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common_benchmark_suite/openxla/benchmark/models/jax/gemma/gemma_pipeline.py
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2023 The OpenXLA Authors | ||
| # | ||
| # Licensed under the Apache License v2.0 with LLVM Exceptions. | ||
| # See https://llvm.org/LICENSE.txt for license information. | ||
| # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | ||
|
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| import jax.numpy as jnp | ||
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| from transformers import AutoTokenizer, GemmaTokenizer, FlaxPreTrainedModel, FlaxGemmaForCausalLM, GenerationConfig | ||
| from typing import Any, List, Tuple | ||
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| from openxla.benchmark.models.jax import jax_model_interface | ||
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| class GemmaPipeline(jax_model_interface.JaxInferenceModel): | ||
| """See https://huggingface.co/docs/transformers/model_doc/gemma for more information.""" | ||
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| batch_size: int | ||
| seq_len: int | ||
| model: FlaxGemmaForCausalLM | ||
| params: dict[str, Any] | ||
| model_name: str | ||
| tokenizer: GemmaTokenizer | ||
| tokenization_kwargs: dict[str, Any] | ||
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| def __init__( | ||
| self, | ||
| batch_size: int, | ||
| dtype: Any, | ||
| seq_len: int, | ||
| max_new_tokens: int, | ||
| model_name: str, | ||
| ): | ||
| self.model, self.params = FlaxGemmaForCausalLM.from_pretrained( | ||
| model_name, revision="flax", _do_init=False) | ||
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| if dtype == jnp.float32: | ||
| self.params = self.model.to_fp32(self.params) | ||
| elif dtype == jnp.float16: | ||
| self.params = self.model.to_fp16(self.params) | ||
| elif dtype == jnp.bfloat16: | ||
| self.params = self.model.to_bf16(self.params) | ||
| else: | ||
| raise ValueError(f"Unsupported data type '{dtype}'.") | ||
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| self.model_name = model_name | ||
| self.batch_size = batch_size | ||
| self.seq_len = seq_len | ||
| self.tokenizer = AutoTokenizer.from_pretrained( | ||
| model_name, | ||
| model_max_length=self.seq_len, | ||
| padding_side="left", | ||
| ) | ||
| self.tokenizer.pad_token = self.tokenizer.eos_token | ||
| self.tokenization_kwargs = { | ||
| "return_tensors": "jax", | ||
| } | ||
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| self.generation_config = GenerationConfig.from_pretrained( | ||
| model_name, | ||
| max_new_tokens=max_new_tokens, | ||
| do_sample=False, | ||
| use_cache=True) | ||
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| def generate_default_inputs(self) -> str: | ||
| return "Once upon a time" | ||
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| def preprocess(self, input_text: str) -> Tuple[Any,]: | ||
| batch_input_text = [input_text] * self.batch_size | ||
| inputs = self.tokenizer(text=batch_input_text, **self.tokenization_kwargs) | ||
| return (inputs["input_ids"],) | ||
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| def forward(self, input_text: Any) -> Any: | ||
| output = self.model.generate(input_text, | ||
| params=self.params, | ||
| generation_config=self.generation_config) | ||
| print(f"output: {output}") | ||
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| def postprocess(self, output: Any) -> List[str]: | ||
| return self.tokenizer.batch_decode(output, skip_special_tokens=True) | ||
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| def apply(self, input_text: Any) -> Any: | ||
| raise Exception("Not implemented.") | ||
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| DTYPE_MAP = { | ||
| "fp32": jnp.float32, | ||
| "fp16": jnp.float16, | ||
| "bf16": jnp.bfloat16, | ||
| } | ||
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| def create_model(batch_size: int = 1, | ||
| data_type: str = "fp32", | ||
| seq_len: int = 1024, | ||
| max_new_tokens: int = 256, | ||
| model_name: str = "google/gemma-2b-it", | ||
| **_unused_params) -> GemmaPipeline: | ||
| """Configure and create a JAX Gemma pipeline. | ||
| Args: | ||
| batch_size: input batch size. | ||
| seq_len: input sequence length. | ||
| max_new_tokens: the maximum number of new tokens to generate. | ||
| model_name: The name of the Gemma variant to use. Supported variants include: | ||
| google/gemma-2b-it, google/gemma-7b-it. | ||
| Returns: | ||
| A JAX GemmaPipeline. | ||
| """ | ||
| return GemmaPipeline(batch_size=batch_size, | ||
| dtype=DTYPE_MAP[data_type], | ||
| seq_len=seq_len, | ||
| max_new_tokens=max_new_tokens, | ||
| model_name=model_name) |
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common_benchmark_suite/openxla/benchmark/models/jax/gemma/requirements.txt
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| flax | ||
| jax | ||
| torch | ||
| transformers |