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[xpu] add ep custom ops #3911
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[xpu] add ep custom ops #3911
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| Original file line number | Diff line number | Diff line change |
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| // Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
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| #include <infer_ops.h> | ||
| #include <functional> | ||
| #include "paddle/extension.h" | ||
| #include "paddle/phi/backends/xpu/enforce_xpu.h" | ||
| #include "utility/debug.h" | ||
| #include "utility/env.h" | ||
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|
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| #ifndef PD_BUILD_STATIC_OP | ||
| #define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name) | ||
| #endif | ||
|
|
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| XPU_DECLARE_BOOL(ENABLE_XVLLM_SDNN_INFER, false); | ||
| namespace api = baidu::xpu::api; | ||
|
|
||
| template <typename T> | ||
| std::vector<paddle::Tensor> RmsNormKernel( | ||
| const paddle::Tensor& x, | ||
| const paddle::optional<paddle::Tensor>& bias, | ||
| const paddle::optional<paddle::Tensor>& residual, | ||
| const paddle::Tensor& norm_weight, | ||
| const paddle::optional<paddle::Tensor>& norm_bias, | ||
| const float epsilon, | ||
| const int begin_norm_axis, | ||
| const float quant_scale, | ||
| const int quant_round_type, | ||
| const float quant_max_bound, | ||
| const float quant_min_bound) { | ||
| using XPU_T = typename XPUTypeTrait<T>::Type; | ||
| phi::XPUPlace place(phi::backends::xpu::GetXPUCurrentDeviceId()); | ||
| auto dev_ctx = paddle::experimental::DeviceContextPool::Instance().Get(place); | ||
| auto xpu_ctx = static_cast<const phi::XPUContext*>(dev_ctx); | ||
|
|
||
| int ret = -1; | ||
| auto x_shape = x.shape(); | ||
| PD_CHECK(quant_scale <= 0, "Quantization is not supported"); | ||
| PD_CHECK(begin_norm_axis > 0 && begin_norm_axis <= x_shape.size(), | ||
| "begin_norm_axis check fail"); | ||
| PD_CHECK(norm_bias.get_ptr() == nullptr, | ||
| "rms norm kernel don't support norm_bias"); | ||
|
|
||
| int64_t m = std::accumulate(x_shape.begin(), | ||
| x_shape.begin() + begin_norm_axis, | ||
| static_cast<int64_t>(1), | ||
| std::multiplies<int64_t>()); | ||
| int64_t n = std::accumulate(x_shape.begin() + begin_norm_axis, | ||
| x_shape.end(), | ||
| static_cast<int64_t>(1), | ||
| std::multiplies<int64_t>()); | ||
|
|
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| PD_CHECK(n == norm_weight.shape()[0], | ||
| "The product from begin_norm_axis to the last axis of x must be " | ||
| "equal to the norm_weight's shape[0]"); | ||
| if (bias.get_ptr()) { | ||
| PD_CHECK(n == bias.get_ptr()->shape()[0], | ||
| "The product from begin_norm_axis to the last axis of x must be " | ||
| "equal to the bias's shape[0]"); | ||
| } | ||
|
|
||
| paddle::Tensor out = paddle::empty(x_shape, x.dtype(), x.place()); | ||
| paddle::Tensor residual_out = paddle::empty(x_shape, x.dtype(), x.place()); | ||
| const XPU_T* x_data = reinterpret_cast<const XPU_T*>(x.data<T>()); | ||
| const XPU_T* norm_weight_data = | ||
| reinterpret_cast<const XPU_T*>(norm_weight.data<T>()); | ||
| const XPU_T* bias_data = | ||
| bias.get_ptr() ? reinterpret_cast<const XPU_T*>(bias.get_ptr()->data<T>()) | ||
| : nullptr; | ||
| const XPU_T* residual_data = | ||
| residual.get_ptr() | ||
| ? reinterpret_cast<const XPU_T*>(residual.get_ptr()->data<T>()) | ||
| : nullptr; | ||
| XPU_T* out_data = reinterpret_cast<XPU_T*>(const_cast<T*>(out.data<T>())); | ||
| XPU_T* residual_out_data = nullptr; | ||
| if (residual_data) { | ||
| residual_out_data = | ||
| reinterpret_cast<XPU_T*>(const_cast<T*>(residual_out.data<T>())); | ||
| } | ||
|
|
||
| XPU_T* add_out_data = const_cast<XPU_T*>(x_data); | ||
| if (bias_data) { | ||
| ret = api::broadcast_add( | ||
| xpu_ctx->x_context(), x_data, bias_data, out_data, {m, n}, {n}); | ||
| PD_CHECK(ret == 0, "broadcast_add"); | ||
| add_out_data = out_data; | ||
| } | ||
|
|
||
| bool use_sdnn = FLAGS_ENABLE_XVLLM_SDNN_INFER; | ||
| if (residual_data) { | ||
| ret = infer_ops::add_rms_layer_norm<XPU_T, XPU_T>(xpu_ctx->x_context(), | ||
| add_out_data, | ||
| residual_data, | ||
| out_data, | ||
| m, | ||
| n, | ||
| epsilon, | ||
| norm_weight_data, | ||
| nullptr, | ||
| nullptr, | ||
| residual_out_data, | ||
| nullptr, | ||
| use_sdnn); | ||
| PD_CHECK(ret == 0, "add_rms_layer_norm"); | ||
| } else { | ||
| ret = api::rms_layer_norm<XPU_T, XPU_T>(xpu_ctx->x_context(), | ||
| add_out_data, | ||
| out_data, | ||
| m, | ||
| n, | ||
| epsilon, | ||
| norm_weight_data, | ||
| nullptr, | ||
| nullptr, | ||
| false); | ||
| PD_CHECK(ret == 0, "rms_layer_norm"); | ||
| } | ||
|
|
||
| return {out, residual_out}; | ||
| } | ||
|
|
||
| std::vector<paddle::Tensor> RmsNorm( | ||
| const paddle::Tensor& x, | ||
| const paddle::optional<paddle::Tensor>& bias, | ||
| const paddle::optional<paddle::Tensor>& residual, | ||
| const paddle::Tensor& norm_weight, | ||
| const paddle::optional<paddle::Tensor>& norm_bias, | ||
| const float epsilon, | ||
| const int begin_norm_axis, | ||
| const float quant_scale, | ||
| const int quant_round_type, | ||
| const float quant_max_bound, | ||
| const float quant_min_bound) { | ||
| const auto x_type = x.dtype(); | ||
|
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||
| #define APPLY_RMS_NORM_KERNEL(TX) \ | ||
| return RmsNormKernel<TX>(x, \ | ||
| bias, \ | ||
| residual, \ | ||
| norm_weight, \ | ||
| norm_bias, \ | ||
| epsilon, \ | ||
| begin_norm_axis, \ | ||
| quant_scale, \ | ||
| quant_round_type, \ | ||
| quant_max_bound, \ | ||
| quant_min_bound); | ||
|
|
||
| if (x_type == paddle::DataType::BFLOAT16) { | ||
| APPLY_RMS_NORM_KERNEL(paddle::bfloat16); | ||
| } else if (x_type == paddle::DataType::FLOAT16) { | ||
| APPLY_RMS_NORM_KERNEL(paddle::float16); | ||
| } else if (x_type == paddle::DataType::FLOAT32) { | ||
| APPLY_RMS_NORM_KERNEL(float); | ||
| } else { | ||
| PD_THROW("RmsNorm not support x_type=", static_cast<int>(x_type)); | ||
| return {}; | ||
| } | ||
| #undef APPLY_RMS_NORM_KERNEL | ||
| } | ||
|
|
||
| std::vector<std::vector<int64_t>> RmsNormInferShape( | ||
| const std::vector<int64_t>& x_shape, | ||
| const paddle::optional<std::vector<int64_t>>& bias_shape, | ||
| const paddle::optional<std::vector<int64_t>>& residual_shape, | ||
| const std::vector<int64_t>& norm_weight_shape, | ||
| const paddle::optional<std::vector<int64_t>>& norm_bias_shape, | ||
| const float epsilon, | ||
| const int begin_norm_axis, | ||
| const float quant_scale, | ||
| const int quant_round_type, | ||
| const float quant_max_bound, | ||
| const float quant_min_bound) { | ||
| PD_CHECK(begin_norm_axis > 0 && begin_norm_axis <= x_shape.size(), | ||
| "begin_norm_axis check fail"); | ||
| int64_t m = std::accumulate(x_shape.begin(), | ||
| x_shape.begin() + begin_norm_axis, | ||
| static_cast<int64_t>(1), | ||
| std::multiplies<int64_t>()); | ||
| return {x_shape, x_shape, {m}}; | ||
| } | ||
|
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| std::vector<paddle::DataType> RmsNormInferDtype( | ||
| const paddle::DataType& x_dtype, | ||
| const paddle::optional<paddle::DataType>& bias_dtype, | ||
| const paddle::optional<paddle::DataType>& residual_dtype, | ||
| const paddle::DataType& norm_weight_dtype, | ||
| const paddle::optional<paddle::DataType>& norm_bias_dtype, | ||
| const float epsilon, | ||
| const int begin_norm_axis, | ||
| const float quant_scale, | ||
| const int quant_round_type, | ||
| const float quant_max_bound, | ||
| const float quant_min_bound) { | ||
| // out, residual_out | ||
| return {x_dtype, x_dtype}; | ||
| } | ||
|
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| PD_BUILD_STATIC_OP(fused_rms_norm_xpu) | ||
| .Inputs({"x", | ||
| paddle::Optional("bias"), | ||
| paddle::Optional("residual"), | ||
| "norm_weight", | ||
| paddle::Optional("norm_bias")}) | ||
| .Outputs({"out", "residul_out"}) | ||
| .Attrs({"epsilon:float", | ||
| "begin_norm_axis:int", | ||
| "quant_scale:float", | ||
| "quant_round_type:int", | ||
| "quant_max_bound:float", | ||
| "quant_min_bound:float"}) | ||
| .SetKernelFn(PD_KERNEL(RmsNorm)) | ||
| .SetInferShapeFn(PD_INFER_SHAPE(RmsNormInferShape)) | ||
| .SetInferDtypeFn(PD_INFER_DTYPE(RmsNormInferDtype)); | ||
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为什么需要给xpu单独写一个fused_rms_norm算子呢？和gpu版本的算子有什么区别？
kernel实现可以是硬件相关的，但是算子定义应该是硬件无关的。