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# 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, Optional
from .base import HfQuantizer
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_accelerate_available, is_fbgemm_gpu_available, is_torch_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_parameters_quantization = True
requires_calibration = False
required_packages = ["fbgemm-gpu", "accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
def validate_environment(self, *args, **kwargs):
if not is_torch_available():
raise ImportError(
"Using fbgemm fp8 quantization requires torch >= 2.1.0"
"Please install the latest version of torch ( pip install --upgrade torch )"
)
if not is_fbgemm_gpu_available():
raise ImportError(
"Using fbgemm fp8 quantization 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("0.32.2"):
raise ImportError(
"Loading an FP8 quantized model requires accelerate > 0.32.1 (`pip install --upgrade accelerate`)"
)
if not torch.cuda.is_available():
raise RuntimeError("Using FP8 quantized models with fbgemm kernels requires a GPU")
compute_capability = torch.cuda.get_device_capability()
major, minor = 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 device available, make sure to set "
"your model on a GPU device in order to run your model. To remove this warning, pass device_map = 'cuda'. "
)
elif device_map is not None:
if (
not self.pre_quantized
and isinstance(device_map, dict)
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 is None:
dtype = torch.bfloat16
logger.info(
"Overriding dtype=%s with `dtype=torch.bloat16` due to "
"requirements of `fbgemm-gpu` to enable model loading in fp8. "
"Pass your own dtype to specify the dtype of the remaining non-linear layers or pass"
" dtype=torch.bfloat16 to remove this warning.",
dtype,
)
elif dtype == torch.float16:
raise ValueError(
"You cannot use FP8 with dtype=torch.float16.We recommend you passing 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 create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
**kwargs,
):
from ..integrations import FbgemmFp8Linear, FbgemmFp8Llama4TextExperts
module, tensor_name = get_module_from_name(model, param_name)
# Sanity checks
if isinstance(module, FbgemmFp8Linear):
if self.pre_quantized or tensor_name == "bias":
if tensor_name == "weight" and param_value.dtype != torch.float8_e4m3fn:
raise ValueError("Expect quantized weights but got an unquantized weight")
else:
if tensor_name == "weight_scale":
raise ValueError("Expect unquantized weights but got a quantized weight_scale")
if isinstance(module, FbgemmFp8Llama4TextExperts):
if not (self.pre_quantized or tensor_name == "bias"):
if tensor_name == "gate_up_proj_scale" or tensor_name == "down_proj_scale":
raise ValueError("Expect unquantized weights but got a quantized weight_scale")
if isinstance(module, FbgemmFp8Llama4TextExperts):
if tensor_name == "gate_up_proj":
# Process each expert separately
# Transpose the second and third dimension
transposed_param = param_value.transpose(1, 2)
# Reshape to 2D for quantization
original_shape = transposed_param.shape
flattened_param = transposed_param.reshape(-1, original_shape[-1])
# Quantize using per row instead of per column
new_value_flat, weight_scale_flat = torch.ops.fbgemm.quantize_fp8_per_row(flattened_param)
# Reshape back to original dimensions
new_value = new_value_flat.reshape(original_shape)
new_value = new_value.transpose(1, 2)
weight_scale = weight_scale_flat.reshape(original_shape[0], 1, original_shape[1])
elif tensor_name == "down_proj":
# Process each expert separately
# Transpose the weights for proper quantization
transposed_param = param_value.transpose(1, 2)
# Reshape to 2D for quantization
original_shape = transposed_param.shape
flattened_param = transposed_param.reshape(-1, original_shape[-1])
# Quantize using per column
new_value_flat, weight_scale_flat = torch.ops.fbgemm.quantize_fp8_per_row(flattened_param)
# Reshape back to original dimensions
new_value = new_value_flat.reshape(original_shape)
new_value = new_value.transpose(1, 2)
weight_scale = weight_scale_flat.reshape(original_shape[0], original_shape[1], 1)
module._parameters[f"{tensor_name}_scale"] = torch.nn.Parameter(weight_scale.to(target_device))
else:
new_value, weight_scale = torch.ops.fbgemm.quantize_fp8_per_row(param_value)
module._parameters[f"{tensor_name}_scale"] = torch.nn.Parameter(
weight_scale.view(weight_scale.shape[0], 1).to(target_device)
)
module._parameters[tensor_name] = torch.nn.Parameter(new_value.to(target_device))
del param_name
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
return model
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
keep_in_fp32_modules: Optional[list[str]] = None,
**kwargs,
):
from ..integrations import replace_with_fbgemm_fp8_linear
tp_plan = model._tp_plan
self.modules_to_not_convert = self.get_modules_to_not_convert(
model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
)
config = model.config
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,
config=config,
tp_plan=tp_plan,
)
model.config.quantization_config = self.quantization_config
def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
from ..integrations import FbgemmFp8Linear, FbgemmFp8Llama4TextExperts
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, (FbgemmFp8Linear, FbgemmFp8Llama4TextExperts)):
for missing in missing_keys:
if (
(name in missing or name in f"{prefix}.{missing}")
and not missing.endswith(".weight")
and not missing.endswith(".bias")
):
not_missing_keys.append(missing)
return [k for k in missing_keys if k not in not_missing_keys]
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": "local_colwise",
"layers.*.self_attn.q_proj.weight_scale": "local_colwise",
"layers.*.self_attn.k_proj.weight": "local_colwise",
"layers.*.self_attn.k_proj.weight_scale": "local_colwise",
"layers.*.self_attn.v_proj.weight": "local_colwise",
"layers.*.self_attn.v_proj.weight_scale": "local_colwise",
"layers.*.self_attn.o_proj.weight": "local_rowwise",
"layers.*.self_attn": "gather",
# 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": "local_colwise",
"layers.*.feed_forward.shared_expert.gate_proj.weight_scale": "local_colwise",
"layers.*.feed_forward.shared_expert.up_proj.weight": "local_colwise",
"layers.*.feed_forward.shared_expert.up_proj.weight_scale": "local_colwise",
"layers.*.feed_forward.shared_expert.down_proj.weight": "local_rowwise",
"layers.*.feed_forward.experts": "local",
"layers.*.feed_forward": "gather",
"layers.*.feed_forward.experts.*.gate_proj.weight": "local_colwise",
"layers.*.feed_forward.experts.*.gate_proj.weight_scale": "local_colwise",
"layers.*.feed_forward.experts.*.up_proj.weight": "local_colwise",
"layers.*.feed_forward.experts.*.up_proj.weight_scale": "local_colwise",
"layers.*.feed_forward.experts.*.down_proj.weight": "local_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": "local_packed_rowwise",
"layers.*.feed_forward.experts.gate_up_proj_scale": "local_packed_rowwise",
"layers.*.feed_forward.experts.down_proj": "local_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, safe_serialization=None):
return True
@property
def is_trainable(self) -> bool:
return False
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