mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
gongweibao
87 lines
3.3 KiB
C++
87 lines
3.3 KiB
C++
/*
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* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
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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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*/
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#pragma once
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#include <cstddef>
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#include <stdint.h>
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#include <vector>
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namespace kernels {
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namespace cutlass_kernels {
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enum class QuantType { W8_A16, W4_A16, W4_AFP8 };
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constexpr int get_weight_quant_bits(QuantType quant_type) {
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switch (quant_type) {
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case QuantType::W8_A16:
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return 8;
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case QuantType::W4_A16:
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return 4;
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case QuantType::W4_AFP8:
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return 4;
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default:
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PADDLE_THROW("Invalid quant_type");
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return -1;
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}
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}
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// Shapes here can be 2 or 3D. 2-D shapes are [num_rows, num_cols]
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// 3-D shapes are [num_experts, num_rows, num_cols]
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void permute_B_rows_for_mixed_gemm(int8_t* permuted_quantized_tensor,
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int8_t const* quantized_tensor,
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std::vector<size_t> const& shape,
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QuantType quant_type,
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const int64_t arch_version);
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void subbyte_transpose(int8_t* transposed_quantized_tensor,
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int8_t const* quantized_tensor,
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std::vector<size_t> const& shape,
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QuantType quant_type);
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void add_bias_and_interleave_quantized_tensor_inplace(int8_t* tensor,
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const size_t num_elts,
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QuantType quant_type);
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void preprocess_weights_for_mixed_gemm(int8_t* preprocessed_quantized_weight,
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int8_t const* row_major_quantized_weight,
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std::vector<size_t> const& shape,
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QuantType quant_type,
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bool force_interleave = false);
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template <typename ComputeType, typename WeightType>
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void symmetric_quantize(int8_t* processed_quantized_weight,
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ComputeType* scale_ptr,
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WeightType const* input_weight_ptr,
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std::vector<size_t> const& shape,
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QuantType quant_type,
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bool force_interleave);
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// This is exposed so that we can write tests that use the processed weights for
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// CUTLASS but the unprocessed weight to implement a simple reference
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// implementation.
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template <typename ComputeType, typename WeightType>
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void symmetric_quantize(int8_t* processed_quantized_weight,
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int8_t* unprocessed_quantized_weight,
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ComputeType* scale_ptr,
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WeightType const* input_weight_ptr,
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std::vector<size_t> const& shape,
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QuantType quant_type,
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bool force_interleave);
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} // namespace cutlass_kernels
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} // namespace kernels
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