400 lines
14 KiB
Python
400 lines
14 KiB
Python
"""
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Lightweight DeepGEMM JIT compilation warmup without loading model weights.
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Reads model config.json from HF cache to derive kernel shapes, then compiles
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DeepGEMM kernels directly. This avoids the expensive model weight loading step
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that the full `sglang.compile_deep_gemm` requires.
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Supports DeepSeek V2/V3 family models. Falls back to `sglang.compile_deep_gemm`
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for unsupported architectures.
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Usage:
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python3 scripts/ci/cuda/warmup_deep_gemm.py \
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deepseek-ai/DeepSeek-V3-0324:8 \
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deepseek-ai/DeepSeek-V3.2-Exp:8
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"""
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import json
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import os
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import subprocess
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import sys
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import time
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from math import ceil
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from pathlib import Path
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# Configure DeepGEMM cache before importing deep_gemm
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os.environ["DG_JIT_CACHE_DIR"] = os.getenv(
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"SGLANG_DG_CACHE_DIR",
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os.path.join(os.path.expanduser("~"), ".cache", "deep_gemm"),
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)
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os.environ["DG_JIT_USE_NVRTC"] = os.getenv("SGL_DG_USE_NVRTC", "0")
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BLOCK_SIZE = 128
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def get_config_json(model_name):
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"""Load config.json for a cached model from HF cache."""
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cache_dir = os.environ.get(
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"HF_HOME", os.path.join(os.path.expanduser("~"), ".cache", "huggingface")
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)
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hub_dir = os.path.join(cache_dir, "hub")
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safe_name = "models--" + model_name.replace("/", "--")
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snapshots_dir = os.path.join(hub_dir, safe_name, "snapshots")
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if not os.path.isdir(snapshots_dir):
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return None
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snapshots = sorted(
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Path(snapshots_dir).iterdir(), key=lambda p: p.stat().st_mtime, reverse=True
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)
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for snapshot in snapshots:
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config_path = snapshot / "config.json"
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if config_path.exists():
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with open(config_path) as f:
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return json.load(f)
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return None
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def is_deepseek_v2v3(config):
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"""Check if a model is from the DeepSeek V2/V3 family."""
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architectures = config.get("architectures", [])
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model_type = config.get("model_type", "")
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return any(
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"DeepseekV2" in a or "DeepseekV3" in a for a in architectures
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) or model_type in ("deepseek_v2", "deepseek_v3")
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def compute_deepseek_v2v3_shapes(config, tp):
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"""Compute all DeepGEMM (kernel_type, N, K, num_groups) for DeepSeek V2/V3.
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Shape derivation based on:
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- MoE: python/sglang/srt/layers/moe/fused_moe_triton/layer.py
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- MLA: python/sglang/srt/models/deepseek_v2.py
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- FP8: python/sglang/srt/layers/quantization/fp8_kernel.py
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"""
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shapes = []
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hidden_size = config["hidden_size"]
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num_attention_heads = config.get("num_attention_heads", 128)
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kv_lora_rank = config.get("kv_lora_rank", 512)
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qk_nope_head_dim = config.get("qk_nope_head_dim", 128)
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v_head_dim = config.get("v_head_dim", 128)
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n_routed_experts = config.get("n_routed_experts", 0)
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n_shared_experts = config.get("n_shared_experts", 0)
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moe_intermediate_size = config.get("moe_intermediate_size", 0)
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num_local_heads = num_attention_heads // tp
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# Shared expert fusion is enabled by default (disable_shared_experts_fusion=False)
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# so the FusedMoE weight tensor includes shared experts
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num_local_experts = n_routed_experts + n_shared_experts
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# --- MoE expert GEMM shapes ---
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# FusedMoE shards intermediate_size across TP ranks (column parallel for gate/up,
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# row parallel for down). All experts are replicated on each TP rank.
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if n_routed_experts > 0 and moe_intermediate_size > 0:
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moe_inter_per_tp = moe_intermediate_size // tp
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# Gate-Up projection: (tokens, hidden_size) @ (experts, 2*inter_per_tp, hidden_size)^T
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# Both masked and contiguous paths are used at runtime
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shapes.append(("MASKED", moe_inter_per_tp * 2, hidden_size, num_local_experts))
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shapes.append(("CONTIG", moe_inter_per_tp * 2, hidden_size, num_local_experts))
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# Down projection: (tokens, inter_per_tp) @ (experts, hidden_size, inter_per_tp)^T
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shapes.append(("MASKED", hidden_size, moe_inter_per_tp, num_local_experts))
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shapes.append(("CONTIG", hidden_size, moe_inter_per_tp, num_local_experts))
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# --- MLA attention GEMM shapes (masked grouped GEMM) ---
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if kv_lora_rank > 0 and num_local_heads > 0:
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# Q_nope -> compressed K: (heads, m, qk_nope_head_dim) @ (heads, kv_lora_rank, qk_nope_head_dim)^T
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shapes.append(("MASKED", kv_lora_rank, qk_nope_head_dim, num_local_heads))
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# Attention output -> V: (heads, m, kv_lora_rank) @ (heads, v_head_dim, kv_lora_rank)^T
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shapes.append(("MASKED", v_head_dim, kv_lora_rank, num_local_heads))
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# --- kv_b_proj (non-grouped GEMM via FP8 kernel) ---
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# ColumnParallelLinear(kv_lora_rank, num_heads * (qk_nope + v_head_dim))
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# Per TP rank: N = num_local_heads * (qk_nope_head_dim + v_head_dim)
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if kv_lora_rank > 0 and num_local_heads > 0:
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kv_b_proj_n = num_local_heads * (qk_nope_head_dim + v_head_dim)
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shapes.append(("NORMAL", kv_b_proj_n, kv_lora_rank, 1))
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return shapes
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def get_architecture_key(config, tp):
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"""Key for dedup: models with same key share DeepGEMM kernels."""
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if config is None:
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return None
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fields = [
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config.get("hidden_size", 0),
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config.get("moe_intermediate_size", 0),
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config.get("n_routed_experts", 0),
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config.get("n_shared_experts", 0),
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config.get("num_attention_heads", 0),
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config.get("kv_lora_rank", 0),
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config.get("qk_nope_head_dim", 0),
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config.get("v_head_dim", 0),
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tp,
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]
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return tuple(fields)
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def compute_m_list(fast_warmup=False, chunked_prefill_size=8192):
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"""Compute the list of M values to compile (matches compile_utils.py logic)."""
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m_list = []
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if fast_warmup:
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m_list += list(range(1, 1025))
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next_m, sample_step = 1024, 2
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max_prefill_bs = min(chunked_prefill_size, 32 * 1024)
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while next_m < max_prefill_bs:
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m_list += list(range(next_m, 2 * next_m, sample_step))
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next_m *= 2
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sample_step *= 2
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m_list.append(max_prefill_bs)
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m_list = sorted(set(m_list))
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else:
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m_max = 16 * 1024
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if chunked_prefill_size > 8192:
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m_max = chunked_prefill_size * 2
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m_max = min(128 * 1024, m_max)
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m_list = list(range(1, m_max + 1))
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return m_list
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def _empty_token_fp8(size):
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"""Create FP8 token tensor + per-block scale tensor."""
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import torch
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*dims, k = size
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return (
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torch.empty(size, device="cuda", dtype=torch.float8_e4m3fn),
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torch.empty((*dims, ceil(k / BLOCK_SIZE)), device="cuda", dtype=torch.float32),
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)
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def _empty_block_fp8(size):
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"""Create FP8 block tensor + per-block scale tensor."""
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import torch
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*dims, n, k = size
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return (
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torch.empty(size, device="cuda", dtype=torch.float8_e4m3fn),
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torch.empty(
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(*dims, ceil(n / BLOCK_SIZE), ceil(k / BLOCK_SIZE)),
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device="cuda",
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dtype=torch.float32,
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),
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)
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def get_memory_requirement(kernel_type, max_m, n, k, num_groups):
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"""Estimate GPU memory needed in GB for compilation buffers."""
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_GB = 1 << 30
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if kernel_type == "NORMAL":
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return (max_m * k + n * k + max_m * n * 2) / _GB
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elif kernel_type == "CONTIG":
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return (max_m * k + num_groups * n * k + max_m * 4 + max_m * n * 2) / _GB
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elif kernel_type == "MASKED":
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return (
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num_groups * max_m * k
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+ num_groups * n * k
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+ num_groups * 4
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+ num_groups * max_m * n * 2
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) / _GB
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return 0
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def compile_one_shape(kernel_type, n, k, num_groups, m_list):
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"""Compile DeepGEMM kernels for one (kernel_type, N, K, num_groups) shape."""
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import deep_gemm
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import torch
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from tqdm import tqdm
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# Filter M list for contiguous layout alignment
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if kernel_type == "CONTIG":
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m_alignment = deep_gemm.get_mk_alignment_for_contiguous_layout()
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m_list = sorted(set(m for m in m_list if m % m_alignment == 0))
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if not m_list:
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return
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max_m = max(m_list)
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# Reduce max_m if not enough GPU memory
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mem_free = torch.cuda.mem_get_info()[0] / (1 << 30)
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mem_required = get_memory_requirement(kernel_type, max_m, n, k, num_groups)
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if mem_required > mem_free:
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while (
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get_memory_requirement(kernel_type, max_m, n, k, num_groups) > mem_free
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and max_m > 4096
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):
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max_m //= 2
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print(
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f" Memory {mem_free:.1f}GB < required {mem_required:.1f}GB, "
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f"reducing max_m to {max_m}"
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)
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m_list = [m for m in m_list if m <= max_m]
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old_mode = deep_gemm.get_compile_mode()
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deep_gemm.set_compile_mode(1)
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try:
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if kernel_type == "NORMAL":
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lhs_q, lhs_s = _empty_token_fp8((max_m, k))
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rhs_q, rhs_s = _empty_block_fp8((n, k))
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out = torch.empty((max_m, n), device="cuda", dtype=torch.bfloat16)
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for m in tqdm(m_list, desc=f" NORMAL N={n} K={k}"):
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deep_gemm.fp8_gemm_nt((lhs_q[:m], lhs_s[:m]), (rhs_q, rhs_s), out[:m])
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elif kernel_type == "CONTIG":
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lhs_q, lhs_s = _empty_token_fp8((max_m, k))
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rhs_q, rhs_s = _empty_block_fp8((num_groups, n, k))
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m_indices = torch.zeros((max_m,), device="cuda", dtype=torch.int32)
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out = torch.empty((max_m, n), device="cuda", dtype=torch.bfloat16)
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for m in tqdm(m_list, desc=f" CONTIG N={n} K={k} G={num_groups}"):
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deep_gemm.m_grouped_fp8_gemm_nt_contiguous(
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(lhs_q[:m], lhs_s[:m]),
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(rhs_q, rhs_s),
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out[:m],
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m_indices=m_indices[:m],
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)
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elif kernel_type == "MASKED":
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lhs_q, lhs_s = _empty_token_fp8((num_groups, max_m, k))
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rhs_q, rhs_s = _empty_block_fp8((num_groups, n, k))
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masked_m = torch.zeros((num_groups,), device="cuda", dtype=torch.int32)
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out = torch.empty(
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(num_groups, max_m, n), device="cuda", dtype=torch.bfloat16
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)
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for m in tqdm(m_list, desc=f" MASKED N={n} K={k} G={num_groups}"):
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deep_gemm.fp8_m_grouped_gemm_nt_masked(
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(lhs_q, lhs_s),
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(rhs_q, rhs_s),
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out,
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masked_m=masked_m,
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expected_m=m,
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)
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finally:
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deep_gemm.set_compile_mode(old_mode)
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torch.cuda.current_stream().synchronize()
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torch.cuda.empty_cache()
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def compile_shapes_lightweight(shapes, m_list):
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"""Compile all DeepGEMM shapes directly (no model loading)."""
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for i, (kernel_type, n, k, num_groups) in enumerate(shapes, 1):
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print(f"\n[{i}/{len(shapes)}] {kernel_type} N={n} K={k} G={num_groups}")
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t0 = time.time()
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compile_one_shape(kernel_type, n, k, num_groups, m_list)
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elapsed = time.time() - t0
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print(f" Done in {elapsed:.1f}s")
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def fallback_compile_deep_gemm(model, tp):
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"""Fall back to full sglang.compile_deep_gemm (loads model weights)."""
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print(f"Falling back to full compile_deep_gemm for {model} (tp={tp})...")
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cmd = [
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sys.executable,
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"-m",
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"sglang.compile_deep_gemm",
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"--model",
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model,
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"--tp",
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str(tp),
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"--trust-remote-code",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true, "num_threads": 64}',
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]
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result = subprocess.run(cmd)
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if result.returncode != 0:
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print(f"Warning: fallback failed for {model} (exit code {result.returncode})")
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return result.returncode == 0
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def main():
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if len(sys.argv) < 2 or sys.argv[1] in ("-h", "--help"):
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print("Usage: warmup_deep_gemm.py model1:tp1 [model2:tp2 ...]")
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print("\nDerives DeepGEMM kernel shapes from config.json without loading model")
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print(
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"weights. Falls back to full compile_deep_gemm for unknown architectures."
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)
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sys.exit(0)
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# Parse model:tp pairs
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model_tp_pairs = []
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for arg in sys.argv[1:]:
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if ":" not in arg:
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print(f"Error: expected model:tp format, got '{arg}'")
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sys.exit(1)
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model, tp_str = arg.rsplit(":", 1)
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model_tp_pairs.append((model, int(tp_str)))
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fast_warmup = os.environ.get("SGLANG_JIT_DEEPGEMM_FAST_WARMUP", "0").lower() in (
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"1",
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"true",
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)
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print(f"=== DeepGEMM Lightweight Warmup ({len(model_tp_pairs)} model(s)) ===")
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print(f" Fast warmup: {fast_warmup}")
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print(
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f" Cache dir: {os.environ.get('DG_JIT_CACHE_DIR', '~/.cache/deep_gemm')}\n"
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)
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# Load configs and deduplicate by architecture
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seen_keys = {}
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to_process = [] # (model, tp, config_or_None, shapes_or_None)
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for model, tp in model_tp_pairs:
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config = get_config_json(model)
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if config is None:
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print(f" SKIP {model} (tp={tp}): config.json not in HF cache")
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continue
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key = get_architecture_key(config, tp)
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if key in seen_keys:
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print(f" DEDUP {model} (tp={tp}): same shapes as {seen_keys[key]}")
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continue
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if is_deepseek_v2v3(config):
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shapes = compute_deepseek_v2v3_shapes(config, tp)
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seen_keys[key] = model
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to_process.append((model, tp, config, shapes))
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print(f" FOUND {model} (tp={tp}): {len(shapes)} DeepGEMM shape(s)")
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else:
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# Unknown architecture: will use fallback
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seen_keys[key] = model
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to_process.append((model, tp, config, None))
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arch = config.get("architectures", ["unknown"])
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print(f" FOUND {model} (tp={tp}): unknown arch {arch}, will use fallback")
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if not to_process:
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print("\nNo models to process. Done.")
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return
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m_list = compute_m_list(fast_warmup=fast_warmup)
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print(f"\nM list: {len(m_list)} values (range {min(m_list)}-{max(m_list)})")
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for model, tp, config, shapes in to_process:
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print(f"\n{'=' * 60}")
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print(f"Model: {model} (tp={tp})")
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print(f"{'=' * 60}")
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if shapes is None:
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# Unknown architecture: fall back to full compile_deep_gemm
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fallback_compile_deep_gemm(model, tp)
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continue
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# Print shape summary
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for kernel_type, n, k, num_groups in shapes:
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print(f" {kernel_type:8s} N={n:<6d} K={k:<6d} G={num_groups}")
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t0 = time.time()
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compile_shapes_lightweight(shapes, m_list)
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elapsed = time.time() - t0
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print(f"\nCompleted {model} in {elapsed:.1f}s")
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print("\nDeepGEMM lightweight warmup complete.")
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if __name__ == "__main__":
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main()
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