From e8439b7af10dbd32c088c16d053bcee166b3c010 Mon Sep 17 00:00:00 2001 From: wyzhang Date: Mon, 27 Jan 2025 21:26:42 -0800 Subject: [PATCH] Revert past 2 commits which accidentally deletes the code due to copybara issue (#167) * Revert "Reverts 6a3579056f307fed3428102df5823a7ff7cebdc6" This reverts commit b459cc1f297a8564e9c6f14346ad5ef41e2d68c6. * Revert "fixing decode." This reverts commit 6a3579056f307fed3428102df5823a7ff7cebdc6. --- .../jetstream/.github => .github}/CODEOWNERS | 0 .../workflows/e2e_tests.yaml | 0 .../workflows/release.yaml | 0 .../workflows/scripts/create_release.js | 0 .../workflows/unit_tests.yaml | 0 .../py/jetstream/.gitignore => .gitignore | 0 .../py/jetstream/AUTHORS => AUTHORS | 0 .../CONTRIBUTING.md => CONTRIBUTING.md | 0 .../py/jetstream/LICENSE => LICENSE | 0 .../py/jetstream/MANIFEST.in => MANIFEST.in | 0 .../py/jetstream/Makefile => Makefile | 0 .../py/jetstream/README.md => README.md | 0 .../benchmarks => benchmarks}/README.md | 0 .../benchmarks => benchmarks}/__init__.py | 0 .../benchmark_serving.py | 332 ++++++--- .../eval_accuracy.py | 1 + benchmarks/metrics.py | 244 +++++++ benchmarks/mlperf/README.md | 155 ++++ benchmarks/mlperf/backend.py | 323 +++++++++ benchmarks/mlperf/dataset.py | 122 ++++ benchmarks/mlperf/evaluate-accuracy.py | 129 ++++ benchmarks/mlperf/main.py | 223 ++++++ benchmarks/mlperf/mlperf.conf | 111 +++ benchmarks/mlperf/scripts/config_utils.sh | 16 + .../mlperf/scripts/config_w-b16-kv-b16.sh | 12 + .../mlperf/scripts/config_w-i8-kv-b16.sh | 12 + .../mlperf/scripts/config_w-i8-kv-i8.sh | 12 + .../mlperf/scripts/config_w-i8w-kv-b16.sh | 12 + .../mlperf/scripts/config_w-i8w-kv-i8.sh | 12 + .../mlperf/scripts/download_loadgen_data.sh | 9 + .../scripts/generate_server_accuracy_run.sh | 64 ++ .../scripts/generate_server_audit_run.sh | 53 ++ .../generate_server_performance_run.sh | 55 ++ benchmarks/mlperf/scripts/init.sh | 7 + benchmarks/mlperf/scripts/init_loadgen.sh | 13 + benchmarks/mlperf/scripts/init_xprof.sh | 2 + .../mlperf/scripts/launch_microbenchmark.sh | 46 ++ benchmarks/mlperf/scripts/launch_server.sh | 33 + benchmarks/mlperf/scripts/run_utils.sh | 32 + benchmarks/mlperf/scripts/tpu_script.sh | 352 +++++++++ benchmarks/mlperf/user.conf | 29 + benchmarks/mlperf/user100.conf | 28 + benchmarks/mlperf/user2000.conf | 28 + ..._gpt4_tokenized_llama.calibration_1000.pkl | Bin .../benchmarks => benchmarks}/requirements.in | 0 .../llama3 => benchmarks/tests}/__init__.py | 0 benchmarks/tests/test_metrics.py | 177 +++++ ...-prometheus-metrics-in-jetstream-server.md | 2 +- .../online-inference-with-maxtext-engine.md | 0 ...iling-with-jax-profiler-and-tensorboard.md | 0 experimental/jax/README.md | 104 +++ .../jax/inference/entrypoint/__init__.py | 15 + .../entrypoint/mini_offline_benchmarking.py | 75 ++ ..._gpt4_tokenized_llama.calibration_1000.pkl | Bin 0 -> 3708395 bytes .../inference/entrypoint/run_simple_server.py | 31 + experimental/jax/inference/kernel/__init__.py | 23 + .../kernel/attention/tpu/__init__.py | 18 + .../tpu/chunked_prefill_attention.py | 275 ++++++++ .../kernel/attention/tpu/paged_attention.py | 666 ++++++++++++++++++ .../attention/tpu/quantization_utils.py | 112 +++ .../jax/inference/kernel/attention_ops.py | 139 ++++ .../inference/kernel/collective_matmul_ops.py | 40 ++ .../kernel/linear/tpu/collective_matmul.py | 223 ++++++ experimental/jax/inference/model/__init__.py | 20 + experimental/jax/inference/model/llama.py | 382 ++++++++++ .../management/hf_llama_ckpt_conversion.py | 145 ++++ .../inference/model/management/registry.py | 181 +++++ .../jax/inference/model/management/util.py | 34 + .../jax/inference/model/postprocess.py | 88 +++ .../jax/inference/model/sampling/sampler.py | 82 +++ experimental/jax/inference/nn/__init__.py | 22 + experimental/jax/inference/nn/attention.py | 253 +++++++ experimental/jax/inference/nn/embedding.py | 66 ++ experimental/jax/inference/nn/linear.py | 143 ++++ experimental/jax/inference/nn/module.py | 223 ++++++ experimental/jax/inference/nn/norm.py | 55 ++ experimental/jax/inference/nn/parameter.py | 330 +++++++++ .../jax/inference/parallel/__init__.py | 21 + experimental/jax/inference/parallel/config.py | 135 ++++ experimental/jax/inference/parallel/device.py | 21 + experimental/jax/inference/parallel/mesh.py | 107 +++ .../jax/inference/parallel/operations.py | 91 +++ experimental/jax/inference/parallel/util.py | 32 + .../jax/inference/runtime/__init__.py | 15 + .../jax/inference/runtime/batch_scheduler.py | 208 ++++++ experimental/jax/inference/runtime/engine.py | 553 +++++++++++++++ .../jax/inference/runtime/kv_cache.py | 150 ++++ .../jax/inference/runtime/model_executor.py | 530 ++++++++++++++ .../inference/runtime/offline_inference.py | 152 ++++ .../jax/inference/runtime/request_type.py | 108 +++ experimental/jax/inference/server/__init__.py | 15 + .../jax/inference/server/simple_server.py | 87 +++ experimental/jax/inference/utils/__init__.py | 17 + .../jax/inference/utils/pytree_utils.py | 33 + experimental/jax/requirements.txt | 12 + .../tpu/test_chunked_prefill_attention.py | 126 ++++ .../linear/tpu/test_collective_matmul_impl.py | 87 +++ experimental/jax/tests/model/test_llama.py | 161 +++++ experimental/jax/tests/nn/test_attention.py | 230 ++++++ experimental/jax/tests/nn/test_embedding.py | 54 ++ experimental/jax/tests/nn/test_module.py | 126 ++++ experimental/jax/tests/nn/test_norm.py | 90 +++ .../jax/tests/parallel/test_operations.py | 70 ++ .../py/jetstream/core/metrics/prometheus.py | 257 ------- .../py/jetstream => jetstream}/__init__.py | 0 .../py/jetstream => jetstream}/core/README.md | 0 .../jetstream => jetstream}/core/__init__.py | 0 .../core/config_lib.py | 8 +- .../core/implementations/__init__.py | 0 .../core/implementations/mock/README.md | 0 .../core/implementations/mock/__init__.py | 0 .../core/implementations/mock/config.py | 0 .../core/implementations/mock/server.py | 0 .../core/metrics/__init__.py | 0 jetstream/core/metrics/prometheus.py | 291 ++++++++ .../core/orchestrator.py | 62 +- .../core/proto/__init__.py | 0 .../core/proto/jetstream.proto | 0 .../core/proto/jetstream_pb2.py | 0 .../core/proto/jetstream_pb2_grpc.py | 0 .../core/server_lib.py | 24 +- .../core/utils/__init__.py | 0 .../core/utils/async_multifuture.py | 0 .../core/utils/proxy_util.py | 0 .../core/utils/return_sample.py | 0 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similarity index 100% rename from google3/third_party/py/jetstream/benchmarks/README.md rename to benchmarks/README.md diff --git a/google3/third_party/py/jetstream/benchmarks/__init__.py b/benchmarks/__init__.py similarity index 100% rename from google3/third_party/py/jetstream/benchmarks/__init__.py rename to benchmarks/__init__.py diff --git a/google3/third_party/py/jetstream/benchmarks/benchmark_serving.py b/benchmarks/benchmark_serving.py similarity index 71% rename from google3/third_party/py/jetstream/benchmarks/benchmark_serving.py rename to benchmarks/benchmark_serving.py index 97628372..68f0d296 100644 --- a/google3/third_party/py/jetstream/benchmarks/benchmark_serving.py +++ b/benchmarks/benchmark_serving.py @@ -62,6 +62,7 @@ import asyncio from dataclasses import dataclass, field from datetime import datetime +import gc import json import random import time @@ -70,6 +71,7 @@ import grpc +from benchmarks.metrics import EventMetric, CounterMetric from jetstream.core.proto import jetstream_pb2 from jetstream.core.proto import jetstream_pb2_grpc from jetstream.engine.token_utils import load_vocab @@ -107,6 +109,40 @@ def str2bool(v: str) -> bool: raise ValueError(f"Invalid value '{v}'!") +class AsyncCounter: + """An counter class for counting and quota management with asycio, + not thread safe. It's safe with asyncio as value changes are done + outside of await statements. + """ + + def __init__(self, init_value: int, block_on_zero_seconds=0.002): + """ + Args: + init_value: Initial value for the counter. + block_on_zero_seconds: if greater than 0, the counter will spin when + value hits 0, hence can be used for quota management. + """ + self._init_value = init_value + self._value = init_value + self._block_on_zero_seconds = block_on_zero_seconds + + async def inc(self): + self._value += 1 + + async def dec(self): + while True: + if self._value > 0 or self._block_on_zero_seconds <= 0.0: + self._value -= 1 + return + await asyncio.sleep(self._block_on_zero_seconds) + + def value(self): + return self._value + + def delta(self): + return self._init_value - self._value + + @dataclass class BenchmarkMetrics: """Data class to store benchmark metrics.""" @@ -117,12 +153,10 @@ class BenchmarkMetrics: request_throughput: float input_throughput: float output_throughput: float - mean_ttft_ms: float - median_ttft_ms: float - p99_ttft_ms: float - mean_tpot_ms: float - median_tpot_ms: float - p99_tpot_ms: float + + ttft: EventMetric # Time-to-first-token + ttst: EventMetric # Time-to-second-token + tpot: EventMetric # Time-per-output-token @dataclass @@ -136,24 +170,37 @@ class InputRequest: @dataclass class RequestFuncOutput: + """Data class to store the response of a request.""" + input_request: Optional[InputRequest] = None - generated_token_list: list[str] = field(default_factory=list) + generated_token_list: list[int] = field(default_factory=list) generated_text: str = "" success: bool = False - latency: float = 0 - ttft: float = 0 + latency_sec: float = 0 + ttft_sec: float = 0 + ttst_sec: float = 0 prompt_len: int = 0 # Flatten the structure and return only the necessary results def to_dict(self): + if self.input_request: + prompt = self.input_request.prompt + original_output = self.input_request.output + sample_idx = self.input_request.sample_idx + else: + prompt = None + original_output = None + sample_idx = None return { - "prompt": self.input_request.prompt, - "original_output": self.input_request.output, + "prompt": prompt, + "original_output": original_output, "generated_text": self.generated_text, "success": self.success, - "latency": self.latency, + "latency_sec": self.latency_sec, + "ttft_sec": self.ttft_sec, + "ttst_sec": self.ttst_sec, "prompt_len": self.prompt_len, - "sample_idx": self.input_request.sample_idx, + "sample_idx": sample_idx, } @@ -210,12 +257,16 @@ def load_sharegpt_dataset( return dataset -def load_openorca_dataset_pkl(): +def load_openorca_dataset_pkl( + dataset_path: str, +) -> list[tuple[Any, Any]]: + if not dataset_path: + dataset_path = "open_orca_gpt4_tokenized_llama.calibration_1000.pkl" # read pickle file samples = pandas.read_pickle( os.path.join( os.path.dirname(os.path.relpath(__file__)), - "open_orca_gpt4_tokenized_llama.calibration_1000.pkl", + dataset_path, ) ) @@ -376,15 +427,19 @@ def calculate_metrics( total_output = 0 total_input = 0 completed = 0 - per_token_latencies = [] - ttfts = [] + ttft = EventMetric("ttft", "Time-to-first-token", "ms") + ttst = EventMetric("ttst", "Time-to-second-token", "ms") + per_out_token_lat = EventMetric("TPOT", "Time-per-output-token", "ms") + output_sizes = [] for i in range(len(outputs)): if outputs[i].success: + completed += 1 output_len = len( outputs[i].generated_token_list if tokenizer != "test" else ["Ċ", "Ō", "Ɵ"] ) + output_sizes.append(output_len) total_output += output_len total_input += input_requests[i].prompt_len if output_len == 0: @@ -393,9 +448,13 @@ def calculate_metrics( output: {outputs[i]}""" ) continue - per_token_latencies.append(outputs[i].latency / output_len) - ttfts.append(outputs[i].ttft) - completed += 1 + ttft.record(outputs[i].ttft_sec * 1000) + ttst.record(outputs[i].ttst_sec * 1000) + per_out_token_lat.record(outputs[i].latency_sec / output_len * 1000) + + print("Mean output size:", float(np.mean(output_sizes))) + print("Median output size:", float(np.median(output_sizes))) + print("P99 output size:", float(np.percentile(output_sizes, 99))) metrics = BenchmarkMetrics( completed=completed, @@ -404,65 +463,99 @@ def calculate_metrics( request_throughput=completed / dur_s, input_throughput=total_input / dur_s, output_throughput=total_output / dur_s, - mean_ttft_ms=float(np.mean(ttfts) * 1000), - median_ttft_ms=float(np.median(ttfts) * 1000), - p99_ttft_ms=float(np.percentile(ttfts, 99) * 1000), - mean_tpot_ms=float(np.mean(per_token_latencies) * 1000), - median_tpot_ms=float(np.median(per_token_latencies) * 1000), - p99_tpot_ms=float(np.percentile(per_token_latencies, 99) * 1000), + ttft=ttft, + ttst=ttst, + tpot=per_out_token_lat, ) return metrics async def grpc_async_request( - api_url: str, request: Any -) -> tuple[list[str], float, float]: + api_url: str, + request: Any, + prefill_quota: AsyncCounter, + active_req_quota: AsyncCounter, + out_token_cnt: CounterMetric, +) -> tuple[list[int], float, float, float]: """Send grpc synchronous request since the current grpc server is sync.""" options = [("grpc.keepalive_timeout_ms", 10000)] async with grpc.aio.insecure_channel(api_url, options=options) as channel: stub = jetstream_pb2_grpc.OrchestratorStub(channel) - print("Making request") - ttft = 0 - token_list = [] request_start_time = time.perf_counter() response = stub.Decode(request) + token_list = [] + ttft = 0 + ttst = 0 + stream_resp_cnt = 0 async for resp in response: - if ttft == 0: + stream_resp_cnt += 1 + if stream_resp_cnt == 1: + await prefill_quota.inc() ttft = time.perf_counter() - request_start_time - token_list.extend(resp.stream_content.samples[0].token_ids) - latency = time.perf_counter() - request_start_time - return token_list, ttft, latency + if ttft > 2.0: + print(datetime.now(), f"slow TTFT {ttft:.2f}", prefill_quota.value()) + elif stream_resp_cnt == 2: + ttst = time.perf_counter() - request_start_time + resp_tokens = resp.stream_content.samples[0].token_ids + token_list.extend(resp_tokens) + out_token_cnt.increment(len(resp_tokens)) + await active_req_quota.inc() + req_latency = time.perf_counter() - request_start_time + return token_list, ttft, ttst, req_latency async def send_request( api_url: str, tokenizer: Any, input_request: InputRequest, + prefill_quota: AsyncCounter, + active_req_quota: AsyncCounter, + req_complete_cnt: CounterMetric, + out_token_cnt: CounterMetric, pbar: tqdm, ) -> RequestFuncOutput: """Send the request to JetStream server.""" - # Tokenization on client side following MLPerf standard. + # Tokenize on client side following MLPerf standard. token_ids = tokenizer.encode(input_request.prompt) + + # Send the request request = jetstream_pb2.DecodeRequest( token_content=jetstream_pb2.DecodeRequest.TokenContent( token_ids=token_ids ), max_tokens=input_request.output_len, + metadata=jetstream_pb2.DecodeRequest.Metadata( + start_time=time.perf_counter() + ), + ) + out_tokens, ttft_sec, ttst_sec, latency_sec = await grpc_async_request( + api_url, + request, + prefill_quota, + active_req_quota, + out_token_cnt, ) + req_complete_cnt.increment() + + # Collect per-request output and metrics. output = RequestFuncOutput() output.input_request = input_request output.prompt_len = input_request.prompt_len - generated_token_list, ttft, latency = await grpc_async_request( - api_url, request - ) - output.ttft = ttft - output.latency = latency - output.generated_token_list = generated_token_list + output.ttft_sec = ttft_sec + output.ttst_sec = ttst_sec + output.latency_sec = latency_sec + output.generated_token_list = out_tokens # generated_token_list is a list of token ids, decode it to generated_text. - output.generated_text = tokenizer.decode(generated_token_list) + output.generated_text = tokenizer.decode(out_tokens) output.success = True if pbar: + pbar.postfix = ( + f"#reqs: {active_req_quota.delta()}/" + f"{active_req_quota.value()}; " + f"#prefill: {prefill_quota.delta()}/" + f"{prefill_quota.value()}" + ) pbar.update(1) return output @@ -473,69 +566,112 @@ async def benchmark( input_requests: list[InputRequest], request_rate: float, disable_tqdm: bool, -): - """Benchmark the online serving performance.""" - pbar = None if disable_tqdm else tqdm(total=len(input_requests)) + prefill_quota: AsyncCounter, + active_req_quota: AsyncCounter, + is_warmup: bool = False, +) -> tuple[dict[str, float | int], list[RequestFuncOutput]]: + """Benchmark the online serving performance. - print(f"Traffic request rate: {request_rate}") + Args: + api_url: URL (e.g. host:port) of the JetStream server to send requests to. + tokenizer: The tokenizer used to convert texts into tokens that will be set + in requests. + input_requests: A list of requests to send. + request_rate: The number of requests to send per second. + disable_tqdm: Whether progress bar should be disabled or not. + prefill_quota: Quota for limiting pending prefill operations. + active_req_quota: Quota for limiting inflight requests. + is_warmup: Whether this run is to warm up the server. + + Return: + A tuple containing the performance statistics for all requests and a list + of responses from the executed requests. + """ + print(f"Benchmarking with a total number of {len(input_requests)} requests") + print(f"Benchmarking with request rate of {request_rate}") + pbar = None if disable_tqdm else tqdm(total=len(input_requests)) + req_complete_cnt = CounterMetric( + "ReqCompleteCount", "Request Completion Counter" + ) + out_token_cnt = CounterMetric("OutTokenCount", "OutToken Counter") - benchmark_start_time = time.perf_counter() + # Run benchmarking tasks = [] + benchmark_start_time = time.perf_counter() async for request in get_request(input_requests, request_rate): + await prefill_quota.dec() + await active_req_quota.dec() tasks.append( asyncio.create_task( send_request( api_url=api_url, tokenizer=tokenizer, input_request=request, + prefill_quota=prefill_quota, + active_req_quota=active_req_quota, + req_complete_cnt=req_complete_cnt, + out_token_cnt=out_token_cnt, pbar=pbar, ) ) ) outputs = await asyncio.gather(*tasks) - - if not disable_tqdm and pbar: + if pbar is not None: pbar.close() - benchmark_duration = time.perf_counter() - benchmark_start_time - - metrics = calculate_metrics( - input_requests=input_requests, - outputs=outputs, - dur_s=benchmark_duration, - tokenizer=tokenizer, - ) - - print(f"Successful requests: {metrics.completed}") - print(f"Benchmark duration: {benchmark_duration:2f} s") - print(f"Total input tokens: {metrics.total_input}") - print(f"Total generated tokens: {metrics.total_output}") - print(f"Request throughput: {metrics.request_throughput:.2f} requests/s") - print(f"Input token throughput: {metrics.input_throughput:.2f} tokens/s") - print(f"Output token throughput: {metrics.output_throughput:.2f} tokens/s") - print(f"Mean TTFT: {metrics.mean_ttft_ms:.2f} ms") - print(f"Median TTFT: {metrics.median_ttft_ms:.2f} ms") - print(f"P99 TTFT: {metrics.p99_ttft_ms:.2f} ms") - print(f"Mean TPOT: {metrics.mean_tpot_ms:.2f} ms") - print(f"Median TPOT: {metrics.median_tpot_ms:.2f} ms") - print(f"P99 TPOT: {metrics.p99_tpot_ms:.2f} ms") - - result = { - "duration": benchmark_duration, - "completed": metrics.completed, - "total_input_tokens": metrics.total_input, - "total_output_tokens": metrics.total_output, - "request_throughput": metrics.request_throughput, - "input_throughput": metrics.input_throughput, - "output_throughput": metrics.output_throughput, - "mean_ttft_ms": metrics.mean_ttft_ms, - "median_ttft_ms": metrics.median_ttft_ms, - "p99_ttft_ms": metrics.p99_ttft_ms, - "mean_tpot_ms": metrics.mean_tpot_ms, - "median_tpot_ms": metrics.median_tpot_ms, - "p99_tpot_ms": metrics.p99_tpot_ms, - } - return result, outputs + # Compute metrics + output_metrics = {} + if not is_warmup: + # No need to calculate metrics when executing warmup requests + benchmark_duration = time.perf_counter() - benchmark_start_time + metrics = calculate_metrics( + input_requests=input_requests, + outputs=outputs, + dur_s=benchmark_duration, + tokenizer=tokenizer, + ) + print(f"Successful requests: {metrics.completed}") + print(f"Benchmark duration: {benchmark_duration:2f} s") + print(f"Total input tokens: {metrics.total_input}") + print(f"Total generated tokens: {metrics.total_output}") + print(f"Request throughput: {metrics.request_throughput:.2f} requests/s") + print(f"Input token throughput: {metrics.input_throughput:.2f} tokens/s") + print(f"Output token throughput: {metrics.output_throughput:.2f} tokens/s") + + print(f"{metrics.ttft.distribution_summary_str()}") + print(f"{metrics.ttst.distribution_summary_str()}") + print(f"{metrics.tpot.distribution_summary_str()}") + + # Calculate one rate for each 10 sec window. Adjusts the window size if + # needed to use csv output below for plotting the rate over time. + window_size_sec = 10 + print( + f"----- Request complete rate time series " + f"(window_size = {window_size_sec} sec) -----" + ) + print(f"{req_complete_cnt.rate_over_window_to_csv(window_size_sec)}") + print( + f"----- Output token rate time series " + f"(window_size = {window_size_sec} sec) -----" + ) + print(f"{out_token_cnt.rate_over_window_to_csv(window_size_sec)}") + + output_metrics = { + "duration": benchmark_duration, + "completed": metrics.completed, + "total_input_tokens": metrics.total_input, + "total_output_tokens": metrics.total_output, + "request_throughput": metrics.request_throughput, + "input_throughput": metrics.input_throughput, + "output_throughput": metrics.output_throughput, + } + output_metrics = { + **output_metrics, + **metrics.ttft.distribution_summary_dict(), + **metrics.ttst.distribution_summary_dict(), + **metrics.tpot.distribution_summary_dict(), + } + return output_metrics, outputs def mock_requests(total_mock_requests: int): @@ -579,6 +715,9 @@ def main(args: argparse.Namespace): tokenizer_id = args.tokenizer use_hf_tokenizer = args.use_hf_tokenizer + prefill_quota = AsyncCounter(init_value=3) + active_req_quota = AsyncCounter(init_value=450) + api_url = f"{args.server}:{args.port}" tokenizer = get_tokenizer(model_id, tokenizer_id, use_hf_tokenizer) @@ -589,7 +728,7 @@ def main(args: argparse.Namespace): else: dataset = [] if args.dataset == "openorca": - dataset = load_openorca_dataset_pkl() + dataset = load_openorca_dataset_pkl(args.dataset_path) elif args.dataset == "sharegpt": dataset = load_sharegpt_dataset( args.dataset_path, @@ -613,17 +752,20 @@ def main(args: argparse.Namespace): warmup_requests = list(sample_warmup_requests(input_requests)) * 2 if warmup_requests: - print(f"Starting {args.warmup_mode} warmup:") - benchmark_result, request_outputs = asyncio.run( + print(f"Warmup (mode: {args.warmup_mode}) is starting.") + _, _ = asyncio.run( benchmark( api_url=api_url, tokenizer=tokenizer, input_requests=warmup_requests, request_rate=args.request_rate, disable_tqdm=args.disable_tqdm, + prefill_quota=prefill_quota, + active_req_quota=active_req_quota, + is_warmup=True, ) ) - print(f"{args.warmup_mode} warmup completed.") + print(f"Warmup (mode: {args.warmup_mode}) has completed.") # TODO: Replace this with warmup complete signal once supported. # Wait for server completely warmup before running the benchmark. @@ -636,6 +778,8 @@ def main(args: argparse.Namespace): input_requests=input_requests, request_rate=args.request_rate, disable_tqdm=args.disable_tqdm, + prefill_quota=prefill_quota, + active_req_quota=active_req_quota, ) ) @@ -692,7 +836,6 @@ def main(args: argparse.Namespace): if __name__ == "__main__": - parser = argparse.ArgumentParser( description="Benchmark the online serving throughput." ) @@ -836,4 +979,5 @@ def main(args: argparse.Namespace): ) parsed_args = parser.parse_args() + gc.disable() main(parsed_args) diff --git a/google3/third_party/py/jetstream/benchmarks/eval_accuracy.py b/benchmarks/eval_accuracy.py similarity index 99% rename from google3/third_party/py/jetstream/benchmarks/eval_accuracy.py rename to benchmarks/eval_accuracy.py index 559cd2a8..f84562be 100644 --- a/google3/third_party/py/jetstream/benchmarks/eval_accuracy.py +++ b/benchmarks/eval_accuracy.py @@ -64,6 +64,7 @@ def main(args): eval_accuracy(request_outputs_dict) + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( diff --git a/benchmarks/metrics.py b/benchmarks/metrics.py new file mode 100644 index 00000000..1cd122c4 --- /dev/null +++ b/benchmarks/metrics.py @@ -0,0 +1,244 @@ +# Copyright 2025 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 +# +# http://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. + +"""Metrics util classes for collecting and managing metrics.""" + +import datetime + +import numpy as np +from typing import Tuple, List, Dict + + +def _floor_datetime_to_sec(timestamp: datetime.datetime) -> datetime.datetime: + """ "Floor the timestamp to the nearest most recent second""" + return timestamp.replace(microsecond=0) + + +def _now_floored_to_second() -> datetime.datetime: + """Return the current timestamp floored to the nearest most recent second a""" + now = datetime.datetime.now() + return _floor_datetime_to_sec(now) + + +class EventMetric: + """An event metric for distribution stats reporting. Not thread-safe.""" + + def __init__(self, name: str, description: str, unit: str = ""): + self._name = name + self._description = description + self._unit = unit + self._data = [] + + def data(self) -> List[float]: + """Returns all stored data points. + + Returns: + A list of data points in the order that was stored + """ + return self._data + + def record(self, value: float): + """Record a data point + + Args: + value: The data point to be stored. + """ + self._data.append(value) + + def percentile(self, percentile: int) -> float: + """Computes and returns the specified percentile of the collected data. + + Args: + percentile: The percentile to compute. + + Returns: + The computed percentile. + """ + if not 0 <= percentile <= 100: + raise ValueError(f"Percentile {percentile} is not in [0, 100]") + if not self._data: + raise ValueError( + f"No data points in metric {self._name} to compute percentile" + ) + return np.percentile(self._data, percentile) + + def mean(self) -> float: + """Calculates and returns the mean value of the collected data. + + Returns: + The mean value of the collected data + """ + if not self._data: + raise ValueError(f"No data points in metric {self._name} to compute mean") + return np.mean(self._data) + + def distribution_summary_str(self) -> str: + """Generates a string representation of the distribution summary + + Returns: + The string representation of the distribution summary including + mean, p50, p90 and p99. + """ + s = "" + s += f"Mean {self._name}: {self.mean():.2f} {self._unit}\n" + s += f"Median {self._name}: {self.percentile(50):.2f} {self._unit}\n" + s += f"P99 {self._name}: {self.percentile(99):.2f} {self._unit}" + return s + + def distribution_summary_dict(self) -> dict[str, float]: + """Generates a dictionary representation of the distribution summary + + Returns: + A dictionary containing of the distribution summary including mean, + p50, p90 and p99. + """ + return { + f"mean_{self._name}_{self._unit}": self.mean(), + f"median_{self._name}_{self._unit}": self.percentile(50), + f"p99_{self._name}_{self._unit}": self.percentile(99), + } + + +class CounterMetric: + """A count metric for computing rates over time. Not thread-safe.""" + + def __init__(self, name: str, description: str): + self._name = name + self._description = description + self._data: dict[datetime.datetime, int] = {} + + def data(self) -> Dict[datetime.datetime, int]: + """Returns all stored data points. + + Returns: + A dictionary of data points where the key is the timestamp and the value + is the aggregated counts within the second of the timestamp. + """ + return self._data + + def total_count(self) -> int: + """Returns aggregated counts + + Returns: + The aggregated counts. + """ + return sum(self._data.values()) + + def total_duration_sec(self) -> int: + """Returns the duration between the first and last count increment + + Returns: + The duration (in seconds) between the first and last increment + (inclusive of both ends). + """ + start_time = min(self._data.keys()) + end_time = max(self._data.keys()) + return int((end_time - start_time).total_seconds() + 1) + + def increment( + self, count: int = 1, timestamp: datetime.datetime | None = None + ): + """Increment the counter by count + + Args: + count: The amount to increment + timestamp: The timestamp for the increment. Default to now if none is + provided. + """ + if timestamp is None: + cur_time = _now_floored_to_second() + else: + cur_time = _floor_datetime_to_sec(timestamp) + # Add timestamp with default value 0 if doesn't exist + cur_count = self._data.setdefault(cur_time, 0) + self._data[cur_time] = cur_count + count + return + + def rate(self) -> float: + """Calculates the rate of change between the first and last increments. + + Returns: + The rate of change between the first and last increments. + """ + if len(self._data.keys()) < 2: + raise ValueError( + "At least 2 data points are required to compute the rate" + ) + start_time = min(self._data.keys()) + end_time = max(self._data.keys()) + delta_time_sec = (end_time - start_time).total_seconds() + sorted_counts = [count for timestamp, count in sorted(self._data.items())] + delta_count = sum(sorted_counts[1:]) + return delta_count / delta_time_sec + + def rate_over_window( + self, window_size_sec: int + ) -> List[Tuple[datetime.datetime, float]]: + """Calculate the rates over time." + + Args: + window_size_sec: The size of the window in seconds for computing each + individual rate + + Returns: + A list of rates over time, where each element represents the rate of + change for the specified window size. + """ + if len(self._data.keys()) < 2: + raise ValueError( + f"At least 2 different timestamp values are required to calculate " + f"the rate, but have only {len(self._data.keys())}" + ) + rates: List[Tuple[datetime.datetime, float]] = [] + sorted_data = sorted(self._data.items()) + + start_time, _ = sorted_data[0] + end_time, _ = sorted_data[-1] + cur_start_time = start_time + cur_end_time = cur_start_time + datetime.timedelta(seconds=window_size_sec) + cur_total_count = 0 + for data_point in sorted_data: + timestamp, count = data_point + if timestamp >= cur_end_time: + while timestamp >= cur_end_time: + rates.append((cur_start_time, cur_total_count / window_size_sec)) + cur_start_time = cur_end_time + cur_end_time = cur_start_time + datetime.timedelta( + seconds=window_size_sec + ) + cur_total_count = 0 + cur_total_count += count + if cur_start_time <= end_time: + delta_time_sec = (end_time - cur_start_time).total_seconds() + 1 + rates.append((cur_start_time, cur_total_count / delta_time_sec)) + return rates + + def rate_over_window_to_csv(self, window_size_sec: int) -> str: + """Compute and return the rates over time and return them in csv string + + Args: + window_size_sec: The size of the window in seconds for computing each + individual rate + + Returns: + A CSV string representation of the rates over time, with two rows: + the first row contains timestamps, and the second row contains rate + values. + """ + rates = self.rate_over_window(window_size_sec) + # Generate CSV string with two rows + timestamps = "TimeStamp," + ",".join([str(e[0]) for e in rates]) + values = "Value," + ",".join([f"{e[1]:.2f}" for e in rates]) + csv_output = timestamps + "\n" + values + return csv_output diff --git a/benchmarks/mlperf/README.md b/benchmarks/mlperf/README.md new file mode 100644 index 00000000..7a139b12 --- /dev/null +++ b/benchmarks/mlperf/README.md @@ -0,0 +1,155 @@ + +## Create TPU VM. +Follow these [instructions](https://cloud.google.com/tpu/docs/v5e-inference#tpu-vm) to create TPU v5e-8 VM and ssh into the VM + + +## Clone repo +``` +git clone https://github.com/mlcommons/inference.git +``` + +## Install loadgen +``` +apt-get install python3-dev +apt-get install build-essential -y +cd loadgen/ && pip install . +``` + +## Install eval dependencies +``` +pip install \ +transformers==4.31.0 \ +nltk==3.8.1 \ +evaluate==0.4.0 \ +absl-py==1.4.0 \ +rouge-score==0.1.2 \ +sentencepiece==0.1.99 \ +accelerate==0.21.0 +``` + +## Download data file +``` +cd / +export DATA_DISK_DIR=/loadgen_run_data +mkdir -p ${DATA_DISK_DIR} +cd ${DATA_DISK_DIR} +gsutil cp gs://cloud-tpu-inference-public/mlcommons/inference/language/llama2-70b/data/processed-openorca/open_orca_gpt4_tokenized_llama.calibration_1000.pkl . +mv open_orca_gpt4_tokenized_llama.calibration_1000.pkl processed-calibration-data.pkl + +gsutil cp gs://cloud-tpu-inference-public/mlcommons/inference/language/llama2-70b/data/processed-openorca/open_orca_gpt4_tokenized_llama.sampled_24576.pkl . +mv open_orca_gpt4_tokenized_llama.sampled_24576.pkl processed-data.pkl +cd /inference_mlperf4.1 +``` + +## Install Maxtext +``` +cd / +git clone git@github.com:google/maxtext.git +cd maxtext +git checkout offline_inf +cd maxtext/MaxText +``` + +## Checkpoint generation + +Steps to get a quantized llama2-70B checkpoint for v5e-8 + +Note llama2-70B model takes about 140G of memory and will not fit into a v5e-8. It must be downloaded onto a large machine (such as v5p-8) and quantized to a smaller quantized checkpoint to be loaded onto a v5e-8 machine. + +* Obtain a llama2-70b checkpoint and convert it to a maxtext inference checkpoint. Please follow maxtext instructions specified here: https://github.com/google/maxtext/blob/main/getting_started/Run_Llama2.md + +* Convert the checkpoint into a quantized checkpoint + +To create an int8 DRQ checkpoint run the following step: + +1. Define paths to load maxtext checkpoint from and save quantized checkpoint to. + +``` +export LOAD_PARAMS_PATH=gs://${USER}-bkt/llama2-70b-chat/param-only-decode-ckpt-maxtext/checkpoints/0/items + +export SAVE_QUANT_PARAMS_PATH=gs://${USER}-bkt/quantized/llama2-70b-chat +``` + +2. Run the following maxtext script to generate and save an in8 quantized checkpoint + +``` +export TOKENIZER_PATH=maxtext/assets/tokenizer.llama2 +cd maxtext && \ +python MaxText/decode.py MaxText/configs/base.yml tokenizer_path=${TOKENIZER_PATH} load_parameters_path=${LOAD_PARAMS_PATH} max_prefill_predict_length=1024 max_target_length=2048 model_name=llama2-70b ici_fsdp_parallelism=1 ici_autoregressive_parallelism=1 ici_tensor_parallelism=-1 scan_layers=false weight_dtype=bfloat16 per_device_batch_size=11 attention=dot_product quantization=int8 save_quantized_params_path=${SAVE_QUANT_PARAMS_PATH} +``` + +Your checkpoint is generated at `$SAVE_QUANT_PARAMS_PATH`. This is used to set `load_parameters_path` param below in `MAXENGINE_ARGS` env variable. + +## HF login +``` +huggingface-cli login +``` + +## Loadgen settings +``` +cd Google/code/llama2-70b/tpu_v5e_8_jetstream_maxtext/scripts/ +export API_URL=0.0.0.0:9000 +export DATA_DISK_DIR=/loadgen_run_data +export DATASET_TYPE=full # for calibration run, DATASET_TYPE=calibration + +export MODEL_NAME=llama70b +export TOTAL_SAMPLE_COUNT=24576 # for calibration run, TOTAL_SAMPLE_COUNT=1000 +export LOG_INTERVAL=1000 +export BATCH_SIZE_EXP=8 +export USER_CONFIG=user.conf +``` + +## Offline Setup +``` +cd / +git clone git@github.com:google/maxtext.git +cd maxtext +git checkout offline_inf +cd maxtext/MaxText + +# For v5e use +export BATCH_AND_PREFILL_LEN=“256,80|512,40|1024,20” + +# For v6e use +export BATCH_AND_PREFILL_LEN=“256,216|512,108|1024,54” +export TOKENIZER_PATH=maxtext/assets/tokenizer.llama2 + +export MAXENGINE_ARGS="model_name=llama2-70b tokenizer_path=${TOKENIZER_PATH} quantization=int8 quantize_kvcache=True load_parameters_path=${SAVE_QUANT_PARAMS_PATH} checkpoint_is_quantized=True compute_axis_order=0,1,2,3 ar_cache_axis_order=0,1,2,3" +``` + +## Run offline performance + +``` +cd /maxtext/MaxText +bash ./llama_offline_performance_run.sh +``` + +## Run offline accuracy +``` +cd /maxtext/MaxText +bash ./llama_offline_accuracy_run.sh +``` + +## Run offline audit +``` +cd /maxtext/MaxText +bash ./llama_offline_audit_run.sh +``` + +## Run server performance +``` +cd Google/code/llama2-70b/tpu_v5e_8_jetstream_maxtext/scripts/ +bash ./generate_server_performance_run.sh +``` + +## Run server accuracy +``` +cd Google/code/llama2-70b/tpu_v5e_8_jetstream_maxtext/scripts/ +bash ./generate_server_accuracy_run.sh +``` + +## Run server audit +``` +cd Google/code/llama2-70b/tpu_v5e_8_jetstream_maxtext/scripts/ +bash ./generate_server_audit_run.sh +``` diff --git a/benchmarks/mlperf/backend.py b/benchmarks/mlperf/backend.py new file mode 100644 index 00000000..f6a3859c --- /dev/null +++ b/benchmarks/mlperf/backend.py @@ -0,0 +1,323 @@ +# 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 +# +# http://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. + +"""mlperf loadgen interface for LLama2.""" +import array +import concurrent.futures +import dataclasses +import json +import logging +from operator import itemgetter # pylint: disable=g-importing-member +import time +from typing import List, Optional, Any + +import numpy as np + +import dataset + +import mlperf_loadgen as lg + +import grpc +from jetstream.core.proto import jetstream_pb2 +from jetstream.core.proto import jetstream_pb2_grpc + +from transformers import AutoTokenizer + + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger("backend.py") + + +@dataclasses.dataclass +class WarmupSample: + id: int + index: int + + +@dataclasses.dataclass +class StreamResponse: + result: str = "" + + +class ThreadedLMClient: + """Holds a thread pool and a loadgen client for LM inference.""" + + _thread_pool: concurrent.futures.ThreadPoolExecutor + _dataset: dataset.Dataset + _futures = List[concurrent.futures.Future] + + def __init__( + self, + is_stream: bool, + num_threads: int, + api_url: str, + dataset_object: dataset.Dataset, + input_mode: str, + output_mode: str, + tokenizer: Optional[AutoTokenizer] = None, + max_output_len: int = 1024, + log_interval: int = 1000, + ): + log.info(f"Initiating {self.__class__.__name__} ...") + self._is_stream = is_stream + self._input_mode = dataset.validate_sample_mode(input_mode) + self._output_mode = dataset.validate_sample_mode(output_mode) + if self._input_mode == "text" or self._output_mode == "text": + assert tokenizer is not None + self._tokenizer = tokenizer + self._max_output_len = max_output_len + + self._log_interval = log_interval + + self._thread_pool = concurrent.futures.ThreadPoolExecutor(num_threads) + self._api_url = api_url + self._dataset = dataset_object + self._futures = [] + self.pred_outputs = {} + self._resp_cnt = 0 + + log.info("Creating grpc channel with api_url {}".format(api_url)) + options = [("grpc.keepalive_timeout_ms", 10000)] + self._grpc_channel = grpc.insecure_channel(api_url, options=options) + + @property + def tokenizer(self): + return self._tokenizer + + def _log_resp_cnt(self): + self._resp_cnt += 1 + if self._resp_cnt % self._log_interval == 0: + log.info("Completed %d queries", self._resp_cnt) + + def process_single_sample_async(self, query_sample, warmup): + """Executes a single query and marks responses complete asynchronously. + + Args: + query_sample: Single prompt + warmup: Indicates that this is a warmup request. + """ + future = self._thread_pool.submit( + self._process_sample, query_sample, warmup + ) + self._futures.append(future) + + def flush(self): + concurrent.futures.wait(self._futures) + self._futures = [] + + def _grpc_request(self, request, sample, warmup): + """Send grpc synchronous request since the current grpc server is sync.""" + stub = jetstream_pb2_grpc.OrchestratorStub(self._grpc_channel) + token_list = [] + ttft = 0 + start_time = time.perf_counter() + response = stub.Decode(request) + for resp in response: + if not warmup and self._is_stream and ttft == 0: + # TTFT for online mode + ttft = time.perf_counter() - start_time + log.info("TTFT {}ms".format(ttft * 1000)) + response_token_ids = resp.stream_content.samples[0].token_ids + assert len(response_token_ids) == 1 + response_token_ids = np.array(response_token_ids, dtype=np.int64) + response_array = array.array("B", response_token_ids.tobytes()) + response_info = response_array.buffer_info() + first_token_response = lg.QuerySampleResponse( + sample.id, response_info[0], response_info[1] + ) + lg.FirstTokenComplete([first_token_response]) + log.info("mark first token complete") + token_list.extend(resp.stream_content.samples[0].token_ids) + return token_list + + def _process_sample(self, sample, warmup): + """Processes a single sample.""" + sample_data = self._dataset.inputs[sample.index] + if self._input_mode == "text": + token_ids = self._tokenizer.encode(sample_data) + else: + assert self._input_mode == "tokenized" + token_ids = [int(token_id_str) for token_id_str in sample_data.split(",")] + + request = jetstream_pb2.DecodeRequest( + token_content=jetstream_pb2.DecodeRequest.TokenContent( + token_ids=token_ids + ), + max_tokens=self._max_output_len, + ) + generated_token_list = self._grpc_request(request, sample, warmup) + if not warmup: + response_token_ids = generated_token_list + n_tokens = len(response_token_ids) + response_token_ids = np.array(response_token_ids, dtype=np.int64) + response_array = array.array("B", response_token_ids.tobytes()) + response_info = response_array.buffer_info() + response_data = response_info[0] + response_size = response_info[1] * response_array.itemsize + query_sample_response = lg.QuerySampleResponse( + sample.id, response_data, response_size, n_tokens + ) + lg.QuerySamplesComplete([query_sample_response]) + log.info("mark query complete") + + pred_output = self._tokenizer.decode(response_token_ids) + self.pred_outputs[sample.index] = pred_output + self._log_resp_cnt() + + +class SUT: + """SUT.""" + + def __init__( + self, + scenario, + api_url, + is_stream, + input_mode, + output_mode, + max_output_len, + dataset_path, + total_sample_count, + tokenizer_path=None, + perf_count_override=None, + num_client_threads=200, + log_interval=1000, + batch_size_exp=5, + pred_outputs_log_path=None, + dataset_rename_cols="", + ): + log.info(f"Starting {scenario} SUT with {api_url}.") + self._is_stream = is_stream + self._input_mode = dataset.validate_sample_mode(input_mode) + self._output_mode = dataset.validate_sample_mode(output_mode) + assert tokenizer_path is not None + self._tokenizer = self.load_tokenizer(tokenizer_path) + self._max_output_len = max_output_len + self._api_url = api_url + self._dataset_path = dataset_path + self._total_sample_count = total_sample_count + self._perf_count_override = perf_count_override + self._num_client_threads = num_client_threads + self._log_interval = log_interval + self._batch_size_exp = batch_size_exp + self._pred_outputs_log_path = pred_outputs_log_path + + log.info("Loading Dataset ... ") + self.dataset = dataset.Dataset( + dataset_path=self._dataset_path, + input_mode=self._input_mode, + total_sample_count=self._total_sample_count, + perf_count_override=self._perf_count_override, + dataset_rename_cols=dataset_rename_cols, + ) + + client_cls = ThreadedLMClient + self._client = client_cls( + is_stream=self._is_stream, + num_threads=self._num_client_threads, + api_url=self._api_url, + dataset_object=self.dataset, + input_mode=self._input_mode, + output_mode=self._output_mode, + tokenizer=self._tokenizer, + max_output_len=self._max_output_len, + log_interval=self._log_interval, + ) + + self.qsl = lg.ConstructQSL( + self.dataset.total_sample_count, + self.dataset.perf_count, + self.dataset.LoadSamplesToRam, + self.dataset.UnloadSamplesFromRam, + ) + + # We need to add some warmup to improve throughput estimation + log.info("Starting warmup....") + # Warm up with exponentially increasing batch sizes up to 32. + for batch_size_exp in range(self._batch_size_exp): + batch_size = 2**batch_size_exp + for warmup_id, warmup_idx in enumerate(range(batch_size)): + warmup_sample = WarmupSample(id=warmup_id, index=warmup_idx) + self._client.process_single_sample_async(warmup_sample, True) + self._client.flush() + + log.info("Warmup done....") + time.sleep(30) + self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries) + + def load_tokenizer( + self, tokenizer_path: Optional[str] = None + ) -> Optional[AutoTokenizer]: + """Returns tokenizer""" + if tokenizer_path is not None: + tokenizer = AutoTokenizer.from_pretrained( + tokenizer_path, + model_max_length=1024, + padding_side="left", + use_fast=True, + ) + tokenizer.pad_token = tokenizer.eos_token + return tokenizer + + def _sort_issue_queries(self, query_samples): + """Issue queries.""" + query_samples_with_length = [] + for query_sample in query_samples: + query_sample_token_length = self.dataset.inputs_with_token_lengths[ + query_sample.index + ][1] + query_samples_with_length.append( + (query_sample_token_length, query_sample) + ) + sorted_query_samples_with_length = sorted( + query_samples_with_length, key=itemgetter(0) + ) + sorted_query_samples = [x[1] for x in sorted_query_samples_with_length] + return sorted_query_samples + + def issue_queries(self, query_samples): + """Issue queries.""" + num_query_samples = len(query_samples) + if num_query_samples > 1: + log.info(f"Issuing {num_query_samples} queries. ") + query_samples = self._sort_issue_queries(query_samples) + for query_sample in query_samples: + self._client.process_single_sample_async(query_sample, False) + + def flush_queries(self): + """Flush queries.""" + log.info("Loadgen has completed issuing queries... ") + self._client.flush() + + if self._pred_outputs_log_path is not None: + + pred_outputs = [] + for idx, x in self._client.pred_outputs.items(): + pred_output = { + "qsl_idx": idx, + "intput": self._client._dataset.inputs[idx], + "data": x, + } + pred_outputs.append(pred_output) + log.info(f"Generated {len(pred_outputs)} prediction outputs") + + if pred_outputs: + self.accuracy_log = open(self._pred_outputs_log_path, "w") + self.accuracy_log.write(json.dumps(pred_outputs)) + self.accuracy_log.flush() + self.accuracy_log.close() + log.info("Dumpped prediction outputs to accuracy log... ") + + def __del__(self): + print("Finished destroying SUT.") diff --git a/benchmarks/mlperf/dataset.py b/benchmarks/mlperf/dataset.py new file mode 100644 index 00000000..318ab9a1 --- /dev/null +++ b/benchmarks/mlperf/dataset.py @@ -0,0 +1,122 @@ +# 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 +# +# http://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. + +import json +import logging +import os + +import pandas as pd + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger("dataset.py") + + +class Dataset: + + def __init__( + self, + dataset_path: str, + input_mode: str, + total_sample_count: int = 24576, + perf_count_override: int = 0, + dataset_rename_cols: str = "", + ): + if not os.path.isfile(dataset_path): + log.warn( + "Processed pickle file {} not found. Please check that the path is correct".format( + dataset_path + ) + ) + self.dataset_path = dataset_path + + self._input_mode = validate_sample_mode(input_mode) + self.dataset_rename_cols = dataset_rename_cols + self.load_processed_dataset() + + self.total_sample_count = min(len(self.input_ids_strs), total_sample_count) + self.perf_count = perf_count_override or self.total_sample_count + + @property + def input_ids_strs(self): + return self._input_ids_strs + + @property + def input_texts(self): + return self._input_texts + + @property + def input_token_lengths(self): + return self._input_token_lengths + + @property + def inputs(self): + return self._inputs + + @property + def inputs_with_token_lengths(self): + return self._inputs_with_token_lengths + + def load_processed_dataset(self): + processed_data = pd.read_pickle(self.dataset_path) + if self.dataset_rename_cols: + rename_dict = json.loads(self.dataset_rename_cols) + processed_data.rename(columns=rename_dict, inplace=True) + log.info(f"Renaming columns of dataset with mapping: {rename_dict}") + + self._input_ids_strs = [] + for input_ids in processed_data["tok_input"]: + input_ids_str = ",".join([str(input_id) for input_id in input_ids]) + self._input_ids_strs.append(input_ids_str) + + self._input_texts = [] + for input_text in processed_data["input"]: + self._input_texts.append(input_text) + + self._input_token_lengths = [] + for token_length in processed_data["tok_input_length"]: + self._input_token_lengths.append(token_length) + + log.info(f"input_mode is {self._input_mode}") + self._inputs = ( + self._input_ids_strs + if self._input_mode == "tokenized" + else self._input_texts + ) + log.info(f"example sample input is {self._inputs[0]}") + self._inputs_with_token_lengths = [ + (input_ids_str_or_input_text, token_length) + for input_ids_str_or_input_text, token_length in zip( + self._inputs, self._input_token_lengths + ) + ] + + def LoadSamplesToRam(self, sample_list): + pass + + def UnloadSamplesFromRam(self, sample_list): + pass + + def __del__(self): + pass + + +SAMPLE_MODE_CHOICES = ["tokenized", "text"] + + +def validate_sample_mode(sample_mode: str) -> str: + if sample_mode not in SAMPLE_MODE_CHOICES: + raise ValueError( + "The sample_mode should be set to either `tokenized` or `text`." + ) + return sample_mode diff --git a/benchmarks/mlperf/evaluate-accuracy.py b/benchmarks/mlperf/evaluate-accuracy.py new file mode 100644 index 00000000..2fe79030 --- /dev/null +++ b/benchmarks/mlperf/evaluate-accuracy.py @@ -0,0 +1,129 @@ +import argparse +from transformers import AutoTokenizer +import nltk +import evaluate +import numpy as np +import json + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--checkpoint-path", + required=True, + help="Path to Llama2-70b-hf-chat checkpoint", + ) + parser.add_argument( + "--mlperf-accuracy-file", + required=True, + help="path to mlperf_log_accuracy.json", + ) + parser.add_argument( + "--dataset-file", + required=True, + help="path to processed openorca validation set", + ) + parser.add_argument("--verbose", action="store_true", help="verbose messages") + parser.add_argument( + "--dtype", + default="int64", + help="dtype of the accuracy log", + choices=["int32", "int64", "float"], + ) + args = parser.parse_args() + return args + + +def get_groundtruth(processed_dataset_file): + import pandas as pd + + data = pd.read_pickle(processed_dataset_file) + ground_truths = data["output"] + return ground_truths + + +def postprocess_text(preds, targets): + preds = [pred.strip() for pred in preds] + targets = [target.strip() for target in targets] + + # rougeLSum expects newline after each sentence + preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds] + targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets] + + return preds, targets + + +def main(): + + args = get_args() + dataset_path = args.dataset_file + checkpoint_path = args.checkpoint_path + metric = evaluate.load("rouge") + nltk.download("punkt") + + tokenizer = AutoTokenizer.from_pretrained( + checkpoint_path, + model_max_length=2048, + padding_side="left", + use_fast=False, + ) + + targets = get_groundtruth(args.dataset_file) + + target_required = [] + preds_token_ids = [] + + eval_dtype = np.int64 + if args.dtype == "int32": + eval_dtype = np.int32 + elif args.dtype == "float": + eval_dtype = np.float32 + + with open(args.mlperf_accuracy_file, "r") as f: + results = json.load(f) + + seen = set() + gen_tok_len = 0 + for pred in results: + qsl_idx = pred["qsl_idx"] + if qsl_idx in seen: + continue + + seen.add(qsl_idx) + target = targets[qsl_idx] + target_required.append(target) + pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype) + + gen_tok_len += len(pred) + preds_token_ids.append(pred) + + preds_decoded_text = tokenizer.batch_decode( + preds_token_ids, skip_special_tokens=True + ) + + preds, targets = postprocess_text(preds_decoded_text, target_required) + + result = metric.compute( + predictions=preds, + references=targets, + use_stemmer=True, + use_aggregator=False, + ) + result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()} + prediction_lens = [len(pred) for pred in preds] + gen_num = len(preds) + + result = { + **result, + "gen_len": np.sum(prediction_lens), + "gen_num": gen_num, + "gen_tok_len": gen_tok_len, + "tokens_per_sample": round(gen_tok_len / gen_num, 1), + } + + print("\nResults\n") + print(result) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/mlperf/main.py b/benchmarks/mlperf/main.py new file mode 100644 index 00000000..4c3fd7c4 --- /dev/null +++ b/benchmarks/mlperf/main.py @@ -0,0 +1,223 @@ +# 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 +# +# http://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. + +import argparse + +import gc +import logging +import os +import sys + +import backend + +import mlperf_loadgen as lg + +_MLPERF_ID = "mixtral-8x7b" + +sys.path.insert(0, os.getcwd()) + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger("main.py") + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--scenario", + type=str, + choices=["Offline", "Server"], + default="Offline", + help="Scenario", + ) + parser.add_argument( + "--api-url", type=str, default=None, help="SAX published model path." + ) + parser.add_argument("--dataset-path", type=str, default=None, help="") + parser.add_argument("--tokenizer-path", type=str, default=None, help="") + parser.add_argument( + "--accuracy", action="store_true", help="Run accuracy mode" + ) + parser.add_argument("--is-stream", action="store_true", help="") + parser.add_argument( + "--input-mode", + type=str, + choices=["text", "tokenized"], + default="tokenized", + ) + parser.add_argument( + "--output-mode", + type=str, + choices=["text", "tokenized"], + default="tokenized", + ) + parser.add_argument( + "--max-output-len", type=int, default=1024, help="Maximum output len" + ) + parser.add_argument( + "--audit-conf", + type=str, + default="audit.conf", + help="audit config for LoadGen settings during compliance runs", + ) + parser.add_argument( + "--mlperf-conf", + type=str, + default="mlperf.conf", + help="mlperf rules config", + ) + parser.add_argument( + "--user-conf", + type=str, + default="user.conf", + help="user config for user LoadGen settings such as target QPS", + ) + parser.add_argument( + "--total-sample-count", + type=int, + default=24576, + help="Number of samples to use in benchmark.", + ) + parser.add_argument( + "--perf-count-override", + type=int, + default=None, + help="Overwrite number of samples to use in benchmark.", + ) + parser.add_argument( + "--output-log-dir", + type=str, + default="output-logs", + help="Where logs are saved.", + ) + parser.add_argument( + "--enable-log-trace", + action="store_true", + help="Enable log tracing. This file can become quite large", + ) + parser.add_argument( + "--num-client-threads", + type=int, + default=200, + help="Number of client threads to use", + ) + parser.add_argument("--batch-size-exp", type=int, default=6, help="") + parser.add_argument("--log-pred-outputs", action="store_true", help="") + parser.add_argument( + "--log-interval", + type=int, + default=1000, + help="Logging interval in seconds", + ) + parser.add_argument( + "--user-conf-override-path", + type=str, + default="", + help="When given overrides the default user.conf path", + ) + parser.add_argument( + "--rename-dataset-cols", + type=str, + default="", + help=( + "Rename some of the dataset columns to whats expected by code. For example, " + "mixtral dataset uses ref_token_length instead of ref_token_len. Format is a string dict " + 'eg. {"tok_input_len": "tok_input_length"}' + ), + ) + + args = parser.parse_args() + return args + + +scenario_map = { + "offline": lg.TestScenario.Offline, + "server": lg.TestScenario.Server, +} + + +def main(): + args = get_args() + + settings = lg.TestSettings() + settings.scenario = scenario_map[args.scenario.lower()] + if args.user_conf_override_path: + user_conf = args.user_conf_override_path + else: + user_conf = args.user_conf + + settings.FromConfig(args.mlperf_conf, _MLPERF_ID, args.scenario) + settings.FromConfig(user_conf, _MLPERF_ID, args.scenario) + log.info("Mlperf config: %s", args.mlperf_conf) + log.info("User config: %s", user_conf) + + if args.accuracy: + settings.mode = lg.TestMode.AccuracyOnly + log.warning( + "Accuracy run will generate the accuracy logs, but the evaluation of the log is not completed yet" + ) + else: + settings.mode = lg.TestMode.PerformanceOnly + settings.print_timestamps = True + + settings.use_token_latencies = True + + os.makedirs(args.output_log_dir, exist_ok=True) + log_output_settings = lg.LogOutputSettings() + log_output_settings.outdir = args.output_log_dir + log_output_settings.copy_summary_to_stdout = True + log_settings = lg.LogSettings() + log_settings.log_output = log_output_settings + log_settings.enable_trace = args.enable_log_trace + + sut = backend.SUT( + scenario=args.scenario.lower(), + api_url=args.api_url, + is_stream=args.is_stream, + input_mode=args.input_mode, + output_mode=args.output_mode, + max_output_len=args.max_output_len, + dataset_path=args.dataset_path, + total_sample_count=args.total_sample_count, + tokenizer_path=args.tokenizer_path, + perf_count_override=args.perf_count_override, + num_client_threads=args.num_client_threads, + log_interval=args.log_interval, + batch_size_exp=args.batch_size_exp, + pred_outputs_log_path=os.path.join( + args.output_log_dir, "pred_outputs_logger.json" + ) + if args.log_pred_outputs + else None, + dataset_rename_cols=args.rename_dataset_cols, + ) + + lgSUT = lg.ConstructSUT(sut.issue_queries, sut.flush_queries) + log.info("Starting Benchmark run") + lg.StartTestWithLogSettings( + lgSUT, sut.qsl, settings, log_settings, args.audit_conf + ) + + log.info("Run Completed!") + + log.info("Destroying SUT...") + lg.DestroySUT(lgSUT) + + log.info("Destroying QSL...") + lg.DestroyQSL(sut.qsl) + + +if __name__ == "__main__": + # Disable garbage collection to avoid stalls when running tests. + gc.disable() + main() diff --git a/benchmarks/mlperf/mlperf.conf b/benchmarks/mlperf/mlperf.conf new file mode 100644 index 00000000..1d036f4b --- /dev/null +++ b/benchmarks/mlperf/mlperf.conf @@ -0,0 +1,111 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +# User can optionally set this to higher values in user.conf. +resnet50.*.performance_sample_count_override = 1024 +ssd-mobilenet.*.performance_sample_count_override = 256 +retinanet.*.performance_sample_count_override = 64 +bert.*.performance_sample_count_override = 10833 +dlrm.*.performance_sample_count_override = 204800 +dlrm-v2.*.performance_sample_count_override = 204800 +rnnt.*.performance_sample_count_override = 2513 +gptj.*.performance_sample_count_override = 13368 +llama2-70b.*.performance_sample_count_override = 24576 +llama3_1-405b.*.performance_sample_count_override = 8313 +stable-diffusion-xl.*.performance_sample_count_override = 5000 +rgat.*.performance_sample_count_override = 788379 +# set to 0 to let entire sample set to be performance sample +3d-unet.*.performance_sample_count_override = 0 + +# Set seeds. The seeds will be distributed two weeks before the submission. +*.*.qsl_rng_seed = 3066443479025735752 +*.*.sample_index_rng_seed = 10688027786191513374 +*.*.schedule_rng_seed = 14962580496156340209 +# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission. +*.*.test05_qsl_rng_seed = 16799458546791641818 +*.*.test05_sample_index_rng_seed = 5453809927556429288 +*.*.test05_schedule_rng_seed = 5435552105434836064 + + +*.SingleStream.target_latency_percentile = 90 +*.SingleStream.min_duration = 600000 + +*.MultiStream.target_latency_percentile = 99 +*.MultiStream.samples_per_query = 8 +*.MultiStream.min_duration = 600000 +*.MultiStream.min_query_count = 662 +retinanet.MultiStream.target_latency = 528 + +# 3D-UNet uses equal issue mode because it has non-uniform inputs +3d-unet.*.sample_concatenate_permutation = 1 + +# R-GAT uses equal issue mode because it may have non-uniform inputs +rgat.*.sample_concatenate_permutation = 1 + +# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario +gptj.*.sample_concatenate_permutation = 1 +llama2-70b.*.sample_concatenate_permutation = 1 +mixtral-8x7b.*.sample_concatenate_permutation = 1 +llama3_1-405b.*.sample_concatenate_permutation = 1 + +*.Server.target_latency = 10 +*.Server.target_latency_percentile = 99 +*.Server.target_duration = 0 +*.Server.min_duration = 600000 +resnet50.Server.target_latency = 15 +retinanet.Server.target_latency = 100 +bert.Server.target_latency = 130 +dlrm.Server.target_latency = 60 +dlrm-v2.Server.target_latency = 60 +rnnt.Server.target_latency = 1000 +gptj.Server.target_latency = 20000 +stable-diffusion-xl.Server.target_latency = 20000 +# Benchmarks that measure token latencies +llama2-70b.*.use_token_latencies = 1 +mixtral-8x7b.*.use_token_latencies = 1 +llama3_1-405b.*.use_token_latencies = 1 +# gptj benchmark infers token latencies +gptj.*.infer_token_latencies = 1 +gptj.*.token_latency_scaling_factor = 69 +# Only ttft and tpot are tracked for the llama2-70b, mixtral-8x7B & llama3_1-405b benchmark therefore target_latency = 0 +llama2-70b.Server.target_latency = 0 +llama2-70b.Server.ttft_latency = 2000 +llama2-70b.Server.tpot_latency = 200 + +mixtral-8x7b.Server.target_latency = 0 +mixtral-8x7b.Server.ttft_latency = 2000 +mixtral-8x7b.Server.tpot_latency = 200 + +llama3_1-405b.Server.target_latency = 0 +llama3_1-405b.Server.ttft_latency = 6000 +llama3_1-405b.Server.tpot_latency = 175 + +*.Offline.target_latency_percentile = 90 +*.Offline.min_duration = 600000 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit + +resnet50.Offline.min_query_count = 24576 +retinanet.Offline.min_query_count = 24576 +dlrm-v2.Offline.min_query_count = 24576 +bert.Offline.min_query_count = 10833 +gptj.Offline.min_query_count = 13368 +rnnt.Offline.min_query_count = 2513 +3d-unet.Offline.min_query_count = 43 +stable-diffusion-xl.Offline.min_query_count = 5000 +llama2-70b.Offline.min_query_count = 24576 +llama3_1-405b.Offline.min_query_count = 8313 +mixtral-8x7b.Offline.min_query_count = 15000 +rgat.Offline.min_query_count = 788379 + +# These fields should be defined and overridden by user.conf. +*.SingleStream.target_latency = 10 +*.MultiStream.target_latency = 80 +*.Server.target_qps = 1.0 +*.Offline.target_qps = 1.0 diff --git a/benchmarks/mlperf/scripts/config_utils.sh b/benchmarks/mlperf/scripts/config_utils.sh new file mode 100755 index 00000000..866cd585 --- /dev/null +++ b/benchmarks/mlperf/scripts/config_utils.sh @@ -0,0 +1,16 @@ +export base_output_dir=gs://${USER}-tpu/mlperf-4.1 +export experiment_time=$(date +%Y-%m-%d-%H-%M) + +export tpu=v5e-16 +export model_name=llama2-70b +export attention=dot_product +export reshape_q=${reshape_q:=False} +export compute_axis_order=${compute_axis_order:=0,2,1,3} +export prefill_cache_axis_order=${prefill_cache_axis_order:=0,2,1,3} +export ar_cache_axis_order=${ar_cache_axis_order:=0,2,1,3} + +export config_file_path=MaxText/configs/v5e/inference/llama2_70b_v5e-16.yml +export ici_fsdp_parallelism=1 +export ici_autoregressive_parallelism=${ici_autoregressive_parallelism:=2} +export ici_tensor_parallelism=${ici_tensor_parallelism:=8} +export allow_split_physical_axes=True diff --git a/benchmarks/mlperf/scripts/config_w-b16-kv-b16.sh b/benchmarks/mlperf/scripts/config_w-b16-kv-b16.sh new file mode 100755 index 00000000..405f7f01 --- /dev/null +++ b/benchmarks/mlperf/scripts/config_w-b16-kv-b16.sh @@ -0,0 +1,12 @@ + +source config_utils.sh + +export checkpoint_path=gs://runner-maxtext-logs/2024-05-07-23-34/unscanned_chkpt/checkpoints/0/items + +export quant_mode=w-b16-kv-b16 +export checkpoint_is_quantized=False +export quantization= +export quantize_kvcache=False +export kv_quant_axis= +export kv_quant_dtype= +export per_device_batch_size=${per_device_batch_size:=6} diff --git a/benchmarks/mlperf/scripts/config_w-i8-kv-b16.sh b/benchmarks/mlperf/scripts/config_w-i8-kv-b16.sh new file mode 100755 index 00000000..baaa5e04 --- /dev/null +++ b/benchmarks/mlperf/scripts/config_w-i8-kv-b16.sh @@ -0,0 +1,12 @@ + +source config_utils.sh + +export checkpoint_path=gs://morgandu-tpu/checkpoints/quantized/aqt/llama2-70b-chat + +export quant_mode=w-i8-kv-b16 +export checkpoint_is_quantized=True +export quantization=int8 +export quantize_kvcache=False +export kv_quant_axis= +export kv_quant_dtype= +export per_device_batch_size=${per_device_batch_size:=14} diff --git a/benchmarks/mlperf/scripts/config_w-i8-kv-i8.sh b/benchmarks/mlperf/scripts/config_w-i8-kv-i8.sh new file mode 100755 index 00000000..883901e0 --- /dev/null +++ b/benchmarks/mlperf/scripts/config_w-i8-kv-i8.sh @@ -0,0 +1,12 @@ + +source config_utils.sh + +export checkpoint_path=gs://morgandu-tpu/checkpoints/quantized/aqt/llama2-70b-chat + +export quant_mode=w-i8-kv-i8 +export checkpoint_is_quantized=True +export quantization=int8 +export quantize_kvcache=True +export kv_quant_axis=heads_and_dkv +export kv_quant_dtype=int8 +export per_device_batch_size=${per_device_batch_size:=28} diff --git a/benchmarks/mlperf/scripts/config_w-i8w-kv-b16.sh b/benchmarks/mlperf/scripts/config_w-i8w-kv-b16.sh new file mode 100755 index 00000000..daf04912 --- /dev/null +++ b/benchmarks/mlperf/scripts/config_w-i8w-kv-b16.sh @@ -0,0 +1,12 @@ + +source config_utils.sh + +export checkpoint_path=gs://msingh-bkt/checkpoints/quant_llama2-70b-chat/mlperf_070924/int8w_ + +export quant_mode=w-i8w-kv-b16 +export checkpoint_is_quantized=True +export quantization=int8w +export quantize_kvcache=False +export kv_quant_axis= +export kv_quant_dtype= +export per_device_batch_size=${per_device_batch_size:=14} diff --git a/benchmarks/mlperf/scripts/config_w-i8w-kv-i8.sh b/benchmarks/mlperf/scripts/config_w-i8w-kv-i8.sh new file mode 100755 index 00000000..1f8a830d --- /dev/null +++ b/benchmarks/mlperf/scripts/config_w-i8w-kv-i8.sh @@ -0,0 +1,12 @@ + +source config_utils.sh + +export checkpoint_path=gs://msingh-bkt/checkpoints/quant_llama2-70b-chat/mlperf_070924/int8w_ + +export quant_mode=w-i8w-kv-i8 +export checkpoint_is_quantized=True +export quantization=int8w +export quantize_kvcache=True +export kv_quant_axis=heads_and_dkv +export kv_quant_dtype=int8 +export per_device_batch_size=${per_device_batch_size:=28} diff --git a/benchmarks/mlperf/scripts/download_loadgen_data.sh b/benchmarks/mlperf/scripts/download_loadgen_data.sh new file mode 100755 index 00000000..46c7eb23 --- /dev/null +++ b/benchmarks/mlperf/scripts/download_loadgen_data.sh @@ -0,0 +1,9 @@ +export DATA_DISK_DIR=/loadgen_run_data + +mkdir -p ${DATA_DISK_DIR} +cd ${DATA_DISK_DIR} +gsutil cp gs://cloud-tpu-inference-public/mlcommons/inference/language/llama2-70b/data/processed-openorca/open_orca_gpt4_tokenized_llama.calibration_1000.pkl . +mv open_orca_gpt4_tokenized_llama.calibration_1000.pkl processed-calibration-data.pkl + +gsutil cp gs://cloud-tpu-inference-public/mlcommons/inference/language/llama2-70b/data/processed-openorca/open_orca_gpt4_tokenized_llama.sampled_24576.pkl . +mv open_orca_gpt4_tokenized_llama.sampled_24576.pkl processed-data.pkl diff --git a/benchmarks/mlperf/scripts/generate_server_accuracy_run.sh b/benchmarks/mlperf/scripts/generate_server_accuracy_run.sh new file mode 100755 index 00000000..23d495b3 --- /dev/null +++ b/benchmarks/mlperf/scripts/generate_server_accuracy_run.sh @@ -0,0 +1,64 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +source run_utils.sh + +DATASET_NAME=$(get_dataset_name ${DATASET_TYPE}) +export DATASET_PATH=${DATA_DISK_DIR}/${DATASET_NAME}.pkl +export API_URL=${API_URL} +export LOADGEN_RUN_TYPE=server-accuracy +export OUTPUT_LOG_ID=${MODEL_NAME}-${DATASET_TYPE}-${LOADGEN_RUN_TYPE}-${LOADGEN_RUN_TIMESTAMP} +export OUTPUT_LOG_DIR=${DATA_DISK_DIR}/logs/${OUTPUT_LOG_ID} +export OUTPUT_ACCURACY_JSON_PATH=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json + +echo "LOADGEN_RUN_TYPE: ${LOADGEN_RUN_TYPE}" +echo "LOADGEN_RUN_TIMESTAMP: ${LOADGEN_RUN_TIMESTAMP}" +echo "DATASET_PATH: ${DATASET_PATH}" +echo "TOTAL_SAMPLE_COUNT: ${TOTAL_SAMPLE_COUNT}" +echo "API_URL: ${API_URL}" +echo "BATCH_SIZE_EXP: ${BATCH_SIZE_EXP}" +echo "OUTPUT_LOG_DIR: ${OUTPUT_LOG_DIR}" +echo "OUTPUT_ACCURACY_JSON_PATH: ${OUTPUT_ACCURACY_JSON_PATH}" +echo "USER_CONFIG: ${USER_CONFIG}" + +mkdir -p ${OUTPUT_LOG_DIR} && cp ../${USER_CONFIG} ${OUTPUT_LOG_DIR} + +# Accuracy Run +cd ../ && python3 main.py \ + --api-url ${API_URL} \ + --is-stream \ + --accuracy \ + --log-pred-outputs \ + --scenario Server \ + --input-mode tokenized \ + --output-mode tokenized \ + --max-output-len 1024 \ + --mlperf-conf ../mlperf.conf \ + --user-conf ${USER_CONFIG} \ + --audit-conf no-audit \ + --total-sample-count ${TOTAL_SAMPLE_COUNT} \ + --batch-size-exp ${BATCH_SIZE_EXP} \ + --dataset-path ${DATASET_PATH} \ + --tokenizer-path ${TOKENIZER_PATH} \ + --log-interval ${LOG_INTERVAL} \ + --num-client-threads ${NUM_CLIENT_THREADS} \ + --output-log-dir ${OUTPUT_LOG_DIR} 2>&1 | tee ${OUTPUT_LOG_DIR}/server_accuracy_log.log + +# Eval Run +if [ -e ${OUTPUT_ACCURACY_JSON_PATH} ]; then + python3 evaluate-accuracy.py \ + --checkpoint-path meta-llama/Llama-2-70b-chat-hf \ + --mlperf-accuracy-file ${OUTPUT_ACCURACY_JSON_PATH} \ + --dataset-file ${DATASET_PATH} 2>&1 | tee ${OUTPUT_LOG_DIR}/evaluate_server_accuracy_log.log +fi diff --git a/benchmarks/mlperf/scripts/generate_server_audit_run.sh b/benchmarks/mlperf/scripts/generate_server_audit_run.sh new file mode 100755 index 00000000..1a01dff5 --- /dev/null +++ b/benchmarks/mlperf/scripts/generate_server_audit_run.sh @@ -0,0 +1,53 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +source run_utils.sh + +DATASET_NAME=$(get_dataset_name ${DATASET_TYPE}) +export DATASET_PATH=${DATA_DISK_DIR}/${DATASET_NAME}.pkl +export API_URL=${API_URL} +export LOADGEN_RUN_TYPE=server-audit +export OUTPUT_LOG_ID=${MODEL_NAME}-${DATASET_TYPE}-${LOADGEN_RUN_TYPE}-${LOADGEN_RUN_TIMESTAMP} +export OUTPUT_LOG_DIR=${DATA_DISK_DIR}/logs/${OUTPUT_LOG_ID} + +echo "LOADGEN_RUN_TYPE: ${LOADGEN_RUN_TYPE}" +echo "LOADGEN_RUN_TIMESTAMP: ${LOADGEN_RUN_TIMESTAMP}" +echo "DATASET_PATH: ${DATASET_PATH}" +echo "TOTAL_SAMPLE_COUNT: ${TOTAL_SAMPLE_COUNT}" +echo "API_URL: ${API_URL}" +echo "BATCH_SIZE_EXP: ${BATCH_SIZE_EXP}" +echo "OUTPUT_LOG_DIR: ${OUTPUT_LOG_DIR}" +echo "USER_CONFIG: ${USER_CONFIG}" + +mkdir -p ${OUTPUT_LOG_DIR} && cp ../${USER_CONFIG} ${OUTPUT_LOG_DIR} + +# Audit Run +cd ../ && python3 main.py \ + --api-url ${API_URL} \ + --is-stream \ + --log-pred-outputs \ + --scenario Server \ + --input-mode tokenized \ + --output-mode tokenized \ + --max-output-len 1024 \ + --mlperf-conf ../mlperf.conf \ + --user-conf ${USER_CONFIG} \ + --audit-conf ../../../compliance/nvidia/TEST06/audit.config \ + --total-sample-count ${TOTAL_SAMPLE_COUNT} \ + --batch-size-exp ${BATCH_SIZE_EXP} \ + --dataset-path ${DATASET_PATH} \ + --tokenizer-path ${TOKENIZER_PATH} \ + --log-interval ${LOG_INTERVAL} \ + --num-client-threads ${NUM_CLIENT_THREADS} \ + --output-log-dir ${OUTPUT_LOG_DIR} 2>&1 | tee ${OUTPUT_LOG_DIR}/server_audit_log.log diff --git a/benchmarks/mlperf/scripts/generate_server_performance_run.sh b/benchmarks/mlperf/scripts/generate_server_performance_run.sh new file mode 100755 index 00000000..e8720180 --- /dev/null +++ b/benchmarks/mlperf/scripts/generate_server_performance_run.sh @@ -0,0 +1,55 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +source run_utils.sh + +DATASET_NAME=$(get_dataset_name ${DATASET_TYPE}) +export DATASET_PATH=${DATA_DISK_DIR}/${DATASET_NAME}.pkl +export API_URL=${API_URL} +export LOADGEN_RUN_TYPE=server-performance +export OUTPUT_LOG_ID=${MODEL_NAME}-${DATASET_TYPE}-${LOADGEN_RUN_TYPE}-${LOADGEN_RUN_TIMESTAMP} +export OUTPUT_LOG_DIR=${DATA_DISK_DIR}/logs/${OUTPUT_LOG_ID} + +echo "LOADGEN_RUN_TYPE: ${LOADGEN_RUN_TYPE}" +echo "LOADGEN_RUN_TIMESTAMP: ${LOADGEN_RUN_TIMESTAMP}" +echo "DATASET_PATH: ${DATASET_PATH}" +echo "TOTAL_SAMPLE_COUNT: ${TOTAL_SAMPLE_COUNT}" +echo "API_URL: ${API_URL}" +echo "BATCH_SIZE_EXP: ${BATCH_SIZE_EXP}" +echo "OUTPUT_LOG_DIR: ${OUTPUT_LOG_DIR}" +echo "USER_CONFIG: ${USER_CONFIG}" + +mkdir -p ${OUTPUT_LOG_DIR} && cp ../${USER_CONFIG} ${OUTPUT_LOG_DIR} +MIXTRAL_COLS_RENAME="{\"tok_input_len\": \"tok_input_length\", \"tok_ref_output_len\": \"tok_output_length\"}" + +# Perf Run +cd ../ && python3 main.py \ + --api-url ${API_URL} \ + --is-stream \ + --log-pred-outputs \ + --scenario Server \ + --input-mode tokenized \ + --output-mode tokenized \ + --max-output-len 1024 \ + --mlperf-conf ../mlperf.conf \ + --user-conf ${USER_CONFIG} \ + --audit-conf no-audit \ + --total-sample-count ${TOTAL_SAMPLE_COUNT} \ + --batch-size-exp ${BATCH_SIZE_EXP} \ + --dataset-path ${DATASET_PATH} \ + --tokenizer-path ${TOKENIZER_PATH} \ + --log-interval ${LOG_INTERVAL} \ + --num-client-threads ${NUM_CLIENT_THREADS} \ + --rename-dataset-cols "${MIXTRAL_COLS_RENAME}" \ + --output-log-dir ${OUTPUT_LOG_DIR} 2>&1 | tee ${OUTPUT_LOG_DIR}/server_performance_log.log diff --git a/benchmarks/mlperf/scripts/init.sh b/benchmarks/mlperf/scripts/init.sh new file mode 100755 index 00000000..4781d690 --- /dev/null +++ b/benchmarks/mlperf/scripts/init.sh @@ -0,0 +1,7 @@ +git clone https://github.com/google/jax.git +cd jax +git reset 44359cb30ab5cdbe97e6b78c2c64fe9f8add29ca --hard +pip install -e . +gsutil cp gs://zhihaoshan-maxtext-profiling/jax_proxy_stream_buffer/jaxlib-0.4.31.dev20240719-cp310-cp310-manylinux2014_x86_64-mlperf_version_3.whl . +mv jaxlib-0.4.31.dev20240719-cp310-cp310-manylinux2014_x86_64-mlperf_version_3.whl jaxlib-0.4.31.dev20240719-cp310-cp310-manylinux2014_x86_64.whl +pip install jaxlib-0.4.31.dev20240719-cp310-cp310-manylinux2014_x86_64.whl diff --git a/benchmarks/mlperf/scripts/init_loadgen.sh b/benchmarks/mlperf/scripts/init_loadgen.sh new file mode 100755 index 00000000..b62f1e87 --- /dev/null +++ b/benchmarks/mlperf/scripts/init_loadgen.sh @@ -0,0 +1,13 @@ +cd /inference_mlperf4.1/language/llama2-70b/tpu/scripts/ +export API_URL=0.0.0.0:9000 + +export DATA_DISK_DIR=/loadgen_run_data +export MODEL_NAME=llama70b +export LOG_INTERVAL=1000 +export BATCH_SIZE_EXP=10 +export USER_CONFIG=user.conf + +export DATASET_TYPE=full +export TOTAL_SAMPLE_COUNT=24576 +export NUM_CLIENT_THREADS=600 + diff --git a/benchmarks/mlperf/scripts/init_xprof.sh b/benchmarks/mlperf/scripts/init_xprof.sh new file mode 100755 index 00000000..347d4636 --- /dev/null +++ b/benchmarks/mlperf/scripts/init_xprof.sh @@ -0,0 +1,2 @@ +pip install keyring keyrings.google-artifactregistry-auth # install keyring +pip install --extra-index-url https://us-central1-python.pkg.dev/cloud-tpu-multipod-dev/multipod-python-repo/simple/ previewutilities \ No newline at end of file diff --git a/benchmarks/mlperf/scripts/launch_microbenchmark.sh b/benchmarks/mlperf/scripts/launch_microbenchmark.sh new file mode 100755 index 00000000..3764ff71 --- /dev/null +++ b/benchmarks/mlperf/scripts/launch_microbenchmark.sh @@ -0,0 +1,46 @@ +echo "config: ${config}" +source ./${config}.sh +export run_name=${model_name}_${tpu}_${attention}_ici_${ici_tensor_parallelism}-${ici_autoregressive_parallelism}_${reshape_q}_${quant_mode}_pbs${per_device_batch_size}_${compute_axis_order//,/}-${prefill_cache_axis_order//,/}-${ar_cache_axis_order//,/} + +export inference_microbenchmark_stages=${inference_microbenchmark_stages:="prefill,generate"} +export inference_microbenchmark_prefill_lengths=${inference_microbenchmark_prefill_lengths:="64,128,256,512,1024"} +echo "inference_microbenchmark_stages: ${inference_microbenchmark_stages}" +echo "inference_microbenchmark_prefill_lengths: ${inference_microbenchmark_prefill_lengths}" + +cd /maxtext +export run_dir=${base_output_dir}/microbenchmark/${run_name}/${experiment_time}/ +echo "run_dir: ${run_dir}" +gsutil cp ${config_file_path} ${run_dir}/ + +python3 MaxText/inference_microbenchmark.py \ + ${config_file_path} \ + model_name=${model_name} \ + tokenizer_path=assets/tokenizer.llama2 \ + load_parameters_path=${checkpoint_path} \ + async_checkpointing=false \ + weight_dtype=bfloat16 \ + attention=dot_product \ + reshape_q=${reshape_q} \ + scan_layers=false \ + max_prefill_predict_length=1024 \ + max_target_length=2048 \ + base_output_directory=${base_output_dir}/microbenchmark \ + run_name=${run_name}/${experiment_time} \ + save_config_to_gcs=true \ + profiler=xplane \ + enable_single_controller=true \ + ici_tensor_parallelism=${ici_tensor_parallelism} \ + ici_autoregressive_parallelism=${ici_autoregressive_parallelism} \ + allow_split_physical_axes=${allow_split_physical_axes} \ + inference_microbenchmark_prefill_lengths=${inference_microbenchmark_prefill_lengths} \ + inference_microbenchmark_stages=${inference_microbenchmark_stages} \ + inference_microbenchmark_loop_iters=10 \ + per_device_batch_size=${per_device_batch_size} \ + quantization=${quantization} \ + quantize_kvcache=${quantize_kvcache} \ + kv_quant_axis=${kv_quant_axis} \ + kv_quant_dtype=${kv_quant_dtype} \ + checkpoint_is_quantized=${checkpoint_is_quantized} \ + compute_axis_order=${compute_axis_order} \ + prefill_cache_axis_order=${prefill_cache_axis_order} \ + ar_cache_axis_order=${ar_cache_axis_order} 2>&1 | tee results.log && gsutil mv results.log ${run_dir}/ diff --git a/benchmarks/mlperf/scripts/launch_server.sh b/benchmarks/mlperf/scripts/launch_server.sh new file mode 100755 index 00000000..e433b605 --- /dev/null +++ b/benchmarks/mlperf/scripts/launch_server.sh @@ -0,0 +1,33 @@ +echo "config: ${config}" +source ./${config}.sh +export run_name=${model_name}_${tpu}_${attention}_ici_${ici_tensor_parallelism}-${ici_autoregressive_parallelism}_${reshape_q}_${quant_mode}_pbs${per_device_batch_size}_${compute_axis_order//,/}-${prefill_cache_axis_order//,/}-${ar_cache_axis_order//,/} + +cd /maxtext +python3 MaxText/maxengine_server.py \ + ${config_file_path} \ + model_name=${model_name} \ + tokenizer_path=assets/tokenizer.llama2 \ + load_parameters_path=${checkpoint_path} \ + async_checkpointing=false \ + weight_dtype=bfloat16 \ + attention=dot_product \ + reshape_q=${reshape_q} \ + scan_layers=false \ + max_prefill_predict_length=1024 \ + max_target_length=2048 \ + base_output_directory=${base_output_dir}/server \ + run_name=${run_name}/${experiment_time} \ + save_config_to_gcs=true \ + enable_single_controller=true \ + ici_tensor_parallelism=${ici_tensor_parallelism} \ + ici_autoregressive_parallelism=${ici_autoregressive_parallelism} \ + allow_split_physical_axes=${allow_split_physical_axes} \ + per_device_batch_size=${per_device_batch_size} \ + quantization=${quantization} \ + quantize_kvcache=${quantize_kvcache} \ + kv_quant_axis=${kv_quant_axis} \ + kv_quant_dtype=${kv_quant_dtype} \ + checkpoint_is_quantized=${checkpoint_is_quantized} \ + compute_axis_order=${compute_axis_order} \ + prefill_cache_axis_order=${prefill_cache_axis_order} \ + ar_cache_axis_order=${ar_cache_axis_order} \ No newline at end of file diff --git a/benchmarks/mlperf/scripts/run_utils.sh b/benchmarks/mlperf/scripts/run_utils.sh new file mode 100644 index 00000000..8271f10b --- /dev/null +++ b/benchmarks/mlperf/scripts/run_utils.sh @@ -0,0 +1,32 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Tokenizer +# export TOKENIZER_PATH=meta-llama/Llama-2-70b-chat-hf +export DATASET_PREFIX=mixtral +export TOKENIZER_PATH=mistralai/Mixtral-8x7B-Instruct-v0.1 +export NUM_CLIENT_THREADS=${NUM_CLIENT_THREADS:=600} + +# Loadgen +export LOADGEN_RUN_TIMESTAMP=$(TZ=America/Los_Angeles date +%Y%m%d%H%M%S%Z) + +get_dataset_name() { + dataset_type=$1 + if [ ${dataset_type} = "full" ] + then echo "${DATASET_PREFIX}-processed-data" + elif [ ${dataset_type} = "calibration" ] + then echo "${DATASET_PREFIX}-processed-calibration-data" + fi +} diff --git a/benchmarks/mlperf/scripts/tpu_script.sh b/benchmarks/mlperf/scripts/tpu_script.sh new file mode 100644 index 00000000..47a88a7c --- /dev/null +++ b/benchmarks/mlperf/scripts/tpu_script.sh @@ -0,0 +1,352 @@ +#!/bin/bash + +# Multi-Host vlp (TODO: replace these params for your own config) +NAME="jwyang-tpu-sh2" +# NAME="jwyang-v5p8-vm" +# ACCELERATOR_TYPE="v5litepod-4" +ACCELERATOR_TYPE="v5litepod-8" +# ACCELERATOR_TYPE="v5p-8" +RUNTIME_VERSION="v2-alpha-tpuv5-lite" +# PROJECT="tpu-prod-env-automated" +PROJECT="cloud-tpu-inference-test" +# PROJECT="tpu-prod-env-small" +# PROJECT="tpu-prod-env-large-cont" +# ZONE="us-east1-c" +ZONE="us-west1-c" +# ZONE="us-east5-a" + +USER=jwyang + +# (TODO: replace these params to your own config) +NUM_WORKERS=1 +TPU_NAME="t1v-n-63d3a09c" + +create_tpu() { + # A temporary solution to clean up the failed and suspended queued resources. + # Otherwise, there will be a quota error. + existing_qr=$(gcloud alpha compute tpus queued-resources list \ + --project ${PROJECT} \ + --zone ${ZONE} \ + --quiet) + while read -r line; do + name=$(echo $line | awk '{print $1}') + status=$(echo $line | awk '{print $5}') + echo ${name} + echo ${status} + if [[ ${status} == "SUSPENDED" || ${status} == "FAILED" ]]; then + gcloud alpha compute tpus queued-resources delete ${name} \ + --project ${PROJECT} \ + --zone ${ZONE} \ + --quiet + fi + done <<< ${existing_qr} + + gcloud alpha compute tpus queued-resources create ${NAME} \ + --description noteardown \ + --node-id ${NAME} \ + --project=${PROJECT} \ + --zone=${ZONE} \ + --accelerator-type=${ACCELERATOR_TYPE} \ + --runtime-version=${RUNTIME_VERSION} \ + --reserved; +} + +list_tpu() { + gcloud compute tpus tpu-vm list --project=${PROJECT} --zone=${ZONE}; +} + +list_queue_resource() { + gcloud alpha compute tpus queued-resources list --project=${PROJECT} --zone=${ZONE}; +} + +delete_tpu() { + gcloud alpha compute tpus tpu-vm delete ${NAME} --project=${PROJECT} --zone=${ZONE}; + gcloud alpha compute tpus queued-resources delete ${NAME} --project=${PROJECT} --zone=${ZONE}; +} + +ssh_to_tpu() { + gcloud compute tpus tpu-vm ssh ${NAME} --zone ${ZONE} --worker ${1} --project ${PROJECT} -- -o ProxyCommand='corp-ssh-helper %h %p' +} + +create_disk() { + for ((i = 0; i < ${NUM_WORKERS}; i++)); do + TPU_WORKER_NAME=${TPU_NAME}-w-${i} + DISK_NAME=${NAME}-w${i}-ssd + + SIZE=35 + if [[ ${i} == 0 ]] + then + SIZE=512 + fi + + gcloud compute disks create ${DISK_NAME} \ + --size ${SIZE} \ + --zone ${ZONE} \ + --type pd-ssd \ + --project=${PROJECT} + + # attach disk to tpu + gcloud alpha compute instances attach-disk ${TPU_WORKER_NAME} \ + --zone=${ZONE} \ + --disk=${DISK_NAME} \ + --mode=rw \ + --project=${PROJECT} + + gcloud compute instances set-disk-auto-delete ${TPU_WORKER_NAME} \ + --zone=${ZONE} \ + --auto-delete \ + --disk=${DISK_NAME} \ + --project=${PROJECT} + + gcloud compute tpus tpu-vm ssh ${NAME} --zone ${ZONE} --worker ${i} --project=${PROJECT} \ + --command="sudo mkfs.ext4 -m 0 -E lazy_itable_init=0,lazy_journal_init=0,discard /dev/sdb && + sudo mkdir -p /mnt/disks/persist && + sudo mount -o discard,defaults /dev/sdb /mnt/disks/persist" \ + -- -o ProxyCommand='corp-ssh-helper %h %p' + done +} + +detach_disks() { + for ((i = 0; i < ${NUM_WORKERS}; i++)); do + TPU_WORKER_NAME=${TPU_NAME}-w-${i} + DISK_NAME=${NAME}-w${i}-ssd + + # attach disk to tpu + gcloud alpha compute instances detach-disk ${TPU_WORKER_NAME} \ + --zone=${ZONE} \ + --disk=${DISK_NAME} \ + --project=${PROJECT} + done +} + +check_disks() { + set -o xtrace + dir_checks="" + for ((i = 0; i < ${NUM_WORKERS}; i++)); do + dir_checks="$dir_checks $( + gcloud compute tpus tpu-vm ssh ${NAME} --zone ${ZONE} --worker ${i} --project=${PROJECT} \ + --command="if [ -d /mnt/disks/persist ]; then echo "exists"; fi" \ + -- -o ProxyCommand='corp-ssh-helper %h %p' + )" + done + num_dir_exists=$(echo "$dir_checks" | wc -w) + echo "Number of workers with disks: $num_dir_exists" + set +o xtrace +} + +copy_relevant_files() { + gcloud compute tpus tpu-vm \ + scp --zone=${ZONE} --project=${PROJECT} --worker=all \ + $PWD/Maxtext/checkpointing.py \ + ${NAME}:~/maxtext/MaxText/checkpointing.py \ + --scp-flag "-o ProxyCommand=corp-ssh-helper %h %p" + + gcloud compute tpus tpu-vm \ + scp --zone=${ZONE} --project=${PROJECT} --worker=all \ + $PWD/Maxtext/maxengine.py \ + ${NAME}:~/maxtext/MaxText/maxengine.py \ + --scp-flag "-o ProxyCommand=corp-ssh-helper %h %p" + + gcloud compute tpus tpu-vm \ + scp --zone=${ZONE} --project=${PROJECT} --worker=all \ + $PWD/Maxtext/layers/quantizations.py \ + ${NAME}:~/maxtext/MaxText/layers/quantizations.py \ + --scp-flag "-o ProxyCommand=corp-ssh-helper %h %p" + +} + + +# LLaMA2-7B JetStream/Maxtext commands + +# # source .env/bin/activate +# your_run_name=jwyang_bs1_llama7b +# python MaxText/inference_microbenchmark.py \ +# MaxText/configs/base.yml \ +# base_output_directory=gs://jwyang-data/maxtext-llama2-7b/microbenchmark \ +# run_name=${your_run_name} \ +# per_device_batch_size=12 \ +# save_config_to_gcs=true \ +# model_name=llama2-7b \ +# tokenizer_path=assets/tokenizer.llama2 \ +# inference_microbenchmark_prefill_lengths=1024 \ +# max_prefill_predict_length=1024 \ +# max_target_length=2048 \ +# ici_fsdp_parallelism=1 \ +# ici_tensor_parallelism=-1 \ +# ici_autoregressive_parallelism=1 \ +# weight_dtype=bfloat16 \ +# enable_profiler=true \ +# scan_layers=false \ +# quantization=int8 \ +# quantize_kvcache=true +# inference_mode=true + + +export model_name=llama2-7b +export tokenizer_path=assets/tokenizer.llama2 +export XLA_FLAGS="--xla_disable_hlo_passes=rematerialization" +export ici_tensor_parallelism=-1 +export ici_autoregressive_parallelism=1 +export per_device_batch_size=1 +export load_parameters_path_chat=gs://jwyang-runner-maxtext-logs/llama2-7b_unscanned_chkpt_2024-04-26-18-28/checkpoints/0/items +export load_parameters_path=gs://jwyang-runner-maxtext-logs/llama2-7b_unscanned_chkpt_2024-04-26-19-40/checkpoints/0/items +export load_parameters_path_chat_quantized=gs://jwyang-data/llama7b-chat-quantized-fixed/0/items + +python MaxText/maxengine_server.py \ + MaxText/configs/base.yml \ + base_output_directory=gs://jwyang-data/maxtext-llama2-7b/microbenchmark \ + load_parameters_path=${load_parameters_path_chat} \ + run_name=$(date +%Y-%m-%d-%H-%M) \ + save_config_to_gcs=true \ + model_name=${model_name} \ + tokenizer_path=${tokenizer_path} \ + inference_microbenchmark_log_file_path=microbenchmark.json \ + inference_microbenchmark_prefill_lengths=1024 \ + inference_microbenchmark_stages=prefill,generate \ + inference_microbenchmark_loop_iters=1000 \ + max_prefill_predict_length=1024 \ + max_target_length=2048 \ + per_device_batch_size=${per_device_batch_size} \ + ici_fsdp_parallelism=1 \ + ici_tensor_parallelism=${ici_tensor_parallelism} \ + ici_autoregressive_parallelism=${ici_autoregressive_parallelism} \ + enable_profiler=false \ + scan_layers=false \ + weight_dtype=bfloat16 + # quantization=int8 + # quantize_kvcache=True + + +export model_name=llama2-7b +export dataset_path=/home/jwyang/llama7b_chat_openorca_input.json +python JetStream/benchmarks/benchmark_serving.py \ + --tokenizer ~/maxtext/assets/tokenizer.llama2 \ + --warmup-first true \ + --save-result \ + --save-request-outputs \ + --request-outputs-file-path /home/jwyang/outputs.json \ + --num-prompts 1000 \ + --max-output-length 1024 \ + --dataset openorca \ + --dataset-path ${dataset_path} + + + +# # 13b model +export model_name=llama2-13b +export tokenizer_path=assets/tokenizer.llama2 +export XLA_FLAGS="--xla_disable_hlo_passes=rematerialization" +export ici_tensor_parallelism=-1 +export ici_autoregressive_parallelism=1 +export per_device_batch_size=1 +export load_parameters_path=gs://runner-maxtext-logs/2024-05-16-23-59/unscanned_chkpt/checkpoints/0/items + + +export experiment_time=$(date +%Y-%m-%d-%H-%M) +echo "export experiment_time=${experiment_time}" +python MaxText/maxengine_server.py \ + MaxText/configs/base.yml \ + base_output_directory=gs://morgandu-tpu/maxtext-logs/microbenchmark/${experiment_time} \ + model_name=llama2-13b \ + async_checkpointing=false \ + load_parameters_path=gs://runner-maxtext-logs/2024-05-16-23-59/unscanned_chkpt/checkpoints/0/items \ + run_name=${experiment_time} \ + inference_microbenchmark_log_file_path=${run_name}.json \ + tokenizer_path=assets/tokenizer.llama2 \ + weight_dtype=bfloat16 \ + inference_microbenchmark_prefill_lengths=1024 \ + inference_microbenchmark_stages=prefill,generate \ + inference_microbenchmark_loop_iters=10 \ + max_prefill_predict_length=1024 \ + max_target_length=2048 \ + ici_fsdp_parallelism=1 \ + ici_tensor_parallelism=-1 \ + ici_autoregressive_parallelism=1 \ + enable_profiler=false \ + scan_layers=false \ + attention=dot_product \ + save_config_to_gcs=true \ + per_device_batch_size=1 + + +python MaxText/inference_microbenchmark.py \ + MaxText/configs/base.yml \ + base_output_directory=gs://morgandu-tpu/maxtext-logs/microbenchmark/${experiment_time} \ + model_name=llama2-13b \ + async_checkpointing=false \ + load_parameters_path=gs://runner-maxtext-logs/2024-05-16-23-59/unscanned_chkpt/checkpoints/0/items \ + run_name=${experiment_time} \ + inference_microbenchmark_log_file_path=${run_name}.json \ + tokenizer_path=assets/tokenizer.llama2 \ + weight_dtype=bfloat16 \ + inference_microbenchmark_prefill_lengths=1024 \ + inference_microbenchmark_stages=prefill,generate \ + inference_microbenchmark_loop_iters=10 \ + max_prefill_predict_length=1024 \ + max_target_length=2048 \ + ici_fsdp_parallelism=1 \ + ici_tensor_parallelism=-1 \ + ici_autoregressive_parallelism=1 \ + enable_profiler=false \ + scan_layers=false \ + attention=dot_product \ + save_config_to_gcs=true \ + per_device_batch_size=1 + + + +# # LLaMA2-70B commands +# # source .env/bin/activate +# your_run_name=jwyang_bs1_llama70b +# python MaxText/inference_microbenchmark.py \ +# MaxText/configs/base.yml \ +# base_output_directory=gs://jwyang-data/maxtext-llama2-70b/microbenchmark \ +# run_name=${your_run_name} \ +# per_device_batch_size=1 \ +# save_config_to_gcs=true \ +# model_name=llama2-70b \ +# tokenizer_path=assets/tokenizer.llama2 \ +# inference_microbenchmark_prefill_lengths=32 \ +# max_prefill_predict_length=32 \ +# max_target_length=64 \ +# ici_fsdp_parallelism=1 \ +# ici_tensor_parallelism=-1 \ +# ici_autoregressive_parallelism=1 \ +# weight_dtype=bfloat16 \ +# enable_profiler=true \ +# scan_layers=false \ +# quantization=int8 \ +# quantize_kvcache=true + + +export model_name=llama2-70b +export tokenizer_path=assets/tokenizer.llama2 +export XLA_FLAGS="--xla_disable_hlo_passes=rematerialization" +export ici_tensor_parallelism=-1 +export ici_autoregressive_parallelism=1 +export per_device_batch_size=1 +export prefill_length=16 +export target_length=32 + +python MaxText/maxengine_server.py \ + MaxText/configs/base.yml \ + base_output_directory=gs://jwyang-data/maxtext-llama2-70b/microbenchmark \ + run_name=$(date +%Y-%m-%d-%H-%M) \ + save_config_to_gcs=true \ + model_name=${model_name} \ + tokenizer_path=${tokenizer_path} \ + inference_microbenchmark_log_file_path=microbenchmark.json \ + inference_microbenchmark_prefill_lengths=${prefill_length} \ + inference_microbenchmark_stages=prefill,generate \ + inference_microbenchmark_loop_iters=1000 \ + max_prefill_predict_length=${prefill_length} \ + max_target_length=${target_length} \ + per_device_batch_size=${per_device_batch_size} \ + ici_fsdp_parallelism=1 \ + ici_tensor_parallelism=${ici_tensor_parallelism} \ + ici_autoregressive_parallelism=${ici_autoregressive_parallelism} \ + enable_profiler=false \ + scan_layers=false \ + weight_dtype=bfloat16 \ + quantization=int8 \ + quantize_kvcache=True \ No newline at end of file diff --git a/benchmarks/mlperf/user.conf b/benchmarks/mlperf/user.conf new file mode 100644 index 00000000..4a53a70b --- /dev/null +++ b/benchmarks/mlperf/user.conf @@ -0,0 +1,29 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +llama2-70b.*.performance_sample_count_override = 24576 +mixtral-8x7b.*.performance_sample_count_override = 15000 + +llama2-70b.*.min_duration = 600000 +mixtral-8x7b.*.min_duration = 30000 + + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit +llama2-70b.Offline.min_query_count = 24576 +llama2-70b.Server.min_query_count = 24576 +mixtral-8x7b.Offline.min_query_count = 15000 +mixtral-8x7b.Server.min_query_count = 15000 + +# These fields should be defined and overridden by user.conf. +*.Offline.target_qps = 5.0 +llama2-70b.Server.target_qps = 1.0 +mixtral-8x7b.Server.target_qps = 11.0 + + +*.sample_concatenate_permutation = 1 diff --git a/benchmarks/mlperf/user100.conf b/benchmarks/mlperf/user100.conf new file mode 100644 index 00000000..995de1e2 --- /dev/null +++ b/benchmarks/mlperf/user100.conf @@ -0,0 +1,28 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +llama2-70b.*.performance_sample_count_override = 100 +mixtral-8x7b.*.performance_sample_count_override = 100 + +llama2-70b.*.min_duration = 60 +mixtral-8x7b.*.min_duration = 30 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit +llama2-70b.Offline.min_query_count = 100 +llama2-70b.Server.min_query_count = 100 +mixtral-8x7b.Offline.min_query_count = 100 +mixtral-8x7b.Server.min_query_count = 100 + + +# These fields should be defined and overridden by user.conf. +*.Offline.target_qps = 5.0 +llama2-70b.Server.target_qps = 1.0 +mixtral-8x7b.Server.target_qps = 9.0 + +*.sample_concatenate_permutation = 1 diff --git a/benchmarks/mlperf/user2000.conf b/benchmarks/mlperf/user2000.conf new file mode 100644 index 00000000..51855597 --- /dev/null +++ b/benchmarks/mlperf/user2000.conf @@ -0,0 +1,28 @@ +# The format of this config file is 'key = value'. +# The key has the format 'model.scenario.key'. Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +llama2-70b.*.performance_sample_count_override = 2000 +mixtral-8x7b.*.performance_sample_count_override = 2000 + +llama2-70b.*.min_duration = 60000 +mixtral-8x7b.*.min_duration = 30000 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit +llama2-70b.Offline.min_query_count = 2000 +llama2-70b.Server.min_query_count = 2000 +mixtral-8x7b.Offline.min_query_count = 2000 +mixtral-8x7b.Server.min_query_count = 2000 + + +# These fields should be defined and overridden by user.conf. +*.Offline.target_qps = 5.0 +llama2-70b.Server.target_qps = 1.0 +mixtral-8x7b.Server.target_qps = 9.0 + +*.sample_concatenate_permutation = 1 diff --git a/google3/third_party/py/jetstream/benchmarks/open_orca_gpt4_tokenized_llama.calibration_1000.pkl b/benchmarks/open_orca_gpt4_tokenized_llama.calibration_1000.pkl similarity index 100% rename from google3/third_party/py/jetstream/benchmarks/open_orca_gpt4_tokenized_llama.calibration_1000.pkl rename to benchmarks/open_orca_gpt4_tokenized_llama.calibration_1000.pkl diff --git a/google3/third_party/py/jetstream/benchmarks/requirements.in b/benchmarks/requirements.in similarity index 100% rename from google3/third_party/py/jetstream/benchmarks/requirements.in rename to benchmarks/requirements.in diff --git a/google3/third_party/py/jetstream/external_tokenizers/llama3/__init__.py b/benchmarks/tests/__init__.py similarity index 100% rename from google3/third_party/py/jetstream/external_tokenizers/llama3/__init__.py rename to benchmarks/tests/__init__.py diff --git a/benchmarks/tests/test_metrics.py b/benchmarks/tests/test_metrics.py new file mode 100644 index 00000000..08288f93 --- /dev/null +++ b/benchmarks/tests/test_metrics.py @@ -0,0 +1,177 @@ +# Copyright 2025 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 +# +# http://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. + +"""Tests for metrics.""" + +import unittest + +import benchmarks.metrics as metrics +import datetime +import re + + +class TestEventMetric(unittest.TestCase): + """ "Tests for event metric (i.e. distribution).""" + + def setUp(self): + self._metric = metrics.EventMetric( + "requestLatency", "Latency of requests", "ms" + ) + + def test_record_adds_a_data_event(self): + m = self._metric + m.record(1.0) + data_points = m.data() + self.assertEqual(1, len(data_points)) + self.assertEqual(1.0, data_points[0]) + + def test_percentile_returns_correct_percentile(self): + m = self._metric + n = 11 + for i in range(0, n): + m.record(i) + self.assertEqual(m.percentile(50), 5) + self.assertEqual(m.percentile(90), 9) + self.assertEqual(m.percentile(100), 10) + + def test_mean_returns_correct_mean_value(self): + m = self._metric + n = 3 + for i in range(0, n): + m.record(i) + self.assertEqual(sum(range(0, n)) / n, m.mean()) + + def test_distribution_summary_str_returns_expected_str(self): + m = self._metric + n = 100 + for i in range(0, n): + m.record(i) + summary = m.distribution_summary_str() + self.assertTrue(re.search(r"Mean requestLatency", summary)) + self.assertTrue(re.search(r"Median requestLatency", summary)) + self.assertTrue(re.search(r"P99 requestLatency", summary)) + + def test_distribution_summary_dict_returns_expected_dict(self): + m = self._metric + n = 100 + for i in range(0, n): + m.record(i) + summary = m.distribution_summary_dict() + self.assertIn("mean_requestLatency_ms", summary) + self.assertIn("median_requestLatency_ms", summary) + self.assertIn("p99_requestLatency_ms", summary) + + +class TestCounterMetric(unittest.TestCase): + """Tests for counter metric (i.e. monotonically increasing counter).""" + + def setUp(self): + self._counter = metrics.CounterMetric( + "RequestCompleteCount", "Number of completed requests" + ) + + def test_increment_increases_total_count(self): + m = self._counter + old_total_cnt = m.total_count() + m.increment() + new_total_cnt = m.total_count() + self.assertEqual(1, new_total_cnt - old_total_cnt) + + def test_increment_within_same_second_update_counts_for_the_second(self): + """Test to ensure one entry used to cumulate counts within a second""" + m = self._counter + timestamp = datetime.datetime.strptime( + "2025-01-01 00:00:00", "%Y-%m-%d %H:%M:%S" + ) + m.increment(1, timestamp) + m.increment(2, timestamp) + data = m.data() + self.assertEqual(1, len(data.keys())) + self.assertEqual(3, data[timestamp]) + + def test_increment_at_different_seconds_creates_separate_entries(self): + """Test to ensure separate entries used for different seconds""" + m = self._counter + timestamp_first = datetime.datetime.strptime( + "2025-01-01 00:00:00", "%Y-%m-%d %H:%M:%S" + ) + m.increment(1, timestamp_first) + timestamp_second = datetime.datetime.strptime( + "2025-01-01 00:00:01", "%Y-%m-%d %H:%M:%S" + ) + m.increment(2, timestamp_second) + data = m.data() + self.assertEqual(2, len(data.keys())) + self.assertEqual(1, data[timestamp_first]) + self.assertEqual(2, data[timestamp_second]) + + def test_rate_returns_expected(self): + m = self._counter + n = 10 + start_time = datetime.datetime.strptime( + "2025-01-01 00:00:00", "%Y-%m-%d %H:%M:%S" + ) + delta_time_sec = 1 + for i in range(0, n): + m.increment( + 1, start_time + datetime.timedelta(seconds=delta_time_sec * i) + ) + # n counts across n seconds, thus rate = 1 + self.assertEqual(1, m.rate()) + + def test_rate_over_window_returns_expected(self): + m = self._counter + n = 10 + start_time = datetime.datetime.strptime( + "2025-01-01 00:00:00", "%Y-%m-%d %H:%M:%S" + ) + delta_time_sec = 1 + for i in range(0, n): + m.increment( + 1, start_time + datetime.timedelta(seconds=delta_time_sec * i) + ) + + rates_with_timestamp = m.rate_over_window(window_size_sec=5) + + rates = [rate for timestamp, rate in rates_with_timestamp] + # 10 seconds with 1 count in each second. One rate per window_size_sec=5 sec + # so rate = 1 for [0, 5) and rate = 1 [5, 10) + self.assertEqual([1, 1], rates) + + def test_rate_over_window_to_csv_returns_correct(self): + m = self._counter + n = 10 + start_time = datetime.datetime.strptime( + "2025-01-01 00:00:00", "%Y-%m-%d %H:%M:%S" + ) + delta_time_sec = 1 + for i in range(0, n): + m.increment( + 1, start_time + datetime.timedelta(seconds=delta_time_sec * i) + ) + + csv_output = m.rate_over_window_to_csv(window_size_sec=5) + + rows = csv_output.split("\n") + self.assertEqual(2, len(rows)) + expected_timestamps = "TimeStamp,2025-01-01 00:00:00,2025-01-01 00:00:05" + got_timestamps = rows[0] + self.assertEqual(expected_timestamps, got_timestamps) + expected_values = "Value,1.00,1.00" + got_values = rows[1] + self.assertEqual(expected_values, got_values) + + +if __name__ == "__main__": + unittest.main() diff --git a/google3/third_party/py/jetstream/docs/observability-prometheus-metrics-in-jetstream-server.md b/docs/observability-prometheus-metrics-in-jetstream-server.md similarity index 94% rename from google3/third_party/py/jetstream/docs/observability-prometheus-metrics-in-jetstream-server.md rename to docs/observability-prometheus-metrics-in-jetstream-server.md index 079b132a..04d7be4c 100644 --- a/google3/third_party/py/jetstream/docs/observability-prometheus-metrics-in-jetstream-server.md +++ b/docs/observability-prometheus-metrics-in-jetstream-server.md @@ -80,6 +80,6 @@ echo '{ }' | kubectl apply -f - ``` -The metrics can now be queried in the Google Cloud Metrics Explorer. When adding a metrics query with the `+Add Query` button the new metrics should be found under the `Prometheus Target > Jetstream` submenu. +The metrics can now be queried in the [Google Cloud Metrics Explorer](https://pantheon.corp.google.com/monitoring/metrics-explorer). When adding a metrics query with the `+Add Query` button the new metrics should be found under the `Prometheus Target > Jetstream` submenu. Additional guides on the metrics explorer can be found [here](https://cloud.google.com/monitoring/charts/metrics-selector). \ No newline at end of file diff --git a/google3/third_party/py/jetstream/docs/online-inference-with-maxtext-engine.md b/docs/online-inference-with-maxtext-engine.md similarity index 100% rename from google3/third_party/py/jetstream/docs/online-inference-with-maxtext-engine.md rename to docs/online-inference-with-maxtext-engine.md diff --git a/google3/third_party/py/jetstream/docs/profiling-with-jax-profiler-and-tensorboard.md b/docs/profiling-with-jax-profiler-and-tensorboard.md similarity index 100% rename from google3/third_party/py/jetstream/docs/profiling-with-jax-profiler-and-tensorboard.md rename to docs/profiling-with-jax-profiler-and-tensorboard.md diff --git a/experimental/jax/README.md b/experimental/jax/README.md new file mode 100644 index 00000000..7b6d7698 --- /dev/null +++ b/experimental/jax/README.md @@ -0,0 +1,104 @@ +# An experimental JAX inference framework for prototyping new ideas. + +## About + + It has the following features (some of them are limited version): + +``` + Performance: + 1. Paged Attention + 2. Chunked Prefill and Piggybacking Decode + 3. Collective Matmul + + Framework: + 1. Pythonic model builder + 2. JAX manual sharding + 3. Interface for different hardware supports + 4. On-the-flying HF model conversion and deployment +``` + +## Quick Start + +So far, the experimental code only works for llama2 7b and TPU v5e-8. The whole process only takes less than 10 mins if you have a Cloud TPU v5e-8 ready. + +### 1. Create Cloud TPU v5e-8 on Google Cloud: + +``` +gcloud alpha compute tpus queued-resources create ${QR_NAME} \ + --node-id ${NODE_NAME} \ + --project ${PROJECT_ID} \ + --zone ${ZONE} \ + --accelerator-type v5litepod-8 \ + --runtime-version v2-alpha-tpuv5-lite +``` + +For more [information](https://cloud.google.com/tpu/docs/queued-resources) + + +### 2. Set up the LLM Server and serve request: +SSH into your Cloud TPU VM first and run the following command: + +Set up a new Python env. +``` +virtualenv jax-inference +source jax-inference/bin/activate +``` + +Clone the repo and install the dependencies. +``` +git clone https://github.com/AI-Hypercomputer/JetStream.git + +cd JetStream/experimental/jax + +pip install -r requirements.txt +``` + +Log in to the Hugging Face (make sure your account has the permission to access `meta-llama/Llama-2-7b-chat-hf`) + +``` +huggingface-cli login +``` + + +### 3. Offline Benchmarking: + +Note: the current setup is using 8-ways TP which is just for experiment and compare with current JetStream + MaxText number. + +``` +export PYTHONPATH=$(pwd) +export JAX_COMPILATION_CACHE_DIR="/tmp/jax_cache" +python inference/entrypoint/mini_offline_benchmarking.py +``` + +Offline Benchmarking result: + +This number is around `45%` better than the current MaxText and JetStream (as of 2024/08/16) number in the same situation. + + +``` +Benchmarking result: + Total requests: 1000 + Total input tokens: 218743 + Total output tokens: 291740 + Input token throughput: 2980.654636529649 tokens/sec + Output token throughput: 3975.332621666338 tokens/sec +``` + +Note: The online number should be even more better than the current MaxText and JetStream as the experimental framework runs the prefill and decode together in one model forward pass. + +### 4. Online Serving Example: + +Start server: + +``` +python inference/entrypoint/run_simple_server.py & +``` + +Send request: + +``` +curl --no-buffer -H 'Content-Type: application/json' \ + -d '{ "prompt": "Today is a good day" }' \ + -X POST \ + localhost:8000/generate +``` \ No newline at end of file diff --git a/experimental/jax/inference/entrypoint/__init__.py b/experimental/jax/inference/entrypoint/__init__.py new file mode 100644 index 00000000..e7c0b714 --- /dev/null +++ b/experimental/jax/inference/entrypoint/__init__.py @@ -0,0 +1,15 @@ +""" +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. +""" diff --git a/experimental/jax/inference/entrypoint/mini_offline_benchmarking.py b/experimental/jax/inference/entrypoint/mini_offline_benchmarking.py new file mode 100644 index 00000000..53a85663 --- /dev/null +++ b/experimental/jax/inference/entrypoint/mini_offline_benchmarking.py @@ -0,0 +1,75 @@ +""" +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. +""" + +import os +import time +import pandas +from inference.runtime.request_type import * +from inference.runtime import offline_inference + + +def load_openorca_dataset_pkl(): + # Read pickle file + current_dir = os.path.dirname(__file__) + samples = pandas.read_pickle( + f"{current_dir}/open_orca_gpt4_tokenized_llama.calibration_1000.pkl" + ) + + prompts = [] + outputs = [] + for _, row in samples.iterrows(): + prompts.append(row["input"]) + outputs.append(row["output"]) + + return [(prompt, output) for prompt, output in zip(prompts, outputs)] + + +def benchmarking(): + dataset = load_openorca_dataset_pkl() + + ds = dataset[:1000] + ds = [d[0] for d in ds] + + inference_instance = offline_inference.OfflineInference() + + start_time = time.perf_counter() + res_list: list[Response] = inference_instance(ds) + end_time = time.perf_counter() + duration = end_time - start_time + + input_tokens = [] + for res in res_list: + input_tokens = input_tokens + res.input_tokens + + output_tokens = [] + for res in res_list: + output_tokens = output_tokens + res.generated_tokens + + num_input_tokens = len(input_tokens) + num_output_tokens = len(output_tokens) + + print("Benchmarking result: ") + # Hardcode the number of requests as 1000 based on the test + # dataset. + print(" Total requests: 1000") + print(" Total input tokens:", num_input_tokens) + print(" Total output tokens:", num_output_tokens) + print(f" Input token throughput: {num_input_tokens/duration} tokens/sec") + print(f" Output token throughput: {num_output_tokens/duration} tokens/sec") + + +if __name__ == "__main__": + benchmarking() diff --git a/experimental/jax/inference/entrypoint/open_orca_gpt4_tokenized_llama.calibration_1000.pkl b/experimental/jax/inference/entrypoint/open_orca_gpt4_tokenized_llama.calibration_1000.pkl new file mode 100644 index 0000000000000000000000000000000000000000..cde4330e0516df00ba894f0af5fe55ae1dda84a8 GIT binary patch literal 3708395 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