[Intel HPU] add example benchmark scripts for hpu (#5304)

* [Intel HPU] add example benchmark scripts for hpu

* Revise the code based on the copilot comments

* update code based on comments

* update ci ops version

examples/intel_hpu/bench_gsm8k.py

@@ -0,0 +1,246 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+"""Metric evaluation for Fastdeploy + ERNIE-4.5-Turbo"""
+# adapted from https://github.com/sgl-project/sglang/blob/main/benchmark/gsm8k/bench_other.py
+import argparse
+import ast
+import json
+import re
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import requests
+from tqdm import tqdm
+
+INVALID = -9999999
+
+
+def call_generate(prompt, **kwargs):
+    """
+    Generates response based on the input prompt.
+
+    Args:
+        prompt (str): The input prompt text.
+        **kwargs: Keyword arguments, including server IP address and port number.
+
+    Returns:
+        str: The response generated based on the prompt.
+
+    """
+    url = f"http://{kwargs['ip']}:{kwargs['port']}/v1/chat/completions"
+    headers = {"Content-Type": "application/json"}
+    data = {
+        "messages": [
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        "temperature": 0.6,
+        "max_tokens": 2047,
+        "top_p": 0.95,
+        "do_sample": True,
+    }
+
+    response = requests.post(url, headers=headers, data=json.dumps(data))
+    out = response.json()
+    return out["choices"][0]["message"]["content"]
+
+
+def get_one_example(lines, i, include_answer):
+    """
+    Retrieves a question-answer example from the given list of text lines.
+
+    Args:
+        lines (list of dict): A list of question-answer pairs.
+        i (int): The index of the question-answer pair to retrieve from lines.
+        include_answer (bool): Whether to include the answer in the returned string.
+
+    Returns:
+        str: A formatted question-answer string in the format "Question: <question>\nAnswer: <answer>".
+
+    """
+    ret = "Question: " + lines[i]["question"] + "\nAnswer:"
+    if include_answer:
+        ret += " " + lines[i]["answer"]
+    return ret
+
+
+def get_few_shot_examples(lines, k):
+    """
+    Selects k examples from the given list of text lines and concatenates them into a single string.
+
+    Args:
+        lines (list): A list containing text lines.
+        k (int): The number of examples to select.
+
+    Returns:
+        str: A string composed of k examples, separated by two newline characters.
+    """
+    ret = ""
+    for i in range(k):
+        ret += get_one_example(lines, i, True) + "\n\n"
+    return ret
+
+
+def get_answer_value(answer_str):
+    """
+    Extracts numerical values from an answer string and returns them.
+
+    Args:
+        answer_str (str): The string containing the answer.
+
+    Returns:
+        The extracted numerical value; returns "INVALID" if extraction fails.
+    """
+    answer_str = answer_str.replace(",", "")
+    numbers = re.findall(r"\d+", answer_str)
+    if len(numbers) < 1:
+        return INVALID
+    try:
+        return ast.literal_eval(numbers[-1])
+    except SyntaxError:
+        return INVALID
+
+
+def read_jsonl(filename: str):
+    """
+    Reads a JSONL file.
+
+    Args:
+        filename (str): Path to the JSONL file.
+
+    Yields:
+        dict: A dictionary object corresponding to each line in the JSONL file.
+    """
+    with open(filename) as fin:
+        for line in fin:
+            if line.startswith("#"):
+                continue
+            yield json.loads(line)
+
+
+def main(args):
+    """
+    Process inputs and generate answers by calling the model in parallel using a thread pool.
+
+    Args:
+        args (argparse.Namespace):
+            - num_questions (int): Number of questions to process.
+            - num_shots (int): Number of few-shot learning examples.
+            - ip (str): IP address of the model service.
+            - port (int): Port number of the model service.
+            - parallel (int): Number of questions to process in parallel.
+            - result_file (str): File path to store the results.
+
+    Returns:
+        None
+
+    """
+    # Read data
+    filename = "test.jsonl"
+
+    lines = list(read_jsonl(filename))
+
+    # Construct prompts
+    num_questions = args.num_questions
+    num_shots = args.num_shots
+    few_shot_examples = get_few_shot_examples(lines, num_shots)
+
+    questions = []
+    labels = []
+    for i in range(len(lines[:num_questions])):
+        questions.append(get_one_example(lines, i, False))
+        labels.append(get_answer_value(lines[i]["answer"]))
+    assert all(l != INVALID for l in labels)
+
+    states = [None] * len(labels)
+
+    # Use thread pool
+    def get_one_answer(i):
+        answer = call_generate(
+            prompt=few_shot_examples + questions[i],
+            # stop=["Question", "Assistant:", "<|separator|>"],
+            ip=args.ip,
+            port=args.port,
+        )
+        states[i] = answer
+
+    tic = time.time()
+    if args.parallel == 1:
+        for i in tqdm(range(len(questions))):
+            get_one_answer(i)
+    else:
+        with ThreadPoolExecutor(args.parallel) as executor:
+            list(
+                tqdm(
+                    executor.map(get_one_answer, list(range(len(questions)))),
+                    total=len(questions),
+                )
+            )
+
+    latency = time.time() - tic
+    preds = []
+
+    with open(args.acc_log, "w") as fout:
+        for i in range(len(states)):
+            preds.append(get_answer_value(states[i]))
+            answer = get_answer_value(states[i])
+            fout.write("\n################################################################\n")
+            fout.write("-----------prompt--------------\n")
+            fout.write(f"{few_shot_examples + questions[i]}\n")
+            fout.write("-----------answer--------------\n")
+            fout.write(f"answer= {states[i]}\n")
+            fout.write("-----------accuracy--------------\n")
+            fout.write(f"Correct={answer==labels[i]}, pred={answer}, label={labels[i]} \n")
+
+    # Compute accuracy
+    acc = np.mean(np.array(preds) == np.array(labels))
+    invalid = np.mean(np.array(preds) == INVALID)
+
+    # Print results
+    print(f"Accuracy: {acc:.3f}")
+    print(f"Invalid: {invalid:.3f}")
+    print(f"Latency: {latency:.3f} s")
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "gsm8k",
+            "backend": "paddlepaddle",
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "accuracy": round(acc, 3),
+            "num_requests": args.num_questions,
+            "other": {
+                "num_questions": args.num_questions,
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ip", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=str, default="8188")
+    parser.add_argument("--num-shots", type=int, default=10)
+    parser.add_argument("--data-path", type=str, default="test.jsonl")
+    parser.add_argument("--num-questions", type=int, default=1319)
+    parser.add_argument("--result-file", type=str, default="result.jsonl")
+    parser.add_argument("--parallel", type=int, default=1)
+    parser.add_argument("--acc-log", type=str, default="accuracy.log")
+    args = parser.parse_args()
+    main(args)

examples/intel_hpu/benchmark_paddle_hpu_cli.sh

@@ -0,0 +1,72 @@
+#!/bin/bash
+
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+# set -x
+
+model="ERNIE-4.5-21B-A3B-Paddle"
+model_log_name="ERNIE-4.5-21B-A3B-Paddle"
+model_yaml="yaml/eb45-21b-a3b-32k-bf16.yaml"
+# model="ERNIE-4.5-300B-A47B-Paddle"
+# model_log_name="ERNIE-4.5-300B-A47B-Paddle"
+# model_yaml="yaml/eb45-300b-a47b-32k-bf16.yaml"
+
+export SERVER_PORT=8188
+export no_proxy=localhost,127.0.0.1,0.0.0.0,10.0.0.0/8,192.168.1.0/24
+
+input_lengths=(1024 2048)
+output_lengths=(1024)
+batch_sizes=(1 2 4 8 16 32 64 128)
+
+workspace=$(pwd)
+cd $workspace
+log_home=$workspace/benchmark_fastdeploy_logs/$(TZ='Asia/Shanghai' date '+WW%V')_$(TZ='Asia/Shanghai' date +%F-%H-%M-%S)_${model_log_name}_FixedLen
+
+mkdir -p ${log_home}
+
+for input_length in "${input_lengths[@]}"
+do
+    for output_length in "${output_lengths[@]}"
+    do
+        for batch_size in "${batch_sizes[@]}"
+        do
+            > log/hpu_model_runner_profile.log
+            num_prompts=$(( batch_size * 3))
+            log_name_prefix="benchmarkdata_${model_log_name}_inputlength_${input_length}_outputlength_${output_length}_batchsize_${batch_size}_numprompts_${num_prompts}"
+            log_name=${log_name_prefix}_$(TZ='Asia/Shanghai' date +%F-%H-%M-%S)
+            echo "running benchmark with input length ${input_length}, output length ${output_length}, batch size ${batch_size}, log name ${log_name}"
+            cmd="python ../../benchmarks/benchmark_serving.py \
+                --backend openai-chat \
+                --model $model \
+                --endpoint /v1/chat/completions \
+                --host 0.0.0.0 \
+                --port ${SERVER_PORT} \
+                --dataset-name random \
+                --random-input-len ${input_length} \
+                --random-output-len ${output_length} \
+                --random-range-ratio 0 \
+                --hyperparameter-path ../../benchmarks/${model_yaml} \
+                --percentile-metrics ttft,tpot,itl,e2el,s_ttft,s_itl,s_e2el,s_decode,input_len,s_input_len,output_len \
+                --metric-percentiles 80,95,99,99.9,99.95,99.99 \
+                --num-prompts ${num_prompts} \
+                --max-concurrency  ${batch_size} \
+                --ignore-eos"
+            echo $cmd | tee -a ${log_home}/${log_name}.log
+            eval $cmd >> ${log_home}/${log_name}.log 2>&1
+
+            cp log/hpu_model_runner_profile.log ${log_home}/${log_name}_profile.log
+        done
+    done
+done

examples/intel_hpu/benchmark_paddle_hpu_cli_sharegpt.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+# set -x
+
+model="ERNIE-4.5-21B-A3B-Paddle"
+model_log_name="ERNIE-4.5-21B-A3B-Paddle"
+model_yaml="yaml/eb45-21b-a3b-32k-bf16.yaml"
+# model="ERNIE-4.5-300B-A47B-Paddle"
+# model_log_name="ERNIE-4.5-300B-A47B-Paddle"
+# model_yaml="yaml/eb45-300b-a47b-32k-bf16.yaml"
+export SERVER_PORT=8188
+export no_proxy=.intel.com,intel.com,localhost,127.0.0.1,0.0.0.0,10.0.0.0/8,192.168.1.0/24
+
+CARD_NUM=$1
+
+if [[ "$CARD_NUM" == "1" ]]; then
+       batch_size=128
+else
+       batch_size=64
+fi
+
+num_prompts=2000
+
+workspace=$(pwd)
+cd $workspace
+log_home=$workspace/benchmark_fastdeploy_logs/$(TZ='Asia/Shanghai' date '+WW%V')_$(TZ='Asia/Shanghai' date +%F-%H-%M-%S)_${model_log_name}
+
+mkdir -p ${log_home}
+
+log_name_prefix="benchmarkdata_${model_log_name}_sharegpt"
+log_name=${log_name_prefix}_$(TZ='Asia/Shanghai' date +%F-%H-%M-%S)
+echo "running benchmark with sharegpt log name ${log_name}"
+cmd="python ../../benchmarks/benchmark_serving.py \
+       --backend openai-chat \
+       --model $model \
+       --endpoint /v1/chat/completions \
+       --host 0.0.0.0 \
+       --port ${SERVER_PORT} \
+       --dataset-name EBChat \
+       --dataset-path ./filtered_sharedgpt_2000_input_1136_output_200_fd.json \
+       --hyperparameter-path ../../benchmarks/${model_yaml} \
+       --percentile-metrics ttft,tpot,itl,e2el,s_ttft,s_itl,s_e2el,s_decode,input_len,s_input_len,output_len \
+       --metric-percentiles 80,95,99,99.9,99.95,99.99 \
+       --max-concurrency  ${batch_size} \
+       --num-prompts ${num_prompts} \
+       --sharegpt-output-len 4096 \
+       --save-result "
+echo $cmd | tee -a ${log_home}/${log_name}.log
+eval $cmd >> ${log_home}/${log_name}.log 2>&1
+cp log/hpu_model_runner_profile.log ${log_home}/${log_name}_profile.log

examples/intel_hpu/benchmark_paddle_hpu_server.sh

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+#!/bin/bash
+export GC_KERNEL_PATH=/usr/lib/habanalabs/libtpc_kernels.so
+export GC_KERNEL_PATH=/usr/local/lib/python3.10/dist-packages/paddle_custom_device/intel_hpu/libcustom_tpc_perf_lib.so:$GC_KERNEL_PATH
+export INTEL_HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PADDLE_DISTRI_BACKEND=xccl
+export PADDLE_XCCL_BACKEND=intel_hpu
+# export FLAGS_intel_hpu_recipe_cache_config=/tmp/recipe,false,10240
+export FLAGS_intel_hpu_recipe_cache_num=20480
+export SERVER_PORT=8188
+export ENGINE_WORKER_QUEUE_PORT=8002
+export METRICS_PORT=8001
+export CACHE_QUEUE_PORT=8003
+export HABANA_PROFILE=0
+export HPU_VISIBLE_DEVICES=0
+rm -rf log 2>/dev/null
+FD_ENC_DEC_BLOCK_NUM=8 HPU_PERF_BREAKDOWN_SYNC_MODE=1 HPU_WARMUP_BUCKET=1 HPU_WARMUP_MODEL_LEN=4096 FD_ATTENTION_BACKEND=HPU_ATTN \
+    python -m fastdeploy.entrypoints.openai.api_server \
+    --model ERNIE-4.5-21B-A3B-Paddle \
+    --port ${SERVER_PORT} \
+    --engine-worker-queue-port ${ENGINE_WORKER_QUEUE_PORT} \
+    --metrics-port ${METRICS_PORT} \
+    --cache-queue-port ${CACHE_QUEUE_PORT} \
+    --tensor-parallel-size 1 \
+    --max-model-len 32768 \
+    --max-num-seqs 128 \
+    --block-size 128 \
+    --num-gpu-blocks-override 3100 \
+    --kv-cache-ratio 0.991 \
+    --no-enable-prefix-caching \
+    --graph-optimization-config '{"use_cudagraph":false}'
+
+# (2k + 1k) / 128(block_size) * 128(batch) = 3072
+# export HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# rm -rf log 2>/dev/null
+# FD_ENC_DEC_BLOCK_NUM=8 HPU_PERF_BREAKDOWN_SYNC_MODE=1 HPU_WARMUP_BUCKET=1 HPU_WARMUP_MODEL_LEN=3072 FD_ATTENTION_BACKEND=HPU_ATTN \
+#     python -m fastdeploy.entrypoints.openai.api_server \
+#     --model ERNIE-4.5-300B-A47B-Paddle \
+#     --port ${SERVER_PORT} \
+#     --engine-worker-queue-port ${ENGINE_WORKER_QUEUE_PORT} \
+#     --metrics-port ${METRICS_PORT} \
+#     --cache-queue-port ${CACHE_QUEUE_PORT} \
+#     --tensor-parallel-size 8 \
+#     --max-model-len 32768 \
+#     --max-num-seqs 128 \
+#     --block-size 128 \
+#     --num-gpu-blocks-override 3100 \
+#     --kv-cache-ratio 0.991 \
+#     --no-enable-prefix-caching \
+#     --graph-optimization-config '{"use_cudagraph":false}'

examples/intel_hpu/benchmark_paddle_hpu_server_sharegpt.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export GC_KERNEL_PATH=/usr/lib/habanalabs/libtpc_kernels.so
+export GC_KERNEL_PATH=/usr/local/lib/python3.10/dist-packages/paddle_custom_device/intel_hpu/libcustom_tpc_perf_lib.so:$GC_KERNEL_PATH
+export INTEL_HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PADDLE_DISTRI_BACKEND=xccl
+export PADDLE_XCCL_BACKEND=intel_hpu
+# export FLAGS_intel_hpu_recipe_cache_config=/tmp/recipe,false,10240
+export FLAGS_intel_hpu_recipe_cache_num=20480
+export SERVER_PORT=8188
+export ENGINE_WORKER_QUEUE_PORT=8002
+export METRICS_PORT=8001
+export CACHE_QUEUE_PORT=8003
+export HABANA_PROFILE=0
+
+CARD_NUM=$1
+
+if [[ "$CARD_NUM" == "1" ]]; then
+    export HPU_VISIBLE_DEVICES=0
+    export MODEL="ERNIE-4.5-21B-A3B-Paddle"
+    export GPU_BLOCKS=5000
+elif [[ "$CARD_NUM" == "8" ]]; then
+    export HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+    export MODEL="ERNIE-4.5-300B-A47B-Paddle"
+    export GPU_BLOCKS=3000
+else
+    exit 0
+fi
+
+rm -rf log 2>/dev/null
+FD_ENC_DEC_BLOCK_NUM=8 HPU_PERF_BREAKDOWN_SYNC_MODE=1 HPU_WARMUP_BUCKET=0 FD_ATTENTION_BACKEND=HPU_ATTN ENABLE_V1_KVCACHE_SCHEDULER=0 \
+    python -m fastdeploy.entrypoints.openai.api_server --model ${MODEL} --port ${SERVER_PORT} \
+    --engine-worker-queue-port ${ENGINE_WORKER_QUEUE_PORT} --metrics-port ${METRICS_PORT} \
+    --cache-queue-port ${CACHE_QUEUE_PORT} --tensor-parallel-size ${CARD_NUM} --max-model-len 16384 \
+    --max-num-seqs 128 --block-size 128  --kv-cache-ratio 0.5 --num-gpu-blocks-override ${GPU_BLOCKS} \
+    --graph-optimization-config '{"use_cudagraph":false}'

examples/intel_hpu/draw_benchmark_data.py

@@ -0,0 +1,173 @@
+import csv
+import os
+import re
+import sys
+from datetime import datetime
+
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+
+log_patterns = [
+    re.compile(
+        r"benchmarkdata_(.+?)_inputlength_(\d+)_outputlength_(\d+)_batchsize_(\d+)_numprompts_(\d+)_.*_profile\.log$"
+    ),
+]
+
+
+def draw_time_graph(log_dir, log_filename, max_num_seqs, mode):
+    # Store extracted time and BT values
+    timestamps_model = []
+    times_model = []
+    bt_values_model = []
+    block_list_shapes_model = []
+    block_indices_shapes_model = []
+    timestamps_pp = []
+    times_pp = []
+    bt_values_pp = []
+
+    # Use regex to extract Model execution time and BT information
+    pattern_model = re.compile(
+        r"(\d+-\d+-\d+ \d+:\d+:\d+,\d+) .* Model execution time\(ms\): ([\d\.]+), BT=(\d+), block_list_shape=\[(\d+)\], block_indices_shape=\[(\d+)\]"
+    )
+    pattern_pp = re.compile(
+        r"(\d+-\d+-\d+ \d+:\d+:\d+,\d+) .* PostProcessing execution time\(ms\): ([\d\.]+), BT=(\d+)"
+    )
+    # Read log file
+    with open(os.path.join(log_dir, log_filename), "r") as file:
+        for line in file:
+            match_model = pattern_model.search(line)
+            if match_model:
+                bt_value = int(match_model.group(3))
+                timestamps_model.append(datetime.strptime(match_model.group(1), "%Y-%m-%d %H:%M:%S,%f"))
+                if mode == "prefill" and bt_value <= max_num_seqs:
+                    times_model.append(None)
+                    bt_values_model.append(None)
+                    continue
+                if mode == "decode" and bt_value > max_num_seqs:
+                    times_model.append(None)
+                    bt_values_model.append(None)
+                    continue
+                times_model.append(float(match_model.group(2)))
+                bt_values_model.append(bt_value)
+                block_list_shapes_model.append(int(match_model.group(4)))
+                block_indices_shapes_model.append(int(match_model.group(5)))
+            else:
+                match_pp = pattern_pp.search(line)
+                if match_pp:
+                    bt_value = int(match_pp.group(3))
+                    timestamps_pp.append(datetime.strptime(match_pp.group(1), "%Y-%m-%d %H:%M:%S,%f"))
+                    if mode == "prefill" and bt_value <= max_num_seqs:
+                        times_pp.append(None)
+                        bt_values_pp.append(None)
+                        continue
+                    if mode == "decode" and bt_value > max_num_seqs:
+                        times_pp.append(None)
+                        bt_values_pp.append(None)
+                        continue
+                    times_pp.append(float(match_pp.group(2)))
+                    bt_values_pp.append(bt_value)
+
+    # Plot graphs
+    plt.figure(figsize=(15, 7))
+
+    date_format = mdates.DateFormatter("%m-%d %H:%M:%S")
+    # Plot time graph
+    plt.subplot(2, 1, 1)
+    ax1 = plt.gca()
+    ax2 = ax1.twinx()
+    ax1.plot(timestamps_model, times_model, label="Model Execution Time (ms)", color="blue")
+    ax2.plot(timestamps_pp, times_pp, label="PostProcessing Time (ms)", color="red")
+    ax1.set_ylabel("Model Execution Time (ms)")
+    ax2.set_ylabel("PostProcessing Time (ms)")
+    ax1.xaxis.set_major_formatter(date_format)
+    # Merge legends
+    lines_1, labels_1 = ax1.get_legend_handles_labels()
+    lines_2, labels_2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines_1 + lines_2, labels_1 + labels_2)
+
+    # Plot BT value graph
+    plt.subplot(2, 1, 2)
+    plt.plot(timestamps_model, bt_values_model, label="BT  [" + mode + "]", color="orange")
+    plt.ylabel("BT Value")
+    plt.xlabel(log_filename, fontsize=8)
+
+    plt.gca().xaxis.set_major_formatter(date_format)
+    plt.legend()
+
+    plt.tight_layout()
+    output_filename = log_filename[:-4] + "_analysis_" + mode + ".png"
+    plt.savefig(os.path.join(log_dir, output_filename), dpi=300)
+    plt.close()
+
+    # Write to CSV file
+    if mode == "all":
+        csv_filename = log_filename[:-4] + "_analysis.csv"
+        with open(os.path.join(log_dir, csv_filename), "w", newline="") as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow(
+                [
+                    "Timestamp",
+                    "ModelTime(ms)",
+                    "BT",
+                    "block_list_shape",
+                    "block_indices_shape",
+                    "Timestamp",
+                    "PostProcessing(ms)",
+                    "BT",
+                ]
+            )
+            for i in range(len(times_model)):
+                writer.writerow(
+                    [
+                        timestamps_model[i],
+                        times_model[i],
+                        bt_values_model[i],
+                        block_list_shapes_model[i],
+                        block_indices_shapes_model[i],
+                        timestamps_pp[i],
+                        times_pp[i],
+                        bt_values_pp[i],
+                    ]
+                )
+
+
+def main():
+    if len(sys.argv) > 1:
+        log_dir = sys.argv[1]
+    else:
+        log_dir = "."
+    try:
+        from natsort import natsorted
+
+        natsort_available = True
+    except ImportError:
+        natsort_available = False
+
+    files = []
+    for f in os.listdir(log_dir):
+        for pat in log_patterns:
+            if pat.match(f):
+                files.append(f)
+                break
+    if natsort_available:
+        files = natsorted(files)
+    else:
+        import re as _re
+
+        def natural_key(s):
+            return [int(text) if text.isdigit() else text.lower() for text in _re.split("([0-9]+)", s)]
+
+        files.sort(key=natural_key)
+
+    for file in files:
+        for idx, pat in enumerate(log_patterns):
+            m = pat.match(file)
+            if m:
+                draw_time_graph(log_dir, file, 128, "prefill")
+                draw_time_graph(log_dir, file, 128, "decode")
+                draw_time_graph(log_dir, file, 128, "all")
+
+
+if __name__ == "__main__":
+    print("Starting to draw logs...")
+    main()

examples/intel_hpu/eb45-300b-a47b-32k-bf16.yaml

@@ -0,0 +1,5 @@
+max_model_len: 32768
+max_num_seqs: 128
+kv_cache_ratio: 0.75
+tensor_parallel_size: 8
+max_num_batched_tokens: 32768

examples/intel_hpu/intel_hpu_serving_benchmark.md

@@ -0,0 +1,70 @@
+# Intel HPU serving benchmark
+These scripts are used to launch FastDeploy Paddle large model inference service for performance and stress testing.
+
+## Main HPU-Specific Parameter
+- `HPU_WARMUP_BUCKET`: Whether to enable warmup (1 means enabled)
+- `HPU_WARMUP_MODEL_LEN`: Model length for warmup (including input and output)
+- `MAX_PREFILL_NUM`: Maximum batch in prefill stage, default 3
+- `BATCH_STEP_PREFILL`: Batch step in prefill stage, default 1
+- `SEQUENCE_STEP_PREFILL`: Sequence step in prefill stage, default 128, same as block size
+- `CONTEXT_BLOCK_STEP_PREFILL`: Step size for block hit when prefill caching is enabled, default 1
+- `BATCH_STEP_DECODE`: Batch step in decode stage, default 4
+- `BLOCK_STEP_DECODE`: Block step in decode stage, default 16
+- `FLAGS_intel_hpu_recipe_cache_num`: Limit for HPU recipe cache number
+- `FLAGS_intel_hpu_recipe_cache_config`: HPU recipe cache config, can be used for warmup optimization
+- `GC_KERNEL_PATH`: The default path of the HPU TPC kernels library
+- `HABANA_PROFILE`: Whether to enable profiler (1 means enabled)
+- `PROFILE_START`: Profiler start step.
+- `PROFILE_END`: Profiler end step.
+
+## Usage
+### 1. Start server
+There are different setup scripts are provided to start the vllm server, one for RandomDataset and the other for ShareGPT.
+
+Before running, please make sure to correctly set the model path and port number in the script.
+```bash
+./benchmark_paddle_hpu_server.sh
+./benchmark_paddle_hpu_server_sharegpt.sh
+```
+You can use HPU_VISIBLE_DEVICES in the script to select the HPU card.
+
+### 2. Run client
+Correspondingly, there are different client test scripts. `benchmark_paddle_hpu_cli.sh` supports both variable and fixed length tests.
+
+Before running, please make sure to correctly set the model path, port number, and input/output settings in the script.
+```bash
+./benchmark_paddle_hpu_cli.sh
+./benchmark_paddle_hpu_cli_sharegpt.sh
+```
+
+### 3. Parse logs
+After batch testing, run the following script to automatically parse the logs and generate a CSV file.
+```python
+python parse_benchmark_logs.py benchmark_fastdeploy_logs/[the targeted folder]
+```
+The performance data will be saved as a CSV file.
+
+### 4. Analyse logs
+During HPU_MODEL_RUNNER execution, performance logs are generated. The following script can parse these logs and produce performance graphs to help identify bottlenecks.
+```python
+python draw_benchmark_data.py benchmark_fastdeploy_logs/[the targeted folder]
+```
+The script will save the model execution times and batch tokens as a CSV file and plot them in a graph.
+
+### 5. Accuracy test
+Accuracy testing uses GSM8K. Use the following conversion to generate the test file.
+```python
+>>> import pandas as pd
+>>> df = pd.read_parquet('tests/ce/accuracy_cases/gsm8k.parquet', engine='pyarrow')
+>>> df.to_json('test.jsonl', orient='records', lines=True)
+```
+Run the following command to perform the accuracy test.
+```bash
+python -u bench_gsm8k.py --port 8188 --num-questions 1319 --num-shots 5 --parallel 64
+```
+
+### 6. Offline demo
+To run a offline demo on HPU quickly, after set model_path in offline_demo.py, run the start script directly.
+```bash
+./run_offline_demo.sh
+```

examples/intel_hpu/offline_demo.py

@@ -0,0 +1,53 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+"""
+
+from fastdeploy.engine.sampling_params import SamplingParams
+from fastdeploy.entrypoints.llm import LLM
+
+model_name_or_path = "ERNIE-4.5-21B-A3B-Paddle"
+# model_name_or_path = "ERNIE-4.5-300B-A47B-Paddle"
+
+# Hyperparameter settings
+input_bs = 1
+input_seq = None  # 1000
+max_out_tokens = 128
+server_max_bs = 128
+TP = 1
+
+# num_gpu_blocks_override = ceil((input_seq + max_out_tokens) / 128) * server_max_bs
+num_gpu_blocks_override = 2000
+sampling_params = SamplingParams(max_tokens=max_out_tokens)
+graph_optimization_config = {"use_cudagraph": False}
+llm = LLM(
+    model=model_name_or_path,
+    tensor_parallel_size=TP,
+    engine_worker_queue_port=8602,
+    num_gpu_blocks_override=num_gpu_blocks_override,
+    block_size=128,
+    max_model_len=32768,
+    max_num_seqs=server_max_bs,
+    graph_optimization_config=graph_optimization_config,
+)
+
+if input_seq is None:
+    prompt = "user: who are you?"
+else:
+    prompt = "hi " * input_seq
+prompts = [prompt] * input_bs
+for i in range(2):
+    output = llm.generate(prompts=prompts, use_tqdm=True, sampling_params=sampling_params)
+
+print(output)

examples/intel_hpu/parse_benchmark_logs.py

@@ -0,0 +1,195 @@
+import csv
+import os
+import re
+import sys
+
+log_patterns = [
+    re.compile(
+        r"benchmarkdata_(.+?)_inputlength_(\d+)_outputlength_(\d+)_batchsize_(\d+)_numprompts_(\d+)_.*(?<!_profile)\.log$"
+    ),
+    re.compile(r"benchmarkdata_(.+?)_sharegpt_prompts_(\d+)_concurrency_(\d+)_.*(?<!_profile)\.log$"),
+]
+
+metrics = [
+    ("Mean Decode", r"Mean Decode:\s+([\d\.]+)"),
+    ("Mean TTFT (ms)", r"Mean TTFT \(ms\):\s+([\d\.]+)"),
+    ("Mean S_TTFT (ms)", r"Mean S_TTFT \(ms\):\s+([\d\.]+)"),
+    ("Mean TPOT (ms)", r"Mean TPOT \(ms\):\s+([\d\.]+)"),
+    ("Mean ITL (ms)", r"Mean ITL \(ms\):\s+([\d\.]+)"),
+    ("Mean S_ITL (ms)", r"Mean S_ITL \(ms\):\s+([\d\.]+)"),
+    ("Mean E2EL (ms)", r"Mean E2EL \(ms\):\s+([\d\.]+)"),
+    ("Mean S_E2EL (ms)", r"Mean S_E2EL \(ms\):\s+([\d\.]+)"),
+    ("Mean Input Length", r"Mean Input Length:\s+([\d\.]+)"),
+    ("Mean Output Length", r"Mean Output Length:\s+([\d\.]+)"),
+    ("Request throughput (req/s)", r"Request throughput \(req/s\):\s+([\d\.]+)"),
+    ("Output token throughput (tok/s)", r"Output token throughput \(tok/s\):\s+([\d\.]+)"),
+    ("Total Token throughput (tok/s)", r"Total Token throughput \(tok/s\):\s+([\d\.]+)"),
+]
+
+
+def parse_benchmark_log_file(filepath):
+    with open(filepath, "r", encoding="utf-8") as f:
+        content = f.read()
+    result = {}
+    for name, pattern in metrics:
+        match = re.search(pattern, content)
+        result[name] = match.group(1) if match else ""
+    return result
+
+
+def parse_profile_log_file(file_path):
+    prepare_input_times = []
+    model_times = []
+    postprocessing_times = []
+    steppaddle_times = []
+
+    with open(file_path, "r") as file:
+        for line in file:
+            prepare_input_match = re.search(r"_prepare_inputs time\(ms\): (\d+\.\d+)", line)
+            model_match = re.search(r"Model execution time\(ms\): (\d+\.\d+)", line)
+            postprocessing_match = re.search(r"PostProcessing execution time\(ms\): (\d+\.\d+)", line)
+            steppaddle_match = re.search(r"StepPaddle execution time\(ms\): (\d+\.\d+)", line)
+
+            if prepare_input_match:
+                prepare_input_times.append(float(prepare_input_match.group(1)))
+            if model_match:
+                model_times.append(float(model_match.group(1)))
+            if postprocessing_match:
+                postprocessing_times.append(float(postprocessing_match.group(1)))
+            if steppaddle_match:
+                steppaddle_times.append(float(steppaddle_match.group(1)))
+
+    return prepare_input_times, model_times, postprocessing_times, steppaddle_times
+
+
+def calculate_times(times, separate_first):
+    if len(times) < 2:
+        return times[0], None
+    if separate_first:
+        first_time = times[0]
+        average_time = sum(times[1:]) / len(times[1:])
+        return first_time, average_time
+    else:
+        return None, sum(times) / len(times)
+
+
+def main():
+    if len(sys.argv) > 1:
+        log_dir = sys.argv[1]
+    else:
+        log_dir = "."
+    try:
+        from natsort import natsorted
+
+        natsort_available = True
+    except ImportError:
+        natsort_available = False
+    all_files = set(os.listdir(log_dir))
+    files = []
+    for f in os.listdir(log_dir):
+        for pat in log_patterns:
+            if pat.match(f):
+                files.append(f)
+                break
+    if natsort_available:
+        files = natsorted(files)
+    else:
+        import re as _re
+
+        def natural_key(s):
+            return [int(text) if text.isdigit() else text.lower() for text in _re.split("([0-9]+)", s)]
+
+        files.sort(key=natural_key)
+    rows = []
+
+    for file in files:
+        m = None
+        matched_idx = -1
+        for idx, pat in enumerate(log_patterns):
+            m = pat.match(file)
+            if m:
+                matched_idx = idx
+                break
+        if not m:
+            continue
+        # model_name, input_len, output_len, batch_size, num_prompts
+        # model_name, num_prompts, max_concurrency
+        if matched_idx == 0:
+            model_name, input_len, output_len, batch_size, num_prompts = m.groups()
+        elif matched_idx == 1:
+            model_name, num_prompts, max_concurrency = m.groups()
+            input_len = "-"
+            output_len = "-"
+        if file.endswith(".log"):
+            profile_file = file[:-4] + "_profile.log"
+        else:
+            profile_file = ""
+        model_first = model_average = postprocessing_average = steppaddle_average = ""
+        if profile_file in all_files:
+            prepare_input_times, model_times, postprocessing_times, steppaddle_times = parse_profile_log_file(
+                os.path.join(log_dir, profile_file)
+            )
+            _, pia = calculate_times(prepare_input_times, False)
+            mf, ma = calculate_times(model_times, True)
+            _, pa = calculate_times(postprocessing_times, False)
+            _, sa = calculate_times(steppaddle_times, False)
+            prepare_input_average = pia if pia is not None else ""
+            model_first = mf if mf is not None else ""
+            model_average = ma if ma is not None else ""
+            postprocessing_average = pa if pa is not None else ""
+            steppaddle_average = sa if sa is not None else ""
+        data = parse_benchmark_log_file(os.path.join(log_dir, file))
+        data["dataset"] = "Fixed-Length" if matched_idx == 0 else "ShareGPT"
+        data["model_name"] = model_name
+        data["input_length"] = input_len
+        data["output_length"] = output_len
+        data["batch_size"] = batch_size if matched_idx == 0 else max_concurrency
+        data["num_prompts"] = num_prompts
+        data["prepare_input_average"] = prepare_input_average
+        data["model_execute_first"] = model_first
+        data["model_execute_average"] = model_average
+        data["postprocessing_execute_average"] = postprocessing_average
+        data["steppaddle_execute_average"] = steppaddle_average
+        rows.append(data)
+
+    import datetime
+
+    import pytz
+
+    shanghai_tz = pytz.timezone("Asia/Shanghai")
+    now = datetime.datetime.now(shanghai_tz)
+    ts = now.strftime("%Y%m%d_%H%M%S")
+    log_dir_name = os.path.basename(os.path.abspath(log_dir))
+    if log_dir_name == "" or log_dir == "." or log_dir == "/":
+        csv_filename = f"benchmark_summary_{ts}.csv"
+    else:
+        csv_filename = f"benchmark_summary_{log_dir_name}_{ts}.csv"
+    fieldnames = (
+        [
+            "model_name",
+            "dataset",
+            "input_length",
+            "output_length",
+            "batch_size",
+            "num_prompts",
+        ]
+        + [name for name, _ in metrics]
+        + [
+            "prepare_input_average",
+            "model_execute_first",
+            "model_execute_average",
+            "postprocessing_execute_average",
+            "steppaddle_execute_average",
+        ]
+    )
+    with open(csv_filename, "w", newline="", encoding="utf-8") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow(row)
+    print(f"CSV saved as: {csv_filename}")
+
+
+if __name__ == "__main__":
+    print("Starting to parse logs...")
+    main()

examples/intel_hpu/run_offline_demo.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+export GC_KERNEL_PATH=/usr/lib/habanalabs/libtpc_kernels.so
+export GC_KERNEL_PATH=/usr/local/lib/python3.10/dist-packages/paddle_custom_device/intel_hpu/libcustom_tpc_perf_lib.so:$GC_KERNEL_PATH
+export INTEL_HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PADDLE_DISTRI_BACKEND=xccl
+export PADDLE_XCCL_BACKEND=intel_hpu
+# export HPU_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export HPU_VISIBLE_DEVICES=0
+export HABANA_PROFILE=0
+export PROFILE_START=1
+export PROFILE_END=3
+# export HABANA_LOGS=hpu_logs
+# export LOG_LEVEL_ALL=0
+# export FLAGS_intel_hpu_runtime_debug=1
+# export FLAGS_intel_hpu_reciperunner_debug=1
+
+rm -rf log
+FD_ATTENTION_BACKEND=HPU_ATTN python offline_demo.py

scripts/run_ci_hpu.sh

@@ -26,8 +26,8 @@ python -m pip uninstall fastdeploy_intel_hpu -y
 #to install paddlepaddle
 pip install paddlepaddle==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
 #to install paddlecustomdevice? (paddle-intel-hpu)
-pip install https://paddle-qa.bj.bcebos.com/suijiaxin/HPU/paddle_intel_hpu-0.0.1-cp310-cp310-linux_x86_64.whl
-pip install https://paddle-qa.bj.bcebos.com/suijiaxin/HPU/paddlenlp_ops-0.0.0-cp310-cp310-linux_x86_64.whl
+pip install https://paddle-qa.bj.bcebos.com/suijiaxin/HPU/paddle_intel_hpu-0.0.2-cp310-cp310-linux_x86_64.whl
+pip install https://paddle-qa.bj.bcebos.com/suijiaxin/HPU/paddlenlp_ops-0.0.2-cp310-cp310-linux_x86_64.whl
 
 #to build and install fastdeploy
 echo "build whl"