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Re-apply make style #40106

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Merged
merged 1 commit into from
Aug 12, 2025
Merged

Re-apply make style #40106

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24 changes: 12 additions & 12 deletions src/transformers/integrations/mxfp4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,12 +314,12 @@ def should_convert_module(current_key_name, patterns):
def dequantize(module, param_name, param_value, target_device, dq_param_name, **kwargs):
from ..integrations.tensor_parallel import shard_and_distribute_module

model = kwargs.get("model", None)
empty_param = kwargs.get("empty_param", None)
casting_dtype = kwargs.get("casting_dtype", None)
to_contiguous = kwargs.get("to_contiguous", None)
rank = kwargs.get("rank", None)
device_mesh = kwargs.get("device_mesh", None)
model = kwargs.get("model")
empty_param = kwargs.get("empty_param")
casting_dtype = kwargs.get("casting_dtype")
to_contiguous = kwargs.get("to_contiguous")
rank = kwargs.get("rank")
device_mesh = kwargs.get("device_mesh")

for proj in ["gate_up_proj", "down_proj"]:
if proj in param_name:
Expand Down Expand Up @@ -357,12 +357,12 @@ def load_and_swizzle_mxfp4(module, param_name, param_value, target_device, **kwa
)
from ..integrations.tensor_parallel import shard_and_distribute_module

model = kwargs.get("model", None)
empty_param = kwargs.get("empty_param", None)
casting_dtype = kwargs.get("casting_dtype", None)
to_contiguous = kwargs.get("to_contiguous", None)
rank = kwargs.get("rank", None)
device_mesh = kwargs.get("device_mesh", None)
model = kwargs.get("model")
empty_param = kwargs.get("empty_param")
casting_dtype = kwargs.get("casting_dtype")
to_contiguous = kwargs.get("to_contiguous")
rank = kwargs.get("rank")
device_mesh = kwargs.get("device_mesh")

for proj in ["gate_up_proj", "down_proj"]:
if proj in param_name:
Expand Down
12 changes: 6 additions & 6 deletions src/transformers/quantizers/quantizer_mxfp4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ def validate_environment(self, *args, **kwargs):
global triton_kernels_hub
triton_kernels_hub = get_kernel("kernels-community/triton_kernels")

device_map = kwargs.get("device_map", None)
device_map = kwargs.get("device_map")
if device_map is None:
logger.warning_once(
"You have loaded an FP4 model on CPU and have a CUDA device available, make sure to set "
Expand Down Expand Up @@ -210,11 +210,11 @@ def create_quantized_param(
# we take this path if already quantized but not in a compatible way
# The params going here are either gate_up_proj_blocks, or down_proj_blocks, or gate_up_proj_scales, or down_proj_scales
else:
empty_param = kwargs.get("empty_param", None)
casting_dtype = kwargs.get("casting_dtype", None)
to_contiguous = kwargs.get("to_contiguous", None)
rank = kwargs.get("rank", None)
device_mesh = kwargs.get("device_mesh", None)
empty_param = kwargs.get("empty_param")
casting_dtype = kwargs.get("casting_dtype")
to_contiguous = kwargs.get("to_contiguous")
rank = kwargs.get("rank")
device_mesh = kwargs.get("device_mesh")
if ("blocks" in param_name or "scales" in param_name) and self.quantization_config.dequantize:
# blocks and scales have the same length that's this works for both
module, _ = get_module_from_name(model, param_name[: -len("_blocks")])
Expand Down