[ci]: add int4_1 & int4_1k (#1653)

* [feat]: init amd adaption

* [feat]: add blis support

* [fix]: fix setup and moe kernel warpper

* [fix](setup.py): support rebuild with cache and import kt_kernel works
fine

* [feat]: add moe_kernel converter for amd and implement the load
method(haven't tested yet)

* [feat](moe_kernel/moe.hpp): delete unused memory when using save

* [fix](moe_kernel): update PLAIN for pack

* [fix](moe_kernel): rm printf debug

* [fix](moe_kernel): skip gpu experts

* [fix](moe_kernel/moe.hpp): update include memory path

* [feat](moe_kernel/moe.hpp): support expert deferral

* [feat]: finish amd

* [ci]: add int4_1 & int4_1k

---------

Co-authored-by: mrhaoxx <mr.haoxx@gmail.com>

kt-kernel/.githooks/pre-commit

@@ -6,6 +6,8 @@ set -euo pipefail
 REPO_ROOT="$(git rev-parse --show-toplevel)"
 # kt-kernel project directory within the monorepo
 KERNEL_DIR="$REPO_ROOT/kt-kernel"
+# Relative path for matching staged files under repo root
+REL_KERNEL_DIR="kt-kernel"
 BUILD_DIR="$KERNEL_DIR/build"
 FORMAT_TARGET="format"
 CLANG_FORMAT_BIN="${CLANG_FORMAT_BIN:-clang-format}"
@@ -22,44 +24,48 @@ if ! command -v "$BLACK_BIN" >/dev/null 2>&1; then
   echo "[pre-commit] black not found (looked for $BLACK_BIN). Skipping Python format." >&2
 fi
 
-# Configure kt-kernel build directory if missing (quiet)
+## Format only staged changes within kt-kernel
-if [ ! -d "$BUILD_DIR" ] || { [ ! -f "$BUILD_DIR/Makefile" ] && [ ! -f "$BUILD_DIR/build.ninja" ]; }; then
+# Collect staged files (Added/Modified/Copied/Renamed)
-  echo "[pre-commit] configuring kt-kernel (cmake) ..." >&2
+mapfile -d '' STAGED < <(git diff --cached --name-only -z --diff-filter=AMCR)
-  cmake -S "$KERNEL_DIR" -B "$BUILD_DIR" >/dev/null
+
+PY_CHANGED=()
+CPP_CHANGED=()
+
+for f in "${STAGED[@]}"; do
+  case "$f" in
+    "$REL_KERNEL_DIR"/*)
+      ext="${f##*.}"
+      case "$ext" in
+        py)
+          PY_CHANGED+=("$f")
+          ;;
+        c|cc|cpp|cxx|h|hh|hpp|hxx|cu|cuh)
+          CPP_CHANGED+=("$f")
+          ;;
+      esac
+      ;;
+  esac
+done
+
+# Run clang-format only on staged C/C++ files
+if command -v "$CLANG_FORMAT_BIN" >/dev/null 2>&1 && [ ${#CPP_CHANGED[@]} -gt 0 ]; then
+  echo "[pre-commit] clang-format on ${#CPP_CHANGED[@]} files" >&2
+  for f in "${CPP_CHANGED[@]}"; do
+    "$CLANG_FORMAT_BIN" -i "$f"
+  done
 fi
 
-# Run format target (prefer ninja if present)
+## Run black only on staged Python files
-# Run clang-format target when available and tool present
+if command -v "$BLACK_BIN" >/dev/null 2>&1 && [ ${#PY_CHANGED[@]} -gt 0 ]; then
-if command -v "$CLANG_FORMAT_BIN" >/dev/null 2>&1; then
+  echo "[pre-commit] black on ${#PY_CHANGED[@]} files" >&2
-  if [ -f "$BUILD_DIR/build.ninja" ]; then
+  "$BLACK_BIN" "${PY_CHANGED[@]}"
-    (cd "$BUILD_DIR" && ninja -k0 "$FORMAT_TARGET" >/dev/null)
-  else
-    (cd "$BUILD_DIR" && make "$FORMAT_TARGET")
-  fi
 fi
 
-# Run black on staged python files (or entire repo if you prefer)
+# Stage any formatting changes for tracked, formatted files only
-if command -v "$BLACK_BIN" >/dev/null 2>&1; then
+FMT_FILES=("${PY_CHANGED[@]}" "${CPP_CHANGED[@]}")
-  # Run black only on kt-kernel's python and scripts directories
+if [ ${#FMT_FILES[@]} -gt 0 ] && ! git diff --quiet --exit-code -- "${FMT_FILES[@]}"; then
-  BLACK_PATHS=""
-  if [ -d "$KERNEL_DIR/python" ]; then
-    BLACK_PATHS="$BLACK_PATHS $KERNEL_DIR/python"
-  fi
-  if [ -d "$KERNEL_DIR/scripts" ]; then
-    BLACK_PATHS="$BLACK_PATHS $KERNEL_DIR/scripts"
-  fi
-  if [ -n "$BLACK_PATHS" ]; then
-    echo "[pre-commit] running black on:$BLACK_PATHS" >&2
-    # shellcheck disable=SC2086
-    $BLACK_BIN $BLACK_PATHS
-  fi
-fi
-
-# Stage any formatting changes for tracked files
-if ! git diff --quiet --exit-code; then
   echo "[pre-commit] Formatting applied; updating index." >&2
-  # Add only modified tracked files (exclude untracked new files not staged yet unless user staged them)
+  git add "${FMT_FILES[@]}"
-  git add -u
   echo "[pre-commit] Re-run git commit to proceed after reviewing changes." >&2
   exit 1
 fi

kt-kernel/test/per_commit/test_moe_amx_bench_int4_1.py

@@ -0,0 +1,315 @@
+#!/usr/bin/env python
+# coding=utf-8
+"""AMX MOE INT4 benchmark tests for KT-Kernel.
+
+Benchmarks performance (bandwidth and FLOPS) of AMX-accelerated INT4 MOE operations.
+"""
+
+import os
+import sys
+import time
+import json
+import subprocess
+import platform
+import pytest
+
+# Add parent directory to path for CI registration
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from ci.ci_register import register_cpu_ci
+
+# Register this test for CPU CI with estimated runtime of 300 seconds
+register_cpu_ci(est_time=300, suite="default")
+
+# Check if dependencies are available
+try:
+    import torch
+    import kt_kernel_ext
+    from tqdm import tqdm
+
+    HAS_DEPS = True
+except ImportError as e:
+    HAS_DEPS = False
+    import_error = str(e)
+
+# Test parameters (from original bench_moe_amx.py)
+expert_num = 16
+hidden_size = 7168
+intermediate_size = 2048
+max_len = 25600
+num_experts_per_tok = 8
+layer_num = 2
+qlen = 1024
+warm_up_iter = 1000
+test_iter = 2000
+
+# Worker configuration
+worker_config_dict = {
+    "subpool_count": 2,
+    "subpool_numa_map": [0, 1],
+    "subpool_thread_count": [30, 30],
+}
+CPUINFER_PARAM = 60
+
+
+def get_git_commit():
+    """Get current git commit information."""
+    result = {}
+    try:
+        commit = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
+        commit_msg = subprocess.check_output(["git", "log", "-1", "--pretty=%B"]).decode("utf-8").strip()
+        result["commit"] = commit
+        result["commit_message"] = commit_msg
+
+        dirty_output = subprocess.check_output(["git", "status", "--porcelain"]).decode("utf-8").strip()
+        if dirty_output:
+            result["dirty"] = True
+            result["dirty_files"] = dirty_output.splitlines()
+        else:
+            result["dirty"] = False
+    except Exception as e:
+        result["commit"] = None
+        result["commit_message"] = None
+        result["dirty"] = None
+        result["error"] = str(e)
+    return result
+
+
+def get_system_info():
+    """Get system information including CPU model, memory, cores, and sockets."""
+    info = {}
+    uname = platform.uname()
+    info["system_name"] = uname.system
+    info["node_name"] = uname.node
+
+    # Get CPU model (Linux only)
+    cpu_model = None
+    if os.path.exists("/proc/cpuinfo"):
+        try:
+            with open("/proc/cpuinfo", "r") as f:
+                for line in f:
+                    if "model name" in line:
+                        cpu_model = line.split(":", 1)[1].strip()
+                        break
+        except Exception as e:
+            cpu_model = f"Error: {e}"
+    info["cpu_model"] = cpu_model
+
+    # Get memory size in GB (Linux only)
+    mem_total_gb = None
+    if os.path.exists("/proc/meminfo"):
+        try:
+            with open("/proc/meminfo", "r") as f:
+                for line in f:
+                    if "MemTotal" in line:
+                        mem_kb = float(line.split(":", 1)[1].split()[0])
+                        mem_total_gb = round(mem_kb / (1024 * 1024), 2)
+                        break
+        except Exception as e:
+            mem_total_gb = f"Error: {e}"
+    info["memory_size_GB"] = mem_total_gb
+
+    # Get CPU core count
+    info["cpu_core_count"] = os.cpu_count()
+
+    # Get socket count
+    sockets = set()
+    if os.path.exists("/proc/cpuinfo"):
+        try:
+            with open("/proc/cpuinfo", "r") as f:
+                for line in f:
+                    if "physical id" in line:
+                        sockets.add(line.split(":", 1)[1].strip())
+        except Exception as e:
+            sockets = set()
+    info["cpu_socket_count"] = len(sockets) if len(sockets) > 0 else 1
+
+    return info
+
+
+def record_results(result, filename):
+    """Append results to JSONL file."""
+    with open(filename, "a") as f:
+        f.write(json.dumps(result) + "\n")
+
+
+@pytest.mark.cpu
+def test_moe_amx_int4_1_benchmark():
+    """Benchmark AMX INT4 MOE performance."""
+    if not HAS_DEPS:
+        pytest.skip(f"Dependencies not available: {import_error}")
+
+    quant_mode = "int4"
+    bytes_per_elem = 0.5
+
+    # Setup output file
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    json_path = os.path.join(script_dir, "bench_moe_amx_int4_1.jsonl")
+
+    with torch.inference_mode():
+        # Initialize CPUInfer with worker config
+        worker_config = kt_kernel_ext.WorkerPoolConfig()
+        worker_config.subpool_count = worker_config_dict["subpool_count"]
+        worker_config.subpool_numa_map = worker_config_dict["subpool_numa_map"]
+        worker_config.subpool_thread_count = worker_config_dict["subpool_thread_count"]
+        CPUInfer = kt_kernel_ext.CPUInfer(worker_config)
+
+        # Initialize MOE layers
+        moes = []
+        for layer_index in range(layer_num):
+            gate_proj = (
+                torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            up_proj = (
+                torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            down_proj = (
+                torch.randn((expert_num, hidden_size, intermediate_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size, 0)
+            config.max_len = max_len
+            config.gate_proj = gate_proj.data_ptr()
+            config.up_proj = up_proj.data_ptr()
+            config.down_proj = down_proj.data_ptr()
+            config.pool = CPUInfer.backend_
+
+            moe = kt_kernel_ext.moe.AMXInt4_MOE(config)
+            CPUInfer.submit(moe.load_weights_task())
+            CPUInfer.sync()
+            moes.append(moe)
+
+        # Generate test data
+        gen_iter = 3000
+        expert_ids = (
+            torch.rand(gen_iter * qlen, expert_num, device="cpu")
+            .argsort(dim=-1)[:, :num_experts_per_tok]
+            .reshape(gen_iter, qlen * num_experts_per_tok)
+            .to("cpu")
+            .contiguous()
+        )
+        weights = (
+            torch.rand((gen_iter, qlen, num_experts_per_tok), dtype=torch.float32, device="cpu").to("cpu").contiguous()
+        )
+        input_tensor = (
+            torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
+        )
+        output_tensor = (
+            torch.empty((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
+        )
+        bsz_tensor = torch.tensor([qlen], device="cpu")
+
+        # Warm-up iterations
+        print(f"Running warm-up for {warm_up_iter} iterations...")
+        for i in tqdm(range(warm_up_iter), desc="Warm-up"):
+            CPUInfer.submit(
+                moes[i % layer_num].forward_task(
+                    bsz_tensor.data_ptr(),
+                    num_experts_per_tok,
+                    expert_ids[i % gen_iter].data_ptr(),
+                    weights[i % gen_iter].data_ptr(),
+                    input_tensor[i % layer_num].data_ptr(),
+                    output_tensor[i % layer_num].data_ptr(),
+                    False,
+                )
+            )
+            CPUInfer.sync()
+
+        # Test iterations
+        print(f"Running test for {test_iter} iterations...")
+        start = time.perf_counter()
+        for i in tqdm(range(test_iter), desc="Testing"):
+            CPUInfer.submit(
+                moes[i % layer_num].forward_task(
+                    bsz_tensor.data_ptr(),
+                    num_experts_per_tok,
+                    expert_ids[i % gen_iter].data_ptr(),
+                    weights[i % gen_iter].data_ptr(),
+                    input_tensor[i % layer_num].data_ptr(),
+                    output_tensor[i % layer_num].data_ptr(),
+                    False,
+                )
+            )
+            CPUInfer.sync()
+        end = time.perf_counter()
+        total_time = end - start
+
+        # Calculate performance metrics
+        time_per_iter_us = total_time / test_iter * 1e6
+        bandwidth = (
+            hidden_size
+            * intermediate_size
+            * 3
+            * num_experts_per_tok
+            * (1 / 8 * 256 * (1 - (31 / 32) ** qlen))
+            * bytes_per_elem
+            * test_iter
+            / total_time
+            / 1e9
+        )  # GB/s
+        flops = (
+            hidden_size * intermediate_size * qlen * 3 * num_experts_per_tok * 2 * test_iter / total_time / 1e12
+        )  # TFLOPS
+
+        print("Quant mode: ", quant_mode)
+        print("Time(s): ", total_time)
+        print("Iteration: ", test_iter)
+        print("Time(us) per iteration: ", time_per_iter_us)
+        print("Bandwidth: ", bandwidth, "GB/s")
+        print("Flops: ", flops, "TFLOPS")
+
+        # Record results
+        result = {
+            "quant_mode": quant_mode,
+            "total_time_seconds": total_time,
+            "iterations": test_iter,
+            "time_per_iteration_us": time_per_iter_us,
+            "bandwidth_GBs": bandwidth,
+            "flops_TFLOPS": flops,
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            "test_parameters": {
+                "expert_num": expert_num,
+                "hidden_size": hidden_size,
+                "intermediate_size": intermediate_size,
+                "max_len": max_len,
+                "num_experts_per_tok": num_experts_per_tok,
+                "layer_num": layer_num,
+                "qlen": qlen,
+                "warm_up_iter": warm_up_iter,
+                "test_iter": test_iter,
+                "CPUInfer_parameter": CPUINFER_PARAM,
+            },
+        }
+        result.update(get_git_commit())
+        result.update(get_system_info())
+        record_results(result, json_path)
+
+        print(f"Results saved to {json_path}")
+
+
+def run_all_tests():
+    """Run all tests in this file (for standalone execution)."""
+    if not HAS_DEPS:
+        print(f"Dependencies not available: {import_error}")
+        print("Skipping AMX MOE INT4 benchmark tests")
+        return
+
+    try:
+        print("Running AMX MOE INT4 benchmark test...")
+        test_moe_amx_int4_1_benchmark()
+        print("AMX MOE INT4 benchmark test passed")
+        print("\nAll tests passed!")
+    except Exception as e:
+        print(f"\nTest failed: {e}")
+        import traceback
+
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    run_all_tests()

kt-kernel/test/per_commit/test_moe_amx_bench_int4_1k.py

@@ -0,0 +1,329 @@
+#!/usr/bin/env python
+# coding=utf-8
+"""AMX MOE INT4 1K Group benchmark tests for KT-Kernel.
+
+Benchmarks performance (bandwidth and FLOPS) of AMX-accelerated INT4 MOE operations
+with 1K group quantization (AMXInt4_1KGroup_MOE).
+"""
+
+import os
+import sys
+import time
+import json
+import subprocess
+import platform
+import pytest
+
+# Add parent directory to path for CI registration
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from ci.ci_register import register_cpu_ci
+
+# Register this test for CPU CI with estimated runtime of 300 seconds
+register_cpu_ci(est_time=300, suite="default")
+
+# Check if dependencies are available
+try:
+    import torch
+    import kt_kernel_ext
+    from tqdm import tqdm
+    HAS_DEPS = True
+except ImportError as e:
+    HAS_DEPS = False
+    import_error = str(e)
+
+# Test parameters (from bench_moe_amx_k.py)
+expert_num = 16
+hidden_size = 7168
+intermediate_size = 2048
+max_len = 25600
+num_experts_per_tok = 8
+layer_num = 2
+qlen = 1024
+warm_up_iter = 1000
+test_iter = 2000
+k_group_size = 128
+
+# Worker configuration
+worker_config_dict = {
+    "subpool_count": 2,
+    "subpool_numa_map": [0, 1],
+    "subpool_thread_count": [30, 30],
+}
+CPUINFER_PARAM = 60
+
+
+def get_git_commit():
+    """Get current git commit information."""
+    result = {}
+    try:
+        commit = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
+        commit_msg = subprocess.check_output(["git", "log", "-1", "--pretty=%B"]).decode("utf-8").strip()
+        result["commit"] = commit
+        result["commit_message"] = commit_msg
+
+        dirty_output = subprocess.check_output(["git", "status", "--porcelain"]).decode("utf-8").strip()
+        if dirty_output:
+            result["dirty"] = True
+            result["dirty_files"] = dirty_output.splitlines()
+        else:
+            result["dirty"] = False
+    except Exception as e:
+        result["commit"] = None
+        result["commit_message"] = None
+        result["dirty"] = None
+        result["error"] = str(e)
+    return result
+
+
+def get_system_info():
+    """Get system information including CPU model, memory, cores, and sockets."""
+    info = {}
+    uname = platform.uname()
+    info["system_name"] = uname.system
+    info["node_name"] = uname.node
+
+    # Get CPU model (Linux only)
+    cpu_model = None
+    if os.path.exists("/proc/cpuinfo"):
+        try:
+            with open("/proc/cpuinfo", "r") as f:
+                for line in f:
+                    if "model name" in line:
+                        cpu_model = line.split(":", 1)[1].strip()
+                        break
+        except Exception as e:
+            cpu_model = f"Error: {e}"
+    info["cpu_model"] = cpu_model
+
+    # Get memory size in GB (Linux only)
+    mem_total_gb = None
+    if os.path.exists("/proc/meminfo"):
+        try:
+            with open("/proc/meminfo", "r") as f:
+                for line in f:
+                    if "MemTotal" in line:
+                        mem_kb = float(line.split(":", 1)[1].split()[0])
+                        mem_total_gb = round(mem_kb / (1024 * 1024), 2)
+                        break
+        except Exception as e:
+            mem_total_gb = f"Error: {e}"
+    info["memory_size_GB"] = mem_total_gb
+
+    # Get CPU core count
+    info["cpu_core_count"] = os.cpu_count()
+
+    # Get socket count
+    sockets = set()
+    if os.path.exists("/proc/cpuinfo"):
+        try:
+            with open("/proc/cpuinfo", "r") as f:
+                for line in f:
+                    if "physical id" in line:
+                        sockets.add(line.split(":", 1)[1].strip())
+        except Exception as e:
+            sockets = set()
+    info["cpu_socket_count"] = len(sockets) if len(sockets) > 0 else 1
+
+    return info
+
+
+def record_results(result, filename):
+    """Append results to JSONL file."""
+    with open(filename, "a") as f:
+        f.write(json.dumps(result) + "\n")
+
+
+@pytest.mark.cpu
+def test_moe_amx_int4_1k_benchmark():
+    """Benchmark AMX INT4 1K Group MOE performance."""
+    if not HAS_DEPS:
+        pytest.skip(f"Dependencies not available: {import_error}")
+
+    quant_mode = "int4_1k"
+    bytes_per_elem = 0.5
+
+    # Setup output file
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    json_path = os.path.join(script_dir, "bench_moe_amx_int4_1k.jsonl")
+
+    with torch.inference_mode():
+        # Initialize CPUInfer with worker config
+        worker_config = kt_kernel_ext.WorkerPoolConfig()
+        worker_config.subpool_count = worker_config_dict["subpool_count"]
+        worker_config.subpool_numa_map = worker_config_dict["subpool_numa_map"]
+        worker_config.subpool_thread_count = worker_config_dict["subpool_thread_count"]
+        CPUInfer = kt_kernel_ext.CPUInfer(worker_config)
+
+        # Physical to logical map for weight loading
+        physical_to_logical_map = torch.tensor(
+            data=range(expert_num),
+            device="cpu",
+            dtype=torch.int64
+        ).contiguous()
+
+        # Initialize MOE layers
+        moes = []
+        for layer_index in range(layer_num):
+            gate_proj = (
+                torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            up_proj = (
+                torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            down_proj = (
+                torch.randn((expert_num, hidden_size, intermediate_size), dtype=torch.float32, device="cuda")
+                .to("cpu")
+                .contiguous()
+            )
+            config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size, 0)
+            config.max_len = max_len
+            config.gate_proj = gate_proj.data_ptr()
+            config.up_proj = up_proj.data_ptr()
+            config.down_proj = down_proj.data_ptr()
+            config.pool = CPUInfer.backend_
+
+            # Configure quantization for INT4 1K Group
+            config.quant_config.bits = 4
+            config.quant_config.group_size = k_group_size
+            config.quant_config.zero_point = True
+            config.gate_scale = 0
+
+            moe = kt_kernel_ext.moe.AMXInt4_1KGroup_MOE(config)
+            CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
+            CPUInfer.sync()
+            moes.append(moe)
+
+        # Generate test data
+        gen_iter = 3000
+        expert_ids = (
+            torch.rand(gen_iter * qlen, expert_num, device="cpu")
+            .argsort(dim=-1)[:, :num_experts_per_tok]
+            .reshape(gen_iter, qlen * num_experts_per_tok)
+            .to("cpu")
+            .contiguous()
+        )
+        weights = (
+            torch.rand((gen_iter, qlen, num_experts_per_tok), dtype=torch.float32, device="cpu").to("cpu").contiguous()
+        )
+        input_tensor = (
+            torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
+        )
+        output_tensor = (
+            torch.empty((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device="cuda").to("cpu").contiguous()
+        )
+        bsz_tensor = torch.tensor([qlen], device="cpu")
+
+        # Warm-up iterations
+        print(f"Running warm-up for {warm_up_iter} iterations...")
+        for i in tqdm(range(warm_up_iter), desc="Warm-up"):
+            CPUInfer.submit(
+                moes[i % layer_num].forward_task(
+                    bsz_tensor.data_ptr(),
+                    num_experts_per_tok,
+                    expert_ids[i % gen_iter].data_ptr(),
+                    weights[i % gen_iter].data_ptr(),
+                    input_tensor[i % layer_num].data_ptr(),
+                    output_tensor[i % layer_num].data_ptr(),
+                    False,
+                )
+            )
+            CPUInfer.sync()
+
+        # Test iterations
+        print(f"Running test for {test_iter} iterations...")
+        start = time.perf_counter()
+        for i in tqdm(range(test_iter), desc="Testing"):
+            CPUInfer.submit(
+                moes[i % layer_num].forward_task(
+                    bsz_tensor.data_ptr(),
+                    num_experts_per_tok,
+                    expert_ids[i % gen_iter].data_ptr(),
+                    weights[i % gen_iter].data_ptr(),
+                    input_tensor[i % layer_num].data_ptr(),
+                    output_tensor[i % layer_num].data_ptr(),
+                    False,
+                )
+            )
+            CPUInfer.sync()
+        end = time.perf_counter()
+        total_time = end - start
+
+        # Calculate performance metrics
+        time_per_iter_us = total_time / test_iter * 1e6
+        bandwidth = (
+            hidden_size
+            * intermediate_size
+            * 3
+            * num_experts_per_tok
+            * (1 / 8 * 256 * (1 - (31 / 32) ** qlen))
+            * bytes_per_elem
+            * test_iter
+            / total_time
+            / 1e9
+        )  # GB/s
+        flops = (
+            hidden_size * intermediate_size * qlen * 3 * num_experts_per_tok * 2 * test_iter / total_time / 1e12
+        )  # TFLOPS
+
+        print("Quant mode: ", quant_mode)
+        print("Time(s): ", total_time)
+        print("Iteration: ", test_iter)
+        print("Time(us) per iteration: ", time_per_iter_us)
+        print("Bandwidth: ", bandwidth, "GB/s")
+        print("Flops: ", flops, "TFLOPS")
+
+        # Record results
+        result = {
+            "quant_mode": quant_mode,
+            "total_time_seconds": total_time,
+            "iterations": test_iter,
+            "time_per_iteration_us": time_per_iter_us,
+            "bandwidth_GBs": bandwidth,
+            "flops_TFLOPS": flops,
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            "test_parameters": {
+                "expert_num": expert_num,
+                "hidden_size": hidden_size,
+                "intermediate_size": intermediate_size,
+                "max_len": max_len,
+                "num_experts_per_tok": num_experts_per_tok,
+                "layer_num": layer_num,
+                "qlen": qlen,
+                "warm_up_iter": warm_up_iter,
+                "test_iter": test_iter,
+                "CPUInfer_parameter": CPUINFER_PARAM,
+                "k_group_size": k_group_size,
+            },
+        }
+        result.update(get_git_commit())
+        result.update(get_system_info())
+        record_results(result, json_path)
+
+        print(f"Results saved to {json_path}")
+
+
+def run_all_tests():
+    """Run all tests in this file (for standalone execution)."""
+    if not HAS_DEPS:
+        print(f"Dependencies not available: {import_error}")
+        print("Skipping AMX MOE INT4 1K Group benchmark tests")
+        return
+
+    try:
+        print("Running AMX MOE INT4 1K Group benchmark test...")
+        test_moe_amx_int4_1k_benchmark()
+        print("AMX MOE INT4 1K Group benchmark test passed")
+        print("\nAll tests passed!")
+    except Exception as e:
+        print(f"\nTest failed: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    run_all_tests()