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