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https://github.com/NVIDIA/TensorRT-LLM.git
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d879430b04
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
331 lines
13 KiB
Python
331 lines
13 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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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 (PositionEmbeddingType, Tensor, allreduce,
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gather_last_token_logits)
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from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm)
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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from ...quantization import QuantMode
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from ..generation_mixin import GenerationMixin
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class GPTJDecoderLayer(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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rotary_dim,
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dtype=None,
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hidden_act='relu',
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tp_group=None,
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tp_size=1,
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quant_mode=QuantMode(0)):
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super().__init__()
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.rotary_dim = rotary_dim
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self.dtype = dtype
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self.hidden_act = hidden_act
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.quant_mode = quant_mode
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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=hidden_size,
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num_attention_heads=num_attention_heads,
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rotary_embedding_percentage=rotary_dim /
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(hidden_size // num_attention_heads),
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position_embedding_type=PositionEmbeddingType.rope_gptj,
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max_position_embeddings=max_position_embeddings,
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dtype=dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=False,
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tp_group=None,
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tp_size=tp_size,
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quant_mode=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=hidden_act,
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dtype=dtype,
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tp_group=None,
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tp_size=tp_size,
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quant_mode=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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if not default_net(
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).plugin_config.layernorm_plugin and trt.__version__[:3] == '8.6':
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raise AssertionError(
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"You need to enable the LayerNorm plugin for GPT-J with TensorRT 8.6. Please set plugin_config.layernorm_plugin"
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)
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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(hidden_states,
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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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attention_output = attention_output
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = attention_output + feed_forward_hidden_states
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if self.tp_size > 1:
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hidden_states = allreduce(hidden_states, self.tp_group)
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hidden_states = 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 GPTJModel(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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rotary_dim,
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dtype=None,
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mapping=Mapping(),
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quant_mode=QuantMode(0)):
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super().__init__()
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self.mapping = mapping
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self.vocab_embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
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self.layers = ModuleList([
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GPTJDecoderLayer(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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rotary_dim=rotary_dim,
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dtype=dtype,
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hidden_act=hidden_act,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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quant_mode=quant_mode) for _ in range(num_layers)
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])
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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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use_cache=False,
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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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kv_cache_params.fill_none_tensor_list(len(self.layers))
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if use_cache:
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presents = []
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for layer, past, pointer, host_pointer, max_attention_window_size in zip(
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self.layers, kv_cache_params.past_key_value,
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kv_cache_params.kv_cache_block_pointers,
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kv_cache_params.host_kv_cache_block_pointers,
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kv_cache_params.host_max_attention_window_sizes):
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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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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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host_max_attention_window_sizes=max_attention_window_size,
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host_sink_token_length=kv_cache_params.
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host_sink_token_length,
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kv_cache_block_pointers=[pointer],
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host_kv_cache_block_pointers=[host_pointer],
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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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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 GPTJForCausalLM(GPTJModel, 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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rotary_dim,
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dtype,
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logits_dtype='float32',
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mapping=Mapping(),
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quant_mode=QuantMode(0)):
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if isinstance(dtype, str):
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self._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._dtype = dtype
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self._kv_dtype = dtype
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self.quant_mode = quant_mode
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if quant_mode.has_int8_kv_cache():
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self._kv_dtype = str_dtype_to_trt('int8')
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elif quant_mode.has_fp8_kv_cache():
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self._kv_dtype = str_dtype_to_trt('fp8')
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if isinstance(logits_dtype, str):
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self._logits_dtype = str_dtype_to_trt(logits_dtype)
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else:
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assert isinstance(logits_dtype, trt.DataType)
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self._logits_dtype = logits_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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super().__init__(num_layers, num_heads, hidden_size, vocab_size,
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hidden_act, max_position_embeddings, rotary_dim, dtype,
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mapping, quant_mode)
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self._vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
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self.lm_head = ColumnLinear(hidden_size,
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self._vocab_size_padded,
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bias=True,
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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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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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kv_cache_params=None,
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attention_params=None):
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hidden_states = super().forward(input_ids, use_cache, kv_cache_params,
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attention_params)
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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._logits_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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max_num_tokens: int = None):
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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_kv = self._num_heads
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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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paged_kv_cache = default_net().plugin_config.paged_kv_cache
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tokens_per_block = default_net().plugin_config.tokens_per_block
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use_custom_all_reduce = default_net(
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).plugin_config.use_custom_all_reduce
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model_inputs = self.prepare_basic_inputs(
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max_batch_size=max_batch_size,
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max_beam_width=max_beam_width,
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max_input_len=max_input_len,
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max_new_tokens=max_new_tokens,
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num_kv_heads=num_heads_kv,
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head_size=head_size,
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num_layers=self._num_layers,
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kv_dtype=self._kv_dtype,
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num_heads=self._num_heads,
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dtype=self._dtype,
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remove_input_padding=remove_input_padding,
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use_gpt_attention_plugin=use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin,
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use_custom_all_reduce=use_custom_all_reduce,
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paged_kv_cache=paged_kv_cache,
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tokens_per_block=tokens_per_block,
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mapping=self.mapping,
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max_num_tokens=max_num_tokens)
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return (
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model_inputs['input_ids'], model_inputs['position_ids'], True,
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model_inputs['last_token_ids'],
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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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host_max_attention_window_sizes=model_inputs[
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'host_max_attention_window_sizes'],
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host_sink_token_length=model_inputs['host_sink_token_length'],
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kv_cache_block_pointers=model_inputs[
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'kv_cache_block_pointers_list'],
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host_kv_cache_block_pointers=model_inputs[
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'host_kv_cache_block_pointers_list'],
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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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