98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
import os
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import torch
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from tests.quantization.utils import is_quant_method_supported
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from vllm import LLM, SamplingParams
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from vllm.config import CompilationLevel
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from vllm.platforms import current_platform
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TEST_MODELS = [
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("facebook/opt-125m", {}),
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("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
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"dtype": torch.float16,
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"quantization": "compressed-tensors"
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}),
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("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", {
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"dtype": torch.float16,
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"quantization": "fp8"
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}),
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("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
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"quantization": "compressed-tensors"
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}),
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("meta-llama/Meta-Llama-3-8B", {}),
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]
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if is_quant_method_supported("aqlm"):
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TEST_MODELS.append(("ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf", {
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"quantization": "aqlm"
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}))
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# TODO: figure out why this fails.
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if False and is_quant_method_supported("gguf"): # noqa: SIM223
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TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", {
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"quantization": "gguf"
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}))
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if is_quant_method_supported("gptq"):
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TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {
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"quantization": "gptq"
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}))
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if is_quant_method_supported("gptq_marlin"):
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TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", {
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"quantization": "gptq_marlin"
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}))
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if is_quant_method_supported("gptq_marlin_24"):
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TEST_MODELS.append(("alexm-nm/tinyllama-24-marlin24-4bit-g128", {
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"quantization": "gptq_marlin_24"
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}))
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if is_quant_method_supported("marlin"):
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TEST_MODELS.append(("robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin", {
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"quantization": "marlin"
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}))
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if not current_platform.is_rocm() and is_quant_method_supported("awq"):
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TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {
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"quantization": "AWQ"
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}))
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def check_full_graph_support(model,
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model_kwargs,
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optimization_level,
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tp_size=1):
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# make sure these models can be captured in full graph mode
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os.environ["VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "1"
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# The base meta llama uses too much memory.
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if (model == "meta-llama/Meta-Llama-3-8B"
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and optimization_level >= CompilationLevel.PIECEWISE):
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return
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print(f"MODEL={model}")
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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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sampling_params = SamplingParams(temperature=0)
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llm = LLM(model=model,
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enforce_eager=True,
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tensor_parallel_size=tp_size,
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disable_custom_all_reduce=True,
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compilation_config=optimization_level,
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**model_kwargs)
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outputs = llm.generate(prompts, sampling_params)
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# Print the 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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