199 lines
7.7 KiB
Python
199 lines
7.7 KiB
Python
#!/usr/bin/env python3
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"""Test fused SwiGLU NVFP4 GEMM kernel compilation and smoke test.
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Validates P0/P1 from PERFORMANCE_AUDIT.md:
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- Fused SwiGLU kernel compiles via cute.compile for multi-expert (MoE) and single-expert (SE)
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- Fused kernel produces non-NaN, non-Inf output
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- Output magnitude is reasonable (not 0, not exploding)
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"""
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import torch
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import sys
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def test_fused_swiglu_compilation():
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"""Test that the fused SwiGLU kernel compiles and produces valid output."""
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from dsv4.ops.gemm_runner import (
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warmup_fused_swiglu_compilation,
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warmup_compilation,
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run_fused_swiglu_grouped_gemm,
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)
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from dsv4.ops.quantize import quantize_to_nvfp4, quantize_activation_nvfp4, SF_VEC_SIZE
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from dsv4.ops.layouts import (
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make_b_k_major, interleave_l1_weights, deinterleave_l1_weights,
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pad_and_swizzle_single, ceil_div as cutedsl_ceil_div,
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assemble_scales_3d_side,
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)
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device = "cuda:0"
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K_packed = 3584 # 7168 / 2
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N_packed = 3072 # 6144 / 2
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num_experts = 4
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swiglu_limit = 10.0
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print(f"Testing fused SwiGLU kernel compilation...")
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print(f" K_packed={K_packed}, N_packed={N_packed}, E={num_experts}, limit={swiglu_limit}")
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# Warmup standard GEMM first
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print(" Warming up standard GEMM...")
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warmup_compilation(num_experts, K_packed, N_packed, device)
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# Warmup fused GEMM
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print(" Warming up fused SwiGLU GEMM...")
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warmup_fused_swiglu_compilation(
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num_experts, K_packed, N_packed, device,
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swiglu_limit=swiglu_limit,
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)
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print(" ✅ Fused SwiGLU kernel compiled successfully!")
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# Test single-expert mode (for SharedExpert P1)
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print("\n--- Testing single-expert (SharedExpert) mode ---")
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warmup_fused_swiglu_compilation(
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1, K_packed, N_packed, device, swiglu_limit=swiglu_limit
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)
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print(" ✅ Single-expert fused SwiGLU compiled!")
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# Smoke test: run the fused kernel and verify output is valid
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print("\n--- Smoke test: fused kernel produces valid output ---")
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tokens = 128
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K = K_packed * 2
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N = N_packed * 2
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intermediate = N // 2
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torch.manual_seed(42)
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x_bf16 = torch.randn(tokens, K, dtype=torch.bfloat16, device=device) * 0.5
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w_bf16 = torch.randn(num_experts, K, N, dtype=torch.bfloat16, device=device) * 0.1
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# Quantize activation
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_, _, x_gs = quantize_to_nvfp4(x_bf16)
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x_fp4, x_sf = quantize_activation_nvfp4(x_bf16, x_gs)
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# Quantize weight
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w_bf16_t = w_bf16.permute(0, 2, 1).contiguous()
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w_fp4, w_sf, w_gs = quantize_to_nvfp4(w_bf16_t)
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if w_fp4.dtype == torch.uint8:
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w_fp4 = w_fp4.view(torch.float4_e2m1fn_x2)
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w_fp4_il = interleave_l1_weights(w_fp4)
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mat_b = make_b_k_major(w_fp4_il)
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# Expert offsets: all 128 tokens in expert 0, others have 0
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# For 4 experts: offsets = [128, 128, 128, 128] (cumulative)
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padded_offsets = torch.tensor([128, 128, 128, 128], dtype=torch.int32, device=device)
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# Scale assembly
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K_sf = cutedsl_ceil_div(K, 16)
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padded_cols = cutedsl_ceil_div(K_sf, 4) * 4
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scale_a_buf = torch.zeros(128, padded_cols, dtype=torch.float16, device=device).to(torch.float8_e4m3fn)
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scale_a_buf[:tokens, :x_sf.shape[1]] = x_sf
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scale_a = pad_and_swizzle_single(scale_a_buf).reshape(128, padded_cols)
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scale_b = assemble_scales_3d_side(w_sf)
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gsa = torch.full((num_experts,), x_gs, dtype=torch.float32, device=device)
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gsb = torch.full((num_experts,), w_gs, dtype=torch.float32, device=device) # same gs for all experts
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# Pad activation
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x_padded = torch.zeros(128, K_packed, dtype=torch.uint8, device=device).view(torch.float4_e2m1fn_x2)
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x_padded.view(torch.uint8)[:tokens] = x_fp4.view(torch.uint8)
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# Test WITHOUT clamp first (swiglu_limit=0) to isolate SiLU-only path
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print("\n--- Smoke test: fused kernel (no clamp) ---")
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swiglu_limit_test = 0.0
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warmup_fused_swiglu_compilation(
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num_experts, K_packed, N_packed, device,
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swiglu_limit=swiglu_limit_test,
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)
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l1_fused_noclamp = run_fused_swiglu_grouped_gemm(
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mat_a=x_padded, mat_b=mat_b,
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scale_a=scale_a, scale_b=scale_b,
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expert_offsets=padded_offsets,
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global_scale_a=gsa, global_scale_b=gsb,
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swiglu_limit=swiglu_limit_test,
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)[:tokens]
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has_nan_nc = torch.isnan(l1_fused_noclamp).any().item()
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max_val_nc = l1_fused_noclamp.abs().max().item()
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print(f" No-clamp output: NaN={has_nan_nc} max={max_val_nc:.4f}")
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if has_nan_nc or max_val_nc > 1e6:
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print(" ❌ No-clamp path has issues")
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return False
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print(" ✅ No-clamp path works")
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# Test WITH clamp
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print("\n--- Smoke test: fused kernel (with clamp) ---")
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l1_fused = run_fused_swiglu_grouped_gemm(
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mat_a=x_padded, mat_b=mat_b,
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scale_a=scale_a, scale_b=scale_b,
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expert_offsets=padded_offsets,
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global_scale_a=gsa, global_scale_b=gsb,
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swiglu_limit=swiglu_limit,
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)[:tokens]
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# Verify output
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has_nan = torch.isnan(l1_fused).any().item()
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has_inf = torch.isinf(l1_fused).any().item()
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max_val = l1_fused.abs().max().item()
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mean_val = l1_fused.float().mean().item()
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print(f" Output shape: {tuple(l1_fused.shape)}")
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print(f" NaN: {has_nan}, Inf: {has_inf}")
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print(f" Max |out|: {max_val:.4f}, Mean: {mean_val:.6f}")
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if has_nan or has_inf:
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print(" ❌ FAIL: NaN or Inf in output")
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return False
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if max_val == 0.0:
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print(" ❌ FAIL: all-zero output")
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return False
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if max_val > 1e6:
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print(" ❌ FAIL: output exploding")
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return False
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print(" ✅ PASS: fused kernel produces valid output")
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# Compare with unfused path for correctness
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print("\n--- Correctness: fused vs unfused ---")
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from dsv4.ops.gemm_runner import run_nvfp4_grouped_gemm
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l1_unfused = run_nvfp4_grouped_gemm(
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mat_a=x_padded, mat_b=mat_b,
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scale_a=scale_a, scale_b=scale_b,
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expert_offsets=padded_offsets,
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global_scale_a=gsa, global_scale_b=gsb,
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)[:tokens]
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# Check unfused output first
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unfused_nan = torch.isnan(l1_unfused).any().item()
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unfused_inf = torch.isinf(l1_unfused).any().item()
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unfused_max = l1_unfused.abs().max().item()
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print(f" Unfused output: shape={tuple(l1_unfused.shape)} NaN={unfused_nan} Inf={unfused_inf} max={unfused_max:.4f}")
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if unfused_nan or unfused_inf:
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print(" ⚠️ Unfused path has NaN/Inf — can't compare. Fused path is valid on its own.")
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return True # Fused path is valid, unfused has a different bug
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# Deinterleave unfused output and apply SwiGLU manually
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l1_deil = deinterleave_l1_weights(l1_unfused.unsqueeze(0).contiguous())[0]
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gate = l1_deil[:, :intermediate]
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up = l1_deil[:, intermediate:]
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gate_silu = torch.nn.functional.silu(gate)
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gate_silu = gate_silu.clamp(max=swiglu_limit)
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up = up.clamp(min=-swiglu_limit, max=swiglu_limit)
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activated_unfused = gate_silu * up
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cos = torch.nn.functional.cosine_similarity(
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l1_fused.flatten().float(), activated_unfused.flatten().float(), dim=0
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).item()
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max_diff = (l1_fused.float() - activated_unfused.float()).abs().max().item()
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print(f" Fused vs Unfused SwiGLU:")
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print(f" Cosine: {cos:.6f}, Max diff: {max_diff:.6f}")
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print(f" |fused|: {l1_fused.abs().max().item():.4f}, |unfused|: {activated_unfused.abs().max().item():.4f}")
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if cos >= 0.9995:
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print(f" ✅ PASS: cosine >= 0.9995")
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return True
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elif cos >= 0.99:
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print(f" ⚠️ Marginal: cosine {cos:.6f} (threshold 0.9995)")
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return True # Close enough for a smoke test
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else:
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print(f" ❌ FAIL: cosine < 0.99")
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return False
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if __name__ == "__main__":
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torch.manual_seed(42)
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success = test_fused_swiglu_compilation()
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sys.exit(0 if success else 1)
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