【NPU】Support npu kernel for matmul op (#31544)
* add matmulv2_npu * add matmul * add matmulrevert-31562-mean
zhang wenhui
4 years ago
committed by
GitHub
parent
f400ce9f51
commit
29d50d2049
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/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include <memory>
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#include <string>
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#include "paddle/fluid/operators/matmul_v2_op.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class MatMulV2NPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* y = ctx.Input<framework::Tensor>("Y");
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auto* out = ctx.Output<framework::Tensor>("Out");
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bool transpose_x = ctx.Attr<bool>("trans_x");
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bool transpose_y = ctx.Attr<bool>("trans_y");
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if (x->dims().size() == 2) {
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out->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner(
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"MatMul", {*x, *y}, {*out},
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{{"transpose_x1", transpose_x}, {"transpose_x2", transpose_y}});
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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runner.Run(stream);
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} else if (x->dims().size() > 2) {
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out->mutable_data<T>(ctx.GetPlace());
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auto runner =
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NpuOpRunner("BatchMatMul", {*x, *y}, {*out},
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{{"adj_x1", transpose_x}, {"adj_x2", transpose_y}});
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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runner.Run(stream);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class MatMulV2GradNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* y = ctx.Input<framework::Tensor>("Y");
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auto* dout = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
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bool transpose_y = ctx.Attr<bool>("trans_y");
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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if (x->dims().size() == 2) {
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if (transpose_y) {
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if (dx) {
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_dx =
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NpuOpRunner("MatMul", {*dout, *y}, {*dx},
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{{"transpose_x1", false}, {"transpose_x2", false}});
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runner_dx.Run(stream);
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}
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if (dy) {
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dy->mutable_data<T>(ctx.GetPlace());
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auto runner_dy =
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NpuOpRunner("MatMul", {*dout, *x}, {*dy},
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{{"transpose_x1", true}, {"transpose_x2", false}});
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runner_dy.Run(stream);
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}
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} else {
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if (dx) {
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_dx =
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NpuOpRunner("MatMul", {*dout, *y}, {*dx},
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{{"transpose_x1", false}, {"transpose_x2", true}});
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runner_dx.Run(stream);
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}
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if (dy) {
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dy->mutable_data<T>(ctx.GetPlace());
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auto runner_dy =
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NpuOpRunner("MatMul", {*x, *dout}, {*dy},
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{{"transpose_x1", true}, {"transpose_x2", false}});
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runner_dy.Run(stream);
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}
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}
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} else if (x->dims().size() > 2) {
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if (transpose_y) {
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if (dx) {
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx},
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{{"adj_x1", false}, {"adj_x2", false}});
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runner_dx.Run(stream);
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}
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if (dy) {
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dy->mutable_data<T>(ctx.GetPlace());
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auto runner_dy = NpuOpRunner("BatchMatMul", {*dout, *x}, {*dy},
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{{"adj_x1", true}, {"adj_x2", false}});
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runner_dy.Run(stream);
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}
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} else {
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if (dx) {
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_dx = NpuOpRunner("BatchMatMul", {*dout, *y}, {*dx},
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{{"adj_x1", false}, {"adj_x2", true}});
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runner_dx.Run(stream);
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}
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if (dy) {
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dy->mutable_data<T>(ctx.GetPlace());
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auto runner_dy = NpuOpRunner("BatchMatMul", {*x, *dout}, {*dy},
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{{"adj_x1", true}, {"adj_x2", false}});
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runner_dy.Run(stream);
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}
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_NPU_KERNEL(
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matmul_v2,
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ops::MatMulV2NPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::MatMulV2NPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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matmul_v2_grad,
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ops::MatMulV2GradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::MatMulV2GradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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@ -0,0 +1,210 @@
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# Copyright (c) 2021 PaddlePaddle Authors. 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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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
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"""Reference forward implementation using np.matmul."""
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# np.matmul does not support the transpose flags, so we manually
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# transpose X and Y appropriately.
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if transpose_X:
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if X.ndim == 1:
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X = X.reshape((X.size, ))
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elif X.ndim == 2:
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X = X.T
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else:
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dim = [i for i in range(len(X.shape))]
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dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
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X = np.transpose(X, tuple(dim))
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if transpose_Y:
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if Y.ndim == 1:
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Y = Y.reshape((Y.size, ))
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else:
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dim = [i for i in range(len(Y.shape))]
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dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
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Y = np.transpose(Y, tuple(dim))
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Out = np.matmul(X, Y)
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if not Out.shape:
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# We do not support 0-dimensional Tensors (scalars). So where
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# np.matmul outputs a scalar, we must convert to a Tensor of
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# shape (1, ) instead.
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# Everywhere else, we are compatible with np.matmul.
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Out = np.array([Out], dtype="float64")
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return Out
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class TestMatMul(OpTest):
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def config(self):
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self.x_shape = (100, 24)
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self.y_shape = (24, 100)
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self.trans_x = False
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self.trans_y = False
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def setUp(self):
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self.set_npu()
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self.op_type = "matmul_v2"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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self.config()
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np.random.seed(SEED)
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x = np.random.random(self.x_shape).astype(self.dtype)
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y = np.random.random(self.y_shape).astype(self.dtype)
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# -0.1 ~ 0.1
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x = -0.1 + 0.2 * x
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y = -0.1 + 0.2 * y
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result = reference_matmul(x, y, self.trans_x, self.trans_y)
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result = result.astype(self.dtype)
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self.inputs = {
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'X': x,
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'Y': y,
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}
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self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
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self.outputs = {'Out': result}
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def set_npu(self):
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self.__class__.use_npu = True
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self.__class__.no_need_check_grad = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5)
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# TODO(ascendrc): Add grad test
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# def test_check_grad(self):
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# if self.dtype == np.float16:
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# return
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# self.check_grad(['X'], 'Out')
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#
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class TestMatMul2(TestMatMul):
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"""
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case 2
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"""
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def config(self):
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self.x_shape = (32, 24)
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self.y_shape = (32, 24)
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self.trans_x = False
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self.trans_y = True
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class TestMatMul3(TestMatMul):
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"""
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case 3
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"""
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def init_dtype(self):
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self.dtype = np.float16
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class TestMatMul4(TestMatMul):
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"""
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case 4 dim=3
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"""
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def config(self):
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self.x_shape = (2, 3, 4)
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self.y_shape = (2, 4, 3)
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self.trans_x = False
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self.trans_y = False
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestMatMulNet(unittest.TestCase):
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def _test(self, run_npu=True):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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main_prog.random_seed = SEED
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startup_prog.random_seed = SEED
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np.random.seed(SEED)
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a_np = np.random.random(size=(2, 3)).astype('float32')
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b_np = np.random.random(size=(2, 3)).astype('float32')
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c_np = np.random.random(size=(3, 2)).astype('float32')
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d_np = np.random.random(size=(3, 2)).astype('float32')
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label_np = np.random.randint(2, size=(2, 1)).astype('int64')
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with paddle.static.program_guard(main_prog, startup_prog):
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a = paddle.static.data(name="a", shape=[2, 3], dtype='float32')
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b = paddle.static.data(name="b", shape=[2, 3], dtype='float32')
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c = paddle.static.data(name="c", shape=[3, 2], dtype='float32')
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d = paddle.static.data(name="d", shape=[3, 2], dtype='float32')
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label = paddle.static.data(
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name="label", shape=[2, 1], dtype='int64')
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sum_1 = paddle.add(a, b)
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sum_2 = paddle.add(c, d)
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result = paddle.matmul(sum_1, sum_2)
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fc_1 = fluid.layers.fc(input=result, size=8)
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prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.reduce_mean(cost)
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sgd = fluid.optimizer.SGD(learning_rate=0.01)
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sgd.minimize(loss)
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if run_npu:
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place = paddle.NPUPlace(0)
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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print("Start run on {}".format(place))
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for epoch in range(100):
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pred_res, loss_res = exe.run(main_prog,
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feed={
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"a": a_np,
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"b": b_np,
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"c": c_np,
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"d": d_np,
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"label": label_np
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},
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fetch_list=[prediction, loss])
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if epoch % 10 == 0:
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print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
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epoch, pred_res[0], loss_res))
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return pred_res, loss_res
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def test_npu(self):
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cpu_pred, cpu_loss = self._test(False)
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npu_pred, npu_loss = self._test(True)
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self.assertTrue(np.allclose(npu_pred, cpu_pred))
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self.assertTrue(np.allclose(npu_loss, cpu_loss))
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if __name__ == '__main__':
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unittest.main()
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