diff --git a/kt-kernel/.githooks/pre-commit b/kt-kernel/.githooks/pre-commit index e3d42e0a..65d6f9fb 100755 --- a/kt-kernel/.githooks/pre-commit +++ b/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 $BLACK_BIN). Skipping Python format." >&2 fi -# Configure kt-kernel build directory if missing (quiet) -if [ ! -d "$BUILD_DIR" ] || { [ ! -f "$BUILD_DIR/Makefile" ] && [ ! -f "$BUILD_DIR/build.ninja" ]; }; then - echo "[pre-commit] configuring kt-kernel (cmake) ..." >&2 - cmake -S "$KERNEL_DIR" -B "$BUILD_DIR" >/dev/null +## Format only staged changes within kt-kernel +# Collect staged files (Added/Modified/Copied/Renamed) +mapfile -d '' STAGED < <(git diff --cached --name-only -z --diff-filter=AMCR) + +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 clang-format target when available and tool present -if command -v "$CLANG_FORMAT_BIN" >/dev/null 2>&1; then - if [ -f "$BUILD_DIR/build.ninja" ]; then - (cd "$BUILD_DIR" && ninja -k0 "$FORMAT_TARGET" >/dev/null) - else - (cd "$BUILD_DIR" && make "$FORMAT_TARGET") - fi +## Run black only on staged Python files +if command -v "$BLACK_BIN" >/dev/null 2>&1 && [ ${#PY_CHANGED[@]} -gt 0 ]; then + echo "[pre-commit] black on ${#PY_CHANGED[@]} files" >&2 + "$BLACK_BIN" "${PY_CHANGED[@]}" fi -# Run black on staged python files (or entire repo if you prefer) -if command -v "$BLACK_BIN" >/dev/null 2>&1; then - # Run black only on kt-kernel's python and scripts directories - 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 $BLACK_PATHS - fi -fi - -# Stage any formatting changes for tracked files -if ! git diff --quiet --exit-code; then +# Stage any formatting changes for tracked, formatted files only +FMT_FILES=("${PY_CHANGED[@]}" "${CPP_CHANGED[@]}") +if [ ${#FMT_FILES[@]} -gt 0 ] && ! git diff --quiet --exit-code -- "${FMT_FILES[@]}"; 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 -u + git add "${FMT_FILES[@]}" echo "[pre-commit] Re-run git commit to proceed after reviewing changes." >&2 exit 1 fi diff --git a/kt-kernel/test/per_commit/test_moe_amx_bench_int4_1.py b/kt-kernel/test/per_commit/test_moe_amx_bench_int4_1.py new file mode 100644 index 00000000..088f70c0 --- /dev/null +++ b/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() diff --git a/kt-kernel/test/per_commit/test_moe_amx_bench_int4_1k.py b/kt-kernel/test/per_commit/test_moe_amx_bench_int4_1k.py new file mode 100644 index 00000000..cdc2f854 --- /dev/null +++ b/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()