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https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 1993-2024 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 typing import Sequence
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import torch
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from tensorrt_llm import PluginBase
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from tensorrt_llm._utils import TensorWrapper, convert_to_torch_tensor
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from tensorrt_llm.python_plugin import SymTensor, trtllm_plugin
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from .lookup_kernel import lookup_kernel
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@trtllm_plugin("TritonLookUp")
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class LookUpPlugin(PluginBase):
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def __init__(self, use_torch_tensor, fp32_output):
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super().__init__()
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self.use_torch_tensor = use_torch_tensor
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self.fp32_output = fp32_output
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def shape_dtype_inference(self, inputs: Sequence[SymTensor]) -> SymTensor:
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shape = inputs[1].shape
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shape[0] = inputs[0].shape[0] + inputs[1].shape[0] - inputs[1].shape[0]
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return SymTensor(
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inputs[1].dtype if not self.fp32_output else torch.float32, shape)
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def forward(self, inputs: Sequence[TensorWrapper],
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outputs: Sequence[TensorWrapper]):
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assert len(inputs) == 2
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assert inputs[0].dtype in [torch.int32 or torch.int64]
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assert inputs[1].dtype in [torch.float32, torch.float16, torch.bfloat16]
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assert (self.fp32_output and outputs[0].dtype
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== torch.float32) or outputs[0].dtype == inputs[1].dtype
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x = inputs[0]
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y = inputs[1]
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z = outputs[0]
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if self.use_torch_tensor:
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x = convert_to_torch_tensor(x)
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y = convert_to_torch_tensor(y)
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z = convert_to_torch_tensor(z)
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MAX_BLOCK_NUM = 65536
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MAX_BLOCK_SIZE = 512
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grid = lambda meta: (min(MAX_BLOCK_NUM, x.shape[0]) * min(
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MAX_BLOCK_SIZE, y.shape[1]), )
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lookup_kernel[grid](x, y, z, y.shape[0], y.shape[1], x.shape[0])
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