wehub-resource-sync
266 lines
9.1 KiB
Python
266 lines
9.1 KiB
Python
# Copyright (c) 2025 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 unittest
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import numpy as np
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import paddle
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class TestMmOutAndGrad(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x_shape = [3, 4]
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self.y_shape = [4, 5]
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self.x_np = np.random.rand(*self.x_shape).astype(np.float32)
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self.y_np = np.random.rand(*self.y_shape).astype(np.float32)
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def do_test(self, test_type):
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x = paddle.to_tensor(self.x_np, stop_gradient=False)
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y = paddle.to_tensor(self.y_np, stop_gradient=False)
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out = paddle.empty((3, 5), dtype='float32')
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out.stop_gradient = False
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if test_type == 'raw':
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result = paddle.mm(x, y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'out':
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paddle.mm(x, y, out=out)
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out.mean().backward()
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return out, x.grad, y.grad
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def test_mm_out(self):
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out_std, x_grad_std, y_grad_std = self.do_test('raw')
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out, x_grad, y_grad = self.do_test('out')
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np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-20)
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np.testing.assert_allclose(
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x_grad.numpy(), x_grad_std.numpy(), rtol=1e-20
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)
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np.testing.assert_allclose(
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y_grad.numpy(), y_grad_std.numpy(), rtol=1e-20
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)
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class TestMmOutScenarios(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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def test_mm_out_scenarios(self):
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def run_mm(test_type):
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x = paddle.to_tensor(
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[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], stop_gradient=False
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)
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y = paddle.to_tensor(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False
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)
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out = paddle.zeros([3, 3], dtype='float32')
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out.stop_gradient = False
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if test_type == "return":
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out = paddle.mm(x, y)
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elif test_type == "input_out":
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paddle.mm(x, y, out=out)
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elif test_type == "both_return":
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out = paddle.mm(x, y, out=out)
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elif test_type == "both_input_out":
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tmp = paddle.mm(x, y, out=out)
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expected = np.array(x.numpy()) @ np.array(y.numpy())
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np.testing.assert_allclose(
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out.numpy(), expected, rtol=1e-5, atol=1e-5
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)
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loss = out.sum()
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loss.backward()
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return out, x.grad, y.grad, out.grad
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out1, x1, y1, o1 = run_mm("return")
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out2, x2, y2, o2 = run_mm("input_out")
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out3, x3, y3, o3 = run_mm("both_return")
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out4, x4, y4, o4 = run_mm("both_input_out")
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np.testing.assert_allclose(
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out1.numpy(), out2.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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out1.numpy(), out3.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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out1.numpy(), out4.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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x1.numpy(), x2.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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x1.numpy(), x3.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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x1.numpy(), x4.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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y1.numpy(), y2.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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y1.numpy(), y3.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_allclose(
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y1.numpy(), y4.numpy(), rtol=1e-20, atol=1e-20
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)
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np.testing.assert_equal(o1, None)
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np.testing.assert_equal(o2, None)
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np.testing.assert_equal(o3, None)
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np.testing.assert_equal(o4, None)
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class TestMmOutBatchedAndShapes(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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def _check_out(self, x_np, y_np):
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x = paddle.to_tensor(x_np, stop_gradient=False)
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y = paddle.to_tensor(y_np, stop_gradient=False)
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expected = paddle.mm(x, y)
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out = paddle.empty(expected.shape, dtype=expected.dtype)
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paddle.mm(x, y, out=out)
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np.testing.assert_allclose(
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out.numpy(), expected.numpy(), rtol=1e-5, atol=1e-5
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)
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def test_2d(self):
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self._check_out(
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np.random.rand(3, 4).astype(np.float32),
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np.random.rand(4, 5).astype(np.float32),
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)
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def test_3d_batched(self):
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self._check_out(
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np.random.rand(2, 3, 4).astype(np.float32),
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np.random.rand(2, 4, 5).astype(np.float32),
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)
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def test_broadcast_3d_2d(self):
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self._check_out(
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np.random.rand(2, 3, 4).astype(np.float32),
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np.random.rand(4, 5).astype(np.float32),
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)
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def test_1d_2d(self):
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self._check_out(
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np.random.rand(4).astype(np.float32),
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np.random.rand(4, 5).astype(np.float32),
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)
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def test_2d_1d(self):
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self._check_out(
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np.random.rand(3, 4).astype(np.float32),
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np.random.rand(4).astype(np.float32),
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)
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class TestMmOutDtypeDynamicOnly(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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def _skip_if_no_bf16_cuda(self):
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if not paddle.is_compiled_with_cuda() or paddle.is_compiled_with_rocm():
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self.skipTest("CUDA is required for mm out_dtype")
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if paddle.device.cuda.get_device_capability()[0] < 8:
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self.skipTest(
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"BF16 mm out_dtype requires CUDA compute capability >= 8"
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)
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def test_bf16_to_fp32(self):
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self._skip_if_no_bf16_cuda()
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x = paddle.randn([3, 4], dtype='bfloat16')
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y = paddle.randn([4, 5], dtype='bfloat16')
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out = paddle.mm(x, y, out_dtype=paddle.float32)
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ref = paddle.mm(x.astype('float32'), y.astype('float32'))
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self.assertEqual(out.dtype, paddle.float32)
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np.testing.assert_allclose(
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out.numpy(), ref.numpy(), rtol=1e-2, atol=1e-2
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)
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def test_bf16_to_fp32_transposed_rhs(self):
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self._skip_if_no_bf16_cuda()
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x = paddle.randn([3, 4], dtype='bfloat16')
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weight = paddle.randn([5, 4], dtype='bfloat16')
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out = paddle.mm(x, weight.t(), out_dtype=paddle.float32)
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ref = paddle.mm(x.astype('float32'), weight.t().astype('float32'))
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self.assertEqual(out.dtype, paddle.float32)
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np.testing.assert_allclose(
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out.numpy(), ref.numpy(), rtol=1e-2, atol=1e-2
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)
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def test_bf16_to_fp32_out(self):
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self._skip_if_no_bf16_cuda()
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x = paddle.randn([3, 4], dtype='bfloat16')
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y = paddle.randn([4, 5], dtype='bfloat16')
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out = paddle.empty([3, 5], dtype='float32')
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ret = paddle.mm(x, y, out_dtype=paddle.float32, out=out)
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ref = paddle.mm(x.astype('float32'), y.astype('float32'))
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self.assertEqual(ret.dtype, paddle.float32)
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np.testing.assert_allclose(
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ret.numpy(), ref.numpy(), rtol=1e-2, atol=1e-2
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)
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np.testing.assert_allclose(
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out.numpy(), ref.numpy(), rtol=1e-2, atol=1e-2
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)
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def test_out_dtype_rejects_unsupported_cases(self):
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self._skip_if_no_bf16_cuda()
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x = paddle.randn([3, 4], dtype='float32')
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y = paddle.randn([4, 5], dtype='float32')
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with self.assertRaises(TypeError):
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paddle.mm(x, y, out_dtype=paddle.float32)
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x_bf16 = paddle.randn([3, 4], dtype='bfloat16')
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y_bf16 = paddle.randn([4, 5], dtype='bfloat16')
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with self.assertRaises(TypeError):
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paddle.mm(x_bf16, y_bf16, out_dtype=paddle.bfloat16)
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with self.assertRaises(ValueError):
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paddle.mm(
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x_bf16.reshape([1, 3, 4]),
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y_bf16.reshape([1, 4, 5]),
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out_dtype=paddle.float32,
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)
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with self.assertRaises(TypeError):
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paddle.mm(
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x_bf16,
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y_bf16,
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out_dtype=paddle.float32,
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out=paddle.empty([3, 5], dtype='bfloat16'),
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)
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def test_static_out_dtype_fails_closed(self):
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paddle.enable_static()
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try:
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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x = paddle.static.data('x', [3, 4], dtype='bfloat16')
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y = paddle.static.data('y', [4, 5], dtype='bfloat16')
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with self.assertRaises(NotImplementedError):
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paddle.mm(x, y, out_dtype=paddle.float32)
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finally:
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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