chore: import upstream snapshot with attribution

kt-kernel/bench/bench_k2_write_buffer.py

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+#!/usr/bin/env python
+# coding=utf-8
+"""
+Benchmark write_weight_scale_to_buffer for AMX_K2_MOE_TP (int4 packed weights + bf16 scales).
+
+Uses two MOE instances that alternate writing to simulate realistic multi-layer scenarios.
+"""
+import json
+import os
+import platform
+import subprocess
+import sys
+import time
+
+from tqdm import tqdm
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "build"))
+
+from kt_kernel import kt_kernel_ext
+import torch
+
+# Benchmark parameters
+expert_num = 384
+num_experts_per_tok = expert_num
+gpu_tp_count = 4
+
+warm_up_iter = 3
+test_iter = 7
+
+gpu_experts_num = expert_num
+
+hidden_size = 7168
+intermediate_size = 2048
+group_size = 32
+max_len = 1
+
+physical_to_logical_map = torch.arange(expert_num, dtype=torch.int64, device="cpu").contiguous()
+CPUInfer = kt_kernel_ext.CPUInfer(80)
+
+
+def get_git_commit():
+    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():
+    info = {}
+    uname = platform.uname()
+    info["system_name"] = uname.system
+    info["node_name"] = uname.node
+
+    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
+
+    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
+
+    info["cpu_core_count"] = os.cpu_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:
+            sockets = set()
+    info["cpu_socket_count"] = len(sockets) if len(sockets) > 0 else 1
+
+    return info
+
+
+script_path = os.path.abspath(__file__)
+script_dir = os.path.dirname(script_path)
+script_name = os.path.splitext(os.path.basename(script_path))[0]
+json_path = os.path.join(script_dir, script_name + ".jsonl")
+
+
+def record_results(result, filename=json_path):
+    with open(filename, "a") as f:
+        f.write(json.dumps(result) + "\n")
+
+
+def allocate_weights():
+    per_mat_weight_bytes = (hidden_size * intermediate_size) // 2
+    per_mat_scale_elems = (hidden_size * intermediate_size) // group_size
+
+    gate_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
+    up_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
+    down_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
+
+    gate_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
+    up_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
+    down_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
+
+    return (
+        gate_q.contiguous(),
+        up_q.contiguous(),
+        down_q.contiguous(),
+        gate_scale.contiguous(),
+        up_scale.contiguous(),
+        down_scale.contiguous(),
+        per_mat_weight_bytes,
+        per_mat_scale_elems,
+    )
+
+
+def build_moe(layer_idx=0):
+    """Build a single MOE instance with the given layer_idx."""
+    (
+        gate_q,
+        up_q,
+        down_q,
+        gate_scale,
+        up_scale,
+        down_scale,
+        per_mat_weight_bytes,
+        per_mat_scale_elems,
+    ) = allocate_weights()
+
+    config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size)
+    config.max_len = max_len
+    config.layer_idx = layer_idx
+    config.quant_config.bits = 4
+    config.quant_config.group_size = group_size
+    config.quant_config.zero_point = False
+    config.pool = CPUInfer.backend_
+
+    config.gate_proj = gate_q.data_ptr()
+    config.up_proj = up_q.data_ptr()
+    config.down_proj = down_q.data_ptr()
+    config.gate_scale = gate_scale.data_ptr()
+    config.up_scale = up_scale.data_ptr()
+    config.down_scale = down_scale.data_ptr()
+
+    moe = kt_kernel_ext.moe.AMXInt4_KGroup_MOE(config)
+    CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
+    CPUInfer.sync()
+
+    keep_tensors = {
+        "gate_q": gate_q,
+        "up_q": up_q,
+        "down_q": down_q,
+        "gate_scale": gate_scale,
+        "up_scale": up_scale,
+        "down_scale": down_scale,
+    }
+
+    buffer_shapes = {
+        "per_mat_weight_bytes": per_mat_weight_bytes,
+        "per_mat_scale_elems": per_mat_scale_elems,
+    }
+
+    return moe, buffer_shapes, keep_tensors
+
+
+def allocate_buffers(buffer_shapes):
+    """Allocate shared output buffers for single expert."""
+    per_mat_weight_bytes = buffer_shapes["per_mat_weight_bytes"]
+    per_mat_scale_elems = buffer_shapes["per_mat_scale_elems"]
+
+    weight_bytes_per_expert_per_tp = per_mat_weight_bytes // gpu_tp_count
+    scale_elems_per_expert_per_tp = per_mat_scale_elems // gpu_tp_count
+
+    # Each buffer stores data for a single expert
+    w13_weight_bufs = [torch.empty(2 * weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
+    w13_scale_bufs = [torch.empty(2 * scale_elems_per_expert_per_tp, dtype=torch.bfloat16) for _ in range(gpu_tp_count)]
+    w2_weight_bufs = [torch.empty(weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
+    w2_scale_bufs = [torch.empty(scale_elems_per_expert_per_tp, dtype=torch.bfloat16) for _ in range(gpu_tp_count)]
+
+    buffer_ptrs = {
+        "w13_weight_ptrs": [buf.data_ptr() for buf in w13_weight_bufs],
+        "w13_scale_ptrs": [buf.data_ptr() for buf in w13_scale_bufs],
+        "w2_weight_ptrs": [buf.data_ptr() for buf in w2_weight_bufs],
+        "w2_scale_ptrs": [buf.data_ptr() for buf in w2_scale_bufs],
+    }
+
+    keep_tensors = {
+        "w13_weight_bufs": w13_weight_bufs,
+        "w13_scale_bufs": w13_scale_bufs,
+        "w2_weight_bufs": w2_weight_bufs,
+        "w2_scale_bufs": w2_scale_bufs,
+    }
+
+    return buffer_ptrs, keep_tensors
+
+
+def bench_write_buffer():
+    # Build two MOE instances with different layer_idx
+    moe_0, buffer_shapes, keep_tensors_0 = build_moe(layer_idx=0)
+    moe_1, _, keep_tensors_1 = build_moe(layer_idx=1)
+    moes = [moe_0, moe_1]
+
+    # Allocate shared buffers
+    buffer_ptrs, buffer_keep_tensors = allocate_buffers(buffer_shapes)
+
+    total_weights = hidden_size * intermediate_size * expert_num * 3
+    # Throughput accounting: scale bytes (bf16) + weight bytes (int4 packed)
+    bytes_per_call = total_weights // group_size * 2 + total_weights // 2
+
+    # Warm-up: alternate between two MOEs
+    for _ in tqdm(range(warm_up_iter), desc="Warm-up"):
+        for moe_idx, moe in enumerate(moes):
+            for expert_id in range(gpu_experts_num):
+                CPUInfer.submit(
+                    moe.write_weight_scale_to_buffer_task(
+                        gpu_tp_count=gpu_tp_count,
+                        expert_id=expert_id,
+                        **buffer_ptrs,
+                    )
+                )
+                CPUInfer.sync()
+
+    total_time = 0
+    for iter_idx in tqdm(range(test_iter), desc="Testing"):
+        start = time.perf_counter()
+        # Alternate between two MOEs
+        for moe_idx, moe in enumerate(moes):
+            for expert_id in range(gpu_experts_num):
+                CPUInfer.submit(
+                    moe.write_weight_scale_to_buffer_task(
+                        gpu_tp_count=gpu_tp_count,
+                        expert_id=expert_id,
+                        **buffer_ptrs,
+                    )
+                )
+                CPUInfer.sync()
+        end = time.perf_counter()
+        iter_time = end - start
+        total_time += iter_time
+        print(f"Iter {iter_idx}: {iter_time*1000:.2f} ms")
+        time.sleep(0.3)
+
+    # bytes_per_call is for one MOE, we have 2 MOEs
+    bytes_per_iter = bytes_per_call * 2
+    time_per_iter_ms = total_time / test_iter * 1000
+    bandwidth_gbs = bytes_per_iter * test_iter / total_time / 1e9
+
+    print(f"\n{'='*60}")
+    print("K2 write_weight_scale_to_buffer benchmark (2 MOEs alternating)")
+    print(f"{'='*60}")
+    print(f"Time per iteration: {time_per_iter_ms:.2f} ms")
+    print(f"Bandwidth: {bandwidth_gbs:.2f} GB/s")
+    print(f"Experts per MOE: {gpu_experts_num}, MOEs: 2")
+    print(f"Time per expert: {time_per_iter_ms/(gpu_experts_num*2)*1000:.2f} us")
+
+    result = {
+        "op": "write_weight_scale_to_buffer_k2",
+        "time_per_iteration_ms": time_per_iter_ms,
+        "bandwidth_GBs": bandwidth_gbs,
+        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+        "test_parameters": {
+            "expert_num": expert_num,
+            "hidden_size": hidden_size,
+            "intermediate_size": intermediate_size,
+            "group_size": group_size,
+            "gpu_tp_count": gpu_tp_count,
+            "bytes_per_iter": bytes_per_iter,
+            "num_moes": 2,
+        },
+    }
+    result.update(get_git_commit())
+    result.update(get_system_info())
+    record_results(result)
+
+
+if __name__ == "__main__":
+    bench_write_buffer()