[Kernel] Vectorized FP8 quantize kernel (#5396)

Inspired by #5146, this PR improves FP8 quantize kernel by vectorizing data transfer to better utilize memory bandwidth. Microbenchmark shows that this improved kernel can achieve 1.0x-1.5x speedup (especially when hidden size is large).

In details, we applied 3 optimizations:

- Use inverted scale so that most divisions are changed to multiplications.
- Unroll the loop by 4 times to improve ILP.
- Use vectorized 4 to transfer data between HBM and SRAM.

tests/quantization/test_fp8.py

@@ -5,6 +5,7 @@ Run `pytest tests/quantization/test_fp8.py --forked`.
 import pytest
 import torch
 
+from vllm._custom_ops import scaled_fp8_quant
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
 
@@ -22,3 +23,49 @@ def test_load_fp16_model(vllm_runner) -> None:
         fc1 = model.model.decoder.layers[0].fc1
         assert isinstance(fc1.quant_method, Fp8LinearMethod)
         assert fc1.weight.dtype == torch.float8_e4m3fn
+
+
+@pytest.mark.skipif(
+    capability < QUANTIZATION_METHODS["fp8"].get_min_capability(),
+    reason="FP8 is not supported on this GPU type.")
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+def test_scaled_fp8_quant(dtype) -> None:
+
+    def quantize_ref(tensor, inv_scale):
+        # The reference implementation that fully aligns to
+        # the kernel being tested.
+        finfo = torch.finfo(torch.float8_e4m3fn)
+        scale = inv_scale.reciprocal()
+        qweight = (tensor.to(torch.float32) * scale).clamp(min=finfo.min,
+                                                           max=finfo.max)
+        qweight = qweight.to(torch.float8_e4m3fn)
+        return qweight
+
+    def per_tensor_dequantize(tensor, inv_scale, dtype):
+        fake_qweight = tensor.to(dtype)
+        dq_weight = fake_qweight * inv_scale
+        return dq_weight
+
+    # Note that we use a shape % 4 != 0 to cover edge cases,
+    # because scaled_fp8_quant is vectorized by 4.
+    x = (torch.randn(size=(11, 11), device="cuda") * 13).to(dtype)
+
+    # Dynamic quantization
+    ref_y, inv_scale = scaled_fp8_quant(x, None)
+    ref_y = per_tensor_dequantize(ref_y, inv_scale, dtype)
+
+    # Reference dynamic quantizaton
+    y = quantize_ref(x, inv_scale)
+    assert torch.allclose(ref_y, per_tensor_dequantize(y, inv_scale, dtype))
+
+    # Static quantization
+    y, _ = scaled_fp8_quant(x, inv_scale)
+    assert torch.allclose(ref_y, per_tensor_dequantize(y, inv_scale, dtype))
+
+    # Padding
+    y, _ = scaled_fp8_quant(x, inv_scale, batch_dim_padding=17)
+    assert y.shape[0] == 17
+    assert torch.allclose(
+        ref_y,
+        per_tensor_dequantize(torch.narrow(y, 0, 0, x.shape[0]), inv_scale,
+                              dtype))