[TPU] Add Load-time W8A16 quantization for TPU Backend

vllm/config.py

@@ -244,6 +244,7 @@ def _verify_quantization(self) -> None:
             "fp8", "marlin", "gptq_marlin_24", "gptq_marlin", "awq_marlin",
             "fbgemm_fp8", "compressed_tensors", "compressed-tensors"
         ]
+        tpu_supported_quantization = ["tpu_int8"]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()
 
@@ -282,6 +283,11 @@ def _verify_quantization(self) -> None:
                 raise ValueError(
                     f"{self.quantization} quantization is currently not "
                     f"supported in ROCm.")
+            if is_tpu(
+            ) and self.quantization not in tpu_supported_quantization:
+                raise ValueError(
+                    f"{self.quantization} quantization is currently not "
+                    f"supported in TPU Backend.")
             if self.quantization not in optimized_quantization_methods:
                 logger.warning(
                     "%s quantization is not fully "

vllm/model_executor/layers/quantization/__init__.py

@@ -22,11 +22,13 @@
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
+from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
 QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "aqlm": AQLMConfig,
     "awq": AWQConfig,
     "deepspeedfp": DeepSpeedFPConfig,
+    "tpu_int8": Int8TpuConfig,
     "fp8": Fp8Config,
     "fbgemm_fp8": FBGEMMFp8Config,
     # The order of gptq methods is important for config.py iteration over

vllm/model_executor/layers/quantization/tpu_int8.py

@@ -0,0 +1,118 @@
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+ACTIVATION_SCHEMES = ["none"]
+
+
+class Int8TpuConfig(QuantizationConfig):
+    """Int8 Quantization Config class for TPU Backend."""
+
+    def __init__(
+        self,
+        activation_scheme: str = "none",
+    ) -> None:
+        if activation_scheme not in ACTIVATION_SCHEMES:
+            raise ValueError(
+                f"Unsupported activation scheme {activation_scheme}")
+        self.activation_scheme = activation_scheme
+
+    def get_name(self) -> str:
+        return "tpu_int8"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "This function should not be called with TPU Backend")
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "Int8TpuConfig":
+        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        return cls(activation_scheme=activation_scheme)
+
+    def get_quant_method(self, layer: Module,
+                         prefix: str) -> Optional["TPUInt8LinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return TPUInt8LinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class TPUInt8LinearMethod(LinearMethodBase):
+    """Int8 Linear method for TPU Quant. """
+
+    def __init__(self, quant_config: Int8TpuConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: Module, input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        weight = Parameter(torch.empty(sum(output_partition_sizes),
+                                       input_size_per_partition,
+                                       dtype=params_dtype),
+                           requires_grad=False)
+        layer.register_parameter("weight", weight)
+        set_weight_attrs(weight, {
+            **extra_weight_attrs,
+            "input_dim": 1,
+            "output_dim": 0,
+        })
+
+    def _quantize_weight(
+            self, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        weight_dtype = weight.dtype
+        weight = weight.cpu().to(torch.float32)
+        n_bit = 8
+        eps = 1e-5
+        max_int = 2**(n_bit - 1) - 1
+        min_int = -(2**(n_bit - 1))
+        max_val = weight.abs().amax(dim=-1, keepdim=True)
+        max_val = max_val.clamp(min=eps)
+        qscale = max_val / max_int
+        qweight = torch.clamp(torch.round(weight * (1.0 / qscale)), min_int,
+                              max_int).to(torch.int8)
+        qscale = qscale.squeeze().to(weight_dtype)
+        return qweight, qscale
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        device = layer.weight.device
+        qweight, qscale = self._quantize_weight(layer.weight)
+        qweight = qweight.to(device)
+        qscale = qscale.to(device)
+        layer.weight = Parameter(qweight, requires_grad=False)
+        layer.scale = Parameter(qscale, requires_grad=False)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        try:
+            import torch_xla.experimental.xla_quantized_matmul  # noqa: F401
+        except ImportError as err:
+            raise ImportError(
+                "Please install torch_xla by following the instructions at "
+                "https://docs.vllm.ai/en/latest/getting_started/tpu-installation.html "  # noqa: E501
+                "to run vLLM on TPU.") from err
+        weight = layer.weight
+        scale = layer.scale
+        out = torch.ops.xla.quantized_matmul(x, weight, scale)
+        if bias is not None:
+            out = out + bias
+        return out

vllm/model_executor/model_loader/loader.py

@@ -94,14 +94,15 @@ def _get_quantization_config(
     """Get the quantization config."""
     if model_config.quantization is not None:
         quant_config = get_quant_config(model_config, load_config)
-        capability = current_platform.get_device_capability()
-        capability = capability[0] * 10 + capability[1]
-        if capability < quant_config.get_min_capability():
-            raise ValueError(
-                f"The quantization method {model_config.quantization} is not "
-                "supported for the current GPU. "
-                f"Minimum capability: {quant_config.get_min_capability()}. "
-                f"Current capability: {capability}.")
+        if not is_tpu():
+            capability = current_platform.get_device_capability()
+            capability = capability[0] * 10 + capability[1]
+            if capability < quant_config.get_min_capability():
+                raise ValueError(
+                    f"The quantization method {model_config.quantization} "
+                    "is not supported for the current GPU. "
+                    f"Minimum capability: {quant_config.get_min_capability()}. "
+                    f"Current capability: {capability}.")
         supported_dtypes = quant_config.get_supported_act_dtypes()
         if model_config.dtype not in supported_dtypes:
             raise ValueError(