mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
343 lines
13 KiB
Python
343 lines
13 KiB
Python
import tempfile
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import pydantic_core
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import pytest
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import yaml
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import tensorrt_llm.bindings.executor as tle
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from tensorrt_llm import LLM as TorchLLM
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from tensorrt_llm._tensorrt_engine import LLM
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from tensorrt_llm.llmapi.llm_args import *
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from tensorrt_llm.llmapi.utils import print_traceback_on_error
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from .test_llm import llama_model_path
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def test_LookaheadDecodingConfig():
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# from constructor
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config = LookaheadDecodingConfig(max_window_size=4,
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max_ngram_size=3,
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max_verification_set_size=4)
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assert config.max_window_size == 4
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assert config.max_ngram_size == 3
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assert config.max_verification_set_size == 4
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# from dict
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config = LookaheadDecodingConfig.from_dict({
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"max_window_size": 4,
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"max_ngram_size": 3,
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"max_verification_set_size": 4
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})
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assert config.max_window_size == 4
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assert config.max_ngram_size == 3
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assert config.max_verification_set_size == 4
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# to pybind
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pybind_config = config._to_pybind()
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assert isinstance(pybind_config, tle.LookaheadDecodingConfig)
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assert pybind_config.max_window_size == 4
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assert pybind_config.max_ngram_size == 3
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assert pybind_config.max_verification_set_size == 4
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def test_update_llm_args_with_extra_dict_with_speculative_config():
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yaml_content = """
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speculative_config:
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decoding_type: Lookahead
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max_window_size: 4
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max_ngram_size: 3
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verification_set_size: 4
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"""
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with tempfile.NamedTemporaryFile(delete=False) as f:
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f.write(yaml_content.encode('utf-8'))
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f.flush()
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f.seek(0)
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dict_content = yaml.safe_load(f)
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llm_args = TrtLlmArgs(model=llama_model_path)
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llm_args_dict = update_llm_args_with_extra_dict(llm_args.to_dict(),
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dict_content)
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llm_args = TrtLlmArgs(**llm_args_dict)
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assert llm_args.speculative_config.max_window_size == 4
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assert llm_args.speculative_config.max_ngram_size == 3
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assert llm_args.speculative_config.max_verification_set_size == 4
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def check_defaults(py_config_cls, pybind_config_cls):
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py_config = py_config_cls()
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pybind_config = pybind_config_cls()
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# get member variables from pybinding_config
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for member in PybindMirror.get_pybind_variable_fields(pybind_config_cls):
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py_value = getattr(py_config, member)
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pybind_value = getattr(pybind_config, member)
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assert py_value == pybind_value, f"{member} default value is not equal"
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def test_KvCacheConfig_declaration():
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config = KvCacheConfig(enable_block_reuse=True,
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max_tokens=1024,
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max_attention_window=[1024, 1024, 1024],
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sink_token_length=32,
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free_gpu_memory_fraction=0.5,
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host_cache_size=1024,
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onboard_blocks=True,
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cross_kv_cache_fraction=0.5,
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secondary_offload_min_priority=1,
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event_buffer_max_size=0,
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enable_partial_reuse=True,
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copy_on_partial_reuse=True)
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pybind_config = config._to_pybind()
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assert pybind_config.enable_block_reuse == True
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assert pybind_config.max_tokens == 1024
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assert pybind_config.max_attention_window == [1024, 1024, 1024]
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assert pybind_config.sink_token_length == 32
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assert pybind_config.free_gpu_memory_fraction == 0.5
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assert pybind_config.host_cache_size == 1024
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assert pybind_config.onboard_blocks == True
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assert pybind_config.cross_kv_cache_fraction == 0.5
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assert pybind_config.secondary_offload_min_priority == 1
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assert pybind_config.event_buffer_max_size == 0
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assert pybind_config.enable_partial_reuse == True
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assert pybind_config.copy_on_partial_reuse == True
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def test_KvCacheConfig_default_values():
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check_defaults(KvCacheConfig, tle.KvCacheConfig)
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def test_CapacitySchedulerPolicy():
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val = CapacitySchedulerPolicy.MAX_UTILIZATION
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assert PybindMirror.maybe_to_pybind(
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val) == tle.CapacitySchedulerPolicy.MAX_UTILIZATION
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def test_ContextChunkingPolicy():
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val = ContextChunkingPolicy.EQUAL_PROGRESS
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assert PybindMirror.maybe_to_pybind(
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val) == tle.ContextChunkingPolicy.EQUAL_PROGRESS
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def test_DynamicBatchConfig_declaration():
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config = DynamicBatchConfig(enable_batch_size_tuning=True,
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enable_max_num_tokens_tuning=True,
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dynamic_batch_moving_average_window=10)
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pybind_config = PybindMirror.maybe_to_pybind(config)
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assert pybind_config.enable_batch_size_tuning == True
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assert pybind_config.enable_max_num_tokens_tuning == True
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assert pybind_config.dynamic_batch_moving_average_window == 10
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def test_SchedulerConfig_declaration():
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config = SchedulerConfig(
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capacity_scheduler_policy=CapacitySchedulerPolicy.MAX_UTILIZATION,
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context_chunking_policy=ContextChunkingPolicy.EQUAL_PROGRESS,
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dynamic_batch_config=DynamicBatchConfig(
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enable_batch_size_tuning=True,
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enable_max_num_tokens_tuning=True,
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dynamic_batch_moving_average_window=10))
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pybind_config = PybindMirror.maybe_to_pybind(config)
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assert pybind_config.capacity_scheduler_policy == tle.CapacitySchedulerPolicy.MAX_UTILIZATION
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assert pybind_config.context_chunking_policy == tle.ContextChunkingPolicy.EQUAL_PROGRESS
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assert PybindMirror.pybind_equals(pybind_config.dynamic_batch_config,
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config.dynamic_batch_config._to_pybind())
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def test_PeftCacheConfig_default_values():
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check_defaults(PeftCacheConfig, tle.PeftCacheConfig)
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def test_PeftCacheConfig_declaration():
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config = PeftCacheConfig(num_host_module_layer=1,
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num_device_module_layer=1,
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optimal_adapter_size=64,
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max_adapter_size=128,
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num_put_workers=1,
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num_ensure_workers=1,
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num_copy_streams=1,
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max_pages_per_block_host=24,
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max_pages_per_block_device=8,
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device_cache_percent=0.5,
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host_cache_size=1024,
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lora_prefetch_dir=".")
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pybind_config = PybindMirror.maybe_to_pybind(config)
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assert pybind_config.num_host_module_layer == 1
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assert pybind_config.num_device_module_layer == 1
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assert pybind_config.optimal_adapter_size == 64
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assert pybind_config.max_adapter_size == 128
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assert pybind_config.num_put_workers == 1
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assert pybind_config.num_ensure_workers == 1
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assert pybind_config.num_copy_streams == 1
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assert pybind_config.max_pages_per_block_host == 24
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assert pybind_config.max_pages_per_block_device == 8
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assert pybind_config.device_cache_percent == 0.5
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assert pybind_config.host_cache_size == 1024
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assert pybind_config.lora_prefetch_dir == "."
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class TestTorchLlmArgsCudaGraphSettings:
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def test_cuda_graph_batch_sizes_case_0(self):
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# set both cuda_graph_batch_sizes and cuda_graph_max_batch_size, and
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# cuda_graph_batch_sizes is not equal to generated
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with pytest.raises(ValueError):
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TorchLlmArgs(model=llama_model_path,
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use_cuda_graph=True,
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cuda_graph_batch_sizes=[1, 2, 3],
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cuda_graph_max_batch_size=128)
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def test_cuda_graph_batch_sizes_case_0_1(self):
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# set both cuda_graph_batch_sizes and cuda_graph_max_batch_size, and
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# cuda_graph_batch_sizes is equal to generated
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args = TorchLlmArgs(model=llama_model_path,
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use_cuda_graph=True,
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cuda_graph_padding_enabled=True,
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cuda_graph_batch_sizes=TorchLlmArgs.
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_generate_cuda_graph_batch_sizes(128, True),
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cuda_graph_max_batch_size=128)
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assert args.cuda_graph_batch_sizes == TorchLlmArgs._generate_cuda_graph_batch_sizes(
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128, True)
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assert args.cuda_graph_max_batch_size == 128
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def test_cuda_graph_batch_sizes_case_1(self):
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# set cuda_graph_batch_sizes only
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args = TorchLlmArgs(model=llama_model_path,
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use_cuda_graph=True,
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cuda_graph_padding_enabled=True,
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cuda_graph_batch_sizes=[1, 2, 4])
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assert args.cuda_graph_batch_sizes == [1, 2, 4]
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def test_cuda_graph_batch_sizes_case_2(self):
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# set cuda_graph_max_batch_size only
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args = TorchLlmArgs(model=llama_model_path,
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use_cuda_graph=True,
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cuda_graph_padding_enabled=True,
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cuda_graph_max_batch_size=128)
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assert args.cuda_graph_batch_sizes == TorchLlmArgs._generate_cuda_graph_batch_sizes(
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128, True)
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assert args.cuda_graph_max_batch_size == 128
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class TestTrtLlmArgs:
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def test_dynamic_setattr(self):
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with pytest.raises(pydantic_core._pydantic_core.ValidationError):
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args = TrtLlmArgs(model=llama_model_path, invalid_arg=1)
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with pytest.raises(ValueError):
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args = TrtLlmArgs(model=llama_model_path)
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args.invalid_arg = 1
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class TestTorchLlmArgs:
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@print_traceback_on_error
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def test_runtime_sizes(self):
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llm = TorchLLM(
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llama_model_path,
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max_beam_width=4,
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max_num_tokens=256,
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max_seq_len=128,
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max_batch_size=8,
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)
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assert llm.args.max_beam_width == 4
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assert llm.args.max_num_tokens == 256
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assert llm.args.max_seq_len == 128
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assert llm.args.max_batch_size == 8
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assert llm._executor_config.max_beam_width == 4
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assert llm._executor_config.max_num_tokens == 256
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assert llm._executor_config.max_seq_len == 128
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assert llm._executor_config.max_batch_size == 8
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def test_dynamic_setattr(self):
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with pytest.raises(pydantic_core._pydantic_core.ValidationError):
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args = TorchLlmArgs(model=llama_model_path, invalid_arg=1)
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with pytest.raises(ValueError):
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args = TorchLlmArgs(model=llama_model_path)
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args.invalid_arg = 1
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def test_from_build_config(self):
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build_config = BuildConfig(
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max_beam_width=4,
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max_batch_size=8,
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max_num_tokens=256,
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)
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args = TorchLlmArgs.from_kwargs(model=llama_model_path,
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build_config=build_config)
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assert args.max_batch_size == build_config.max_batch_size
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assert args.max_num_tokens == build_config.max_num_tokens
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assert args.max_beam_width == build_config.max_beam_width
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class TestTrtLlmArgs:
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def test_build_config_default(self):
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args = TrtLlmArgs(model=llama_model_path)
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# It will create a default build_config
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assert args.build_config
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assert args.build_config.max_beam_width == 1
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def test_build_config_change(self):
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build_config = BuildConfig(
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max_beam_width=4,
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max_batch_size=8,
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max_num_tokens=256,
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)
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args = TrtLlmArgs(model=llama_model_path, build_config=build_config)
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assert args.build_config.max_beam_width == build_config.max_beam_width
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assert args.build_config.max_batch_size == build_config.max_batch_size
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assert args.build_config.max_num_tokens == build_config.max_num_tokens
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def test_LLM_with_build_config(self):
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build_config = BuildConfig(
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max_beam_width=4,
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max_batch_size=8,
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max_num_tokens=256,
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)
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args = TrtLlmArgs(model=llama_model_path, build_config=build_config)
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assert args.build_config.max_beam_width == build_config.max_beam_width
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assert args.build_config.max_batch_size == build_config.max_batch_size
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assert args.build_config.max_num_tokens == build_config.max_num_tokens
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assert args.max_beam_width == build_config.max_beam_width
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def test_to_dict_and_from_dict(self):
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build_config = BuildConfig(
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max_beam_width=4,
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max_batch_size=8,
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max_num_tokens=256,
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)
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args = TrtLlmArgs(model=llama_model_path, build_config=build_config)
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args_dict = args.to_dict()
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new_args = TrtLlmArgs.from_kwargs(**args_dict)
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assert new_args.to_dict() == args_dict
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def test_build_config_from_engine(self):
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build_config = BuildConfig(max_batch_size=8, max_num_tokens=256)
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tmp_dir = tempfile.mkdtemp()
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with LLM(model=llama_model_path, build_config=build_config) as llm:
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llm.save(tmp_dir)
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args = TrtLlmArgs(
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model=tmp_dir,
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# runtime values
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max_num_tokens=16,
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max_batch_size=4,
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)
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assert args.build_config.max_batch_size == build_config.max_batch_size
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assert args.build_config.max_num_tokens == build_config.max_num_tokens
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assert args.max_num_tokens == 16
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assert args.max_batch_size == 4
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