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https://github.com/NVIDIA/TensorRT-LLM.git
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deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
134 lines
5.2 KiB
Python
134 lines
5.2 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 ctypes
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from collections import OrderedDict
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from pathlib import Path
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from typing import List
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import numpy as np
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import tensorrt as trt
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from tensorrt_llm._common import default_trtnet
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from tensorrt_llm._utils import str_dtype_to_trt
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from tensorrt_llm.functional import Tensor, _create_tensor
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from tensorrt_llm.module import Module
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TRT_LLM_PLUGIN_NAMESPACE = 'tensorrt_llm'
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LAYER_NAME = 'TritonFlashAttentionLayer'
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FMHA_KERNEL_BLOCK_SIZE = 128
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def _load_triton_plugin_lib():
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triton_plugin_dir = Path(__file__).parent.absolute()
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plugin_lib = triton_plugin_dir / 'build/libtrt_llm_custom_plugins.so'
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handle = ctypes.CDLL(plugin_lib, mode=ctypes.RTLD_GLOBAL)
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if handle is None:
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raise ImportError('TensorRT-LLM Triton Plugin is unavailable')
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handle.initOpenAiTritonPlugins.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
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handle.initOpenAiTritonPlugins.restype = ctypes.c_bool
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assert handle.initOpenAiTritonPlugins(
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None, TRT_LLM_PLUGIN_NAMESPACE.encode('utf-8'))
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_load_triton_plugin_lib()
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def flash_attention_op(num_heads: int, head_size: int, softmax_scale: float,
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inputs: List[trt.ITensor]) -> Tensor:
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# Create a plugin instance.
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plugin_creator = trt.get_plugin_registry().get_plugin_creator(
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'TritonFlashAttention', '1', TRT_LLM_PLUGIN_NAMESPACE)
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assert plugin_creator is not None
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pfc = trt.PluginFieldCollection([
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trt.PluginField("num_heads", np.array([num_heads], np.int32),
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trt.PluginFieldType.INT32),
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trt.PluginField("head_size", np.array([head_size], np.int32),
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trt.PluginFieldType.INT32),
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trt.PluginField("softmax_scale", np.array([softmax_scale], np.float32),
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trt.PluginFieldType.FLOAT32),
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trt.PluginField("type_id", np.array([int(inputs[0].dtype)], np.int32),
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trt.PluginFieldType.INT32)
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])
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plugin = plugin_creator.create_plugin("flash_attention", pfc)
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layer = default_trtnet().add_plugin_v2(inputs, plugin)
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return _create_tensor(layer.get_output(0), layer)
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class FmhaLayer(Module):
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def __init__(self, num_heads: int, head_size: int, softmax_scale: float,
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dtype: str):
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super().__init__()
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self.num_heads = num_heads
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self.head_size = head_size
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self.softmax_scale = softmax_scale
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self.dtype = str_dtype_to_trt(dtype)
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def forward(self, Q: Tensor, K: Tensor, V: Tensor):
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inputs = [Q, K, V]
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out = flash_attention_op(num_heads=self.num_heads,
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head_size=self.head_size,
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softmax_scale=self.softmax_scale,
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inputs=[p.trt_tensor for p in inputs])
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out.mark_output('out', self.dtype)
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return out
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def prepare_inputs(self, max_batch_size: int, max_len: int) -> List[Tensor]:
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'''
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@brief: Prepare inputs Tensors for the model, the given sizes are used to
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determine the 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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bs_range = [1, (max_batch_size + 1) // 2, max_batch_size]
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max_len_range = [1, (max_len + 1) // 2, max_len]
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dynamic_shape = [-1, self.num_heads, -1, self.head_size]
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Q = Tensor(name='Q',
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dtype=self.dtype,
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shape=dynamic_shape,
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dim_range=OrderedDict([
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('batch_size', [bs_range]),
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('num_heads', [self.num_heads]),
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('seq_len', [max_len_range]),
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('head_size', [self.head_size]),
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]))
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K = Tensor(name='K',
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dtype=self.dtype,
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shape=dynamic_shape,
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dim_range=OrderedDict([
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('batch_size', [bs_range]),
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('num_heads', [self.num_heads]),
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('seq_len', [max_len_range]),
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('head_size', [self.head_size]),
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]))
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V = Tensor(name='V',
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dtype=self.dtype,
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shape=dynamic_shape,
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dim_range=OrderedDict([
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('batch_size', [bs_range]),
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('num_heads', [self.num_heads]),
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('seq_len', [max_len_range]),
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('head_size', [self.head_size]),
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]))
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return [Q, K, V]
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def get_engine_name(head_size, dtype):
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return f'{LAYER_NAME}_{FMHA_KERNEL_BLOCK_SIZE}_d{head_size}_{dtype}.engine'
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