[TPU] support fp8 kv cache quantization (#19292)

Signed-off-by: Chengji Yao <chengjiyao@google.com>

tests/entrypoints/llm/test_accuracy.py

@@ -15,15 +15,18 @@ import pytest
 from vllm.platforms import current_platform
 
 MODEL_NAMES = [
-    "Qwen/Qwen2-1.5B-Instruct",
+    "Qwen/Qwen3-1.7B",
     "google/gemma-3-1b-it",
 ]
+FP8_KV_MODEL_NAMES = [
+    "Qwen/Qwen3-1.7B",
+]
 NUM_CONCURRENT = 500
 TASK = "gsm8k"
 FILTER = "exact_match,strict-match"
 RTOL = 0.03
 EXPECTED_VALUES = {
-    "Qwen/Qwen2-1.5B-Instruct": 0.58,
+    "Qwen/Qwen3-1.7B": 0.68,
     "google/gemma-3-1b-it": 0.25,
 }
 
@@ -70,10 +73,9 @@ def test_lm_eval_accuracy_v1_engine(model, monkeypatch: pytest.MonkeyPatch):
         if current_platform.is_tpu():
             # Limit compilation time for TPU V1
 
-            if model == "google/gemma-3-1b-it":
-                # TPU + google/gemma-3-1b-it + xet doesn't work well.
-                m.setenv("HF_HUB_DISABLE_XET", "1")
-
+            # xet doesn't work well for both Qwen/Qwen3-1.7B and
+            # google/gemma-3-1b-it
+            m.setenv("HF_HUB_DISABLE_XET", "1")
             more_args = "max_model_len=2048,max_num_seqs=64"
 
             # Add TP test (if provided)
@@ -83,9 +85,27 @@ def test_lm_eval_accuracy_v1_engine(model, monkeypatch: pytest.MonkeyPatch):
         run_test(model, more_args)
 
 
-def test_lm_eval_accuracy_v0_engine(monkeypatch: pytest.MonkeyPatch):
-    """Run with the V0 Engine."""
+@pytest.mark.skipif(not current_platform.is_cuda()
+                    and not current_platform.is_tpu(),
+                    reason="V1 is currently only supported on CUDA and TPU")
+@pytest.mark.parametrize("model", FP8_KV_MODEL_NAMES)
+def test_lm_eval_accuracy_v1_engine_fp8_kv_cache(
+        model, monkeypatch: pytest.MonkeyPatch):
+    """Run with the V1 Engine."""
 
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "0")
-        run_test("Qwen/Qwen2-1.5B-Instruct")
+        m.setenv("VLLM_USE_V1", "1")
+
+        more_args = None
+        if current_platform.is_tpu():
+            # Limit compilation time for TPU V1
+
+            # xet doesn't work well for Qwen/Qwen3-1.7B
+            m.setenv("HF_HUB_DISABLE_XET", "1")
+            more_args = "max_model_len=2048,max_num_seqs=128,kv_cache_dtype=fp8"
+
+            # Add TP test (if provided)
+            if TPU_TP_TEST_STR:
+                more_args += ",{}".format(TPU_TP_TEST_STR)
+
+        run_test(model, more_args)

tests/v1/tpu/test_pallas.py

@@ -95,4 +95,6 @@ def test_ragged_paged_attention():
             sm_scale=scale,
             sliding_window=sliding_window,
             soft_cap=logits_soft_cap,
+            k_scale=1.0,
+            v_scale=1.0,
         )

vllm/engine/arg_utils.py

@@ -1358,10 +1358,10 @@ class EngineArgs:
                 and not envs.is_set("VLLM_ATTENTION_BACKEND")
             ) or envs.VLLM_ATTENTION_BACKEND == "FLASH_ATTN_VLLM_V1"
             supported = False
-            if current_platform.is_rocm() or (
-                    current_platform.is_cuda()
-                    and current_platform.is_device_capability(100)
-            ):  # handle hpu also for OOT platform
+            if (current_platform.is_rocm()
+                    or (current_platform.is_cuda()
+                        and current_platform.is_device_capability(100))
+                    or current_platform.is_tpu()):
                 supported = True
             elif fp8_attention and will_use_fa:
                 from vllm.attention.utils.fa_utils import (

vllm/platforms/tpu.py

@@ -35,7 +35,9 @@ class TpuPlatform(Platform):
     device_control_env_var: str = "TPU_VISIBLE_CHIPS"
     simple_compile_backend: str = "openxla"
 
-    supported_quantization: list[str] = ["tpu_int8", "compressed-tensors"]
+    supported_quantization: list[str] = [
+        "fp8", "tpu_int8", "compressed-tensors"
+    ]
 
     additional_env_vars: list[str] = [
         "TPU_CHIPS_PER_HOST_BOUNDS", "TPU_HOST_BOUNDS"

vllm/v1/attention/backends/pallas.py

@@ -24,6 +24,19 @@ logger = init_logger(__name__)
 # TPU requires the head size to be a multiple of 128.
 TPU_HEAD_SIZE_ALIGNMENT = 128
 
+# Note: TPU can fp8 as storage dtype but doesn't support converting from uint8
+# from to fp32 directly. That's why it has a dtype mapping different from GPU
+TPU_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+    "float": torch.float,
+    "fp8": torch.float8_e4m3fn,
+    "fp8_e4m3": torch.float8_e4m3fn,
+    "fp8_e5m2": torch.float8_e5m2,
+    "int8": torch.int8,
+    "uint8": torch.uint8,
+}
+
 
 class PallasAttentionBackend(AttentionBackend):
 
@@ -152,8 +165,6 @@ class PallasAttentionBackendImpl(AttentionImpl):
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
         if alibi_slopes is not None:
             raise NotImplementedError("Alibi slopes is not supported.")
-        if kv_cache_dtype != "auto":
-            raise NotImplementedError("FP8 KV cache dtype is not supported.")
 
         if attn_type != AttentionType.DECODER:
             raise NotImplementedError("Encoder self-attention and "
@@ -161,6 +172,11 @@ class PallasAttentionBackendImpl(AttentionImpl):
                                       "are not implemented for "
                                       "PallasAttentionBackendImpl")
 
+        self.kv_cache_quantized_dtype = None
+        if kv_cache_dtype != "auto":
+            self.kv_cache_quantized_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE.get(
+                kv_cache_dtype.lower().strip())
+
     def forward(
         self,
         layer: AttentionLayer,
@@ -194,7 +210,6 @@ class PallasAttentionBackendImpl(AttentionImpl):
                 output = torch.ones_like(query)
             return output
 
-        assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
         num_tokens, hidden_size = query.shape
         query = query.view(num_tokens, self.num_heads, self.head_size)
         key = key.view(-1, self.num_kv_heads, self.head_size)
@@ -215,10 +230,21 @@ class PallasAttentionBackendImpl(AttentionImpl):
             # Skip this if sharing KV cache with an earlier attention layer.
             slot_mapping = attn_metadata.slot_mapping
             write_to_kv_cache(
-                key, value, kv_cache, slot_mapping,
+                key,
+                value,
+                kv_cache,
+                slot_mapping,
                 attn_metadata.num_slices_per_kv_cache_update_block,
-                attn_metadata.num_kv_update_slices)
-
+                attn_metadata.num_kv_update_slices,
+                self.kv_cache_quantized_dtype,
+                layer._k_scale_float,
+                layer._v_scale_float,
+            )
+
+        if self.kv_cache_quantized_dtype is not None and (
+                layer._k_scale_float == 0.0 or layer._v_scale_float == 0.0):
+            raise ValueError(
+                "k_scale_float and v_scale_float must be non-zero")
         output = torch.ops.xla.ragged_paged_attention(
             query,
             kv_cache,
@@ -236,6 +262,8 @@ class PallasAttentionBackendImpl(AttentionImpl):
             sm_scale=self.scale,
             sliding_window=self.sliding_window,
             soft_cap=self.logits_soft_cap,
+            k_scale=layer._k_scale_float,
+            v_scale=layer._v_scale_float,
         )
 
         if self.head_size % TPU_HEAD_SIZE_ALIGNMENT != 0:
@@ -251,18 +279,32 @@ def write_to_kv_cache(
     slot_mapping: torch.Tensor,
     num_slices_per_kv_cache_update_block: int,
     num_kv_update_slices: torch.Tensor,
+    kv_cache_quantized_dtype: Optional[torch.dtype] = None,
+    k_scale: float = 1.0,
+    v_scale: float = 1.0,
 ) -> None:
     """ Write the key and values to the KV cache.
 
     Args:
-        key: shape = [num_tokens, num_kv_heads * head_size]
-        value: shape = [num_tokens, num_kv_heads *  head_size]
+        key: shape = [num_tokens, num_kv_heads, head_size]
+        value: shape = [num_tokens, num_kv_heads, head_size]
         kv_cache = [num_blocks, block_size, num_kv_heads * 2, head_size]
         num_slices_per_kv_cache_update_block: int
     """
     _, page_size, num_combined_kv_heads, head_size = kv_cache.shape
     head_size = cdiv(head_size,
                      TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+
+    if kv_cache_quantized_dtype is not None:
+        dtype_info = torch.finfo(kv_cache_quantized_dtype)
+        key = key.to(torch.float32) / k_scale
+        # NOTE: clamp is added here to avoid out of range of quantized dtype
+        key = torch.clamp(key, dtype_info.min, dtype_info.max)
+        key = key.to(kv_cache_quantized_dtype)
+        value = value.to(torch.float32) / v_scale
+        value = torch.clamp(value, dtype_info.min, dtype_info.max)
+        value = value.to(kv_cache_quantized_dtype)
+
     kv = torch.cat([key, value], axis=-1).reshape(-1, num_combined_kv_heads,
                                                   head_size)
 

vllm/v1/worker/tpu_model_runner.py

@@ -32,9 +32,10 @@ from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargs,
 from vllm.multimodal.utils import group_mm_inputs_by_modality
 from vllm.pooling_params import PoolingTask
 from vllm.sequence import IntermediateTensors
-from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, cdiv,
-                        is_pin_memory_available, prev_power_of_2)
-from vllm.v1.attention.backends.pallas import (PallasAttentionBackend,
+from vllm.utils import (LayerBlockType, cdiv, is_pin_memory_available,
+                        prev_power_of_2)
+from vllm.v1.attention.backends.pallas import (TPU_STR_DTYPE_TO_TORCH_DTYPE,
+                                               PallasAttentionBackend,
                                                PallasMetadata,
                                                get_page_size_bytes)
 from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
@@ -142,11 +143,11 @@ class TPUModelRunner(LoRAModelRunnerMixin):
         if cache_config.cache_dtype == "auto":
             model_dtype = self.dtype
             if isinstance(model_dtype, str):
-                self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
+                self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
             else:
                 self.kv_cache_dtype = model_dtype
         else:
-            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
+            self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[
                 cache_config.cache_dtype]
         self._hidden_states_dtype = self.dtype