wehub-resource-sync
296 lines
10 KiB
Python
296 lines
10 KiB
Python
# Copyright (c) 2023 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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import os
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import numpy as np
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import paddle
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import paddle.distributed as dist
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class TestMatmulApiForSemiAutoParallel:
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def __init__(self):
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self._dtype = os.getenv("dtype")
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self._backend = os.getenv("backend")
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self._seed = eval(os.getenv("seed"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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def check_tensor_eq(self, a, b):
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np1 = a.numpy()
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np2 = b.numpy()
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np.testing.assert_allclose(np1, np2, rtol=1e-04, verbose=True)
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def test_body(
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self,
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x_shape,
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y_shape,
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x_placements,
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y_placements,
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trans_x=False,
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trans_y=False,
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):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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x_np = np.random.random(size=x_shape).astype(self._dtype)
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y_np = np.random.random(size=y_shape).astype(self._dtype)
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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x.stop_gradient = False
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y.stop_gradient = False
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dist_x = dist.shard_tensor(x_np, self._mesh, x_placements)
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dist_y = dist.shard_tensor(y_np, self._mesh, y_placements)
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dist_x.stop_gradient = False
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dist_y.stop_gradient = False
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out = paddle.matmul(x, y, transpose_x=trans_x, transpose_y=trans_y)
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dist_out = paddle.matmul(
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dist_x, dist_y, transpose_x=trans_x, transpose_y=trans_y
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)
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self.check_tensor_eq(out, dist_out)
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out.backward()
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dist_out.backward()
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self.check_tensor_eq(x.grad, dist_x.grad)
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self.check_tensor_eq(y.grad, dist_y.grad)
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return dist_out, dist_x.grad, dist_y.grad
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def test_matmul_x_row_shard(self):
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# case1: mk[0,-1],kn[-1,-1] -> mk[0,-1],kn[-1,-1] = mn[0,-1] partial[]
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[64, 32],
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y_shape=[32, 48],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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)
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# verify output local shape and dist attr
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np.testing.assert_equal(dist_out._local_shape, [32, 48], verbose=True)
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np.testing.assert_equal(
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dist_out.placements, [dist.Shard(0)], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [32, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(0)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [32, 48], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Partial()], verbose=True
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)
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def test_batch_matmul(self):
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# case1: amk[0,-1, -1],kn[-1,-1] -> amk[0,-1,-1],kn[-1,-1] = amn[0,-1, -1] partial[]
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[2, 64, 32],
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y_shape=[32, 48],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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)
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# verify output local shape and dist attr
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np.testing.assert_equal(
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dist_out._local_shape, [1, 64, 48], verbose=True
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)
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np.testing.assert_equal(
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dist_out.placements, [dist.Shard(0)], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [1, 64, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(0)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [32, 48], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Partial()], verbose=True
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)
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def test_matmul_x_column_shard(self):
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# case2: mk[-1, 0],kn[-1,-1] --> mk[-1, 0],kn[0, -1] = nm[-1, -1] partial[0]
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[64, 32],
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y_shape=[32, 48],
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x_placements=[dist.Shard(1)],
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y_placements=[dist.Replicate()],
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)
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# verify local shape
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np.testing.assert_equal(dist_out._local_shape, [64, 48], verbose=True)
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np.testing.assert_equal(
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dist_out.placements, [dist.Partial()], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [64, 16], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(1)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [32, 48], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Replicate()], verbose=True
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)
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def test_matmul_x_column_shard_trans_x_y(self):
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# case1: mk[-1,0],kn[-1,-1] -> mk[0,-1],kn[-1,-1] = mn[0,-1] partial[], trans x, trans y
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[32, 64],
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y_shape=[48, 32],
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x_placements=[dist.Shard(1)],
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y_placements=[dist.Replicate()],
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trans_x=True,
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trans_y=True,
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)
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# verify output local shape and dist attr
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np.testing.assert_equal(dist_out._local_shape, [32, 48], verbose=True)
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np.testing.assert_equal(
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dist_out.placements, [dist.Shard(0)], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [32, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(1)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [48, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Partial()], verbose=True
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)
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def test_matmul_x_column_shard_trans_x(self):
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# case1: mk[-1,0],kn[-1,-1] -> mk[0,-1],kn[-1,-1] = mn[0,-1] partial[], trans x
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[32, 64],
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y_shape=[32, 48],
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x_placements=[dist.Shard(1)],
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y_placements=[dist.Replicate()],
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trans_x=True,
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trans_y=False,
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)
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# verify output local shape and dist attr
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np.testing.assert_equal(dist_out._local_shape, [32, 48], verbose=True)
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np.testing.assert_equal(
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dist_out.placements, [dist.Shard(0)], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [32, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(1)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [32, 48], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Partial()], verbose=True
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)
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def test_matmul_x_row_shard_trans_y(self):
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# case1: mk[0,-1],kn[-1,-1] -> mk[0,-1],kn[-1,-1] = mn[0,-1] partial[], trans y
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dist_out, dist_x_grad, dist_y_grad = self.test_body(
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x_shape=[64, 32],
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y_shape=[48, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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trans_x=False,
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trans_y=True,
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)
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# verify output local shape and dist attr
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np.testing.assert_equal(dist_out._local_shape, [32, 48], verbose=True)
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np.testing.assert_equal(
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dist_out.placements, [dist.Shard(0)], verbose=True
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)
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# verify x_grad local shape and dist attr
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np.testing.assert_equal(
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dist_x_grad._local_shape, [32, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_x_grad.placements, [dist.Shard(0)], verbose=True
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)
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# verify y_grad local shape and dist attr
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np.testing.assert_equal(
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dist_y_grad._local_shape, [48, 32], verbose=True
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)
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np.testing.assert_equal(
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dist_y_grad.placements, [dist.Partial()], verbose=True
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)
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def test_matmul_with_complex_type(self):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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x_np = np.random.random(size=[64, 32]).astype(np.complex128)
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y_np = np.random.random(size=[32, 48]).astype(np.float32)
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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x.stop_gradient = False
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y.stop_gradient = False
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dist_x = dist.shard_tensor(x_np, self._mesh, [dist.Replicate()])
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dist_y = dist.shard_tensor(y_np, self._mesh, [dist.Replicate()])
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dist_x.stop_gradient = False
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dist_y.stop_gradient = False
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out = paddle.matmul(x, y, transpose_x=False, transpose_y=False)
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dist_out = paddle.matmul(
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dist_x, dist_y, transpose_x=False, transpose_y=False
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)
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self.check_tensor_eq(out, dist_out)
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out.backward()
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dist_out.backward()
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self.check_tensor_eq(x.grad, dist_x.grad)
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self.check_tensor_eq(y.grad, dist_y.grad)
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def run_test_case(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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elif self._backend == "gpu":
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paddle.set_device("gpu:" + str(dist.get_rank()))
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else:
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raise ValueError("Only support cpu or gpu backend.")
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self.test_matmul_x_row_shard()
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self.test_matmul_x_column_shard()
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self.test_matmul_x_column_shard_trans_x_y()
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self.test_matmul_x_column_shard_trans_x()
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self.test_matmul_x_row_shard_trans_y()
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self.test_matmul_with_complex_type()
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self.test_batch_matmul()
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if __name__ == '__main__':
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TestMatmulApiForSemiAutoParallel().run_test_case()
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