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[XPU] support fp16 for mea and group_norm (#70633)
* [XPU] support fp16 for mea and group_norm * fix typo
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test/xpu/test_variable_length_memory_efficient_attention_xpu.py
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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. | ||
|
|
||
| import unittest | ||
|
|
||
| import numpy as np | ||
|
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||
| import paddle | ||
| from paddle import base | ||
| from paddle.incubate.nn.functional import ( | ||
| variable_length_memory_efficient_attention, | ||
| ) | ||
|
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| paddle.seed(2023) | ||
|
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|
|
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| def create_attn_mask( | ||
| mask_type, | ||
| batch_size, | ||
| seq_lens, | ||
| ): | ||
| max_seq_len = max(seq_lens) | ||
| mask = paddle.zeros( | ||
| [batch_size, 1, max_seq_len, max_seq_len], dtype=mask_type | ||
| ) | ||
| for i in range(batch_size): | ||
| seq_len = seq_lens[i] | ||
| mask[i, 0, :seq_len, :seq_len] = ( | ||
| paddle.tril(paddle.ones(shape=(seq_len, seq_len), dtype=mask_type)) | ||
| - 1 | ||
| ) * 1e4 | ||
| return mask | ||
|
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||
|
|
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| def naive_attention_impl(query, key, value, mask, scale): | ||
| batch = query.shape[0] | ||
| heads = query.shape[1] | ||
| seq_len = query.shape[2] | ||
| head_dim = query.shape[3] | ||
| kv_head = key.shape[1] | ||
|
|
||
| key = key.reshape([batch, kv_head, 1, seq_len, head_dim]) | ||
| key = paddle.tile(key, [1, 1, heads // kv_head, 1, 1]) | ||
| key = key.reshape([batch, heads, seq_len, head_dim]) | ||
|
|
||
| value = value.reshape([batch, kv_head, 1, seq_len, head_dim]) | ||
| value = paddle.tile(value, [1, 1, heads // kv_head, 1, 1]) | ||
| value = value.reshape([batch, heads, seq_len, head_dim]) | ||
|
|
||
| qk_res = paddle.matmul(query, key, transpose_y=True) | ||
| attention = qk_res * scale | ||
| attention = attention + mask | ||
| softmax_result = paddle.nn.functional.softmax(attention, -1) | ||
| result = paddle.matmul(softmax_result, value) | ||
| return result | ||
|
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|
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| class TestMemEffAttentionVariableAPI(unittest.TestCase): | ||
| def setUp(self): | ||
| self.name = "MemEffAPIVariable_fp32" | ||
| self.place = paddle.XPUPlace(0) | ||
| self.batch_size = 1 | ||
| self.num_head = 8 | ||
| self.kv_num_head = 8 | ||
| self.seq_len = 64 | ||
| self.dim_head = 16 | ||
| self.seq_lens = paddle.to_tensor( | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| "int32", | ||
| ) | ||
| self.shape = ( | ||
| self.batch_size, | ||
| self.num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.shape_kv = ( | ||
| self.batch_size, | ||
| self.kv_num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.dtype = 'float32' | ||
| self.attention_mask = create_attn_mask( | ||
| self.dtype, | ||
| self.batch_size, | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| ) | ||
| self.scale = 1.0 / np.sqrt(self.shape[-1]) | ||
|
|
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| def test_all(self): | ||
| paddle.disable_static() | ||
|
|
||
| query = np.random.random(self.shape) | ||
| q = paddle.to_tensor( | ||
| query, place=self.place, dtype=self.dtype, stop_gradient=False | ||
| ) | ||
| key = np.random.random(self.shape_kv) | ||
| k = paddle.to_tensor( | ||
| key, place=self.place, dtype=self.dtype, stop_gradient=False | ||
| ) | ||
| value = np.random.random(self.shape_kv) | ||
| v = paddle.to_tensor( | ||
| value, place=self.place, dtype=self.dtype, stop_gradient=False | ||
| ) | ||
|
|
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| out_ = naive_attention_impl(q, k, v, self.attention_mask, self.scale) | ||
|
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| out = variable_length_memory_efficient_attention( | ||
| q, | ||
| k, | ||
| v, | ||
| self.seq_lens, | ||
| self.seq_lens, | ||
| self.attention_mask, | ||
| self.scale, | ||
| ) | ||
|
|
||
| for i in range(self.batch_size): | ||
| np.testing.assert_allclose( | ||
| out.numpy()[i, :, : self.seq_lens[i], :], | ||
| out_[i, :, : self.seq_lens[i], :], | ||
| rtol=5e-03, | ||
| atol=1e-03, | ||
| ) | ||
|
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|
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| class TestMemEffAPIVariableDtypeFP16(TestMemEffAttentionVariableAPI): | ||
| def setUp(self): | ||
| self.name = "MemEffAPIVariable_fp16" | ||
| self.place = paddle.XPUPlace(0) | ||
| self.batch_size = 3 | ||
| self.num_head = 16 | ||
| self.kv_num_head = 16 | ||
| self.seq_len = 64 | ||
| self.dim_head = 32 | ||
| self.seq_lens = paddle.to_tensor( | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| "int32", | ||
| ) | ||
| self.shape = ( | ||
| self.batch_size, | ||
| self.num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.shape_kv = ( | ||
| self.batch_size, | ||
| self.kv_num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.dtype = 'float16' | ||
| self.attention_mask = create_attn_mask( | ||
| self.dtype, | ||
| self.batch_size, | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| ) | ||
| self.scale = 1.0 / np.sqrt(self.shape[-1]) | ||
|
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|
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| class TestMemEffAPIVariableDtypeFP16Static(unittest.TestCase): | ||
| def setUp(self): | ||
| self.name = "MemEffAPIVariableStatic_fp16" | ||
| self.place = paddle.XPUPlace(0) | ||
| self.batch_size = 3 | ||
| self.num_head = 16 | ||
| self.kv_num_head = 16 | ||
| self.seq_len = 64 | ||
| self.dim_head = 32 | ||
| self.seq_lens = paddle.to_tensor( | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| "int32", | ||
| ).numpy() | ||
| self.shape = ( | ||
| self.batch_size, | ||
| self.num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.shape_kv = ( | ||
| self.batch_size, | ||
| self.kv_num_head, | ||
| self.seq_len, | ||
| self.dim_head, | ||
| ) | ||
| self.dtype = 'float16' | ||
| self.attention_mask = create_attn_mask( | ||
| self.dtype, | ||
| self.batch_size, | ||
| [ | ||
| self.seq_len, | ||
| ] | ||
| * self.batch_size, | ||
| ).numpy() | ||
| self.q = np.random.random(self.shape).astype(self.dtype) | ||
| self.k = np.random.random(self.shape_kv).astype(self.dtype) | ||
| self.v = np.random.random(self.shape_kv).astype(self.dtype) | ||
| self.scale = 1.0 / np.sqrt(self.shape[-1]) | ||
|
|
||
| self.ref_out = naive_attention_impl( | ||
| paddle.to_tensor(self.q), | ||
| paddle.to_tensor(self.k), | ||
| paddle.to_tensor(self.v), | ||
| paddle.to_tensor(self.attention_mask), | ||
| self.scale, | ||
| ) | ||
|
|
||
| def test_all(self): | ||
| paddle.enable_static() | ||
| with paddle.static.program_guard( | ||
| paddle.static.Program(), paddle.static.Program() | ||
| ): | ||
| q = paddle.static.data( | ||
| name="query", shape=self.shape, dtype=self.dtype | ||
| ) | ||
| k = paddle.static.data( | ||
| name="key", shape=self.shape_kv, dtype=self.dtype | ||
| ) | ||
| v = paddle.static.data( | ||
| name="value", shape=self.shape_kv, dtype=self.dtype | ||
| ) | ||
| mask = paddle.static.data( | ||
| name="mask", | ||
| shape=[self.batch_size, 1, self.seq_len, self.seq_len], | ||
| dtype=self.dtype, | ||
| ) | ||
| seq_lens = paddle.static.data( | ||
| name="seq_lens", shape=[self.batch_size, 1], dtype="int32" | ||
| ) | ||
| out = variable_length_memory_efficient_attention( | ||
| q, k, v, seq_lens, seq_lens, mask, self.scale | ||
| ) | ||
| exe = base.Executor() | ||
| res = exe.run( | ||
| feed={ | ||
| "query": self.q, | ||
| "key": self.k, | ||
| "value": self.v, | ||
| "seq_lens": self.seq_lens, | ||
| "mask": self.attention_mask, | ||
| }, | ||
| fetch_list=[out], | ||
| ) | ||
| paddle.disable_static() | ||
| np.testing.assert_allclose(res[0], self.ref_out, rtol=5e-03, atol=1e-03) | ||
|
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|
|
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| if __name__ == '__main__': | ||
| unittest.main() |