mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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chen
29a313a402
* support FA4 sm100 * flash attn backend support mask * flash attn backend run flashmask correct * add test for flash_attn_backend and flash_attn_func * check * add test for fa4 * requirements.txt add fa4 whl * check test on sm100 * fix CI conflict * add enable_torch_proxy for flash_mask * lazy import fa4 * check * fix tests import * check test_load_mpt import
134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
"""
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# 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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"""
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import unittest
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import numpy as np
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import paddle
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from utils import OpPerformanceTester
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from fastdeploy.model_executor.ops.triton_ops import qk_rmsnorm_fused
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paddle.set_default_dtype("bfloat16")
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paddle.seed(99)
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class TestQKNorm(unittest.TestCase):
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def setUp(self) -> None:
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# Qwen3-30B-A3B TP1
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self.hidden_size = 2048
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self.num_attention_heads = 32
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self.num_key_value_heads = 4
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self.num_hidden_layers = 48
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self.head_dim = 128
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self.rms_norm_eps = 1e-6
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self.tp_size = 1
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# # Qwen3-235B-A22B TP4
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# self.hidden_size = 4096
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# self.num_attention_heads = 64
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# self.num_key_value_heads = 4
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# self.num_hidden_layers = 94
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# self.head_dim = 128
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# self.rms_norm_eps = 1e-6
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# self.tp_size = 4
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# # GLM_4.6 TP4
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# self.hidden_size = 5120
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# self.num_attention_heads = 96
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# self.num_key_value_heads = 8
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# self.num_hidden_layers = 92
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# self.head_dim = 128
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# self.rms_norm_eps = 1e-5
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# self.tp_size = 4
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self.num_kv_heads_replicas = max(1, self.tp_size // self.num_key_value_heads)
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self.q_size = self.num_attention_heads * self.head_dim // self.tp_size
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self.kv_size = self.num_key_value_heads * self.head_dim * self.num_kv_heads_replicas // self.tp_size
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self.q_norm_weight = paddle.randn([self.head_dim], paddle.bfloat16)
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self.k_norm_weight = paddle.randn([self.head_dim], paddle.bfloat16)
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def qk_norm_paddle(self, qkv_out):
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q, k, v = qkv_out.split([self.q_size, self.kv_size, self.kv_size], axis=-1)
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q_by_head = q.reshape([*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim])
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q_by_head = paddle.incubate.nn.functional.fused_rms_norm(
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q_by_head, self.q_norm_weight, None, self.rms_norm_eps, begin_norm_axis=2
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)[0]
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q = q_by_head.reshape(q.shape)
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k_by_head = k.reshape([*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim])
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k_by_head = paddle.incubate.nn.functional.fused_rms_norm(
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k_by_head, self.k_norm_weight, None, self.rms_norm_eps, begin_norm_axis=2
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)[0]
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k = k_by_head.reshape(k.shape)
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qkv_out = paddle.concat([q, k, v], axis=-1)
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return qkv_out
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def qk_norm_triton_fused(self, qkv_out):
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qkv_out = qk_rmsnorm_fused(
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qkv_out,
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self.q_norm_weight,
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self.k_norm_weight,
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self.rms_norm_eps,
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self.q_size,
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self.kv_size,
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self.head_dim,
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)
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return qkv_out
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def test_qk_norm_paddle_performance(self):
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tester_paddle = OpPerformanceTester(
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op_name="qk_norm_paddle",
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op_fn=self.qk_norm_paddle,
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num_layers=self.num_hidden_layers,
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)
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tester_paddle.benchmark(
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input_size=self.head_dim
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* (self.num_attention_heads // self.tp_size + 2 * self.num_key_value_heads // self.tp_size),
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batch_sizes=[1, 8, 64, 128, 1024, 2048, 4096, 8192],
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)
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def test_qk_norm_fused_performance(self):
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tester = OpPerformanceTester(
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op_name="qk_norm_triton_fused",
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op_fn=self.qk_norm_triton_fused,
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num_layers=self.num_hidden_layers,
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)
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tester.benchmark(
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input_size=self.head_dim
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* (self.num_attention_heads // self.tp_size + 2 * self.num_key_value_heads // self.tp_size),
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batch_sizes=[1, 8, 64, 128, 1024, 2048, 4096, 8192],
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)
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def test_qk_norm_result(self):
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x = paddle.randn(
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[
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128,
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self.head_dim
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* (self.num_attention_heads // self.tp_size + 2 * self.num_key_value_heads // self.tp_size),
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],
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paddle.bfloat16,
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)
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out_paddle = self.qk_norm_paddle(x)
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out_triton_fused = self.qk_norm_triton_fused(x)
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np.testing.assert_allclose(out_triton_fused.numpy(), out_paddle.numpy(), rtol=1e-4, atol=1e-4)
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if __name__ == "__main__":
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unittest.main()
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