# Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from .base import HfQuantizer if TYPE_CHECKING: from ..modeling_utils import PreTrainedModel from ..utils import ( is_accelerate_available, is_fbgemm_gpu_available, is_kernels_available, is_torch_available, is_torch_cuda_available, is_torch_xpu_available, logging, ) from .quantizers_utils import get_module_from_name if is_torch_available(): import torch logger = logging.get_logger(__name__) class FbgemmFp8HfQuantizer(HfQuantizer): """ FP8 quantization using fbgemm kernels """ requires_calibration = False def __init__(self, quantization_config, **kwargs): super().__init__(quantization_config, **kwargs) def validate_environment(self, *args, **kwargs): if not is_torch_cuda_available() and not is_torch_xpu_available(): raise ImportError("Using fbgemm fp8 quantization requires a GPU or XPU") if is_torch_xpu_available() and not is_kernels_available(): raise ImportError("Using FP8 fbgemm on XPU requires kernels (`pip install kernels`)") if is_torch_cuda_available() and not is_fbgemm_gpu_available(): raise ImportError( "Loading an FP8 fbgemm quantized model on CUDA requires fbgemm-gpu library" "Please install the latest version of fbgemm-gpu library by following : https://pytorch.org/FBGEMM/fbgemm_gpu-development/InstallationInstructions.html#fbgemm-gpu-install-libraries" ) if not is_accelerate_available(): raise ImportError( "Loading an FP8 quantized model requires accelerate (`pip install --upgrade accelerate`)" ) if is_torch_cuda_available(): compute_capability = torch.cuda.get_device_capability() major, _ = compute_capability if major < 9: raise ValueError( "FP8 quantized models is only supported on GPUs with compute capability >= 9.0 (e.g H100)" ) device_map = kwargs.get("device_map") if device_map is None: logger.warning_once( "You have loaded an FP8 model on CPU and have a CUDA/XPU device available, make sure to set " "your model on a GPU/XPU device in order to run your model. To remove this warning, pass device_map = 'cuda' or 'xpu' or 'auto'. " ) elif isinstance(device_map, dict): if not self.pre_quantized and ("cpu" in device_map.values() or "disk" in device_map.values()): raise ValueError( "You are attempting to load an FP8 model with a device_map that contains a CPU or disk device." "This is not supported when the model is quantized on the fly. " "Please use a quantized checkpoint or remove the CPU or disk device from the device_map." ) def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype": if dtype != torch.bfloat16: logger.warning_once( f"Setting dtype to {dtype}, but only bfloat16 is supported right now. Overwriting torch_dtype to bfloat16." ) dtype = torch.bfloat16 return dtype def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool: from ..integrations import FbgemmFp8Linear, FbgemmFp8Llama4TextExperts module, tensor_name = get_module_from_name(model, param_name) if isinstance(module, FbgemmFp8Linear): if self.pre_quantized or tensor_name == "bias": return False else: return True if isinstance(module, FbgemmFp8Llama4TextExperts): if self.pre_quantized or tensor_name == "bias": return False else: return True return False def _process_model_before_weight_loading( self, model: "PreTrainedModel", **kwargs, ): from ..integrations import replace_with_fbgemm_fp8_linear self.modules_to_not_convert = self.get_modules_to_not_convert( model, self.quantization_config.modules_to_not_convert, model._keep_in_fp32_modules ) model = replace_with_fbgemm_fp8_linear( model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config, pre_quantized=self.pre_quantized, tp_plan=model._tp_plan, ) def update_tp_plan(self, config): if "Llama4" in config.__class__.__name__: text_plan = { # We are using a different tp plan with local_colwise and local_rowwise for the attention because fbgemm operations cannot be parallelized # With local_colwise and local_rowwise, all the operations are done locally, and we add a gather operation to gather the results instead of # using dtensors "layers.*.self_attn.q_proj.weight": "colwise", "layers.*.self_attn.q_proj.weight_scale": "colwise", "layers.*.self_attn.k_proj.weight": "colwise", "layers.*.self_attn.k_proj.weight_scale": "colwise", "layers.*.self_attn.v_proj.weight": "colwise", "layers.*.self_attn.v_proj.weight_scale": "colwise", "layers.*.self_attn.o_proj.weight": "rowwise", # We keep the same sequence_parallel plan for layernorms "layers.*.input_layernorm.weight": "sequence_parallel", "layers.*.post_attention_layernorm.weight": "sequence_parallel", "norm.weight": "sequence_parallel", # We keep the same local_colwise and local_rowwise plan for the feed forward shared expert # We also add scales for the shared expert, for local_colwise the scale is also local_colwise # For local_rowwise the scale is replicated, so we don't need to add it "layers.*.feed_forward.shared_expert.gate_proj.weight": "colwise", "layers.*.feed_forward.shared_expert.gate_proj.weight_scale": "colwise", "layers.*.feed_forward.shared_expert.up_proj.weight": "colwise", "layers.*.feed_forward.shared_expert.up_proj.weight_scale": "colwise", "layers.*.feed_forward.shared_expert.down_proj.weight": "rowwise", "layers.*.feed_forward.experts.*.gate_proj.weight": "colwise", "layers.*.feed_forward.experts.*.gate_proj.weight_scale": "colwise", "layers.*.feed_forward.experts.*.up_proj.weight": "colwise", "layers.*.feed_forward.experts.*.up_proj.weight_scale": "colwise", "layers.*.feed_forward.experts.*.down_proj.weight": "rowwise", # For Fused implementation we use local_packed_rowwise for the gate_up_proj, and the same for the packed scales # We use local_colwise for the down_proj, and the scales are replicated so we don't add them "layers.*.feed_forward.experts.gate_up_proj": "packed_rowwise", "layers.*.feed_forward.experts.gate_up_proj_scale": "packed_rowwise", "layers.*.feed_forward.experts.down_proj": "colwise", } if config.get_text_config() is not None: config.get_text_config().base_model_tp_plan = text_plan else: config.base_model_tp_plan = text_plan return config return config def is_serializable(self): return True @property def is_trainable(self) -> bool: return False def get_quantize_ops(self): from ..integrations.fbgemm_fp8 import FbgemmFp8Quantize return FbgemmFp8Quantize(self)