60508ffda9
Co-authored-by: Faraz Shahsavan <faraz.shahsavan@gmail.com> Co-authored-by: ilmarkov <markovilya197@gmail.com> Co-authored-by: Rahul Tuli <rahul@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
"""Tests for sparse cutlass kernels
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Run `pytest tests/kernels/test_semi_structured.py`.
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"""
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from typing import Optional, Tuple, Type
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import pytest
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import torch
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from vllm import _custom_ops as ops
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from vllm.platforms import current_platform
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CUDA_DEVICES = [
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f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
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]
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capability = current_platform.get_device_capability()
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capability = capability[0] * 10 + capability[1]
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def to_fp8(tensor: torch.Tensor):
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finfo = torch.finfo(torch.float8_e4m3fn)
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return torch.round(tensor.clamp(
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min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
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def to_int8(tensor: torch.Tensor):
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return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)
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def rand_int8(shape: tuple, device: str = "cuda"):
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return to_int8(torch.rand(shape, device=device) * 255 - 128)
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def to_bf16(tensor: torch.Tensor) -> torch.Tensor:
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return tensor.to(dtype=torch.bfloat16)
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def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
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return tensor.to(dtype=torch.float16)
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def prune_to_2_4(tensor):
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# Reshape tensor to [N, 4] where N is number of groups of 4
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original_shape = tensor.shape
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reshaped = tensor.reshape(-1, 4)
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# Get indices of top 2 absolute values in each group of 4
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_, indices = torch.topk(torch.abs(reshaped), k=2, dim=1)
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# Create binary mask
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mask = torch.zeros_like(reshaped)
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mask.scatter_(dim=1,
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index=indices,
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src=torch.ones_like(indices, dtype=mask.dtype))
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# Apply mask and reshape back
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pruned = reshaped * mask
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# Turn all -0.0 to 0.0
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pruned[pruned == -0.0] = 0.0
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return pruned.reshape(original_shape)
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def make_rand_sparse_tensors(
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dtype: torch.dtype, m: int, n: int, k: int
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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a = torch.randn((m, k), device='cuda') * 5
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b = torch.randn((n, k), device='cuda').t() * 5
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b = prune_to_2_4(b.t()).t()
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if dtype == torch.int8:
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a, b = to_int8(a), to_int8(b)
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elif dtype == torch.float8_e4m3fn:
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a, b = to_fp8(a), to_fp8(b)
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elif dtype == torch.float16:
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a, b = to_fp16(a), to_fp16(b)
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elif dtype == torch.bfloat16:
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a, b = to_bf16(a), to_bf16(b)
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else:
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raise ValueError("unsupported dtype")
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b_compressed, e = ops.cutlass_sparse_compress(b.t())
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# Compressed B, Metadata, Original A, B
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return b_compressed, e, a, b
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def baseline_scaled_mm(a: torch.Tensor,
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b: torch.Tensor,
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scale_a: torch.Tensor,
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scale_b: torch.Tensor,
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out_dtype: Type[torch.dtype],
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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output = (scale_a * (scale_b * (torch.mm(
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a.to(dtype=torch.float32), b.to(dtype=torch.float32))))).to(out_dtype)
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if bias is not None:
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output = output + bias
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return output
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@pytest.mark.skipif(not current_platform.has_device_capability(90),
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reason="Sparse FP8 is not yet supported on this GPU type.")
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# Test working with a subset of A and B for sparse matmul
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def test_cutlass_sparse_subset():
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big_m = 1024
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m, n, k = 512, 512, 512
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# Create tensors
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b_comp, e, whole_a, b = make_rand_sparse_tensors(torch.float8_e4m3fn,
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big_m, n, k)
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a = whole_a[0:m, 0:k]
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scale_a = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
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scale_b = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
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out = ops.cutlass_scaled_sparse_mm(a,
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b_comp,
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e,
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scale_a,
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scale_b,
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out_dtype=torch.bfloat16)
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baseline = baseline_scaled_mm(a,
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b,
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scale_a,
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scale_b,
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out_dtype=torch.bfloat16)
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torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
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