TE EP integration to MoEBlock

.gitignore

@@ -43,3 +43,6 @@ tensor_dumps/
 artifacts/
 .DS_Store
 .claude/
+
+# NCCL EP shared library staged by setup.py for wheel packaging.
+/transformer_engine/libnccl_ep.so*

.gitmodules

@@ -7,3 +7,6 @@
 [submodule "3rdparty/cutlass"]
 	path = 3rdparty/cutlass
 	url = https://github.com/NVIDIA/cutlass.git
+[submodule "3rdparty/nccl"]
+	path = 3rdparty/nccl
+	url = https://github.com/NVIDIA/nccl.git

build_tools/jax.py

@@ -120,6 +120,9 @@ def setup_jax_extension(
     if bool(int(os.getenv("NVTE_WITH_CUBLASMP", 0))):
         cxx_flags.append("-DNVTE_WITH_CUBLASMP")
 
+    if bool(int(os.getenv("NVTE_BUILD_WITH_NCCL_EP", "1"))):
+        cxx_flags.append("-DNVTE_WITH_NCCL_EP")
+
     # Define TE/JAX as a Pybind11Extension
     from pybind11.setup_helpers import Pybind11Extension
 

examples/jax/ep/bench/ep_bench.py

@@ -0,0 +1,308 @@
+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+"""JAX EP perf bench — dispatch/combine (raw fwd + custom_vjp wrapper) on a 1DP x EP mesh.
+
+One process per GPU; launch via run_ep_bench.sh. Each stage is jitted and
+timed separately with NVTX ranges (prepare runs once outside the loop).
+Rank-0 prints mean wall in us; nsys / --xplane attribute kernels per stage.
+"""
+
+import argparse
+import os
+import sys
+import time
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from jax.sharding import Mesh, NamedSharding, PartitionSpec
+
+from transformer_engine.jax.cpp_extensions import ep as tex_ep
+from transformer_engine.jax.ep import EpLayerConfig, ep_bootstrap, ep_dispatch, ep_combine
+from transformer_engine.jax.sharding import MeshResource, global_shard_guard
+
+
+def _parse_args():
+    p = argparse.ArgumentParser(description="TE-JAX EP perf bench (dispatch_fwd + combine_fwd)")
+    p.add_argument("--coordinator-address", required=True)
+    p.add_argument("--process-id", type=int, required=True)
+    p.add_argument("--num-processes", type=int, required=True)
+    p.add_argument("--tokens-per-rank", type=int, default=8192)
+    p.add_argument("--hidden", type=int, default=7168)
+    p.add_argument("--top-k", type=int, default=8)
+    p.add_argument("--num-experts", type=int, default=256)
+    p.add_argument("--dp-size", type=int, default=1)
+    p.add_argument("--warmup", type=int, default=2)
+    p.add_argument("--iters", type=int, default=10)
+    p.add_argument(
+        "--max-num-sms",
+        type=int,
+        default=0,
+        help="Max SMs for dispatch / combine / preprocess kernels (0 = auto).",
+    )
+    p.add_argument(
+        "--mode-label",
+        default=None,
+        help="Optional label suffix for NVTX range names so nsys can partition kernels.",
+    )
+    p.add_argument(
+        "--second-step",
+        action="store_true",
+        help=(
+            "Time only the 2nd step (1 warmup iter, 1 timed iter). Use to isolate "
+            "JIT-cache-warm-but-no-steady-state-batching overhead from steady-state perf."
+        ),
+    )
+    p.add_argument(
+        "--xplane",
+        default=None,
+        help="If set, jax.profiler dumps an XPlane trace into this dir (rank 0 only).",
+    )
+    return p.parse_args()
+
+
+def _distributed_init(args):
+    jax.distributed.initialize(
+        coordinator_address=args.coordinator_address,
+        num_processes=args.num_processes,
+        process_id=args.process_id,
+        local_device_ids=[args.process_id],
+    )
+
+
+def _build_mesh(args):
+    n = args.num_processes
+    assert n % args.dp_size == 0
+    ep = n // args.dp_size
+    devs = np.asarray(jax.devices()).reshape(args.dp_size, ep)
+    return Mesh(devs, ("dp", "ep")), ep
+
+
+def _make_inputs(args, ep_size):
+    """Round-robin routing, uniform top-k weights; each rank sees ``args.tokens_per_rank`` tokens."""
+    n = args.num_processes
+    T = args.tokens_per_rank
+    H = args.hidden
+    K = args.top_k
+    E = args.num_experts
+    del ep_size
+
+    topk_idx = np.empty((n * T, K), dtype=np.int32)
+    for t in range(n * T):
+        for k in range(K):
+            topk_idx[t, k] = (t * K + k) % E
+    topk_idx = jnp.asarray(topk_idx)
+    topk_w = jnp.full((n * T, K), 1.0 / K, dtype=jnp.float32)
+    tokens = jnp.asarray(
+        np.random.default_rng(0).standard_normal((n * T, H), dtype=np.float32) * 0.5,
+        dtype=jnp.bfloat16,
+    )
+    return tokens, topk_idx, topk_w
+
+
+def main():
+    args = _parse_args()
+    _distributed_init(args)
+    mesh, ep_size = _build_mesh(args)
+    mr = MeshResource(dp_resource="dp", ep_resource="ep")
+    rank = args.process_id
+
+    local_experts = args.num_experts // ep_size
+    recv_capacity_per_rank = args.num_processes * args.tokens_per_rank * args.top_k // 2
+
+    if rank == 0:
+        print(
+            f"[ep_bench] world={args.num_processes} dp={args.dp_size} ep={ep_size}"
+            f" T={args.tokens_per_rank} H={args.hidden} K={args.top_k}"
+            f" E={args.num_experts} (local={local_experts}) recv_pr={recv_capacity_per_rank}"
+            + (f" mode={args.mode_label}" if args.mode_label else ""),
+            flush=True,
+        )
+
+    nvtx_suffix = f"[{args.mode_label}]" if args.mode_label else ""
+
+    in_spec = PartitionSpec(("dp", "ep"), None)
+    ep_spec_3d = PartitionSpec(("dp", "ep"), None, None)
+    ep_spec_2d = PartitionSpec(("dp", "ep"), None)
+    out_spec = (("dp", "ep"), None)
+    T_global = args.num_processes * args.tokens_per_rank
+
+    with mesh, global_shard_guard(mr):
+        ep_bootstrap(
+            world_size=args.num_processes,
+            rank=rank,
+            num_experts=args.num_experts,
+            max_tokens_per_rank=args.tokens_per_rank,
+            recv_capacity_per_rank=recv_capacity_per_rank,
+            hidden_dim=args.hidden,
+            max_num_sms=args.max_num_sms,
+        )
+
+        tokens, topk_idx, topk_w = _make_inputs(args, ep_size)
+        idx_s = jax.lax.with_sharding_constraint(topk_idx, NamedSharding(mesh, in_spec))
+        tok_s = jax.lax.with_sharding_constraint(tokens, NamedSharding(mesh, in_spec))
+        w_s = jax.lax.with_sharding_constraint(topk_w, NamedSharding(mesh, in_spec))
+
+        cfg = EpLayerConfig(top_k=args.top_k, dispatch_output_per_expert_alignment=16)
+
+        @jax.jit
+        def run_prepare(idx):
+            tc, hm = tex_ep.ep_prepare(cfg, idx)
+            return tc, hm
+
+        @jax.jit
+        def run_dispatch(hm, idx, toks, w):
+            recv_t, recv_w = tex_ep.ep_dispatch_fwd(cfg, hm, idx, toks, w, recv_capacity_per_rank)
+            recv_t = jax.lax.with_sharding_constraint(recv_t, NamedSharding(mesh, ep_spec_3d))
+            recv_w = jax.lax.with_sharding_constraint(recv_w, NamedSharding(mesh, ep_spec_2d))
+            return recv_t, recv_w
+
+        @jax.jit
+        def run_dispatch_vjp(idx, toks, w):
+            recv_t, recv_w, _hm, _tc = ep_dispatch(cfg, idx, toks, w, recv_capacity_per_rank)
+            recv_t = jax.lax.with_sharding_constraint(recv_t, NamedSharding(mesh, ep_spec_3d))
+            recv_w = jax.lax.with_sharding_constraint(recv_w, NamedSharding(mesh, ep_spec_2d))
+            return recv_t, recv_w
+
+        @jax.jit
+        def run_combine(hm, recv_t):
+            out = tex_ep.ep_combine_fwd(
+                cfg,
+                hm,
+                recv_t,
+                T_global,
+                out_partition_spec=out_spec,
+            )
+            return out
+
+        @jax.jit
+        def run_combine_vjp(hm, tc, recv_t):
+            # ep_combine is unweighted; bench feeds expert_out directly (caller
+            # would otherwise pre-multiply by recv_topk_weights + mask).
+            out = ep_combine(cfg, hm, tc, recv_t, T_global, out_sharding=out_spec)
+            return out
+
+        tc, handle_mem = run_prepare(idx_s)
+        tc.block_until_ready()
+        handle_mem.block_until_ready()
+
+        recv_t0, recv_w0 = run_dispatch(handle_mem, idx_s, tok_s, w_s)
+        recv_t0.block_until_ready()
+        recv_w0.block_until_ready()
+
+        warmup_n = 1 if args.second_step else args.warmup
+        iters_n = 1 if args.second_step else args.iters
+
+        for _ in range(warmup_n):
+            r, _rw = run_dispatch(handle_mem, idx_s, tok_s, w_s)
+            r.block_until_ready()
+            o = run_combine(handle_mem, r)
+            o.block_until_ready()
+        run_dispatch_vjp(idx_s, tok_s, w_s)[0].block_until_ready()
+        run_combine_vjp(handle_mem, tc, recv_t0).block_until_ready()
+
+        if args.xplane and rank == 0:
+            os.makedirs(args.xplane, exist_ok=True)
+            jax.profiler.start_trace(args.xplane)
+
+        try:
+            import nvtx as _nvtx
+
+            def _push(name):
+                _nvtx.push_range(message=name)
+
+            def _pop():
+                _nvtx.pop_range()
+
+        except ImportError:
+
+            def _push(name):
+                pass
+
+            def _pop():
+                pass
+
+        def _time_stage_wall_us(name, fn):
+            # First timed iter still carries an autotune outlier even after JIT
+            # warmup; run iters_n + 1, drop iter 0 from the average, and push
+            # the NVTX range AFTER iter 0 so nsys' nvtx_kern_sum excludes the
+            # outlier too.
+            total_ns = 0
+            counted = 0
+            for i in range(iters_n + 1):
+                if i == 1:
+                    _push(f"{name}{nvtx_suffix}")
+                t0 = time.perf_counter_ns()
+                fn()
+                dt = time.perf_counter_ns() - t0
+                if i == 0:
+                    continue
+                total_ns += dt
+                counted += 1
+            _pop()
+            return total_ns / 1e3 / counted
+
+        def _do_dispatch():
+            r, _ = run_dispatch(handle_mem, idx_s, tok_s, w_s)
+            r.block_until_ready()
+
+        def _do_dispatch_vjp():
+            r, _ = run_dispatch_vjp(idx_s, tok_s, w_s)
+            r.block_until_ready()
+
+        def _do_combine():
+            o = run_combine(handle_mem, recv_t0)
+            o.block_until_ready()
+
+        def _do_combine_vjp():
+            o = run_combine_vjp(handle_mem, tc, recv_t0)
+            o.block_until_ready()
+
+        d_wall_us = _time_stage_wall_us("dispatch_fwd", _do_dispatch)
+        dv_wall_us = _time_stage_wall_us("ep_dispatch_vjp", _do_dispatch_vjp)
+        c_wall_us = _time_stage_wall_us("combine_fwd", _do_combine)
+        cv_wall_us = _time_stage_wall_us("ep_combine_vjp", _do_combine_vjp)
+
+        if args.xplane and rank == 0:
+            jax.profiler.stop_trace()
+
+    if rank == 0:
+        label = f" [{args.mode_label}]" if args.mode_label else ""
+        print("", flush=True)
+        print(f"| stage             | mean wall (us){label} |", flush=True)
+        print("|-------------------|---------------:|", flush=True)
+        print(f"| dispatch_fwd      | {d_wall_us:14.1f} |", flush=True)
+        print(f"| ep_dispatch_vjp   | {dv_wall_us:14.1f} |", flush=True)
+        print(f"| combine_fwd       | {c_wall_us:14.1f} |", flush=True)
+        print(f"| ep_combine_vjp    | {cv_wall_us:14.1f} |", flush=True)
+        print(f"| (dispatch vjp-fwd)| {dv_wall_us - d_wall_us:14.1f} |", flush=True)
+        print(f"| (combine  vjp-fwd)| {cv_wall_us - c_wall_us:14.1f} |", flush=True)
+        print("", flush=True)
+        print(
+            "[ep_bench] kernel breakout: see nsys nvtx_kern_sum output below "
+            "(produced by run_ep_bench.sh --nsys).",
+            flush=True,
+        )
+
+    # Under nsys: force cudaDeviceReset() to drain CUPTI's in-process kernel
+    # records into the .nsys-rep, then os._exit to skip JAX's coord-service
+    # watchdog. The reset crashes during NCCL EP context teardown, so we only
+    # take this path when the launcher opts in via EP_BENCH_FLUSH_CUPTI=1.
+    if os.environ.get("EP_BENCH_FLUSH_CUPTI", "0") == "1":
+        try:
+            import ctypes
+
+            cudart = ctypes.CDLL("libcudart.so")
+            cudart.cudaDeviceSynchronize()
+            cudart.cudaDeviceReset()
+        except Exception:
+            pass
+        time.sleep(0.5)
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os._exit(0)
+
+
+if __name__ == "__main__":
+    main()