63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
"""Test: verify B matrix weight layout by using one-hot A.
|
|
If A is one-hot (only element j is nonzero), output = row j of B.
|
|
We can verify that column n of the output matches checkpoint row n.
|
|
"""
|
|
import torch, sys
|
|
sys.path.insert(0, 'src')
|
|
from nvfp4_megamoe_kernel.cutlass_nvfp4_gemm.kernel import cutlass_nvfp4_blockscaled_gemm
|
|
from nvfp4_megamoe_kernel.nvfp4_mega_moe import _quantize_to_e2m1, _E2M1_MAGNITUDES
|
|
|
|
torch.manual_seed(123)
|
|
device = "cuda"
|
|
|
|
# Small dimensions: M=1, N=4, K=16
|
|
M, N, K = 1, 4, 16
|
|
|
|
# Create a weight matrix with unique values so we can identify each element
|
|
# Use BF16 values that map to distinct E2M1 values
|
|
# E2M1 magnitudes: [0, 0.5, 1, 1.5, 2, 3, 4, 6]
|
|
# Create w_bf16 where each column has a different pattern
|
|
w_bf16 = torch.tensor([
|
|
[0.5, 1.0, 2.0, 3.0], # K=0
|
|
[1.5, 0.5, 1.0, 2.0], # K=1
|
|
[2.0, 3.0, 0.5, 1.0], # K=2
|
|
[1.0, 2.0, 3.0, 0.5], # K=3
|
|
[3.0, 1.0, 0.5, 2.0], # K=4
|
|
[0.5, 3.0, 2.0, 1.0], # K=5
|
|
[2.0, 0.5, 1.0, 3.0], # K=6
|
|
[1.0, 2.0, 3.0, 0.5], # K=7
|
|
[3.0, 1.0, 2.0, 0.5], # K=8
|
|
[0.5, 2.0, 1.0, 3.0], # K=9
|
|
[2.0, 0.5, 3.0, 1.0], # K=10
|
|
[1.0, 3.0, 0.5, 2.0], # K=11
|
|
[3.0, 2.0, 1.0, 0.5], # K=12
|
|
[0.5, 1.0, 3.0, 2.0], # K=13
|
|
[2.0, 3.0, 0.5, 1.0], # K=14
|
|
[1.0, 0.5, 2.0, 3.0], # K=15
|
|
], dtype=torch.bfloat16, device=device) # (K, N)
|
|
|
|
# Quantize weights
|
|
w_fp4, w_sf = _quantize_to_e2m1(w_bf16.T.float()) # (N, K//2), (N, K//16)
|
|
w_fp4 = w_fp4.T.contiguous() # (K//2, N) = (8, 4)
|
|
w_sf = w_sf.T.contiguous() # (K//16, N) = (1, 4)
|
|
|
|
# BF16 reference: just sum the rows
|
|
ref = w_bf16.sum(dim=0) # (N,) = sum over K
|
|
print(f"BF16 reference sum: {ref.tolist()}")
|
|
|
|
# Now run GEMM with all-ones A (sum all K elements)
|
|
x_bf16 = torch.ones(M, K, dtype=torch.bfloat16, device=device)
|
|
x_fp4, x_sf = _quantize_to_e2m1(x_bf16.float())
|
|
|
|
out = cutlass_nvfp4_blockscaled_gemm(x_fp4, x_sf, w_fp4, w_sf, M, N, K, alpha=1.0)
|
|
print(f"NVFP4 output: {out[0].tolist()}")
|
|
|
|
# The outputs should be proportional to the column sums
|
|
# Since we're using quantized values, they won't match exactly
|
|
# But the RANK ORDER should match (column with highest sum should have highest output)
|
|
ref_order = torch.argsort(ref, descending=True).tolist()
|
|
out_order = torch.argsort(out[0], descending=True).tolist()
|
|
print(f"Reference rank order: {ref_order}")
|
|
print(f"NVFP4 rank order: {out_order}")
|
|
print(f"Rank order match: {ref_order == out_order}")
|