Revert "Add static FP8 attention support"

auto_round/__main__.py

@@ -153,13 +153,6 @@ def __init__(self, *args, **kwargs):
         basic.add_argument(
             "--enable_torch_compile", action="store_true", help="Enable PyTorch compilation for faster execution. "
         )
-        basic.add_argument(
-            "--static_kv_dtype", default=None, type=str, help="Data type for static quantize key and value. "
-        )
-
-        basic.add_argument(
-            "--static_attention_dtype ", default=None, type=str, help="Data type for static quantize attention. "
-        )
 
         tuning = self.add_argument_group("Tuning Arguments")
         tuning.add_argument(
@@ -606,8 +599,6 @@ def tune(args):
         layer_config=layer_config,
         model_dtype=args.model_dtype,
         momentum=args.momentum,
-        static_kv_dtype=args.static_kv_dtype,
-        static_attention_dtype=args.static_attention_dtype,
     )
 
     model_name = args.model.rstrip("/")

auto_round/compressors/base.py

@@ -237,7 +237,6 @@ def __init__(
         enable_deterministic_algorithms = kwargs.pop("enable_deterministic_algorithms", False)
         self.momentum = kwargs.pop("momentum", 0.0)
         static_kv_dtype = kwargs.pop("static_kv_dtype", None)
-        static_attention_dtype = kwargs.pop("static_attention_dtype", None)
         model_dtype = kwargs.pop("model_dtype", None)
         device = kwargs.pop("device", None)
         if envs.AR_USE_MODELSCOPE:
@@ -357,11 +356,6 @@ def __init__(
         if self.static_kv_dtype is not None:
             logger.warning("The static kv is experimental and currently has limited support.")
 
-        # Attention static dtype
-        self.static_attention_dtype = static_attention_dtype
-        if self.static_attention_dtype is not None:
-            logger.warning("The static attention dtype is experimental and currently has limited support.")
-
         self._set_amp_dtype()
         self.cache_device = torch.device("cpu") if self.low_gpu_mem_usage else self.device
         if self.act_bits <= 8 and self.amp_dtype == torch.float16:
@@ -1010,12 +1004,7 @@ def quantize_and_save(
         kwargs.pop("inplace", None)
 
         # Perform model quantization
-        if self.static_attention_dtype is not None:
-            from auto_round.experimental.attention import attention_quant_ctx
-
-            with attention_quant_ctx(self.model, static_attention_dtype=self.static_attention_dtype):
-                model, _ = self.quantize()
-        elif self.static_kv_dtype is not None:
+        if self.static_kv_dtype is not None:
             from auto_round.experimental.kv_cache import kvcache_quant_context
 
             with kvcache_quant_context(self.model, static_kv_dtype=self.static_kv_dtype):

auto_round/experimental/kv_cache.py

@@ -24,12 +24,6 @@
 import torch
 from transformers.cache_utils import DynamicCache
 
-from auto_round.experimental.utils import (
-    is_attention_module,
-    normalize_static_kv_dtype,
-    per_tensor_fp8_qdq,
-    update_parameter_data,
-)
 from auto_round.utils import logger
 
 __all__ = [
@@ -87,6 +81,13 @@ def _pad_and_append_at_idx_(lst: List, idx: int, val: Any) -> list:
     return lst
 
 
+def fp8_per_tensor_qdq(tensor):
+    from auto_round.data_type.fp8 import quant_fp8_sym
+
+    qdq_tensor, scale, _ = quant_fp8_sym(tensor, max_scale=1.0, tensor_max=None, group_size=0, v=0)
+    return qdq_tensor, scale
+
+
 class QuantizedKVParameterCache(DynamicCache):
     """
     Quantized KV cache used in the forward call based on HF's dynamic cache.
@@ -172,8 +173,8 @@ def _quant_dequant(self, tensor: torch.Tensor, kv_type: KVCacheScaleType, layer_
             assert kv_type == KVCacheScaleType.VALUE
             scales = self.v_scales
 
-        qdq_tensor, scale = per_tensor_fp8_qdq(tensor)
-        _pad_and_append_at_idx_(scales, layer_idx, scale.squeeze(0))
+        qdq_tensor, scale = fp8_per_tensor_qdq(tensor)
+        _pad_and_append_at_idx_(scales, layer_idx, scale)
         return qdq_tensor
 
 
@@ -191,9 +192,13 @@ def initialize_quantized_kv_cache(module: torch.nn.Module, dtype=torch.float8_e4
     quantized_kv_cache = QuantizedKVParameterCache(dtype=dtype)
     setattr(module, "kv_cache", quantized_kv_cache)
     logger.debug(f"Initialized quantized kv_cache for {module.__class__.__name__} {getattr(module, 'layer_idx', None)}")
-    init_scale = torch.tensor([0.0], device=next(module.parameters()).device)
-    update_parameter_data(module, init_scale.clone(), KVCacheScaleType.KEY.value)
-    update_parameter_data(module, init_scale.clone(), KVCacheScaleType.VALUE.value)
+
+
+def is_attention_module(module: torch.nn.Module):
+    # FIXME: Handle this better.
+    return "attention" in module.__class__.__name__.lower() and (
+        hasattr(module, "k_proj") or hasattr(module, "v_proj") or hasattr(module, "qkv_proj")
+    )
 
 
 def calibrate_kv_cache_input_hook(
@@ -204,6 +209,7 @@ def calibrate_kv_cache_input_hook(
     kv_cache quantization. Will update the passed in
     kv_cache to singleton QuantizedKVParameterCache.
     """
+    logger.debug(f"calibrate kv_cache input hook for {module.__class__.__name__} {getattr(module, 'layer_idx', None)}")
     kv_cache = getattr(module, "kv_cache")
     #  Start from transformers 4.55.2, the `past_key_value` was renamed to `past_key_values`.
     # https://github.com/huggingface/transformers/blob/52c6c1bb6e27ca87c4faede34a4c2a7404c17c4d/src/transformers/models/llama/modeling_llama.py#L279-L280
@@ -215,14 +221,33 @@ def calibrate_kv_cache_input_hook(
     return args, kwargs
 
 
+def update_parameter_data(module: torch.nn.Module, new_val: torch.Tensor, name: str):
+    """
+    Update the data of a parameter in a module.
+    If the parameter does not exist, it will be created.
+    """
+    if hasattr(module, name):
+        param = getattr(module, name)
+        if isinstance(param, torch.nn.Parameter):
+            param.data = new_val
+        else:
+            module.register_parameter(name, torch.nn.Parameter(new_val))
+    else:
+        logger.warning(
+            "Parameter %s not found in module %s, creating new parameter."
+            % (name, module.__class__.__name__ + str(getattr(module, "layer_idx", "")))
+        )
+        module.register_parameter(name, torch.nn.Parameter(new_val))
+
+
 def calibrate_kv_cache_output_hook(module: torch.nn.Module, _args: Any, _output: torch.Tensor):
     """
     Hook to update k_scale and v_scale parameters when running kv_cache quantization.
     """
-    # logger.debug(
-    #     "Calibrate kv_cache output hook for %s %s"
-    #     % (module.__class__.__name__, str(getattr(module, "layer_idx", None)))
-    # )
+    logger.debug(
+        "Calibrate kv_cache output hook for %s %s"
+        % (module.__class__.__name__, str(getattr(module, "layer_idx", None)))
+    )
     kv_cache = getattr(module, "kv_cache")
     k_scale = kv_cache.k_scales[module.layer_idx]
     v_scale = kv_cache.v_scales[module.layer_idx]
@@ -236,6 +261,28 @@ def prep_attention_module_for_calibration(module: torch.nn.Module):
         module.register_forward_hook(calibrate_kv_cache_output_hook)
 
 
+def normalize_static_kv_dtype(static_kv_dtype: Union[str, torch.dtype]) -> torch.dtype:
+    valid_dtype_name_lst = ["float16", "bfloat16", "fp8", "float32", "float"]
+    valid_torch_dtype = {
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+        "fp8": torch.float8_e4m3fn,
+        "float8_e4m3fn": torch.float8_e4m3fn,
+        "float32": torch.float32,
+        "float": torch.float32,  # Alias for float32
+    }
+    if static_kv_dtype in valid_dtype_name_lst:
+        new_dtype = valid_torch_dtype[static_kv_dtype]
+    elif static_kv_dtype in valid_torch_dtype.values():
+        new_dtype = static_kv_dtype
+    else:
+        raise ValueError(
+            f"Invalid static kv dtype: {static_kv_dtype}. "
+            f"Valid options are: {', '.join(valid_dtype_name_lst  + list(valid_torch_dtype.values()))}."
+        )
+    return new_dtype
+
+
 @contextlib.contextmanager
 def kvcache_quant_context(model: torch.nn.Module, static_kv_dtype=torch.float8_e4m3fn):
     """Context manager for FP8 KV cache quantization operations."""

auto_round/experimental/qmodules/fp8_static.py

@@ -115,8 +115,8 @@ def qdq_input(self, bf16_input: torch.Tensor):
 
     @torch.no_grad()
     def forward(self, bf16_input: torch.Tensor) -> torch.Tensor:
-        original_dtype = bf16_input.dtype
+
         qdq_input = self.qdq_input(bf16_input)
         qdq_weight = self.dequant_weight_online()
-        out = torch.nn.functional.linear(qdq_input.to(original_dtype), qdq_weight.to(original_dtype), self.bias)
+        out = torch.nn.functional.linear(qdq_input, qdq_weight, self.bias)
         return out