test: runner vs pipeline comparison + scale assembly comparison
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202
tests/test_runner_vs_pipeline.py
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202
tests/test_runner_vs_pipeline.py
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#!/usr/bin/env python3
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"""
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Test A: Compare moe_pipeline output vs CuTeDSLMoERunner output.
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Uses the same weights and inputs. If they differ, the runner is broken.
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Runs on the B200 host (not inside Docker):
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source /root/nvfp4-megamoe-kernel/tests/.venv/bin/activate
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python3 tests/test_runner_vs_pipeline.py
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"""
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import os, sys, json, torch
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from safetensors import safe_open
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, REPO_ROOT)
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from cutedsl.moe_pipeline import run_nvfp4_moe
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from vllm.nvfp4_cutedsl import CuTeDSLMoERunner
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from cutedsl.bridge import quantize_to_nvfp4, quantize_weight_to_nvfp4, make_b_k_major, assemble_scales_3d_side, compute_expert_offsets
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MODEL_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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DEVICE = "cuda"
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LAYER_IDX = 0
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E2M1_LUT = torch.tensor([0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0], dtype=torch.float32)
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def find_shards(model_dir):
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index_path = os.path.join(model_dir, "model.safetensors.index.json")
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key_to_shard = {}
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if os.path.exists(index_path):
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with open(index_path) as f:
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index = json.load(f)
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for key, shard in index["weight_map"].items():
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key_to_shard[key] = os.path.join(model_dir, shard)
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return key_to_shard
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def load_layer_tensors(model_dir, layer_idx):
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key_to_shard = find_shards(model_dir)
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layer_prefix = f"layers.{layer_idx}."
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tensors = {}
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for key, shard in key_to_shard.items():
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norm_key = key.removeprefix("model.")
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if not norm_key.startswith(layer_prefix):
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continue
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with safe_open(shard, framework="pt") as f:
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if key in f.keys():
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tensors[norm_key] = f.get_tensor(key)
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return tensors
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def dequantize_nvfp4_weight(packed_uint8, scale_e4m3, global_scale):
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device = packed_uint8.device
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lut = E2M1_LUT.to(device)
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lower = lut[(packed_uint8 & 0x0F).long()]
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upper = lut[((packed_uint8 >> 4) & 0x0F).long()]
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out_features = packed_uint8.shape[0]
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in_features = packed_uint8.shape[1] * 2
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unpacked = torch.empty(out_features, in_features, dtype=torch.float32, device=device)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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block_scale = scale_e4m3.float()
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block_expanded = block_scale.repeat_interleave(16, dim=1)[:, :in_features]
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return (unpacked * block_expanded * global_scale).to(torch.bfloat16)
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def prepare_direct_weights(nvfp4_tensors, layer_idx, expert_indices, intermediate_size):
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"""Direct view-cast path (same as layertest)."""
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l1_fp4, l1_sf, l1_gs = [], [], []
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l2_fp4, l2_sf, l2_gs = [], [], []
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for e in expert_indices:
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gate_w = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight"].to(DEVICE)
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up_w = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight"].to(DEVICE)
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gate_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale"].to(DEVICE)
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up_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale"].to(DEVICE)
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gate_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale_2"].item()
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up_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale_2"].item()
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fused_w = torch.cat([gate_w, up_w], dim=0)
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fused_w_fp4 = fused_w.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous()
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fused_sf = torch.cat([gate_sf, up_sf], dim=0).permute(1, 0).contiguous()
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max_gs = max(gate_gs, up_gs)
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if gate_gs != up_gs:
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f32 = fused_sf.float()
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f32[:, :intermediate_size] *= (gate_gs / max_gs)
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f32[:, intermediate_size:] *= (up_gs / max_gs)
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fused_sf = f32.to(torch.float8_e4m3fn)
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l1_fp4.append(fused_w_fp4)
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l1_sf.append(fused_sf)
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l1_gs.append(max_gs)
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down_w = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight"].to(DEVICE)
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down_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale"].to(DEVICE)
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down_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale_2"].item()
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l2_fp4.append(down_w.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous())
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l2_sf.append(down_sf.permute(1, 0).contiguous())
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l2_gs.append(down_gs)
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return {'l1_fp4': l1_fp4, 'l1_sf': l1_sf, 'l1_gs': l1_gs,
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'l2_fp4': l2_fp4, 'l2_sf': l2_sf, 'l2_gs': l2_gs}
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def main():
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torch.manual_seed(42)
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expert_indices = [0, 1, 2]
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num_experts = len(expert_indices)
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hidden_size = 7168
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intermediate_size = 3072
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top_k = 2
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num_tokens = 4
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print("=" * 70)
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print(" Loading checkpoint")
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print("=" * 70)
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nvfp4_tensors = load_layer_tensors(MODEL_DIR, LAYER_IDX)
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print(f" {len(nvfp4_tensors)} tensors loaded")
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weights = prepare_direct_weights(nvfp4_tensors, LAYER_IDX, expert_indices, intermediate_size)
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hidden_states = torch.randn(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE) * 2.0
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expert_ids = torch.tensor([[0, 1]] * num_tokens, dtype=torch.int32, device=DEVICE)
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expert_weights = torch.tensor([[0.6, 0.4]] * num_tokens, dtype=torch.float32, device=DEVICE)
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# ── Path 1: moe_pipeline (reference, uses quantize_to_nvfp4) ──
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print("\n Running moe_pipeline (dynamic gs)...")
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pipeline_out = run_nvfp4_moe(
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hidden_states.clone(), expert_ids.clone(), expert_weights.clone(),
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weights, expert_indices,
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)
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print(f" Pipeline: amax={pipeline_out.abs().max():.4f}, mean={pipeline_out.float().mean():.6f}")
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# ── Path 2: CuTeDSLMoERunner with checkpoint input_scale (what vLLM uses) ──
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print("\n Running CuTeDSLMoERunner (checkpoint gs)...")
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runner = CuTeDSLMoERunner(num_experts, hidden_size, intermediate_size, device=DEVICE)
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runner.prepare_weights_direct(
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[w.clone() for w in weights['l1_fp4']],
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[w.clone() for w in weights['l1_sf']],
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list(weights['l1_gs']),
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[w.clone() for w in weights['l2_fp4']],
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[w.clone() for w in weights['l2_sf']],
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list(weights['l2_gs']),
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)
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# Set checkpoint input_scale (what vLLM does in finalize_weights)
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igs = nvfp4_tensors[f"layers.{LAYER_IDX}.mlp.experts.0.gate_proj.input_scale"].item()
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runner._l1_activation_global_scale = igs
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runner._l2_activation_global_scale = igs
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print(f" Checkpoint input_scale: {igs:.10f}")
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# Build topk_weights and topk_ids in the format the runner expects
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# runner.run expects topk_ids as expert indices (0-based within our expert set)
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topk_weights = expert_weights
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topk_ids = expert_ids
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runner_out = runner.run(hidden_states.clone(), topk_weights, topk_ids)
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print(f" Runner (ckpt gs): amax={runner_out.abs().max():.4f}, mean={runner_out.float().mean():.6f}")
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cos_ckpt = torch.nn.functional.cosine_similarity(
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runner_out.flatten().unsqueeze(0).float(),
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pipeline_out.flatten().unsqueeze(0).float(),
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).item()
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print(f" Cosine vs pipeline: {cos_ckpt:.6f}")
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# ── Path 3: CuTeDSLMoERunner with dynamic gs ──
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print("\n Running CuTeDSLMoERunner (dynamic gs)...")
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# We can't use quantize_to_nvfp4 in the runner (cudagraph), but we can
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# compute the gs from the input and set it before calling run
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x_igs = (hidden_states.abs().max().item()) / (6.0 * 448.0)
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runner2 = CuTeDSLMoERunner(num_experts, hidden_size, intermediate_size, device=DEVICE)
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runner2.prepare_weights_direct(
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[w.clone() for w in weights['l1_fp4']],
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[w.clone() for w in weights['l1_sf']],
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list(weights['l1_gs']),
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[w.clone() for w in weights['l2_fp4']],
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[w.clone() for w in weights['l2_sf']],
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list(weights['l2_gs']),
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)
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runner2._l1_activation_global_scale = x_igs
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runner2._l2_activation_global_scale = x_igs
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print(f" Dynamic gs (from input amax): {x_igs:.10f}")
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runner2_out = runner2.run(hidden_states.clone(), topk_weights, topk_ids)
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print(f" Runner (dynamic gs): amax={runner2_out.abs().max():.4f}, mean={runner2_out.float().mean():.6f}")
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cos_dyn = torch.nn.functional.cosine_similarity(
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runner2_out.flatten().unsqueeze(0).float(),
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pipeline_out.flatten().unsqueeze(0).float(),
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).item()
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print(f" Cosine vs pipeline: {cos_dyn:.6f}")
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# ── Summary ──
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print(f"\n{'=' * 70}")
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print(f" RESULTS")
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print(f"{'=' * 70}")
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print(f" Runner with checkpoint gs vs pipeline: {cos_ckpt:.6f}")
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print(f" Runner with dynamic gs vs pipeline: {cos_dyn:.6f}")
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if cos_dyn > 0.95:
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print(f" ✅ Dynamic gs fixes the problem — gs is the only bug")
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elif cos_dyn < 0.5 and cos_ckpt < 0.5:
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print(f" ❌ Both runner paths are broken — scale assembly is also wrong")
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else:
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print(f" ⚠️ Partial match — multiple issues")
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if __name__ == "__main__":
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main()
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108
tests/test_scale_assembly.py
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108
tests/test_scale_assembly.py
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#!/usr/bin/env python3
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"""
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Test B: Compare _assemble_scales_cudagraph_safe vs assemble_scales_2d_side.
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Both should produce identical output given the same x_sf and expert_offsets.
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If they differ, the cudagraph-safe path has a bug.
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Runs on the B200 host:
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source /root/nvfp4-megamoe-kernel/tests/.venv/bin/activate
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python3 tests/test_scale_assembly.py
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"""
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import os, sys, torch
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, REPO_ROOT)
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from cutedsl.bridge import quantize_to_nvfp4, assemble_scales_2d_side
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from cutedsl.kernel.moe.torch_scaled_grouped_mm import pad_and_swizzle_single, ceil_div
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from vllm.nvfp4_cutedsl import CuTeDSLMoERunner
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def test_scale_assembly():
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"""Compare the two scale assembly methods with realistic data."""
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DEVICE = "cuda"
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num_experts = 3
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hidden_size = 7168
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intermediate_size = 3072
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# Create a runner just to use its _assemble_scales_cudagraph_safe
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runner = CuTeDSLMoERunner(num_experts, hidden_size, intermediate_size, device=DEVICE)
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# Trigger _ensure_stacked and buffer allocation with dummy weights
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def rand_fp4(*shape):
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return torch.randint(0, 256, shape, dtype=torch.uint8, device=DEVICE).view(torch.float4_e2m1fn_x2)
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def rand_sf(*shape):
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return torch.rand(shape, dtype=torch.float16, device=DEVICE).to(torch.float8_e4m3fn)
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runner.prepare_weights_direct(
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[rand_fp4(3584, intermediate_size * 2) for _ in range(num_experts)],
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[rand_sf(3584 // 16, intermediate_size * 2) for _ in range(num_experts)],
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[0.1] * num_experts,
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[rand_fp4(1536, hidden_size) for _ in range(num_experts)],
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[rand_sf(1536 // 16, hidden_size) for _ in range(num_experts)],
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[0.1] * num_experts,
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)
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runner._ensure_stacked()
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# Test with different token distributions
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test_cases = [
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("4 tokens, expert 0 gets 2, expert 1 gets 2, expert 2 gets 0", [2, 2, 0]),
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("8 tokens, expert 0 gets 4, expert 1 gets 3, expert 2 gets 1", [4, 3, 1]),
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("4 tokens, expert 0 gets 4, expert 1 gets 0, expert 2 gets 0", [4, 0, 0]),
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("3 tokens, expert 0 gets 1, expert 1 gets 1, expert 2 gets 1", [1, 1, 1]),
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]
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all_pass = True
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for desc, tokens_per_expert in test_cases:
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total_tokens = sum(tokens_per_expert)
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# Create input and quantize
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x = torch.randn(total_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE) * 2.0
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x_fp4, x_sf, x_igs = quantize_to_nvfp4(x)
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# Path 1: assemble_scales_2d_side (per-expert split)
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x_sf_parts = []
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offset = 0
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for tpe in tokens_per_expert:
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x_sf_parts.append(x_sf[offset:offset + tpe])
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offset += tpe
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scale_a_ref = assemble_scales_2d_side(x_sf_parts)
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# Path 2: _assemble_scales_cudagraph_safe (GPU-only)
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expert_offsets = torch.zeros(num_experts + 1, dtype=torch.int32, device=DEVICE)
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expert_offsets[1:] = torch.tensor(tokens_per_expert, dtype=torch.int32).cumsum(0)
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scale_a_cudagraph = runner._assemble_scales_cudagraph_safe(x_sf, expert_offsets)
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# Compare
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# Note: shapes may differ due to padding, but the data in the
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# padded rows should match (up to the total number of rows used by the kernel)
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if scale_a_ref.shape != scale_a_cudagraph.shape:
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print(f" {desc}")
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print(f" Shape mismatch: ref={scale_a_ref.shape}, cg={scale_a_cudagraph.shape}")
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all_pass = False
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continue
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match = torch.equal(scale_a_ref, scale_a_cudagraph)
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if not match:
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# Check how many bytes differ
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diff = (scale_a_ref.view(torch.uint8) != scale_a_cudagraph.view(torch.uint8)).sum().item()
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total = scale_a_ref.numel()
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pct = diff / total * 100
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print(f" {desc}")
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print(f" MISMATCH: {diff}/{total} bytes differ ({pct:.1f}%)")
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print(f" ref range: [{scale_a_ref.view(torch.uint8).min()}, {scale_a_ref.view(torch.uint8).max()}]")
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print(f" cg range: [{scale_a_cudagraph.view(torch.uint8).min()}, {scale_a_cudagraph.view(torch.uint8).max()}]")
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all_pass = False
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else:
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print(f" {desc}: ✅ MATCH")
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print(f"\n{'=' * 70}")
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if all_pass:
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print(" ALL SCALE ASSEMBLY TESTS PASSED ✅")
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else:
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print(" SCALE ASSEMBLY TESTS FAILED ❌")
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print(f"{'=' * 70}")
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return all_pass
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if __name__ == "__main__":
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test_scale_assembly()
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