215 lines
7.3 KiB
Python
215 lines
7.3 KiB
Python
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"""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 Tuple, Type
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import pytest
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import torch
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import torch.nn.functional as F
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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sparse_cutlass_supported)
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from vllm.platforms import current_platform
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from .utils import baseline_scaled_mm, to_fp8, to_int8
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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_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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@pytest.mark.skipif(not sparse_cutlass_supported(),
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reason="Sparse CUTLASS is not 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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MNK_FACTORS = [
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(1, 256, 128),
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(1, 16384, 1024),
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(1, 24576, 512),
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(16, 256, 512),
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(16, 16384, 128),
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(16, 24576, 4096),
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(32, 8192, 4096),
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(32, 16384, 4096),
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(33, 1024, 1024),
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(33, 8192, 128),
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(64, 2048, 512),
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(64, 16384, 1024),
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(100, 8192, 512),
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(128, 32768, 4096),
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(256, 4096, 4096),
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(512, 256, 1024),
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(512, 8192, 4096),
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(512, 16384, 128),
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(512, 24576, 128),
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]
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# Test working with a subset of A and B for sparse matmul
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@pytest.mark.skip(reason="2of4 sparse w16a16 CUTLASS produces bad output.")
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@pytest.mark.skipif(not sparse_cutlass_supported(),
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reason="Sparse CUTLASS is not supported on this GPU type.")
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@pytest.mark.parametrize("m, k, n", MNK_FACTORS)
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@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
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def test_cutlass_sparse_gemm(m: int, k: int, n: int, dtype: Type[torch.dtype]):
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# Create tensors
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b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)
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scale_a = torch.ones((1, 1), device="cuda", dtype=torch.float32)
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scale_b = torch.ones((1, 1), device="cuda", dtype=torch.float32)
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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=dtype)
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baseline = F.linear(a, b.T)
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torch.testing.assert_close(out, baseline, rtol=1e-2, atol=1e-2)
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@pytest.mark.skipif(not sparse_cutlass_supported(),
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reason="Sparse CUTLASS is not supported on this GPU type.")
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@pytest.mark.parametrize("m, k, n", MNK_FACTORS)
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@pytest.mark.skipif(not current_platform.has_device_capability(89),
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reason="FP8 is not supported on this GPU type.")
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def test_cutlass_sparse_fp8_gemm(m: int, n: int, k: int):
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# Create tensors
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b_comp, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n, k)
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scale_a = (torch.randn((1, 1), device="cuda", dtype=torch.float32))
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scale_b = (torch.randn((1, 1), device="cuda", dtype=torch.float32))
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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=1e0, atol=2e0)
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@pytest.mark.skipif(not sparse_cutlass_supported(),
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reason="Sparse CUTLASS is not supported on this GPU type.")
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@pytest.mark.parametrize("m,k,n", MNK_FACTORS)
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@pytest.mark.parametrize("per_act_token", [True, False])
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@pytest.mark.parametrize("per_out_ch", [True, False])
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@pytest.mark.parametrize("use_bias", [True, False])
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def test_cutlass_sparse_int8_gemm(m: int, n: int, k: int, per_act_token: bool,
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per_out_ch: bool, use_bias: bool):
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# Create tensors
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b_comp, e, a, b = make_rand_sparse_tensors(torch.int8, m, n, k)
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scale_a = (torch.randn((1, 1), device="cuda", dtype=torch.float32))
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scale_b = (torch.randn((1, 1), device="cuda", dtype=torch.float32))
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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=1e0, atol=2e0)
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