92f5b57da3
This is a complete rebranding and optimization of the original GPT-OSS codebase for Apple Silicon: 🚀 Features: - Native MLX acceleration for M1/M2/M3/M4 chips - Complete MLX implementation with Mixture of Experts (MoE) - Memory-efficient quantization (4-bit MXFP4) - Drop-in replacement APIs for existing backends - Full tool integration (browser, python, apply_patch) - Comprehensive build system with Metal kernels 📦 What's Included: - gpt_oss/mlx_gpt_oss/ - Complete MLX implementation - All original inference backends (torch, triton, metal, vllm) - Command-line interfaces and Python APIs - Developer tools and evaluation suite - Updated branding and documentation 🍎 Apple Silicon Optimized: - Up to 40 tokens/sec performance on Apple Silicon - Run GPT-OSS-120b in 30GB with quantization - Native Metal kernel acceleration - Memory-mapped weight loading 🔧 Ready to Deploy: - Updated package name to openharmony-mlx - Comprehensive .gitignore for clean releases - Updated README with Apple Silicon focus - All build artifacts cleaned up 🧠 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
# Model parallel inference
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# Note: This script is for demonstration purposes only. It is not designed for production use.
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# See gpt_oss.chat for a more complete example with the Harmony parser.
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# torchrun --nproc-per-node=4 -m gpt_oss.generate -p "why did the chicken cross the road?" model/
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import argparse
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from gpt_oss.tokenizer import get_tokenizer
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def main(args):
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match args.backend:
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case "torch":
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from gpt_oss.torch.utils import init_distributed
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from gpt_oss.torch.model import TokenGenerator as TorchGenerator
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device = init_distributed()
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generator = TorchGenerator(args.checkpoint, device=device)
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case "triton":
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from gpt_oss.torch.utils import init_distributed
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from gpt_oss.triton.model import TokenGenerator as TritonGenerator
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device = init_distributed()
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generator = TritonGenerator(args.checkpoint, context=4096, device=device)
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case "vllm":
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from gpt_oss.vllm.token_generator import TokenGenerator as VLLMGenerator
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generator = VLLMGenerator(args.checkpoint, tensor_parallel_size=2)
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case "mlx":
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from gpt_oss.mlx_gpt_oss.generate import TokenGenerator as MLXGenerator
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generator = MLXGenerator(args.checkpoint)
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case _:
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raise ValueError(f"Invalid backend: {args.backend}")
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tokenizer = get_tokenizer()
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tokens = tokenizer.encode(args.prompt)
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for token, logprob in generator.generate(tokens, stop_tokens=[tokenizer.eot_token], temperature=args.temperature, max_tokens=args.limit, return_logprobs=True):
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tokens.append(token)
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decoded_token = tokenizer.decode([token])
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print(
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f"Generated token: {repr(decoded_token)}, logprob: {logprob}"
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Text generation example")
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parser.add_argument(
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"checkpoint",
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metavar="FILE",
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type=str,
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help="Path to the SafeTensors checkpoint",
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)
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parser.add_argument(
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"-p",
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"--prompt",
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metavar="PROMPT",
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type=str,
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default="How are you?",
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help="LLM prompt",
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)
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parser.add_argument(
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"-t",
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"--temperature",
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metavar="TEMP",
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type=float,
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default=0.0,
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help="Sampling temperature",
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)
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parser.add_argument(
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"-l",
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"--limit",
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metavar="LIMIT",
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type=int,
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default=0,
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help="Limit on the number of tokens (0 to disable)",
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)
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parser.add_argument(
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"-b",
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"--backend",
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metavar="BACKEND",
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type=str,
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default="torch",
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choices=["triton", "torch", "vllm", "mlx"],
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help="Inference backend",
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)
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args = parser.parse_args()
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main(args)
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