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Merge pull request #662 from google:mor--kv-cache-layout-reformat-output
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Copyright 2024 Google LLC | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| https://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
|
||
| """Sweep across inference microbenchmarks.""" | ||
|
|
||
| import os | ||
| import sys | ||
| import json | ||
| import jsonlines | ||
| import inference_microbenchmark | ||
| import pyconfig | ||
| from jax._src.lib import xla_extension | ||
|
|
||
|
|
||
| def main(): | ||
| """ | ||
| User needs to set the config's inference_metadata_file, which is a path to a | ||
| json file. | ||
| This json should contain the following keys: | ||
| - two_axis_order_product_id_list: comma separated string of two_axis_order_product_id | ||
| - prefill_cache_axis_order_list: comma delimited string of prefill_cache_axis_order | ||
| - ar_cache_axis_order_list: comma delimited string of ar_cache_axis_order | ||
| - accelerator: name of the accelerator | ||
| - flatten_microbenchmark_results: Whether or not to flatten results. Should | ||
| be true | ||
| """ | ||
| pyconfig.initialize(sys.argv) | ||
| config = pyconfig.config | ||
| base_run_name = config.run_name | ||
|
|
||
| with open(config.inference_metadata_file, encoding='utf-8') as json_file: | ||
| inference_metadata = json.load(json_file) | ||
| print(f"inference_metadata: {inference_metadata}") | ||
|
|
||
| two_axis_order_product_id_list = inference_metadata['two_axis_order_product_id_list'].split(':') | ||
| prefill_cache_axis_order_list = inference_metadata['prefill_cache_axis_order_list'].split(':') | ||
| ar_cache_axis_order_list = inference_metadata['ar_cache_axis_order_list'].split(':') | ||
|
|
||
| start_two_axis_order_product_id = two_axis_order_product_id_list[0] | ||
| end_two_axis_order_product_id = two_axis_order_product_id_list[-1] | ||
|
|
||
| results = [] | ||
| for ( | ||
| two_axis_order_product_id, | ||
| prefill_cache_axis_order, | ||
| ar_cache_axis_order, | ||
| ) in zip( | ||
| two_axis_order_product_id_list, | ||
| prefill_cache_axis_order_list, | ||
| ar_cache_axis_order_list, | ||
| ): | ||
| print(f"two_axis_order_product_id {two_axis_order_product_id}") | ||
| print(f"prefill_cache_axis_order {prefill_cache_axis_order}") | ||
| print(f"ar_cache_axis_order {ar_cache_axis_order}") | ||
|
|
||
| run_tag = ( | ||
| f"{two_axis_order_product_id}-{prefill_cache_axis_order.replace(',','')}-{ar_cache_axis_order.replace(',','')}" | ||
| ) | ||
| run_name = f"{base_run_name}/{run_tag}" | ||
|
|
||
| tensorboard_dir = os.path.join(config.base_output_directory, run_name, "tensorboard", "") | ||
| pyconfig._config.keys['prefill_cache_axis_order'] = prefill_cache_axis_order # pylint: disable=protected-access | ||
| pyconfig._config.keys['ar_cache_axis_order'] = ar_cache_axis_order # pylint: disable=protected-access | ||
| pyconfig._config.keys['tensorboard_dir'] = tensorboard_dir # pylint: disable=protected-access | ||
| pyconfig._config.keys['run_name'] = run_name # pylint: disable=protected-access | ||
|
|
||
| # Prepare metadata (dimensions) json for XLML | ||
| dimensions_json = { | ||
| "base_output_directory": config.base_output_directory, | ||
| "model_name": config.model_name, | ||
| "tokenizer": config.tokenizer_path, | ||
| "weight_dtype": config.weight_dtype, | ||
| "inference_microbenchmark_prefill_lengths": f"{config.inference_microbenchmark_prefill_lengths}", | ||
| "inference_microbenchmark_stages": config.inference_microbenchmark_stages, | ||
| "inference_microbenchmark_loop_iters": f"{config.inference_microbenchmark_loop_iters}", | ||
| "max_prefill_predict_length": f"{config.max_prefill_predict_length}", | ||
| "max_target_length": f"{config.max_target_length}", | ||
| "per_device_batch_size": f"{config.per_device_batch_size}", | ||
| "ici_fsdp_parallelism": f"{config.ici_fsdp_parallelism}", | ||
| "ici_autoregressive_parallelism": f"{config.ici_autoregressive_parallelism}", | ||
| "ici_tensor_parallelism": f"{config.ici_tensor_parallelism}", | ||
| "profiler": f"{config.profiler}", | ||
| "scan_layers": f"{config.scan_layers}", | ||
| "quantization": config.quantization, | ||
| "quantize_kvcache": f"{config.quantize_kvcache}", | ||
| "attention": config.attention, | ||
| "two_axis_order_product_id": f"{two_axis_order_product_id}", | ||
| "prefill_cache_axis_order": f"{prefill_cache_axis_order}", | ||
| "ar_cache_axis_order": f"{ar_cache_axis_order}", | ||
| "compute_axis_order": f"{config.compute_axis_order}", | ||
| "reshape_q": f"{config.reshape_q}", | ||
| "kv_quant_axis": f"{config.kv_quant_axis}", | ||
| "run_name": f"{run_name}", | ||
| "run_tag": f"{run_tag}", | ||
| "config_json_string": json.dumps( | ||
| pyconfig._config.keys, # pylint: disable=protected-access | ||
| default=lambda x: f"<<non-serializable: {type(x).__qualname__}>>" | ||
| ) | ||
| } | ||
| dimensions_json = { | ||
| **dimensions_json, | ||
| **inference_metadata, | ||
| } | ||
| try: | ||
| microbenchmark_results = inference_microbenchmark.main(config, inference_metadata=inference_metadata) | ||
| metrics = microbenchmark_results['flattened_results'] | ||
| metrics = {k.lower(): v for k, v in metrics.items()} | ||
| dimensions_json['oom'] = 'False' | ||
| print(f"Completed run {two_axis_order_product_id} out of: " | ||
| f"{start_two_axis_order_product_id} to {end_two_axis_order_product_id}") | ||
| except xla_extension.XlaRuntimeError: | ||
| # OOM | ||
| metrics = {} | ||
| dimensions_json['oom'] = 'True' | ||
| print(f"Failed at run {two_axis_order_product_id} out of: " | ||
| f"{start_two_axis_order_product_id} to {end_two_axis_order_product_id}") | ||
|
|
||
| final = {'metrics': metrics, 'dimensions': dimensions_json} | ||
| print(f"Result: {final}") | ||
| results.append(final) | ||
|
|
||
| print(f"All results {results}") | ||
| path = 'inference_microbenchmark_sweep_results.jsonl' | ||
| with jsonlines.open(path, mode="w") as writer: | ||
| writer.write_all(results) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| main() |