mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-10-31 07:43:00 +08:00
noeyy-mino
883c8731aa
## What does this PR do? **Type of change:** Add TRT LLM/vLLM/SGLang functional test cases for newly added checkpoints on HF **Overview:** 1.Since the speculative draft model only supports loading from a local path, we should set the MODELOPT_LOCAL_MODEL_ROOT environment variable. If we don't set it, these test cases will be skipped. 2. Newly added checkpoints: - nvidia/gpt-oss-120b-Eagle3-short-context - nvidia/gpt-oss-120b-Eagle3-throughput - nvidia/EAGLE3-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 - nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 ## Usage ```python pytest tests/examples/llm_ptq/test_deploy.py --run-release ``` ## Testing Run release testing ## Before your PR is "*Ready for review*" Ready for review - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes - **Did you write any new necessary tests?**: No - **Did you add or update any necessary documentation?**: No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**: No ## Additional Information N/A --------- Signed-off-by: noeyy-mino <174223378+noeyy-mino@users.noreply.github.com>
286 lines
10 KiB
Python
286 lines
10 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 itertools
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import subprocess
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import pytest
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import torch
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# Common test prompts for all backends
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COMMON_PROMPTS = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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class ModelDeployer:
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def __init__(
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self,
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backend: str = "trtllm",
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model_id: str = "",
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tensor_parallel_size: int = 1,
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mini_sm: int = 89,
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attn_backend: str = "TRTLLM",
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base_model: str = "",
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eagle3_one_model: bool = True,
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):
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"""
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Initialize the ModelDeployer.
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Args:
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backend: The backend to use ('vllm', 'trtllm', or 'sglang')
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model_id: Path to the model
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tensor_parallel_size: Tensor parallel size for distributed inference
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mini_sm: Minimum SM (Streaming Multiprocessor) requirement for the model
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attn_backend: is for TRT LLM deployment
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"""
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self.backend = backend
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self.model_id = model_id
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self.tensor_parallel_size = tensor_parallel_size
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self.mini_sm = mini_sm
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self.attn_backend = attn_backend
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self.base_model = base_model
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self.eagle3_one_model = eagle3_one_model
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def run(self):
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"""Run the deployment based on the specified backend."""
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if not torch.cuda.is_available() or torch.cuda.device_count() == 0:
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pytest.skip("CUDA is not available")
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return
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if torch.cuda.get_device_capability() < (
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self.mini_sm // 10,
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self.mini_sm % 10,
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):
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pytest.skip(reason=f"Requires sm{self.mini_sm} or higher")
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return
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if torch.cuda.device_count() < self.tensor_parallel_size:
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pytest.skip(reason=f"Requires at least {self.tensor_parallel_size} GPUs")
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return
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if self.backend == "vllm":
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self._deploy_vllm()
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elif self.backend == "trtllm":
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self._deploy_trtllm()
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elif self.backend == "sglang":
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self._deploy_sglang()
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else:
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raise ValueError(f"Unknown backend: {self.backend}")
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# check gpu status
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gpu_status = subprocess.run(
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"nvidia-smi || true", shell=True, capture_output=True, text=True, check=True
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)
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print("\n=== GPU Status Before Test ===")
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print(gpu_status.stdout)
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print("=============================\n")
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def _deploy_trtllm(self):
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"""Deploy a model using TensorRT-LLM."""
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try:
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from tensorrt_llm import LLM, SamplingParams
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from tensorrt_llm.llmapi import CudaGraphConfig, EagleDecodingConfig, KvCacheConfig
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except ImportError:
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pytest.skip("tensorrt_llm package not available")
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sampling_params = SamplingParams(max_tokens=32)
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spec_config = None
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llm = None
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kv_cache_config = KvCacheConfig(enable_block_reuse=True, free_gpu_memory_fraction=0.8)
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if self.model_id == "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8":
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llm = LLM(
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model=self.model_id,
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tensor_parallel_size=self.tensor_parallel_size,
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enable_attention_dp=False,
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attn_backend=self.attn_backend,
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trust_remote_code=True,
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max_batch_size=8,
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kv_cache_config=KvCacheConfig(
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enable_block_reuse=False,
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mamba_ssm_cache_dtype="float32",
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),
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)
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elif self.model_id == "nvidia/EAGLE3-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16":
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spec_config = EagleDecodingConfig(
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max_draft_len=3,
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speculative_model_dir=self.model_id,
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eagle3_one_model=self.eagle3_one_model,
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)
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llm = LLM(
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model=self.model_id,
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tensor_parallel_size=self.tensor_parallel_size,
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enable_attention_dp=False,
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attn_backend=self.attn_backend,
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trust_remote_code=True,
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max_batch_size=8,
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speculative_config=spec_config,
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kv_cache_config=KvCacheConfig(
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enable_block_reuse=False,
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mamba_ssm_cache_dtype="float32",
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),
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)
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elif "eagle" in self.model_id.lower():
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spec_config = EagleDecodingConfig(
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max_draft_len=3,
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speculative_model_dir=self.model_id,
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eagle3_one_model=self.eagle3_one_model,
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)
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cuda_graph = CudaGraphConfig(
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max_batch_size=1,
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)
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llm = LLM(
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model=self.base_model,
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tensor_parallel_size=self.tensor_parallel_size,
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enable_attention_dp=False,
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disable_overlap_scheduler=True,
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enable_autotuner=False,
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speculative_config=spec_config,
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cuda_graph_config=cuda_graph,
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kv_cache_config=kv_cache_config,
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)
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else:
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llm = LLM(
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model=self.model_id,
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tensor_parallel_size=self.tensor_parallel_size,
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enable_attention_dp=False,
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attn_backend=self.attn_backend,
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trust_remote_code=True,
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max_batch_size=8,
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kv_cache_config=kv_cache_config,
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)
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outputs = llm.generate(COMMON_PROMPTS, sampling_params)
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# Print outputs
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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del llm
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def _deploy_vllm(self):
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"""Deploy a model using vLLM."""
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try:
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from vllm import LLM, SamplingParams
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except ImportError:
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pytest.skip("vllm package not available")
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quantization_method = "modelopt"
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if "fp4" in self.model_id.lower():
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quantization_method = "modelopt_fp4"
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llm = LLM(
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model=self.model_id,
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quantization=quantization_method,
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tensor_parallel_size=self.tensor_parallel_size,
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trust_remote_code=True,
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)
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sampling_params = SamplingParams(temperature=0.8, top_p=0.9)
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outputs = llm.generate(COMMON_PROMPTS, sampling_params)
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# Assertions and output
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assert len(outputs) == len(COMMON_PROMPTS), (
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f"Expected {len(COMMON_PROMPTS)} outputs, got {len(outputs)}"
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)
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for i, output in enumerate(outputs):
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assert output.prompt == COMMON_PROMPTS[i], f"Prompt mismatch at index {i}"
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assert hasattr(output, "outputs"), f"Output {i} missing 'outputs' attribute"
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assert len(output.outputs) > 0, f"Output {i} has no generated text"
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assert hasattr(output.outputs[0], "text"), f"Output {i} missing 'text' attribute"
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assert isinstance(output.outputs[0].text, str), f"Output {i} text is not a string"
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assert len(output.outputs[0].text) > 0, f"Output {i} generated empty text"
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print(f"Model: {self.model_id}")
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print(f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}")
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print("-" * 50)
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del llm
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def _deploy_sglang(self):
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"""Deploy a model using SGLang."""
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try:
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import sglang as sgl
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except ImportError:
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pytest.skip("sglang package not available")
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quantization_method = "modelopt"
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if "fp4" in self.model_id.lower():
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quantization_method = "modelopt_fp4"
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if "eagle" in self.model_id.lower():
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llm = sgl.Engine(
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model_path=self.base_model,
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speculative_algorithm="EAGLE3",
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speculative_num_steps=3,
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speculative_eagle_topk=1,
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speculative_num_draft_tokens=4,
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speculative_draft_model_path=self.model_id,
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tp_size=self.tensor_parallel_size,
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trust_remote_code=True,
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mem_fraction_static=0.7,
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context_length=1024,
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)
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elif self.model_id == "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8":
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llm = sgl.Engine(
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model_path=self.model_id,
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quantization=quantization_method,
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tp_size=self.tensor_parallel_size,
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trust_remote_code=True,
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attention_backend="flashinfer",
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)
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else:
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llm = sgl.Engine(
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model_path=self.model_id,
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quantization=quantization_method,
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tp_size=self.tensor_parallel_size,
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trust_remote_code=True,
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)
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print(llm.generate(["What's the age of the earth? "]))
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llm.shutdown()
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del llm
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class ModelDeployerList:
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def __init__(self, **params):
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self.params = {}
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for key, value in params.items():
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if isinstance(value, (list, tuple)):
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self.params[key] = list(value)
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else:
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self.params[key] = [value]
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# Pre-generate all deployers for pytest compatibility
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self._deployers = list(self._generate_deployers())
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def _generate_deployers(self):
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for values in itertools.product(*self.params.values()):
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deployer = ModelDeployer(**dict(zip(self.params.keys(), values)))
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# Set test case ID in format "model_id_backend"
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deployer.test_id = f"{deployer.model_id}_{deployer.backend}"
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yield deployer
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def __iter__(self):
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return iter(self._deployers)
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def __len__(self):
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return len(self._deployers)
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def __getitem__(self, index):
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return self._deployers[index]
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def __str__(self):
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return f"ModelDeployerList({len(self._deployers)} items)"
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def __repr__(self):
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return f"ModelDeployerList({len(self._deployers)} items)"
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