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[PHI] Migrate matmul kernel (PaddlePaddle#48162)
* cleanup unused code * unify is_int8 is_bfloat16 * Simplify matmul_v2 FWD kernel * remove RunKernel methods * remove import namespace * remove headers * clean fluid/phi cross imports * remove fluid axpy_handler * delete fluid methods * activations * OneDNNMemDesc * MKLDNNFormatForSize * MatchShapeToLayout * MKLDNNMemoryFormat * MKLDNNFormat * ReorderMKLDNNHandler * to_void_cast * review suggestions * interpolate * remove fluid depedency * init * ExecuteMatMulV2 * rm fluid kernel * matmul_grad * remove mutable_data * mul_grad * matmul fwd * add extra attr * temp disable passes * re-enable passes * workaround for matmul+act * fix for matmul+eltwise_add * fix typo * merge bugfix PaddlePaddle#48364 * remove merge conflict
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| // Copyright (c) 2022 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 "paddle/phi/kernels/matmul_kernel.h" | ||
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| #include "paddle/phi/backends/onednn/onednn_reuse.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
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| namespace phi { | ||
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| DDim GetDimsForInput(const OneDNNContext &dev_ctx, | ||
| DDim input_dims, | ||
| std::string input_name) { | ||
| auto shape = | ||
| dev_ctx.HasDnnAttr("fused_reshape_" + input_name) | ||
| ? PADDLE_GET_CONST(std::vector<int>, | ||
| dev_ctx.GetDnnAttr("fused_reshape_" + input_name)) | ||
| : std::vector<int>(); | ||
| auto axis = dev_ctx.HasDnnAttr("fused_transpose_" + input_name) | ||
| ? PADDLE_GET_CONST( | ||
| std::vector<int>, | ||
| dev_ctx.GetDnnAttr("fused_transpose_" + input_name)) | ||
| : std::vector<int>(); | ||
| if (!shape.empty() && !axis.empty()) { | ||
| return input_dims.reshape(shape).transpose(axis); | ||
| } | ||
| return input_dims; | ||
| } | ||
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| void CalculateMatrixDims(const std::vector<int64_t> &x_dims, | ||
| const std::vector<int64_t> &y_dims, | ||
| std::vector<int64_t> *x_bd_dims, | ||
| std::vector<int64_t> *y_bd_dims, | ||
| DenseTensor *out, | ||
| const bool is_output_fused) { | ||
| if (x_dims.size() == 1) { | ||
| (*x_bd_dims)[(*x_bd_dims).size() - 1] = x_dims[0]; | ||
| } else if (x_dims.size() == 2) { | ||
| (*x_bd_dims)[(*x_bd_dims).size() - 1] = x_dims[1]; | ||
| (*x_bd_dims)[(*x_bd_dims).size() - 2] = x_dims[0]; | ||
| } else { | ||
| for (size_t i = 0; i < x_dims.size(); ++i) { | ||
| (*x_bd_dims)[(*x_bd_dims).size() - x_dims.size() + i] = x_dims[i]; | ||
| } | ||
| } | ||
| if (y_dims.size() == 1) { | ||
| (*y_bd_dims)[(*x_bd_dims).size() - 2] = y_dims[0]; | ||
| } else if (y_dims.size() == 2) { | ||
| (*y_bd_dims)[(*y_bd_dims).size() - 1] = y_dims[1]; | ||
| (*y_bd_dims)[(*y_bd_dims).size() - 2] = y_dims[0]; | ||
| } else { | ||
| for (size_t i = 0; i < y_dims.size(); ++i) { | ||
| (*y_bd_dims)[(*y_bd_dims).size() - y_dims.size() + i] = y_dims[i]; | ||
| } | ||
| } | ||
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| if (!is_output_fused && x_dims.size() > 2 && y_dims.size() > 2) { | ||
| auto out_dims = vectorize(out->dims()); | ||
| for (size_t i = 0; i < (*x_bd_dims).size() - 2; ++i) { | ||
| PADDLE_ENFORCE_EQ( | ||
| (*x_bd_dims)[i] == (*y_bd_dims)[i] || (*x_bd_dims)[i] == 1 || | ||
| (*y_bd_dims)[i] == 1, | ||
| true, | ||
| errors::InvalidArgument( | ||
| "Tensor dimensions are incorrect for broadcasting." | ||
| "Dimensions in X and Y must be same or equal to 1, but " | ||
| "received x_dim[%d]=%d and y_dims[%d]= %d", | ||
| i, | ||
| (*x_bd_dims)[i], | ||
| i, | ||
| (*y_bd_dims)[i])); | ||
| (out_dims)[i] = std::max((*x_bd_dims)[i], (*y_bd_dims)[i]); | ||
| } | ||
| out->Resize(make_ddim((out_dims))); | ||
| } | ||
| } | ||
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| template <typename T, typename Context> | ||
| void MatmulKernel(const Context &dev_ctx, | ||
| const DenseTensor &x, | ||
| const DenseTensor &y, | ||
| bool transpose_x, | ||
| bool transpose_y, | ||
| DenseTensor *out) { | ||
| if (dev_ctx.HasDnnAttr("head_number")) { | ||
| const auto head_number = | ||
| PADDLE_GET_CONST(int, dev_ctx.GetDnnAttr("head_number")); | ||
| PADDLE_ENFORCE_EQ( | ||
| head_number, | ||
| 1, | ||
| errors::Unimplemented( | ||
| "oneDNN matmul doesn't support multiple heads. Expected " | ||
| "head_number=1. But received `head_number` is %d", | ||
| head_number)); | ||
| } | ||
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| constexpr bool is_int8 = funcs::is_int8<T>(); | ||
| constexpr bool is_bfloat16 = funcs::is_bfloat16<T>(); | ||
| const bool force_fp32_output = | ||
| dev_ctx.HasDnnAttr("force_fp32_output") | ||
| ? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("force_fp32_output")) | ||
| : false; | ||
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| bool fuse_relu = false; | ||
| if (dev_ctx.HasDnnAttr("fuse_activation")) { | ||
| auto act_type = | ||
| PADDLE_GET_CONST(std::string, dev_ctx.GetDnnAttr("fuse_activation")); | ||
| if (act_type == "relu" || act_type == "relu6") { | ||
| fuse_relu = true; | ||
| } | ||
| } | ||
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| auto x_dims = vectorize(GetDimsForInput(dev_ctx, x.dims(), "X")); | ||
| auto y_dims = vectorize(GetDimsForInput(dev_ctx, y.dims(), "Y")); | ||
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| int ndims = std::max(x_dims.size(), y_dims.size()); | ||
| ndims = std::max(ndims, 3); | ||
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| std::vector<int64_t> x_bd_dims(ndims, 1); | ||
| std::vector<int64_t> y_bd_dims(ndims, 1); | ||
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| CalculateMatrixDims(x_dims, | ||
| y_dims, | ||
| &x_bd_dims, | ||
| &y_bd_dims, | ||
| out, | ||
| funcs::IsOutputFused(dev_ctx)); | ||
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| if (force_fp32_output || ((!is_int8) && (!is_bfloat16))) { | ||
| funcs::ExecuteMatmul<T, float>( | ||
| dev_ctx, x, y, x_bd_dims, y_bd_dims, transpose_x, transpose_y, out); | ||
| } else if (is_bfloat16) { | ||
| funcs::ExecuteMatmul<T, paddle::platform::bfloat16>( | ||
| dev_ctx, x, y, x_bd_dims, y_bd_dims, transpose_x, transpose_y, out); | ||
| } else if (fuse_relu) { | ||
| funcs::ExecuteMatmul<T, uint8_t>( | ||
| dev_ctx, x, y, x_bd_dims, y_bd_dims, transpose_x, transpose_y, out); | ||
| } else { | ||
| funcs::ExecuteMatmul<T, int8_t>( | ||
| dev_ctx, x, y, x_bd_dims, y_bd_dims, transpose_x, transpose_y, out); | ||
| } | ||
| } | ||
|
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(matmul, | ||
| OneDNN, | ||
| ONEDNN, | ||
| phi::MatmulKernel, | ||
| float, | ||
| phi::dtype::bfloat16, | ||
| int8_t, | ||
| uint8_t) {} |