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* refactor: move legacy code to archive/ directory - Moved ktransformers, csrc, third_party, merge_tensors to archive/ - Moved build scripts and configurations to archive/ - Kept kt-kernel, KT-SFT, doc, and README files in root - Preserved complete git history for all moved files * refactor: restructure repository to focus on kt-kernel and KT-SFT modules * fix README * fix README * fix README * fix README * docs: add performance benchmarks to kt-kernel section Add comprehensive performance data for kt-kernel to match KT-SFT's presentation: - AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch) - Prefill phase: up to 20× speedup vs baseline - Decode phase: up to 4× speedup - NUMA optimization: up to 63% throughput improvement - Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8 Source: https://lmsys.org/blog/2025-10-22-KTransformers/ This provides users with concrete performance metrics for both core modules, making it easier to understand the capabilities of each component. * refactor: improve kt-kernel performance data with specific hardware and models Replace generic performance descriptions with concrete benchmarks: - Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX - Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B) - Show detailed metrics: total throughput, output throughput, concurrency details - Match KT-SFT presentation style for consistency This provides users with actionable performance data they can use to evaluate hardware requirements and expected performance for their use cases. * fix README * docs: clean up performance table and improve formatting * add pic for README * refactor: simplify .gitmodules and backup legacy submodules - Remove 7 legacy submodules from root .gitmodules (archive/third_party/*) - Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11) - Backup complete .gitmodules to archive/.gitmodules - Add documentation in archive/README.md for researchers who need legacy submodules This reduces initial clone size by ~500MB and avoids downloading unused dependencies. * refactor: move doc/ back to root directory Keep documentation in root for easier access and maintenance. * refactor: consolidate all images to doc/assets/ - Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/ - Remove KT-SFT/assets/ (images already in doc/assets/) - Update KT-SFT/README.md image references to ../doc/assets/ - Eliminates ~7.9MB image duplication - Centralizes all documentation assets in one location * fix pic path for README
160 lines
6.5 KiB
Python
160 lines
6.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Description :
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Author : chenht2022
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Date : 2024-07-25 10:32:05
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Version : 1.0.0
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LastEditors : chenht2022
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LastEditTime : 2024-08-06 10:41:28
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Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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'''
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import os, sys
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import time
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sys.path.append(os.path.dirname(__file__) + '/../build')
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import cpuinfer_ext
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import torch
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expert_num = 160
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hidden_size = 5120
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intermediate_size = 1536
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stride = 16
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group_min_len = 10
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group_max_len = 1024
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n_routed_experts = 6
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layer_num = 10
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qlen = 1
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CPUInfer = cpuinfer_ext.CPUInfer(64)
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warm_up_iter = 1000
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test_iter = 10000
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def bench_moe(quant_mode: str):
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with torch.inference_mode(mode=True):
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hidden_type = 30 # ggml_type::GGML_TYPE_BF16
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if quant_mode == "fp32":
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gate_type = 0 # ggml_type::GGML_TYPE_F32
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up_type = 0 # ggml_type::GGML_TYPE_F32
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down_type = 0 # ggml_type::GGML_TYPE_F32
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bytes_per_elem = 4.000000
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elif quant_mode == "fp16":
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gate_type = 1 # ggml_type::GGML_TYPE_F16
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up_type = 1 # ggml_type::GGML_TYPE_F16
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down_type = 1 # ggml_type::GGML_TYPE_F16
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bytes_per_elem = 2.000000
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elif quant_mode == "bf16":
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gate_type = 30 # ggml_type::GGML_TYPE_BF16
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up_type = 30 # ggml_type::GGML_TYPE_BF16
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down_type = 30 # ggml_type::GGML_TYPE_BF16
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bytes_per_elem = 2.000000
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elif quant_mode == "q8_0":
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gate_type = 8 # ggml_type::GGML_TYPE_Q8_0
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up_type = 8 # ggml_type::GGML_TYPE_Q8_0
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down_type = 8 # ggml_type::GGML_TYPE_Q8_0
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bytes_per_elem = 1.062500
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elif quant_mode == "q6_k":
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gate_type = 14 # ggml_type::GGML_TYPE_Q6_K
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up_type = 14 # ggml_type::GGML_TYPE_Q6_K
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down_type = 14 # ggml_type::GGML_TYPE_Q6_K
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bytes_per_elem = 0.820312
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elif quant_mode == "q5_k_m":
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gate_type = 13 # ggml_type::GGML_TYPE_Q5_K
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up_type = 13 # ggml_type::GGML_TYPE_Q5_K
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down_type = 14 # ggml_type::GGML_TYPE_Q6_K
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bytes_per_elem = 0.731771
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elif quant_mode == "q4_k_m":
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gate_type = 12 # ggml_type::GGML_TYPE_Q4_K
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up_type = 12 # ggml_type::GGML_TYPE_Q4_K
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down_type = 14 # ggml_type::GGML_TYPE_Q6_K
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bytes_per_elem = 0.648437
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elif quant_mode == "q3_k_m":
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gate_type = 11 # ggml_type::GGML_TYPE_Q3_K
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up_type = 11 # ggml_type::GGML_TYPE_Q3_K
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down_type = 13 # ggml_type::GGML_TYPE_Q5_K
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bytes_per_elem = 0.515625
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elif quant_mode == "q2_k":
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gate_type = 10 # ggml_type::GGML_TYPE_Q2_K
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up_type = 10 # ggml_type::GGML_TYPE_Q2_K
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down_type = 11 # ggml_type::GGML_TYPE_Q3_K
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bytes_per_elem = 0.328125
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elif quant_mode == "iq3_xs":
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gate_type = 21 # ggml_type::GGML_TYPE_IQ3_S
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up_type = 21 # ggml_type::GGML_TYPE_IQ3_S
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down_type = 21 # ggml_type::GGML_TYPE_IQ3_S
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bytes_per_elem = 0.429688
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elif quant_mode == "iq2_xxs":
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gate_type = 16 # ggml_type::GGML_TYPE_IQ2_XXS
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up_type = 16 # ggml_type::GGML_TYPE_IQ2_XXS
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down_type = 16 # ggml_type::GGML_TYPE_IQ2_XXS
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bytes_per_elem = 0.257812
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else:
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assert(False)
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moes = []
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gate_projs = []
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up_projs = []
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down_projs = []
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for _ in range(layer_num):
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gate_proj = torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
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up_proj = torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
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down_proj = torch.randn((expert_num, hidden_size, intermediate_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
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config = cpuinfer_ext.moe.MOEConfig(expert_num, n_routed_experts, hidden_size, intermediate_size, stride, group_min_len, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
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moe = cpuinfer_ext.moe.MOE(config)
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gate_projs.append(gate_proj)
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up_projs.append(up_proj)
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down_projs.append(down_proj)
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moes.append(moe)
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expert_ids = torch.stack([torch.stack([torch.randperm(expert_num, dtype=torch.int64, device = "cuda")[:n_routed_experts] for _ in range(qlen)]) for _ in range(layer_num)]).to("cpu").contiguous()
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weights = torch.rand((layer_num, qlen, n_routed_experts), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
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input = torch.randn((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
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output = torch.empty((layer_num, qlen, hidden_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
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# warm up
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for i in range(warm_up_iter):
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CPUInfer.submit(
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moes[i % layer_num].forward(
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qlen,
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n_routed_experts,
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expert_ids[i % layer_num].data_ptr(),
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weights[i % layer_num].data_ptr(),
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input[i % layer_num].data_ptr(),
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output[i % layer_num].data_ptr()
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)
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)
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CPUInfer.sync()
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# test
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start = time.perf_counter()
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for i in range(test_iter):
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CPUInfer.submit(
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moes[i % layer_num].forward(
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qlen,
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n_routed_experts,
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expert_ids[i % layer_num].data_ptr(),
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weights[i % layer_num].data_ptr(),
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input[i % layer_num].data_ptr(),
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output[i % layer_num].data_ptr()
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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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total_time = end - start
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print('Quant mode: ', quant_mode)
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print('Time(s): ', total_time)
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print('Iteration: ', test_iter)
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print('Time(us) per iteration: ', total_time / test_iter * 1000000)
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print('Bandwidth: ', hidden_size * intermediate_size * 3 * n_routed_experts * bytes_per_elem * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
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print('')
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bench_moe("fp32")
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bench_moe("fp16")
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bench_moe("bf16")
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bench_moe("q8_0")
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bench_moe("q6_k")
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bench_moe("q5_k_m")
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bench_moe("q4_k_m")
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bench_moe("q3_k_m")
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bench_moe("q2_k")
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# Not supported on __x86_64__
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# bench_linear("iq3_xs")
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# bench_linear("iq2_xxs")
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