e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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from vllm import LLM, SamplingParams
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# creates XLA hlo graphs for all the context length buckets.
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os.environ['NEURON_CONTEXT_LENGTH_BUCKETS'] = "128,512,1024,2048"
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# creates XLA hlo graphs for all the token gen buckets.
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os.environ['NEURON_TOKEN_GEN_BUCKETS'] = "128,512,1024,2048"
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# Quantizes neuron model weight to int8 ,
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# The default config for quantization is int8 dtype.
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os.environ['NEURON_QUANT_DTYPE'] = "s8"
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# Sample prompts.
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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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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# Create an LLM.
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llm = LLM(
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model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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max_num_seqs=8,
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# The max_model_len and block_size arguments are required to be same as
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# max sequence length when targeting neuron device.
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# Currently, this is a known limitation in continuous batching support
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# in transformers-neuronx.
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# TODO(liangfu): Support paged-attention in transformers-neuronx.
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max_model_len=2048,
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block_size=2048,
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# The device can be automatically detected when AWS Neuron SDK is installed.
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# The device argument can be either unspecified for automated detection,
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# or explicitly assigned.
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device="neuron",
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quantization="neuron_quant",
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override_neuron_config={
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"cast_logits_dtype": "bfloat16",
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},
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tensor_parallel_size=2)
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# Generate texts from the prompts. The output is a list of RequestOutput objects
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# that contain the prompt, generated text, and other information.
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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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