mirror of
https://github.com/kvcache-ai/ktransformers.git
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306 lines
10 KiB
Python
306 lines
10 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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"""
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Benchmark write_weight_scale_to_buffer for AMX_K2_MOE_TP (int4 packed weights + bf16 scales).
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Uses two MOE instances that alternate writing to simulate realistic multi-layer scenarios.
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"""
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import json
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import os
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import platform
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import subprocess
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import sys
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import time
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from tqdm import tqdm
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "build"))
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from kt_kernel import kt_kernel_ext
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import torch
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# Benchmark parameters
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expert_num = 384
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num_experts_per_tok = expert_num
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gpu_tp_count = 4
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warm_up_iter = 3
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test_iter = 7
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gpu_experts_num = expert_num
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hidden_size = 7168
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intermediate_size = 2048
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group_size = 32
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max_len = 1
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physical_to_logical_map = torch.arange(expert_num, dtype=torch.int64, device="cpu").contiguous()
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CPUInfer = kt_kernel_ext.CPUInfer(80)
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def get_git_commit():
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result = {}
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try:
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commit = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip()
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commit_msg = subprocess.check_output(["git", "log", "-1", "--pretty=%B"]).decode("utf-8").strip()
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result["commit"] = commit
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result["commit_message"] = commit_msg
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dirty_output = subprocess.check_output(["git", "status", "--porcelain"]).decode("utf-8").strip()
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if dirty_output:
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result["dirty"] = True
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result["dirty_files"] = dirty_output.splitlines()
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else:
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result["dirty"] = False
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except Exception as e:
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result["commit"] = None
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result["commit_message"] = None
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result["dirty"] = None
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result["error"] = str(e)
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return result
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def get_system_info():
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info = {}
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uname = platform.uname()
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info["system_name"] = uname.system
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info["node_name"] = uname.node
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cpu_model = None
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if os.path.exists("/proc/cpuinfo"):
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try:
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with open("/proc/cpuinfo", "r") as f:
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for line in f:
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if "model name" in line:
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cpu_model = line.split(":", 1)[1].strip()
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break
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except Exception as e:
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cpu_model = f"Error: {e}"
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info["cpu_model"] = cpu_model
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mem_total_gb = None
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if os.path.exists("/proc/meminfo"):
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try:
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with open("/proc/meminfo", "r") as f:
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for line in f:
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if "MemTotal" in line:
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mem_kb = float(line.split(":", 1)[1].split()[0])
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mem_total_gb = round(mem_kb / (1024 * 1024), 2)
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break
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except Exception as e:
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mem_total_gb = f"Error: {e}"
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info["memory_size_GB"] = mem_total_gb
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info["cpu_core_count"] = os.cpu_count()
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sockets = set()
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if os.path.exists("/proc/cpuinfo"):
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try:
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with open("/proc/cpuinfo", "r") as f:
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for line in f:
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if "physical id" in line:
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sockets.add(line.split(":", 1)[1].strip())
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except Exception:
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sockets = set()
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info["cpu_socket_count"] = len(sockets) if len(sockets) > 0 else 1
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return info
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script_path = os.path.abspath(__file__)
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script_dir = os.path.dirname(script_path)
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script_name = os.path.splitext(os.path.basename(script_path))[0]
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json_path = os.path.join(script_dir, script_name + ".jsonl")
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def record_results(result, filename=json_path):
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with open(filename, "a") as f:
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f.write(json.dumps(result) + "\n")
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def allocate_weights():
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per_mat_weight_bytes = (hidden_size * intermediate_size) // 2
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per_mat_scale_elems = (hidden_size * intermediate_size) // group_size
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gate_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
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up_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
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down_q = torch.randint(0, 256, (expert_num * per_mat_weight_bytes,), dtype=torch.uint8)
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gate_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
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up_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
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down_scale = torch.randn(expert_num * per_mat_scale_elems, dtype=torch.bfloat16)
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return (
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gate_q.contiguous(),
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up_q.contiguous(),
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down_q.contiguous(),
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gate_scale.contiguous(),
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up_scale.contiguous(),
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down_scale.contiguous(),
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per_mat_weight_bytes,
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per_mat_scale_elems,
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)
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def build_moe(layer_idx=0):
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"""Build a single MOE instance with the given layer_idx."""
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(
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gate_q,
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up_q,
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down_q,
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gate_scale,
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up_scale,
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down_scale,
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per_mat_weight_bytes,
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per_mat_scale_elems,
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) = allocate_weights()
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config = kt_kernel_ext.moe.MOEConfig(expert_num, num_experts_per_tok, hidden_size, intermediate_size)
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config.max_len = max_len
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config.layer_idx = layer_idx
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config.quant_config.bits = 4
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config.quant_config.group_size = group_size
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config.quant_config.zero_point = False
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config.pool = CPUInfer.backend_
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config.gate_proj = gate_q.data_ptr()
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config.up_proj = up_q.data_ptr()
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config.down_proj = down_q.data_ptr()
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config.gate_scale = gate_scale.data_ptr()
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config.up_scale = up_scale.data_ptr()
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config.down_scale = down_scale.data_ptr()
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moe = kt_kernel_ext.moe.AMXInt4_KGroup_MOE(config)
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CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
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CPUInfer.sync()
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keep_tensors = {
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"gate_q": gate_q,
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"up_q": up_q,
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"down_q": down_q,
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"gate_scale": gate_scale,
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"up_scale": up_scale,
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"down_scale": down_scale,
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}
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buffer_shapes = {
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"per_mat_weight_bytes": per_mat_weight_bytes,
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"per_mat_scale_elems": per_mat_scale_elems,
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}
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return moe, buffer_shapes, keep_tensors
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def allocate_buffers(buffer_shapes):
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"""Allocate shared output buffers for single expert."""
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per_mat_weight_bytes = buffer_shapes["per_mat_weight_bytes"]
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per_mat_scale_elems = buffer_shapes["per_mat_scale_elems"]
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weight_bytes_per_expert_per_tp = per_mat_weight_bytes // gpu_tp_count
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scale_elems_per_expert_per_tp = per_mat_scale_elems // gpu_tp_count
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# Each buffer stores data for a single expert
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w13_weight_bufs = [torch.empty(2 * weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
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w13_scale_bufs = [torch.empty(2 * scale_elems_per_expert_per_tp, dtype=torch.bfloat16) for _ in range(gpu_tp_count)]
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w2_weight_bufs = [torch.empty(weight_bytes_per_expert_per_tp, dtype=torch.uint8) for _ in range(gpu_tp_count)]
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w2_scale_bufs = [torch.empty(scale_elems_per_expert_per_tp, dtype=torch.bfloat16) for _ in range(gpu_tp_count)]
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buffer_ptrs = {
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"w13_weight_ptrs": [buf.data_ptr() for buf in w13_weight_bufs],
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"w13_scale_ptrs": [buf.data_ptr() for buf in w13_scale_bufs],
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"w2_weight_ptrs": [buf.data_ptr() for buf in w2_weight_bufs],
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"w2_scale_ptrs": [buf.data_ptr() for buf in w2_scale_bufs],
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}
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keep_tensors = {
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"w13_weight_bufs": w13_weight_bufs,
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"w13_scale_bufs": w13_scale_bufs,
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"w2_weight_bufs": w2_weight_bufs,
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"w2_scale_bufs": w2_scale_bufs,
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}
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return buffer_ptrs, keep_tensors
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def bench_write_buffer():
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# Build two MOE instances with different layer_idx
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moe_0, buffer_shapes, keep_tensors_0 = build_moe(layer_idx=0)
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moe_1, _, keep_tensors_1 = build_moe(layer_idx=1)
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moes = [moe_0, moe_1]
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# Allocate shared buffers
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buffer_ptrs, buffer_keep_tensors = allocate_buffers(buffer_shapes)
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total_weights = hidden_size * intermediate_size * expert_num * 3
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# Throughput accounting: scale bytes (bf16) + weight bytes (int4 packed)
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bytes_per_call = total_weights // group_size * 2 + total_weights // 2
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# Warm-up: alternate between two MOEs
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for _ in tqdm(range(warm_up_iter), desc="Warm-up"):
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for moe_idx, moe in enumerate(moes):
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for expert_id in range(gpu_experts_num):
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CPUInfer.submit(
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moe.write_weight_scale_to_buffer_task(
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gpu_tp_count=gpu_tp_count,
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expert_id=expert_id,
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**buffer_ptrs,
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)
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)
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CPUInfer.sync()
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total_time = 0
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for iter_idx in tqdm(range(test_iter), desc="Testing"):
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start = time.perf_counter()
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# Alternate between two MOEs
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for moe_idx, moe in enumerate(moes):
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for expert_id in range(gpu_experts_num):
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CPUInfer.submit(
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moe.write_weight_scale_to_buffer_task(
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gpu_tp_count=gpu_tp_count,
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expert_id=expert_id,
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**buffer_ptrs,
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)
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)
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CPUInfer.sync()
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end = time.perf_counter()
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iter_time = end - start
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total_time += iter_time
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print(f"Iter {iter_idx}: {iter_time*1000:.2f} ms")
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time.sleep(0.3)
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# bytes_per_call is for one MOE, we have 2 MOEs
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bytes_per_iter = bytes_per_call * 2
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time_per_iter_ms = total_time / test_iter * 1000
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bandwidth_gbs = bytes_per_iter * test_iter / total_time / 1e9
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print(f"\n{'='*60}")
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print("K2 write_weight_scale_to_buffer benchmark (2 MOEs alternating)")
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print(f"{'='*60}")
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print(f"Time per iteration: {time_per_iter_ms:.2f} ms")
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print(f"Bandwidth: {bandwidth_gbs:.2f} GB/s")
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print(f"Experts per MOE: {gpu_experts_num}, MOEs: 2")
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print(f"Time per expert: {time_per_iter_ms/(gpu_experts_num*2)*1000:.2f} us")
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result = {
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"op": "write_weight_scale_to_buffer_k2",
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"time_per_iteration_ms": time_per_iter_ms,
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"bandwidth_GBs": bandwidth_gbs,
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"test_parameters": {
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"expert_num": expert_num,
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"hidden_size": hidden_size,
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"intermediate_size": intermediate_size,
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"group_size": group_size,
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"gpu_tp_count": gpu_tp_count,
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"bytes_per_iter": bytes_per_iter,
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"num_moes": 2,
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},
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}
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result.update(get_git_commit())
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result.update(get_system_info())
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record_results(result)
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if __name__ == "__main__":
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bench_write_buffer()
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