biondizzle
3fb3c925af
- Split bridge.py -> ops/quantize.py, ops/layouts.py, ops/gemm_runner.py - Renamed classes: CuTeDSLNvfp4Linear -> Nvfp4Linear, etc. - Moved kernel code to dsv4/kernels/ (gemm, attention, compressor, decode, cuda) - Moved PyTorch bridges to dsv4/ops/ - Moved nn.Module layers to dsv4layers/ - Moved reference implementations to dsv4/reference/ - Moved vendored CUTLASS code to vendored/ - Archived ~190 debug tests to tests/archive/ - Kept ~15 canonical tests in tests/unit/ - Updated all import paths - Added stubs for future components (model/, cache/, loader/) - Updated pyproject.toml: dsv4-inference package name
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
"""Test: Validate SiLU in registers (Step 1 of fused SwiGLU).
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Compiles the fused kernel with fused_swiglu=True, runs it, and compares
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the BF16 output with PyTorch SiLU applied to the standard L1 GEMM 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 dsv4.ops.quantize import (
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quantize_weight_to_nvfp4,
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quantize_activation_nvfp4,
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)
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from dsv4.ops.layouts import (
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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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)
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from dsv4.ops.gemm_runner import (
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run_nvfp4_grouped_gemm,
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run_fused_swiglu_grouped_gemm,
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warmup_compilation,
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)
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def test_silu_step1():
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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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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_compilation(num_experts, hidden // 2, (2 * intermediate) // 2, device)
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# 1. 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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silu_ref = torch.nn.functional.silu(out_bf16)
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print(f"Standard L1 output: shape={out_bf16.shape}, amax={out_bf16.abs().amax().item():.4f}")
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print(f"PyTorch SiLU ref: amax={silu_ref.abs().amax().item():.4f}")
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# 2. Fused kernel with SiLU in registers
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print("\nCompiling fused kernel (first time, may take a while)...")
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out_fused = run_fused_swiglu_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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print(f"Fused SiLU output: shape={out_fused.shape}, amax={out_fused.abs().amax().item():.4f}")
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# 3. Compare
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diff = (out_fused - silu_ref).float()
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rel_err = diff.norm() / silu_ref.float().norm()
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max_err = diff.abs().max()
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print(f"\n=== Results ===")
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print(f"Relative error: {rel_err.item():.6f}")
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print(f"Max abs error: {max_err.item():.6f}")
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print(f"PASS" if rel_err.item() < 0.1 else "FAIL (tolerance: 0.1 for NVFP4 quant noise)")
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if __name__ == "__main__":
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test_silu_step1()
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