddings,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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dtype=dtype,
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use_prompt_tuning=use_prompt_tuning,
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tensor_parallel=mapping.tp_size if use_parallel_embedding else 1,
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tensor_parallel_group=mapping.tp_group
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if use_parallel_embedding else None,
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sharding_dim=embedding_sharding_dim,
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tp_rank=mapping.tp_rank)
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self.layers = ModuleList([
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OPTDecoderLayer(hidden_size=hidden_size,
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num_attention_heads=num_heads,
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max_position_embeddings=max_position_embeddings,
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dtype=dtype,
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hidden_act=hidden_act,
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pre_norm=pre_norm,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size) for _ in range(num_layers)
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])
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if self.do_layer_norm_before:
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self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=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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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None):
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hidden_states = self.embedding(input_ids, position_ids,
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prompt_embedding_table, prompt_tasks,
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prompt_vocab_size)
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if kv_cache_params.past_key_value is None:
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kv_cache_params.past_key_value = tuple([None] * len(self.layers))
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if use_cache:
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presents = []
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for layer, past in zip(self.layers, kv_cache_params.past_key_value):
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hidden_states = layer(
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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=KeyValueCacheParams(
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past_key_value=[past],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params)
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if use_cache:
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presents.append(hidden_states[1])
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hidden_states = hidden_states[0]
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if self.do_layer_norm_before:
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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 OPTLMHeadModel(OPTModel, GenerationMixin):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype,
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mapping=Mapping(),
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pre_norm=True,
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do_layer_norm_before=True,
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use_prompt_tuning=False,
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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share_embedding_table=False):
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if share_embedding_table and mapping.tp_size > 1:
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if (not use_parallel_embedding) or (use_parallel_embedding and
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embedding_sharding_dim == 1):
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raise NotImplementedError(
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'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim = 0'
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)
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super().__init__(num_layers, num_heads, hidden_size, vocab_size,
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hidden_act, max_position_embeddings, dtype, mapping,
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pre_norm, do_layer_norm_before, use_prompt_tuning,
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use_parallel_embedding, embedding_sharding_dim)
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vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
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if isinstance(dtype, str):
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self._kv_dtype = str_dtype_to_trt(dtype)
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else:
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assert isinstance(dtype, trt.DataType)
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self._kv_dtype = dtype
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self._dtype = self._kv_dtype
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self._num_layers = num_layers
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self._num_heads = num_heads
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self._hidden_size = hidden_size
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self._vocab_size = vocab_size
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self._tp_size = mapping.tp_size
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self._use_prompt_tuning = use_prompt_tuning
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share_weight = None
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if share_embedding_table:
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share_weight = self.embedding.vocab_embedding.weight
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self.lm_head = ColumnLinear(hidden_size,
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vocab_size_padded,
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bias=False,
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dtype=dtype,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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gather_output=True,
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share_weight=share_weight)
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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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last_token_ids=None,
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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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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None):
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hidden_states = super().forward(input_ids, position_ids, use_cache,
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attention_mask, kv_cache_params,
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attention_params,
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prompt_embedding_table, prompt_tasks,
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prompt_vocab_size)
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if use_cache:
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hidden_states, presents = hidden_states
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hidden_states = gather_last_token_logits(
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hidden_states, last_token_ids,
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default_net().plugin_config.remove_input_padding)
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# [batch_size, hidden_size] -> [batch_size, vocab_size]
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lm_logits = self.lm_head(hidden_states)
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lm_logits.mark_output('logits', self._kv_dtype)
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if use_cache and default_net().plugin_config.paged_kv_cache == False:
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for i, present in enumerate(presents):
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present.mark_output(f'present_key_value_{i}', self._kv_dtype)
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return (lm_logits, presents)
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return lm_logits
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def prepare_inputs(self,
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max_batch_size,
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max_input_len,
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max_new_tokens,
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use_cache,
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max_beam_width,
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prompt_embedding_table_size=32):
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'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
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ranges of the dimensions of when using TRT dynamic shapes.
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@return: a list contains values which can be fed into the self.forward()
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'''
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# Prepare inputs
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head_size = self._hidden_size // self._num_heads
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num_heads = self._num_heads // self._tp_size
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remove_input_padding = default_net().plugin_config.remove_input_padding
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use_gpt_attention_plugin = default_net(
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).plugin_config.gpt_attention_plugin
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use_gemm_plugin = default_net().plugin_config.gemm_plugin
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model_inputs = self.prepare_basic_inputs(
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max_batch_size,
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max_beam_width,
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max_input_len,
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max_new_tokens,
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num_heads,
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head_size,
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self._num_layers,
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self._kv_dtype,
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remove_input_padding,
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use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin)
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bb_range = [
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1, (max_batch_size * max_beam_width + 1) // 2,
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max_batch_size * max_beam_width
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]
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p_embedding_range = [
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1, prompt_embedding_table_size // 2, prompt_embedding_table_size
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]
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num_tokens_range = [
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1, max_batch_size * max_beam_width,
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max(max_input_len * max_batch_size, max_beam_width * max_batch_size)
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]
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[1, 1, max_input_len]
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prompt_embedding_table = None
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tasks = None
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prompt_vocab_size = None
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if self._use_prompt_tuning:
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prompt_embedding_table = Tensor(name='prompt_embedding_table',
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dtype=self._dtype,
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shape=[-1, self._hidden_size],
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dim_range=OrderedDict([
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('prompt_embedding_table_size',
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[p_embedding_range]),
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('hidden_size',
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[self._hidden_size]),
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]))
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if remove_input_padding:
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tasks = Tensor(name='tasks',
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dtype=trt.int32,
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shape=[1, -1],
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dim_range=OrderedDict([
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('batch_size', [1]),
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('input_len', [num_tokens_range]),
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]))
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else:
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tasks = Tensor(name='tasks',
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dtype=trt.int32,
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shape=[-1, 1],
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dim_range=OrderedDict([
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('batch_size', [bb_range]),
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('input_len_for_task', [1]),
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]))
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prompt_vocab_size = Tensor(name='prompt_vocab_size',
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dtype=trt.int32,
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shape=[1],
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dim_range=OrderedDict([('size', [1])]))
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return (model_inputs['input_ids'], model_inputs['position_ids'], True,
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model_inputs['last_token_ids'], model_inputs['attention_mask'],
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KeyValueCacheParams(
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past_key_value=model_inputs['past_key_value'],
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host_past_key_value_lengths=model_inputs[
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'host_past_key_value_lengths'],
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cache_indirection=model_inputs['cache_indirection'],
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),
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AttentionParams(
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sequence_length=model_inputs['sequence_length'],
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context_lengths=model_inputs['context_lengths'],
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host_context_lengths=model_inputs['host_context_lengths'],
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max_context_length=max_input_len,
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host_request_types=model_inputs['host_request_types']),
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prompt_embedding_table, tasks, prompt_vocab_size)
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