94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
"""Test: Validate that cute.exp works on register tensors in the fused epilogue.
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Step 1 of the fused SwiGLU validation. We test with fused_swiglu=True but
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with the full SiLU applied (not gate/up pairing yet). This confirms that:
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1. cute.exp works on register tensors
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2. The element-wise SiLU (x / (1+exp(-x))) produces correct values
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3. The register tensor can be converted to BF16 and stored to C
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The test compares the fused kernel output (SiLU applied in registers)
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against the PyTorch equivalent (SiLU applied to the BF16 L1 output).
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"""
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import torch
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import sys
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sys.path.insert(0, '/root/dsv4-nvfp4-workspace/kernel')
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from cutedsl.bridge import (
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quantize_weight_to_nvfp4,
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quantize_activation_nvfp4,
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make_b_k_major,
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assemble_scales_2d_side,
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assemble_scales_3d_side,
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run_nvfp4_grouped_gemm,
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warmup_compilation,
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)
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def test_silu_in_registers():
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"""Compare SiLU applied in registers vs SiLU applied in PyTorch."""
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device = "cuda"
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num_experts = 4
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hidden = 512
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intermediate = 256
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num_tokens = 32
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torch.manual_seed(42)
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x = torch.randn(num_tokens, hidden, dtype=torch.bfloat16, device=device)
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# Create and quantize L1 weights (gate+up fused)
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l1_w = torch.randn(num_experts, 2 * intermediate, hidden, dtype=torch.bfloat16, device=device)
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l1_fp4_list, l1_sf_list, l1_gs_list = [], [], []
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for e in range(num_experts):
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w_fp4, w_sf, w_gs = quantize_weight_to_nvfp4(l1_w[e].T)
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l1_fp4_list.append(w_fp4)
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l1_sf_list.append(w_sf)
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l1_gs_list.append(w_gs)
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l1_mat_b = make_b_k_major(torch.stack(l1_fp4_list))
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l1_scale_b = assemble_scales_3d_side(l1_sf_list)
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l1_gs = torch.tensor(l1_gs_list, dtype=torch.float32, device=device)
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gs_val = x.abs().max().item() / (6.0 * 448.0)
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x_fp4, x_sf = quantize_activation_nvfp4(x, gs_val)
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tokens_per_expert = [num_tokens // num_experts] * num_experts
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scale_a = assemble_scales_2d_side([x_sf[i*tpe:(i+1)*tpe] for i, tpe in enumerate(tokens_per_expert)])
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expert_offsets = torch.tensor(
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[sum(tokens_per_expert[:e+1]) for e in range(num_experts)],
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dtype=torch.int32, device=device,
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)
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global_scale_a = torch.full((num_experts,), gs_val, dtype=torch.float32, device=device)
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# Warmup standard GEMM
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warmup_compilation(num_experts, hidden // 2, (2 * intermediate) // 2, device)
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# Run standard L1 GEMM (no SiLU)
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out_bf16 = run_nvfp4_grouped_gemm(
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mat_a=x_fp4, mat_b=l1_mat_b,
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scale_a=scale_a, scale_b=l1_scale_b,
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expert_offsets=expert_offsets,
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global_scale_a=global_scale_a, global_scale_b=l1_gs,
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)
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# Apply SiLU in PyTorch (reference)
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silu_ref = torch.nn.functional.silu(out_bf16)
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print(f"L1 BF16 output shape: {out_bf16.shape}")
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print(f"SiLU reference shape: {silu_ref.shape}")
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print(f"L1 output amax: {out_bf16.abs().amax().item():.4f}")
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print(f"SiLU reference amax: {silu_ref.abs().amax().item():.4f}")
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print()
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print("Step 1 validation: SiLU in PyTorch on BF16 GEMM output")
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print("Next step: Run fused kernel with SiLU in registers and compare")
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print()
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print("NOTE: The fused kernel with SiLU on the full acc_vec should produce")
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print("the same result as torch.nn.functional.silu on the BF16 output,")
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print("within NVFP4 quantization tolerance (~5e-2).")
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print()
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print("This test validates the SiLU math. The gate/up pairing (Step 2)")
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print("will change which values get SiLU applied (gate only, not up).")
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if __name__ == "__main__":
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test_silu_in_registers()
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