Revert "[CPU] Add ops for float8 linear (#3052)"

This reverts commit 5e90c47.

Author: jcaip

test/test_ops.py

@@ -40,14 +40,7 @@
 except RuntimeError:
     pytest.skip("torchao.ops not available")
 
-from torchao.quantization import PerGroup, PerRow, PerTensor
-from torchao.quantization.quant_primitives import (
-    _choose_scale_float8,
-    _dequantize_affine_float8,
-    _quantize_affine_float8,
-)
 from torchao.quantization.utils import (
-    get_block_size,
     get_groupwise_affine_qparams,
     groupwise_affine_dequantize_tensor_from_qparams,
     groupwise_affine_quantize_tensor_from_qparams,
@@ -908,91 +901,5 @@ def _test_scaled_embedding_bag_cpu_helper(
         torch.testing.assert_close(refe_out, test_out, atol=1e-5, rtol=1e-5)
 
 
-@pytest.mark.skipif(
-    "CPU" not in torch._C._dispatch_dump("torchao::_scaled_embedding_bag"),
-    reason="cpp kernels not built",
-)
-@pytest.mark.parametrize(
-    "multi_hot, batch_size, vector_size, index_type",
-    EMBEDINGBAG_TEST_PARAMS,
-    ids=str,
-)
-def test_scaled_embedding_bag_int8_cpu(multi_hot, batch_size, vector_size, index_type):
-    _test_scaled_embedding_bag_cpu_helper(
-        multi_hot, batch_size, vector_size, index_type, torch.int8
-    )
-
-
-@pytest.mark.skipif(
-    "CPU" not in torch._C._dispatch_dump("torchao::_scaled_embedding_bag"),
-    reason="cpp kernels not built",
-)
-@pytest.mark.parametrize(
-    "multi_hot, batch_size, vector_size, index_type",
-    EMBEDINGBAG_TEST_PARAMS,
-    ids=str,
-)
-def test_scaled_embedding_bag_fp8_cpu(multi_hot, batch_size, vector_size, index_type):
-    _test_scaled_embedding_bag_cpu_helper(
-        multi_hot, batch_size, vector_size, index_type, torch.float8_e4m3fn
-    )
-
-
-@pytest.mark.skipif(
-    "CPU" not in torch._C._dispatch_dump("torchao::float8_linear_prepack_cpu")
-    or "CPU" not in torch._C._dispatch_dump("torchao::float8_linear_cpu"),
-    reason="cpp kernels not built",
-)
-@pytest.mark.skipif(
-    not torch_version_at_least("2.6.0"), reason="Test only enabled for 2.6+"
-)
-@pytest.mark.parametrize("shape", [(64, 64), (256, 256)])
-@pytest.mark.parametrize("bs", [1, 160])
-@pytest.mark.parametrize("out_dtype", [torch.float, torch.bfloat16, torch.half])
-@pytest.mark.parametrize("bias", [True, False])
-@pytest.mark.parametrize("x_granularity", [PerTensor(), PerRow(), PerGroup(128)])
-@pytest.mark.parametrize("w_granularity", [PerTensor(), PerRow(), PerGroup(128)])
-def test_float8_linear_cpu(shape, bs, out_dtype, bias, x_granularity, w_granularity):
-    in_feature, out_feature = shape
-    if isinstance(x_granularity, PerGroup):
-        if x_granularity.group_size >= in_feature:
-            return
-        if not isinstance(w_granularity, PerGroup):
-            return
-    if isinstance(w_granularity, PerGroup):
-        if w_granularity.group_size >= in_feature:
-            return
-    m = torch.nn.Linear(in_feature, out_feature, bias=bias).eval()
-    b = m.bias
-    x = torch.randn(bs, in_feature)
-    x_block_size = get_block_size(x.shape, x_granularity)
-    x_scale = _choose_scale_float8(
-        x,
-        float8_dtype=torch.float8_e4m3fn,
-        block_size=x_block_size,
-    )
-    x_fp8 = _quantize_affine_float8(x, x_scale, torch.float8_e4m3fn)
-
-    w = m.weight.detach()
-    w_block_size = get_block_size(w.shape, w_granularity)
-    w_scale = _choose_scale_float8(
-        w,
-        float8_dtype=torch.float8_e4m3fn,
-        block_size=w_block_size,
-    )
-    w_fp8 = _quantize_affine_float8(w, w_scale, torch.float8_e4m3fn)
-
-    x_dq = _dequantize_affine_float8(x_fp8, x_scale)
-    w_dq = _dequantize_affine_float8(w_fp8, w_scale)
-    ref = torch.nn.functional.linear(x_dq, w_dq, b).to(out_dtype)
-
-    packed_w, packed_scale = torch.ops.torchao.float8_linear_prepack_cpu(w_fp8, w_scale)
-    y = torch.ops.torchao.float8_linear_cpu(
-        x_fp8, x_scale, packed_w, packed_scale, b, out_dtype
-    )
-
-    torch.testing.assert_close(y, ref, atol=1e-2, rtol=1e-2)
-
-
 if __name__ == "__main__":
     pytest.main(sys.argv)