Files
llmgw/internal/agent/discovery/gpu.go
T
ilkerandClaude Opus 5 6a13e3dddf
CI / test (push) Successful in 45s
Phase 2 (1/2): node discovery, engine adapters, process supervisor
Everything here works without GPUs and is covered by tests. The control plane
connection is not built yet, so llmgw agent itself is still a stub and the phase
exit criterion is not met.

- discovery: IB from /sys/class/infiniband, parsing the formats sysfs actually
  uses and telling native InfiniBand from RoCE; GPUs via nvidia-smi, the
  cgo-free path, with health flags for uncorrected ECC and memory already held
  by another process. A node without IB is not an error, it is a single-node
  island.
- engines: vLLM and SGLang command and environment construction, kept free of
  process handling so it stays testable. The NCCL environment is the part the
  plan marks critical: NCCL_IB_HCA lists only the chosen fabric's adapters,
  because leaving it open lets NCCL pick an adapter on a fabric where the peer
  is unreachable, and the job then hangs instead of failing.
- validation before launch: no mixed GPU types in a tensor-parallel group, the
  node/GPU/TP arithmetic has to add up, and fp8 is refused below compute
  capability 8.9 rather than silently falling back to a slower path.
- supervisor: process groups, because engines fork workers that hold GPU memory
  and outlive a signal sent only to the leader; a bounded log tail, since engine
  output is the only diagnosis when loading fails; Starting separate from
  Loading, because a 70B model legitimately takes minutes; and orphan adoption
  through pid files fingerprinted with the command line, since pids get reused
  and adopting an unrelated process is worse than leaking the original.
- readiness is two questions: /health says the process is up, /v1/models says
  the weights loaded. Routing on /health alone sends traffic into a loading model.
- llmgw agent inventory reports what the scheduler would see on a node.

One plan correction: GPU class sizes come from the driver in GiB, which does not
always match the marketing name. An L40S is sold as 48GB and reports 44.99 GiB,
so its class is l40s-45g, not l40s-48g. Consistency matters more than matching
the box, because that number decides whether a model fits. Plan §2 and §5.1
updated.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-16 02:01:59 +02:00

134 lines
4.4 KiB
Go

package discovery
import (
"encoding/json"
"fmt"
"math"
"regexp"
"slices"
"strconv"
"strings"
)
// GPU is one accelerator on this node.
type GPU struct {
Index int `json:"index"`
UUID string `json:"uuid"`
Name string `json:"name"`
// MemoryTotalMB is the total framebuffer size.
MemoryTotalMB int `json:"memory_total_mb"`
// MemoryUsedMB is what is already allocated. Non-zero on an idle node means
// something other than our engines is using the card (plan §5.1).
MemoryUsedMB int `json:"memory_used_mb"`
// ComputeCapability gates quantization choices: an FP8 profile must not
// land on a card below 8.9 (plan §6.2).
ComputeCapability string `json:"compute_capability"`
NUMANode int `json:"numa_node"`
PCIBusID string `json:"pci_bus_id,omitempty"`
DriverVersion string `json:"driver_version,omitempty"`
CUDAVersion string `json:"cuda_version,omitempty"`
// NVLinkPeers are the indices of GPUs reachable over NVLink. Within a node
// a TP group should stay inside one NVLink domain.
NVLinkPeers []int `json:"nvlink_peers,omitempty"`
// Health carries anything that should keep the GPU out of placement.
Health GPUHealth `json:"health"`
}
// GPUHealth records conditions that make a GPU unsafe to schedule onto.
type GPUHealth struct {
// ECCErrors is the count of uncorrected ECC errors.
ECCErrors int `json:"ecc_errors"`
// TemperatureC is the current die temperature.
TemperatureC int `json:"temperature_c"`
// Problems lists human-readable reasons to avoid this GPU.
Problems []string `json:"problems,omitempty"`
}
// OK reports whether the GPU is fit for placement.
func (h GPUHealth) OK() bool { return len(h.Problems) == 0 }
// nonAlnum collapses everything that is not a letter or digit.
var nonAlnum = regexp.MustCompile(`[^a-z0-9]+`)
// Class derives the GPU class used for placement: same model, same memory.
//
// Two cards only belong in the same tensor-parallel group if they are the same
// class, so the derivation has to be stable across driver versions and naming
// quirks ("NVIDIA H100 80GB HBM3" and "H100-SXM5-80GB" are the same class).
func (g GPU) Class() string {
name := strings.ToLower(g.Name)
name = strings.TrimPrefix(name, "nvidia ")
name = strings.TrimPrefix(name, "tesla ")
// Keep the model token: the first word that contains a digit, which is how
// NVIDIA names every accelerator (h100, a100, l40s, rtx4090).
var model string
for _, tok := range nonAlnum.Split(name, -1) {
if tok == "" || !strings.ContainsFunc(tok, func(r rune) bool { return r >= '0' && r <= '9' }) {
continue
}
// Skip memory and form-factor tokens, which are not the model.
if strings.HasSuffix(tok, "gb") || tok == "80gb" || tok == "sxm4" || tok == "sxm5" || tok == "pcie" {
continue
}
model = tok
break
}
if model == "" {
model = nonAlnum.ReplaceAllString(name, "-")
}
// Round to the nearest GiB: vendors report 81559 MiB for an "80GB" card.
gb := int(math.Round(float64(g.MemoryTotalMB) / 1024))
return fmt.Sprintf("%s-%dg", model, gb)
}
// MarshalJSON adds the derived class, so a consumer of the JSON does not have
// to reimplement the derivation to know which GPUs are interchangeable.
func (g GPU) MarshalJSON() ([]byte, error) {
type alias GPU // prevents recursing back into this method
return json.Marshal(struct {
alias
Class string `json:"class"`
}{alias(g), g.Class()})
}
// Inventory is everything discovery found on this node.
type Inventory struct {
GPUs []GPU `json:"gpus"`
HCAs []HCA `json:"hcas"`
// Source names the backend that produced the GPU list, so a consumer can
// tell "no GPUs" from "discovery did not work".
Source string `json:"source"`
}
// Classes returns the distinct GPU classes present, sorted.
func (inv Inventory) Classes() []string {
seen := map[string]struct{}{}
for _, g := range inv.GPUs {
seen[g.Class()] = struct{}{}
}
out := make([]string, 0, len(seen))
for c := range seen {
out = append(out, c)
}
slices.Sort(out)
return out
}
// parseCSVInt reads an integer from nvidia-smi's CSV output, tolerating the
// "[N/A]" and "Not Supported" values it emits for unsupported queries.
func parseCSVInt(s string) (int, bool) {
s = strings.TrimSpace(s)
s = strings.TrimSuffix(s, " MiB")
s = strings.TrimSuffix(s, " W")
if s == "" || strings.HasPrefix(s, "[") || strings.EqualFold(s, "Not Supported") {
return 0, false
}
v, err := strconv.Atoi(s)
if err != nil {
return 0, false
}
return v, true
}