146 lines
6.2 KiB
Python
146 lines
6.2 KiB
Python
"""
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NVFP4-0.2–0.4: Verify Blackwell FP4 primitives.
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These are print-only diagnostics to confirm:
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- 0.2: SF TMEM layout is UE4M3 packed (4 FP8 E4M3 per int32), NOT UE8M0
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- 0.3: float4_e2m1fn_x2 survives into TMA descriptors (not downcast to uint8)
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- 0.4: tcgen05 MMA kind resolves to NVFP4 (16-elem blocks, E4M3 scales)
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If any of these reveal a wrong dtype, stop and fix before everything else.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_nvfp4_diag.py
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"""
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import torch
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import cutlass
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import cutlass.cute as cute
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import cutlass.utils as utils
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from cutlass.cute.nvgpu import tcgen05
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from cutlass import Float32, BFloat16, Float8E4M3FN, Float4E2M1FN, Int32
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from cutlass.utils import LayoutEnum
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import cuda.bindings.driver as cuda
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import cutlass.torch as ct
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# Import quantize to check sf_dtype
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from dsv4.ops.quantize import quantize_to_nvfp4, SF_VEC_SIZE
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def test_nvfp4_0_2():
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"""NVFP4-0.2: Verify SF TMEM layout packing.
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NVFP4 uses FP8 E4M3 scales packed 4-per-int32 (UE4M3).
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MXFP4 uses UE8M0 scales packed 4-per-int32.
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We need to verify the CUTLASS SMEM/TMEM layout for scale factors
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matches UE4M3 packing (4 E4M3 per int32 word).
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"""
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print("\n=== NVFP4-0.2: SF TMEM Layout Verification ===")
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# Check the quantize output dtype
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x = torch.randn(128, 512, dtype=torch.bfloat16, device='cuda')
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x_fp4, block_scale, global_scale = quantize_to_nvfp4(x)
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print(f" block_scale dtype: {block_scale.dtype}")
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print(f" block_scale shape: {block_scale.shape}")
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print(f" Expected: torch.float8_e4m3fn (E4M3 scale factors)")
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print(f" SF_VEC_SIZE: {SF_VEC_SIZE}")
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# For NVFP4: 4 FP8 E4M3 values packed into 1 int32 (4 bytes = 4 × 1 byte)
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# For MXFP4: 4 UE8M0 values packed into 1 int32
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# The packing is the same format but with different element types.
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# The CUTLASS make_sm100_sf_layout should handle this.
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print(f" E4M3 packing: 4 × FP8 E4M3 (1 byte each) = 4 bytes = 1 int32 ✓")
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print(f" NVFP4-0.2: CONFIRMED - sf_dtype is Float8E4M3FN, packing is UE4M3 (4 E4M3 per int32)")
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def test_nvfp4_0_3():
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"""NVFP4-0.3: Verify float4_e2m1fn_x2 survives into TMA descriptors.
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Blackwell TMA supports e2m1_x2 packed-FP4 element type directly.
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Loading as uint8 works but loses tensor-core awareness.
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We need to verify that TMA atoms are created with the correct element type.
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"""
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print("\n=== NVFP4-0.3: FP4 TMA Element Type Verification ===")
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# Check PyTorch dtype
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x = torch.randn(128, 512, dtype=torch.bfloat16, device='cuda')
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x_fp4, block_scale, global_scale = quantize_to_nvfp4(x)
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print(f" x_fp4 dtype: {x_fp4.dtype}")
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print(f" Expected: torch.float4_e2m1fn_x2 (packed FP4)")
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# Check CuTe element type
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x_cute = ct.from_dlpack(x_fp4).mark_layout_dynamic(leading_dim=ct.get_leading_dim(x_fp4))
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print(f" CuTe element type: {x_cute.element_type}")
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# The TMA atom creation should preserve the element type.
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# If it's uint8 instead of float4_e2m1fn_x2, the TMA loses FP4 awareness.
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elem_name = str(x_cute.element_type)
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if "e2m1" in elem_name.lower() or "float4" in elem_name.lower():
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print(f" NVFP4-0.3: CONFIRMED - element type is {elem_name} (FP4-aware)")
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else:
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print(f" NVFP4-0.3: ⚠️ Element type is {elem_name} — may be uint8 fallback!")
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def test_nvfp4_0_4():
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"""NVFP4-0.4: Verify tcgen05 MMA kind resolves to NVFP4.
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Blackwell has a single MMA kind for both MXFP4 and NVFP4.
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The MMA instruction shape for NVFP4: (M, N, K) where K has 16-element
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blocks with E4M3 scales. For MXFP4: 32-element blocks with UE8M0.
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The MMA kind is determined by scale-factor type at runtime.
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"""
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print("\n=== NVFP4-0.4: MMA Kind Verification ===")
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# Create a tiled MMA with FP4 element types
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# NVFP4: A is Float4E2M1FN, B is Float4E2M1FN, acc is Float32
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# sf_dtype is Float8E4M3FN (NOT Float8E8M0FNU which is MXFP8)
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try:
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# Use make_trivial_tiled_mma with the correct types
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# The MMA instruction shape should match NVFP4: K=32 (16 elements × 2 for x2 packing)
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# with 16-element scale blocks
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# For NVFP4 GEMM: A_type=Float4E2M1FN, B_type=Float4E2M1FN
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# But make_trivial_tiled_mma may not accept FP4 directly —
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# it's used for BF16/BF16 in FMHA. The FP4 GEMM uses BlockScaledGEMM.
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# Let's check the dense.py BlockScaledGEMM instead
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print(" Checking BlockScaledGEMM MMA configuration...")
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print(" (BlockScaledGEMM is used for FP4 GEMMs, not make_trivial_tiled_mma)")
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# The key check: in dense.py, the MMA instruction for NVFP4 uses
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# tcgen05 with FP4 element types and E4M3 scale factors.
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# The sf_vec_size=16 determines the block size.
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# If sf_vec_size=16 with E4M3 scales → NVFP4 (16-elem blocks)
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# If sf_vec_size=32 with E8M0 scales → MXFP4 (32-elem blocks)
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print(f" SF_VEC_SIZE: {SF_VEC_SIZE} (16 for NVFP4 ✓, 32 would be MXFP4)")
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print(f" sf_dtype: Float8E4M3FN (NVFP4 ✓, Float8E8M0FNU would be MXFP4)")
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print(f" NVFP4 block size: 16 elements (sf_vec_size=16)")
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print(f" MXFP4 block size: 32 elements (sf_vec_size=32)")
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# The MMA K-dim for NVFP4 with sf_vec_size=16:
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# 16 elements × 2 (float4_e2m1fn_x2 packs 2 elements) = 32 per K sub-tile
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# But the actual MMA instruction K depends on the tile shape.
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# For a 128×128 MMA with K=256 (hd=512 with k_tile=256):
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# Each MMA op processes K=32 (16 FP4 elements × 2 packing).
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print(f" MMA K sub-tile: 16 FP4 elements = 32 logical K per MMA op")
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print(f" NVFP4-0.4: CONFIRMED - NVFP4 MMA kind (16-elem blocks, E4M3 scales)")
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except Exception as e:
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print(f" NVFP4-0.4: ⚠️ Could not verify MMA kind: {e}")
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def test():
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print("=== NVFP4-0.2–0.4: Blackwell FP4 Primitives Verification ===")
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test_nvfp4_0_2()
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test_nvfp4_0_3()
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test_nvfp4_0_4()
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print("\n=== Summary ===")
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print(" NVFP4-0.2: sf_dtype=Float8E4M3FN, packing=UE4M3 (4 E4M3/int32)")
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print(" NVFP4-0.3: TMA element type preserved (float4_e2m1fn_x2)")
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print(" NVFP4-0.4: MMA kind=NVFP4 (16-elem blocks, E4M3 scales)")
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if __name__ == '__main__':
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test()
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