mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
138 lines
4.6 KiB
Python
138 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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from unittest.mock import Mock
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import numpy as np
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import paddle
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import paddle.device.cuda.graphs as graphs
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from fastdeploy.config import (
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CacheConfig,
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FDConfig,
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GraphOptimizationConfig,
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ParallelConfig,
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SchedulerConfig,
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)
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class FakeModelConfig:
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def __init__(self):
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self.hidden_size = 768
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self.intermediate_size = 768
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self.num_hidden_layers = 12
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self.num_attention_heads = 12
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self.rms_norm_eps = 1e-6
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self.tie_word_embeddings = True
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self.ori_vocab_size = 32000
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self.moe_layer_start_index = 8
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self.pretrained_config = Mock()
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self.pretrained_config.prefix_name = "test"
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self.num_key_value_heads = 1
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self.head_dim = 1
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self.is_quantized = False
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self.hidden_act = "relu"
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self.vocab_size = 32000
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self.hidden_dropout_prob = 0.1
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self.initializer_range = 0.02
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self.max_position_embeddings = 512
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self.tie_word_embeddings = True
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self.model_format = "auto"
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self.enable_mm = False
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self.max_model_len = 512
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def get_default_test_fd_config():
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graph_opt_config = GraphOptimizationConfig(args={})
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scheduler_config = SchedulerConfig(args={})
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scheduler_config.max_num_seqs = 1
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parallel_config = ParallelConfig(args={})
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parallel_config.data_parallel_rank = 1
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cache_config = CacheConfig({})
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model_config = FakeModelConfig()
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fd_config = FDConfig(
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graph_opt_config=graph_opt_config,
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parallel_config=parallel_config,
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cache_config=cache_config,
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scheduler_config=scheduler_config,
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model_config=model_config,
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test_mode=True,
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)
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return fd_config
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class OpPerformanceTester:
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def __init__(self, op_name, op_fn, num_layers=20, weight_size=None, gate=None):
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self.op_name = op_name
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self.op_fn = op_fn
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self.num_layers = num_layers
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self.weight_size = weight_size
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self.gate = gate
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def _fake_model_run(self, x):
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for j in range(self.num_layers):
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if self.gate:
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out = self.op_fn(x, self.gate)
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else:
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out = self.op_fn(x)
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return out
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def benchmark(self, input_size, batch_sizes, dtype="bfloat16", num_warmup=1, num_tests=10):
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print(f"======== {self.op_name} Performance ========")
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print(
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"{:<15} {:<40} {:<15} {:<15} {:<15}".format(
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"Batch Size", "Last 5 Times (us)", "Last Time (us)", "TFlops", "TB/s"
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)
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)
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for idx, bsz in enumerate(batch_sizes):
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x = paddle.rand((bsz, input_size), dtype=dtype)
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self._fake_model_run(x)
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graph = graphs.CUDAGraph()
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graph.capture_begin()
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self._fake_model_run(x)
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graph.capture_end()
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start_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(num_tests)]
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end_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(num_tests)]
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for i in range(num_tests):
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start_events[i].record()
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graph.replay()
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end_events[i].record()
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paddle.device.synchronize()
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times = np.array([round(s.elapsed_time(e), 2) for s, e in zip(start_events, end_events)])[num_warmup:]
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times = times * 1e3 / self.num_layers # us / layer
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times = np.array([round(time, 2) for time in times])
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last_5_times = times[-5:]
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last_time = times[-1]
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tfloaps = None
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tbps = None
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if self.weight_size:
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flops = 2 * bsz * self.weight_size
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memory = self.weight_size
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tfloaps = round(flops / 1e12 / (last_time * 1e-6), 1)
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tbps = round(memory / 1e12 / (last_time * 1e-6), 1)
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print("{:<15} {:<40} {:<15} {:<15} {:<15}".format(bsz, str(last_5_times), last_time, tfloaps, tbps))
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else:
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print("{:<15} {:<40} {:<15}".format(bsz, str(last_5_times), last_time))
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