from typing import Any, Dict, List, Optional, Union import torch from loguru import logger from torch.nn import Module from torch.nn.parameter import Parameter from aphrodite import _custom_ops as ops from aphrodite.common.utils import print_warning_once from aphrodite.modeling.layers.fused_moe import (FusedMoE, FusedMoEMethodBase, fused_moe) from aphrodite.modeling.layers.linear import LinearBase, LinearMethodBase from aphrodite.modeling.utils import set_weight_attrs from aphrodite.platforms import current_platform from aphrodite.quantization.base_config import (QuantizationConfig, QuantizeMethodBase) ACTIVATION_SCHEMES = ["static", "dynamic"] def cutlass_fp8_supported() -> bool: capability = current_platform.get_device_capability() capability = capability[0] * 10 + capability[1] return ops.cutlass_scaled_mm_supports_fp8(capability) class Fp8Config(QuantizationConfig): """Config class for FP8.""" def __init__( self, is_checkpoint_fp8_serialized: bool = False, activation_scheme: str = "dynamic", ) -> None: self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized if is_checkpoint_fp8_serialized: logger.warning("Detected fp8 checkpoint. Please note that the " "format is experimental and subject to change.") if activation_scheme not in ACTIVATION_SCHEMES: raise ValueError( f"Unsupported activation scheme {activation_scheme}") self.activation_scheme = activation_scheme @classmethod def get_name(cls) -> str: return "fp8" @classmethod def get_supported_act_dtypes(cls) -> List[torch.dtype]: return [torch.bfloat16, torch.half] @classmethod def get_min_capability(cls) -> int: return 89 @classmethod def get_config_filenames(cls) -> List[str]: return [] @classmethod def from_config(cls, config: Dict[str, Any]) -> "Fp8Config": quant_method = cls.get_from_keys(config, ["quant_method"]) is_checkpoint_fp8_serialized = ("fp8" in quant_method) activation_scheme = cls.get_from_keys(config, ["activation_scheme"]) return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized, activation_scheme=activation_scheme) def get_quant_method( self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]: from aphrodite.attention.layer import \ Attention # Avoid circular import if isinstance(layer, LinearBase): return Fp8LinearMethod(self) elif isinstance(layer, FusedMoE): return Fp8MoEMethod(self) elif isinstance(layer, Attention): return Fp8KVCacheMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] class Fp8LinearMethod(LinearMethodBase): """Linear method for FP8. Supports loading FP8 checkpoints with static weight scale and dynamic/static activation scale. Also supports loading quantized FP16/BF16 model checkpoints with dynamic activation scaling. The weight scaling factor will be initialized after the model weights are loaded. Limitations: 1. Only support per-tensor quantization due to torch._scaled_mm support. 2. Only support float8_e4m3fn data type due to the limitation of torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856) Args: quant_config: The quantization config. """ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config self.cutlass_fp8_supported = cutlass_fp8_supported() def _create_scale_param( self, scale_name: str, layer: torch.nn.Module, output_partition_sizes: List[int], **extra_weight_attrs, ) -> None: scale = Parameter(torch.empty(len(output_partition_sizes), dtype=torch.float32), requires_grad=False) scale[:] = torch.finfo(torch.float8_e4m3fn).min layer.register_parameter(scale_name, scale) set_weight_attrs(scale, { **extra_weight_attrs, "needs_scalar_to_array": True, }) def create_weights( self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: List[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): del input_size, output_size output_size_per_partition = sum(output_partition_sizes) layer.process_after_load = True layer.logical_widths = output_partition_sizes # WEIGHT weight_dtype = (torch.float8_e4m3fn if self.quant_config.is_checkpoint_fp8_serialized else params_dtype) weight = Parameter(torch.empty(output_size_per_partition, input_size_per_partition, dtype=weight_dtype), requires_grad=False) layer.register_parameter("weight", weight) set_weight_attrs(weight, { **extra_weight_attrs, "input_dim": 1, "output_dim": 0, }) # If checkpoint is serialized fp8, load them. # Otherwise, wait until process_weights_after_loading. if self.quant_config.is_checkpoint_fp8_serialized: # WEIGHT SCALE self._create_scale_param( scale_name="weight_scale", layer=layer, output_partition_sizes=output_partition_sizes, **extra_weight_attrs) # INPUT ACTIVATION SCALE if self.quant_config.activation_scheme == "static": self._create_scale_param( scale_name="input_scale", layer=layer, output_partition_sizes=output_partition_sizes, **extra_weight_attrs) def process_weights_after_loading(self, layer: Module) -> None: if (not hasattr(layer, "process_after_load") or not layer.process_after_load): return # If checkpoint is fp/bf16 (not serialized fp8), quantize the weights. if not self.quant_config.is_checkpoint_fp8_serialized: qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None) layer.weight = Parameter(qweight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) layer.logical_widths = None layer.input_scale = None return # If checkpoint is fp8, requantize the separately quantized logical # weights into a single fp8 weight with a single weight scale. else: # WEIGHT_SCALE / WEIGHT # Loop over logical weights, requantizing with single scale. max_w_scale = layer.weight_scale.max() # QKV / MLP is fused in the on disk checkpoint if any of the # weight scales are still set to the default since we initialize # N weight scales for N shards but we only load 1 weight scale # from disk in this case. As a result, we skip dequant -> requant # since we already have quantized QKV together. unfused_module_in_checkpoint = ( layer.weight_scale[-1] > torch.finfo(torch.float8_e4m3fn).min) if unfused_module_in_checkpoint: start = 0 for idx, logical_width in enumerate(layer.logical_widths): end = start + logical_width weight_dq = per_tensor_dequantize( layer.weight[start:end, :], layer.weight_scale[idx]) layer.weight[start:end, :] = per_tensor_quantize( weight_dq, layer.weight_scale.max()) start = end layer.weight_scale = Parameter(max_w_scale, requires_grad=False) # WEIGHT # Transpose weight for passing to torch._scaled_mm weight = layer.weight layer.weight = Parameter(weight.t(), requires_grad=False) # INPUT ACTIVATION SCALE # Dynamic: set to None (required input to ops.scaled_fp8_quant). # Static: set to max of the input_scales (since they are equal). if self.quant_config.activation_scheme == "dynamic": layer.input_scale = None elif self.quant_config.activation_scheme == "static": layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) else: raise ValueError( f"Unknown scheme {self.quant_config.activation_scheme}") def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: # ops.scaled_fp8_quant supports both dynamic and static quant. # If dynamic, layer.input_scale is None and x_scale computed from x. # If static, layer.input_scale is scalar and x_scale is input_scale. if bias is None and self.cutlass_fp8_supported: qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale) # Fused GEMM_DQ output = ops.cutlass_scaled_mm( qinput, layer.weight, out_dtype=x.dtype, scale_a=x_scale, scale_b=layer.weight_scale, ) else: qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale, batch_dim_padding=17) # Fused GEMM_DQ -- note we padded the input above because # torch._scaled_mm is more performant for matrices with # batch dimension > 16. Note that this could change # in the future. output, _ = torch._scaled_mm( qinput, layer.weight, out_dtype=x.dtype, scale_a=x_scale, scale_b=layer.weight_scale, bias=bias, ) return torch.narrow(output, 0, 0, x.shape[0]) class Fp8MoEMethod(FusedMoEMethodBase): """MoE method for FP8. Supports loading FP8 checkpoints with static weight scale and dynamic/static activation scale. Also supports loading quantized FP16/BF16 model checkpoints with dynamic activation scaling. The weight scaling factor will be initialized after the model weights are loaded. Args: quant_config: The quantization config. """ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config def create_weights(self, layer: Module, num_experts: int, hidden_size: int, intermediate_size: int, params_dtype: torch.dtype, **extra_weight_attrs): layer.process_after_load = True if self.quant_config.is_checkpoint_fp8_serialized: params_dtype = torch.float8_e4m3fn # WEIGHTS w13_weight = torch.nn.Parameter(torch.empty(num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype), requires_grad=False) layer.register_parameter("w13_weight", w13_weight) set_weight_attrs(w13_weight, extra_weight_attrs) w2_weight = torch.nn.Parameter(torch.empty(num_experts, hidden_size, intermediate_size, dtype=params_dtype), requires_grad=False) layer.register_parameter("w2_weight", w2_weight) set_weight_attrs(w2_weight, extra_weight_attrs) # WEIGHT_SCALES # Allocate 2 scales for w1 and w3 respectively. # They will be combined to a single scale after weight loading. w13_scale = torch.nn.Parameter(torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False) layer.register_parameter("w13_scale", w13_scale) w2_scale = torch.nn.Parameter(torch.ones(num_experts, dtype=torch.float32), requires_grad=False) layer.register_parameter("w2_scale", w2_scale) # If loading fp8 checkpoint, pass the weight loaders. # If loading an fp16 checkpoint, do not (we will quantize in # process_weights_after_loading() if self.quant_config.is_checkpoint_fp8_serialized: set_weight_attrs(w13_scale, extra_weight_attrs) set_weight_attrs(w2_scale, extra_weight_attrs) # INPUT_SCALES if self.quant_config.activation_scheme == "static": if not self.quant_config.is_checkpoint_fp8_serialized: raise ValueError( "Found static activation scheme for checkpoint that " "was not serialized fp8.") a13_scale = torch.nn.Parameter(torch.ones(num_experts, dtype=torch.float32), requires_grad=False) layer.register_parameter("a13_scale", a13_scale) set_weight_attrs(a13_scale, extra_weight_attrs) a2_scale = torch.nn.Parameter(torch.ones(num_experts, dtype=torch.float32), requires_grad=False) layer.register_parameter("a2_scale", a2_scale) set_weight_attrs(a2_scale, extra_weight_attrs) else: layer.a13_scale = None layer.a2_scale = None def process_weights_after_loading(self, layer: Module) -> None: if (not hasattr(layer, "process_after_load") or not layer.process_after_load): return # If checkpoint is fp16, quantize in place. if not self.quant_config.is_checkpoint_fp8_serialized: w13_weight = torch.empty_like(layer.w13_weight.data, dtype=torch.float8_e4m3fn) w2_weight = torch.empty_like(layer.w2_weight.data, dtype=torch.float8_e4m3fn) # Re-initialize w13_scale because we directly quantize # merged w13 weights and generate a single scaling factor. layer.w13_scale = torch.nn.Parameter(torch.ones( layer.num_experts, dtype=torch.float32, device=w13_weight.device), requires_grad=False) for expert in range(layer.num_experts): w13_weight[expert, :, :], layer.w13_scale[ expert] = ops.scaled_fp8_quant( layer.w13_weight.data[expert, :, :]) w2_weight[expert, :, :], layer.w2_scale[ expert] = ops.scaled_fp8_quant( layer.w2_weight.data[expert, :, :]) layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False) layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False) return # If checkpoint is fp8, we need to handle that the # MoE kernels require single activation scale and single weight # scale for w13 per expert. else: # Fp8 moe kernels require a single activation scale. # We take the max of all the scales in case they differ. if self.quant_config.activation_scheme == "static": if layer.a13_scale is None or layer.a2_scale is None: raise ValueError( "QuantConfig has static quantization, but found " "activation scales are None.") if (not all_close_1d(layer.a13_scale) or not all_close_1d(layer.a2_scale)): print_warning_once( "Found input_scales that are not equal for " "fp8 MoE layer. Using the maximum across experts " "for each layer. ") layer.a13_scale = torch.nn.Parameter(layer.a13_scale.max(), requires_grad=False) layer.a2_scale = torch.nn.Parameter(layer.a2_scale.max(), requires_grad=False) # Fp8 moe kernel needs single weight scale for w13 per expert. # We take the max then dequant and requant each expert. assert layer.w13_scale is not None shard_size = layer.intermediate_size_per_partition max_w13_scales = layer.w13_scale.max(dim=1).values for expert_id in range(layer.num_experts): start = 0 for shard_id in range(2): dq_weight = per_tensor_dequantize( layer.w13_weight[expert_id][start:start + shard_size, :], layer.w13_scale[expert_id][shard_id]) layer.w13_weight[expert_id][ start:start + shard_size, :] = per_tensor_quantize( dq_weight, max_w13_scales[expert_id]) start += shard_size layer.w13_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False) return def apply(self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, top_k: int, renormalize: bool = True) -> torch.Tensor: return fused_moe(x, layer.w13_weight, layer.w2_weight, router_logits, top_k, renormalize=renormalize, inplace=True, use_fp8=True, w1_scale=layer.w13_scale, w2_scale=layer.w2_scale, a1_scale=layer.a13_scale, a2_scale=layer.a2_scale) class Fp8KVCacheMethod(QuantizeMethodBase): """Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config def create_weights(self, layer: torch.nn.Module): """Create "weight" (aka kv_scale) for an attention layer. Args: layer: The layer that is using the QuantizeMethodBase factory. """ # Initialize the KV cache scale to 1.0 as the default value. # If the kv_scale appears in the checkpoint, it will be # overwritten when loading weights. layer.kv_scale = Parameter(torch.tensor(1.0), requires_grad=False) def apply(self, layer: torch.nn.Module) -> torch.Tensor: raise RuntimeError("Fp8KVCacheMethod.apply should not be called.") def process_weights_after_loading(self, layer: Module) -> None: # If the kv-cache dtype is auto, we enforce the kv-scale to be 1.0 # regardless whether the kv-scale is available in the checkpoint. if layer.kv_cache_dtype != "auto": kv_scale = layer.kv_scale.to("cpu").tolist() if not isinstance(kv_scale, float): raise ValueError("Only support per-tensor scaling factor " "for fp8 KV cache") layer._kv_scale = kv_scale if layer._kv_scale == 1.0 and "e5m2" not in layer.kv_cache_dtype: print_warning_once( "Using KV cache scaling factor 1.0 for fp8_e4m3. This may " "cause accuracy issues. Please make sure kv-cache scaling " "factor is available in the fp8 checkpoint.") del layer.kv_scale def per_tensor_quantize(tensor: torch.Tensor, inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: finfo = torch.finfo(torch.float8_e4m3fn) qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) return qweight.to(torch.float8_e4m3fn) def per_tensor_dequantize( tensor: torch.Tensor, inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: fake_qweight = tensor.to(torch.float16) dq_weight = fake_qweight * inv_scale return dq_weight def all_close_1d(x: torch.Tensor) -> bool: assert len(x.shape) == 1 return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))