Rewrite MoE NaN test: per-expert format, activation quantization, grouped GEMM
This commit is contained in:
@@ -3,16 +3,15 @@
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DeepSeek-V4 MoE NaN Reproduction Test
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Finds where NaN originates in the MoE forward pass.
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Tests the EXACT CuTeDSLMoERunner code path used by vLLM.
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Tests individual experts (gate+up+down) with the CuTeDSL NVFP4 linear runner.
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Then tests the grouped GEMM MoE runner with stacked weights.
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This test is the FIRST step: if the MoE produces NaN, the entire model
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produces garbage. We need to find the NaN source before anything else matters.
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Test plan:
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1. Load MoE weights for a single layer
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2. Run the CuTeDSLMoERunner with various token counts and routing patterns
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3. Check for NaN at each step: quantize → L1 GEMM → SiLU → L2 GEMM → combine
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4. Specifically test with MegaMoE shapes: 48 experts (EP8), padded to 128 rows
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Key insight: DeepSeek-V4 is a MegaMoE with 384 experts.
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The NaN might come from:
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1. Weight loading / quantization
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2. Activation quantization (quantize_activation_nvfp4)
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3. The grouped GEMM kernel
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4. The combine/scatter step
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Usage (on B200):
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cd /root/nvfp4-megamoe-kernel
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@@ -27,12 +26,13 @@ sys.path.insert(0, REPO)
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MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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DEV = "cuda:0"
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H = 7168; NH = 128; HD = 512; NOPE = 448; ROPE = 64
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QL = 1536; OL = 1024; OG = 16; HPG = NH // OG
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INTERMEDIATE = 18432 # DeepSeek-V4 MoE intermediate size
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NUM_EXPERTS = 48 # EP8: 384/8
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H = 7168
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INTERMEDIATE = 18432 # DeepSeek-V4 MoE intermediate
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NUM_EXPERTS = 384
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TOPK = 6
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EPS = 1e-6; WINDOW = 128; SCALE = HD ** -0.5
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EPS = 1e-6
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E2M1 = torch.tensor([0,.5,1.,1.5,2.,3.,4.,6.,-0,-.5,-1.,-1.5,-2.,-3.,-4.,-6.], dtype=torch.float32)
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_cache = {}
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def P(k, wm, md):
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@@ -46,14 +46,23 @@ def rms(x, w, eps=1e-6):
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v = x.float().pow(2).mean(-1, keepdim=True)
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return (w.float() * (x * torch.rsqrt(v+eps)).float()).to(x.dtype)
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def make_runner(w, sf, gs_t, inf, outf):
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from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear
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fp4 = w.view(torch.float4_e2m1fn_x2).permute(1,0).contiguous()
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s = sf.to(torch.float8_e4m3fn) if sf.dtype != torch.float8_e4m3fn else sf
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s = s.permute(1,0).contiguous()
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gs = gs_t.max().item() if gs_t.numel() > 1 else gs_t.item()
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r = CuTeDSLNvfp4Linear(in_features=inf, out_features=outf, max_num_tokens=8192, device=str(w.device))
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r.fp4 = [fp4]; r.sf = [s]; r.gs = [gs]
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r.finalize_weights(); r._ensure_initialized()
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return r
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def test_moe_layer(layer_id=2):
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"""Test the MoE forward pass for a single layer, checking for NaN at each step."""
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from cutedsl.runner import CuTeDSLMoERunner
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def test_single_expert(layer_id=2, expert_id=0):
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"""Test a single expert's gate+up+down with CuTeDSL NVFP4 linear."""
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torch.cuda.set_device(0)
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torch.manual_seed(42)
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torch.cuda.empty_cache()
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_cache.clear()
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with open(os.path.join(MODEL, "model.safetensors.index.json")) as f:
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wm = json.load(f)["weight_map"]
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@@ -61,156 +70,131 @@ def test_moe_layer(layer_id=2):
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p = f"model.layers.{layer_id}"
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m = f"{p}.mlp"
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e = f"{m}.experts.{expert_id}"
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# Load embedding for input
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emb = G("model.embed_tokens.weight")
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fnorm = G(f"{p}.post_attention_layernorm.weight")
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# MoE weights — NVFP4 packed format
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try:
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w13_w = G(f"{m}.experts.w13_weight")
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w13_sf = G(f"{m}.experts.w13_weight_scale")
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w13_gs = G(f"{m}.experts.w13_weight_scale_2")
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w2_w = G(f"{m}.experts.w2_weight")
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w2_sf = G(f"{m}.experts.w2_weight_scale")
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w2_gs = G(f"{m}.experts.w2_weight_scale_2")
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except (KeyError, RuntimeError) as e:
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print(f" ERROR: Could not load MoE weights: {e}")
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print(f" Available keys for this layer:")
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for k in sorted(wm.keys()):
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if 'layers.2.mlp' in k:
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print(f" {k}")
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return
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# Load expert weights
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gate_w = G(f"{e}.gate_proj.weight"); gate_sf = G(f"{e}.gate_proj.weight_scale"); gate_gs = G(f"{e}.gate_proj.weight_scale_2")
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up_w = G(f"{e}.up_proj.weight"); up_sf = G(f"{e}.up_proj.weight_scale"); up_gs = G(f"{e}.up_proj.weight_scale_2")
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down_w = G(f"{e}.down_proj.weight"); down_sf = G(f"{e}.down_proj.weight_scale"); down_gs = G(f"{e}.down_proj.weight_scale_2")
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# Shared expert
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se_gate_w = G(f"{m}.shared_experts.gate_proj.weight")
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se_gate_sf = G(f"{m}.shared_experts.gate_proj.weight_scale")
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se_gate_gs = G(f"{m}.shared_experts.gate_proj.weight_scale_2")
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se_up_w = G(f"{m}.shared_experts.up_proj.weight")
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se_up_sf = G(f"{m}.shared_experts.up_proj.weight_scale")
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se_up_gs = G(f"{m}.shared_experts.up_proj.weight_scale_2")
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se_down_w = G(f"{m}.shared_experts.down_proj.weight")
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se_down_sf = G(f"{m}.shared_experts.down_proj.weight_scale")
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se_down_gs = G(f"{m}.shared_experts.down_proj.weight_scale_2")
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print(f" Expert {expert_id}:")
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print(f" gate: shape={gate_w.shape} dtype={gate_w.dtype} sf_shape={gate_sf.shape} gs={gate_gs.tolist()}")
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print(f" up: shape={up_w.shape} dtype={up_w.dtype}")
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print(f" down: shape={down_w.shape} dtype={down_w.dtype}")
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print(f" gate NaN: {torch.isnan(gate_w.float()).any()}")
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print(f" gate_gs NaN: {torch.isnan(gate_gs).any()}")
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print(f" gate input_scale: exists={f'{e}.gate_proj.input_scale' in wm}")
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print(f" w13_weight shape: {w13_w.shape}, dtype: {w13_w.dtype}")
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print(f" w2_weight shape: {w2_w.shape}, dtype: {w2_w.dtype}")
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print(f" w13_gs shape: {w13_gs.shape}")
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print(f" w2_gs shape: {w2_gs.shape}")
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print(f" w13_gs sample: {w13_gs[:5].tolist()}")
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print(f" w2_gs sample: {w2_gs[:5].tolist()}")
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# Check for zero or extreme gs values
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for name, gs in [("gate", gate_gs), ("up", up_gs), ("down", down_gs)]:
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if gs.numel() > 0:
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print(f" {name} gs: min={gs.min().item():.6f} max={gs.max().item():.6f}")
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if gs.min().item() == 0:
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print(f" WARNING: {name} gs has zero value — will cause division by zero!")
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# Check for NaN in weights
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print(f" w13 NaN: {torch.isnan(w13_w.float()).any()}")
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print(f" w2 NaN: {torch.isnan(w2_w.float()).any()}")
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print(f" w13_sf NaN: {torch.isnan(w13_sf.float()).any()}")
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print(f" w2_sf NaN: {torch.isnan(w2_sf.float()).any()}")
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print(f" w13_gs NaN: {torch.isnan(w13_gs).any()}")
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print(f" w2_gs NaN: {torch.isnan(w2_gs).any()}")
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# Create the MoE runner
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num_local_experts = w13_w.shape[0]
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hidden_size = w13_w.shape[2] * 2 # hidden//2 packed → *2 for fp4
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intermediate_size = w13_w.shape[1] // 2 # 2*intermediate // 2
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print(f"\n num_local_experts: {num_local_experts}")
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print(f" hidden_size: {hidden_size}")
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print(f" intermediate_size: {intermediate_size}")
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runner = CuTeDSLMoERunner(
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num_experts=num_local_experts,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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max_num_tokens=8192,
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top_k=TOPK,
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device=str(DEV),
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)
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# Prepare weights
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l1_fp4 = w13_w.view(torch.float4_e2m1fn_x2)
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l2_fp4 = w2_w.view(torch.float4_e2m1fn_x2)
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l1_sf = w13_sf.to(torch.float8_e4m3fn) if w13_sf.dtype != torch.float8_e4m3fn else w13_sf
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l2_sf = w2_sf.to(torch.float8_e4m3fn) if w2_sf.dtype != torch.float8_e4m3fn else w2_sf
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runner.prepare_weights_from_stacked(
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l1_fp4, l1_sf, w13_gs.tolist(),
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l2_fp4, l2_sf, w2_gs.tolist(),
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)
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r_gate = make_runner(gate_w, gate_sf, gate_gs, H, gate_w.shape[0])
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r_up = make_runner(up_w, up_sf, up_gs, H, up_w.shape[0])
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r_down = make_runner(down_w, down_sf, down_gs, INTERMEDIATE, down_w.shape[0])
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# Test with various token counts
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test_cases = [
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("1 token (decode)", 1),
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("4 tokens", 4),
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("8 tokens", 8),
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("16 tokens", 16),
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]
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for desc, num_tokens in test_cases:
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print(f"\n --- {desc} ---")
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for num_tokens in [1, 4, 8, 16]:
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token_ids = torch.randint(1, 1000, (num_tokens,), dtype=torch.long, device=DEV)
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hidden = emb[token_ids]
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normed = rms(hidden, fnorm, EPS)
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print(f" Input: amax={normed.amax():.4f} NaN={torch.isnan(normed).any()}")
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# Create routing (random top-6 from num_local_experts)
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topk_ids = torch.randint(0, num_local_experts, (num_tokens, TOPK), device=DEV)
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topk_weights = torch.softmax(torch.randn(num_tokens, TOPK, device=DEV), dim=-1)
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with torch.no_grad():
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result = runner.run(normed, topk_weights, topk_ids)
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print(f" Output: amax={result.amax():.4f} NaN={torch.isnan(result).any()}")
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if torch.isnan(result).any():
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# Count NaN rows
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nan_rows = torch.isnan(result).any(dim=1).sum().item()
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print(f" NaN rows: {nan_rows}/{num_tokens}")
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gate_out = r_gate.run(normed)
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up_out = r_up.run(normed)
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# Check if shared expert also produces NaN
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from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear
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def make_runner(w, sf, gs_t, inf, outf):
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fp4 = w.view(torch.float4_e2m1fn_x2).permute(1,0).contiguous()
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s = sf.to(torch.float8_e4m3fn) if sf.dtype != torch.float8_e4m3fn else sf
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s = s.permute(1,0).contiguous()
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gs = gs_t.max().item() if gs_t.numel() > 1 else gs_t.item()
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r = CuTeDSLNvfp4Linear(in_features=inf, out_features=outf, max_num_tokens=8192, device=str(w.device))
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r.fp4 = [fp4]; r.sf = [s]; r.gs = [gs]
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r.finalize_weights(); r._ensure_initialized()
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return r
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# Check gate and up
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gate_nan = torch.isnan(gate_out).any().item()
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up_nan = torch.isnan(up_out).any().item()
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# Shared expert only
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r_gate = make_runner(se_gate_w, se_gate_sf, se_gate_gs, H, se_gate_w.shape[0])
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r_up = make_runner(se_up_w, se_up_sf, se_up_gs, H, se_up_w.shape[0])
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r_down = make_runner(se_down_w, se_down_sf, se_down_gs, INTERMEDIATE, se_down_w.shape[0])
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if gate_nan or up_nan:
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print(f" {num_tokens} tokens: gate NaN={gate_nan} up NaN={up_nan}")
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# Find which row has NaN
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gate_nan_rows = torch.isnan(gate_out).any(dim=1).nonzero().flatten().tolist()
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print(f" Gate NaN rows: {gate_nan_rows}")
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continue
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with torch.no_grad():
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gate_out = r_gate.run(normed)
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up_out = r_up.run(normed)
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activated = F.silu(gate_out) * up_out
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se_result = r_down.run(activated)
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# SiLU activation
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activated = F.silu(gate_out) * up_out
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act_nan = torch.isnan(activated).any().item()
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print(f" Shared expert: amax={se_result.amax():.4f} NaN={torch.isnan(se_result).any()}")
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if act_nan:
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print(f" {num_tokens} tokens: NaN after SiLU activation!")
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continue
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del r_gate, r_up, r_down
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down_out = r_down.run(activated)
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down_nan = torch.isnan(down_out).any().item()
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if down_nan:
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print(f" {num_tokens} tokens: down NaN={down_nan}")
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continue
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print(f" {num_tokens} tokens: amax={down_out.amax():.4f} OK")
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# Test with exactly the vLLM padding pattern
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print(f"\n --- vLLM padding test (8 tokens, top-6, expert offsets) ---")
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num_tokens = 8
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token_ids = torch.randint(1, 1000, (num_tokens,), dtype=torch.long, device=DEV)
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hidden = emb[token_ids]
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normed = rms(hidden, fnorm, EPS)
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topk_ids = torch.randint(0, num_local_experts, (num_tokens, TOPK), device=DEV)
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topk_weights = torch.softmax(torch.randn(num_tokens, TOPK, device=DEV), dim=-1)
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with torch.no_grad():
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result = runner.run(normed, topk_weights, topk_ids)
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print(f" Output: amax={result.amax():.4f} NaN={torch.isnan(result).any()}")
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print(f" Output sample (first 10): {result[0, :10].tolist()}")
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del runner
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del r_gate, r_up, r_down
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torch.cuda.empty_cache()
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_cache.clear()
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def test_quantize_activation():
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"""Test the activation quantization used by the MoE grouped GEMM."""
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from cutedsl.nvfp4_linear import quantize_activation_nvfp4
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torch.cuda.set_device(0)
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for num_tokens in [1, 4, 8, 16]:
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# Create realistic input (after SiLU * up)
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x = torch.randn(num_tokens, INTERMEDIATE, dtype=torch.bfloat16, device=DEV)
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# quantize_activation_nvfp4 returns (x_sf, x_gs) or similar
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# The grouped GEMM needs quantized activation as input
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try:
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result = quantize_activation_nvfp4(x, num_tokens)
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if isinstance(result, tuple):
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for i, r in enumerate(result):
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if r is not None and r.is_floating_point():
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print(f" {num_tokens} tokens: quantize result[{i}] NaN={torch.isnan(r).any()}")
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else:
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print(f" {num_tokens} tokens: quantize result NaN={torch.isnan(result).any()}")
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except Exception as e:
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print(f" {num_tokens} tokens: quantize failed: {e}")
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print()
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def test_grouped_gemm_shapes():
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"""Test the CuTeDSL grouped GEMM with MegaMoE-like shapes."""
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from cutedsl.moe import run_nvfp4_grouped_gemm
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torch.cuda.set_device(0)
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# Create simple test: 4 experts, 8 tokens, top-2
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num_experts = 4
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num_tokens = 8
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hidden_size = 512 # Small for testing
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intermediate_size = 256
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# Allocate weight tensors (random)
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# L1: (num_experts, 2*intermediate_size, hidden_size//2) fp4
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# L2: (num_experts, hidden_size, intermediate_size//2) fp4
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l1_shape = (num_experts, 2 * intermediate_size, hidden_size // 2)
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l2_shape = (num_experts, hidden_size, intermediate_size // 2)
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print(f" Testing grouped GEMM with:")
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print(f" num_experts={num_experts}, num_tokens={num_tokens}")
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print(f" l1_shape={l1_shape}, l2_shape={l2_shape}")
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# This test just checks if the kernel can handle various expert distributions
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# without NaN. The actual weight values don't matter for NaN detection.
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print(f" (Skipping — requires proper weight packing from vLLM model loader)")
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print(f" The CuTeDSL grouped GEMM needs weights in a specific packed format")
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print(f" that the vLLM model loader creates during model initialization.")
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print()
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def main():
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@@ -219,16 +203,21 @@ def main():
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print(" Finds where NaN originates in the MoE forward pass")
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print("=" * 70)
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test_moe_layer(layer_id=2) # C4A layer
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print("\n=== Test 1: Single expert gate+up+down ===")
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for expert_id in [0, 1, 100, 383]:
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test_single_expert(layer_id=2, expert_id=expert_id)
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_cache.clear()
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print("\n=== Test 2: Activation quantization ===")
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test_quantize_activation()
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print("\n=== Test 3: Grouped GEMM shapes ===")
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test_grouped_gemm_shapes()
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print(f"\n{'='*70}")
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print(f" If NaN is found, bisect by testing each step:")
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print(f" 1. quantize_activation_nvfp4(input)")
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print(f" 2. run_nvfp4_grouped_gemm(L1)")
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print(f" 3. SiLU(gate) * up")
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print(f" 4. quantize_activation_nvfp4(activated)")
|
||||
print(f" 5. run_nvfp4_grouped_gemm(L2)")
|
||||
print(f" 6. scatter_add combine")
|
||||
print(f" Summary: If single experts produce NaN, the issue is in weight")
|
||||
print(f" loading or the CuTeDSL NVFP4 linear kernel. If they're fine,")
|
||||
print(f" the NaN comes from the grouped GEMM or the combine step.")
|
||||
print(f"{'='*70}")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user