[quantization] use channel scales for w4a8 + misc fixes (#23570)

Signed-off-by: czhu-cohere <conway.zhu@cohere.com>

tests/quantization/test_compressed_tensors.py

@@ -14,10 +14,10 @@ from compressed_tensors.quantization import QuantizationType
 from tests.models.utils import check_logprobs_close
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
     CompressedTensors24, CompressedTensorsLinearMethod,
-    CompressedTensorsW4A4Fp4, CompressedTensorsW4A16Fp4,
-    CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8Fp8,
-    CompressedTensorsW8A8Int8, CompressedTensorsW8A16Fp8,
-    CompressedTensorsWNA16)
+    CompressedTensorsW4A4Fp4, CompressedTensorsW4A8Fp8,
+    CompressedTensorsW4A16Fp4, CompressedTensorsW4A16Sparse24,
+    CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
+    CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     cutlass_fp4_supported)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
@@ -683,3 +683,39 @@ def test_compressed_tensors_nvfp4(vllm_runner, args):
         output = llm.generate_greedy("Hello my name is", max_tokens=20)
         print(output)
         assert output
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda()
+    or not current_platform.has_device_capability(90),
+    reason="W4A8 FP8 is not yet supported on this GPU type.",
+)
+@pytest.mark.parametrize("args", [
+    ("czhu-cohere/TinyLlama-1.1B-Chat-v1.0-W4A8-e2e", CompressedTensorsW4A8Fp8)
+])
+def test_compressed_tensors_w4a8_fp8(vllm_runner, args):
+    model, scheme = args
+    with vllm_runner(model, enforce_eager=True) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+            o_proj = layer.self_attn.o_proj
+            gate_up_proj = layer.mlp.gate_up_proj
+            down_proj = layer.mlp.down_proj
+
+            for proj in (qkv_proj, o_proj, gate_up_proj, down_proj):
+                assert isinstance(proj.quant_method,
+                                  CompressedTensorsLinearMethod)
+                assert isinstance(proj.scheme, scheme)
+
+                assert proj.weight_packed.dtype is torch.int32
+                assert proj.weight_scale.dtype is torch.float8_e4m3fn
+                assert proj.weight_chan_scale.dtype is torch.float32
+                assert proj.scheme.group_size == 128
+
+        llm.apply_model(check_model)
+        output = llm.generate_greedy("Hello my name is", max_tokens=20)
+        print(output)
+        assert output