[fp8] add fp8 linear (#5967)

* [fp8] add fp8 linear

* [test] fix fp8 linear test condition

* [test] fix fp8 linear test condition

* [test] fix fp8 linear test condition

tests/test_fp8/test_fp8_linear.py

@@ -0,0 +1,45 @@
+import pytest
+import torch
+import torch.nn.functional as F
+from torch.testing import assert_close
+
+from colossalai.accelerator import get_accelerator
+from colossalai.quantization.fp8 import linear_fp8
+from colossalai.utils import get_current_device
+
+D_IN, D_OUT = 16, 32
+B, S = 2, 64
+DTYPE = torch.bfloat16
+
+
+@pytest.mark.skipif(get_accelerator().get_device_capability()[0] < 9, reason="Test requires device capability >= 9.0")
+@pytest.mark.parametrize("use_bias", [True, False])
+@pytest.mark.parametrize("use_batch", [True, False])
+def test_fp8_linear(use_bias: bool, use_batch: bool):
+    # create tensors
+    w = torch.rand(D_OUT, D_IN, device=get_current_device(), dtype=DTYPE, requires_grad=True)
+    ref_w = w.clone().detach().requires_grad_()
+    if use_batch:
+        x_shape = (B, S, D_IN)
+    else:
+        x_shape = (S, D_IN)
+    x = torch.rand(x_shape, device=get_current_device(), dtype=DTYPE, requires_grad=True)
+    ref_x = x.clone().detach().requires_grad_()
+    if use_bias:
+        bias = torch.rand(D_OUT, device=get_current_device(), dtype=DTYPE, requires_grad=True)
+        ref_bias = bias.clone().detach().requires_grad_()
+    else:
+        bias = None
+        ref_bias = None
+
+    out = linear_fp8(x, w, bias)
+    assert out.shape == x_shape[:-1] + (D_OUT,)
+    out.sum().backward()
+    ref_out = F.linear(ref_x, ref_w, ref_bias)
+    ref_out.sum().backward()
+
+    assert_close(out, ref_out, rtol=0.2, atol=0.1)
+    assert_close(x.grad, ref_x.grad, rtol=0.2, atol=0.1)
+    assert_close(w.grad, ref_w.grad, rtol=0.2, atol=0.1)
+    if use_bias:
+        assert_close(bias.grad, ref_bias.grad, rtol=0.2, atol=0.1)