Package upstream model with reproducible local MMEB evaluation

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# Copyright 2025 The HuggingFace 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.
import math
import sys
from collections import defaultdict
from contextlib import contextmanager

import torch


# Record all the torch primitives in advance, so that we can use them without them being modified when we patch torch
# in context managers
TORCH_INIT_FUNCTIONS = {
    "uniform_": torch.nn.init.uniform_,
    "normal_": torch.nn.init.normal_,
    "constant_": torch.nn.init.constant_,
    "ones_": torch.nn.init.ones_,
    "zeros_": torch.nn.init.zeros_,
    "eye_": torch.nn.init.eye_,
    "dirac_": torch.nn.init.dirac_,
    "xavier_uniform_": torch.nn.init.xavier_uniform_,
    "xavier_normal_": torch.nn.init.xavier_normal_,
    "kaiming_uniform_": torch.nn.init.kaiming_uniform_,
    "kaiming_normal_": torch.nn.init.kaiming_normal_,
    "trunc_normal_": torch.nn.init.trunc_normal_,
    "orthogonal_": torch.nn.init.orthogonal_,
    "sparse_": torch.nn.init.sparse_,
}


def uniform_(
    tensor: torch.Tensor, a: float = 0.0, b: float = 1.0, generator: torch.Generator | None = None
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["uniform_"](tensor, a=a, b=b, generator=generator)
    return tensor


def normal_(
    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, generator: torch.Generator | None = None
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["normal_"](tensor, mean=mean, std=std, generator=generator)
    return tensor


def constant_(tensor: torch.Tensor, val: float) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["constant_"](tensor, val=val)
    return tensor


def ones_(tensor: torch.Tensor) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["ones_"](tensor)
    return tensor


def zeros_(tensor: torch.Tensor) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["zeros_"](tensor)
    return tensor


def eye_(tensor: torch.Tensor) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["eye_"](tensor)
    return tensor


def dirac_(tensor: torch.Tensor, groups: int = 1) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["dirac_"](tensor, groups=groups)
    return tensor


def xavier_uniform_(tensor: torch.Tensor, gain: float = 1.0, generator: torch.Generator | None = None) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["xavier_uniform_"](tensor, gain=gain, generator=generator)
    return tensor


def xavier_normal_(tensor: torch.Tensor, gain: float = 1.0, generator: torch.Generator | None = None) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["xavier_normal_"](tensor, gain=gain, generator=generator)
    return tensor


def kaiming_uniform_(
    tensor: torch.Tensor,
    a: float = 0,
    mode: str = "fan_in",
    nonlinearity: str = "leaky_relu",
    generator: torch.Generator | None = None,
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["kaiming_uniform_"](
            tensor, a=a, mode=mode, nonlinearity=nonlinearity, generator=generator
        )
    return tensor


def kaiming_normal_(
    tensor: torch.Tensor,
    a: float = 0,
    mode: str = "fan_in",
    nonlinearity: str = "leaky_relu",
    generator: torch.Generator | None = None,
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["kaiming_normal_"](
            tensor, a=a, mode=mode, nonlinearity=nonlinearity, generator=generator
        )
    return tensor


def trunc_normal_(
    tensor: torch.Tensor,
    mean: float = 0.0,
    std: float = 1.0,
    a: float = -2.0,
    b: float = 2.0,
    generator: torch.Generator | None = None,
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["trunc_normal_"](tensor, mean=mean, std=std, a=a, b=b, generator=generator)
    return tensor


def orthogonal_(
    tensor: torch.Tensor,
    gain: float = 1,
    generator: torch.Generator | None = None,
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["orthogonal_"](tensor, gain=gain, generator=generator)
    return tensor


def sparse_(
    tensor: torch.Tensor, sparsity: float, std: float = 0.01, generator: torch.Generator | None = None
) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        return TORCH_INIT_FUNCTIONS["sparse_"](tensor, sparsity=sparsity, std=std, generator=generator)
    return tensor


def copy_(tensor: torch.Tensor, other: torch.Tensor) -> torch.Tensor:
    if not getattr(tensor, "_is_hf_initialized", False):
        with torch.no_grad():
            return tensor.copy_(other)
    return tensor


def _variance_scaling(tensor, mode="fan_in", distribution="normal"):
    fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(tensor)
    if mode == "fan_in":
        denom = fan_in
    elif mode == "fan_out":
        denom = fan_out
    elif mode == "fan_avg":
        denom = (fan_in + fan_out) / 2

    variance = 1.0 / denom

    if distribution == "truncated_normal":
        trunc_normal_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
    elif distribution == "normal":
        normal_(tensor, std=math.sqrt(variance))
    elif distribution == "uniform":
        bound = math.sqrt(3 * variance)
        uniform_(tensor, -bound, bound)
    else:
        raise ValueError(f"invalid distribution {distribution}")


def lecun_normal_(tensor):
    if not getattr(tensor, "_is_hf_initialized", False):
        _variance_scaling(tensor, mode="fan_in", distribution="truncated_normal")
    return tensor


def default_flax_embed_init_(tensor):
    if not getattr(tensor, "_is_hf_initialized", False):
        _variance_scaling(tensor, mode="fan_in", distribution="normal")
    return tensor


# Here, we need to check several modules imported, and hot patch all of them, as sometimes torch does
# something like `from torch.nn.init import xavier_uniform_` in their internals (e.g in torch.nn.modules.activations,
# where MultiHeadAttention lives), so the function name is binded at import time and just doing
# `setattr(torch.nn.init, name, globals()[name])` is thus not enough
# The following list should be enough for all torch versions we work with
TORCH_MODULES_TO_PATCH = (
    "torch.nn.init",
    "torch.nn.modules.activation",
    "torch.nn.modules.transformer",
    "torch.nn.modules.linear",
    "torch.nn.modules.loss",
    "torch.nn.modules.batchnorm",
    "torch.nn.modules.conv",
    "torch.nn.modules.normalization",
    "torch.nn.modules.rnn",
    "torch.nn.modules.sparse",
)


@contextmanager
def guard_torch_init_functions():
    """
    Guard the `torch.nn.init` primitive functions to behave exactly like the functions in this file, i.e. be
    protected against the `_is_hf_initialized` flag to avoid re-init if the param was already loaded.
    Usually, all models are using the init from `transformers` which are already guarded, but just to make extra sure
    and for remote code, we also use this context manager.
    """
    originals = defaultdict(dict)
    try:
        # Replace all torch funcs by the ones in this file
        for module_name in TORCH_MODULES_TO_PATCH:
            if module_name in sys.modules:
                module = sys.modules[module_name]
                for func_name in TORCH_INIT_FUNCTIONS.keys():
                    if hasattr(module, func_name):
                        originals[module][func_name] = getattr(module, func_name)
                        setattr(module, func_name, globals()[func_name])
        yield
    finally:
        # Set back the original functions on all modules
        for module, functions in originals.items():
            for func_name, func in functions.items():
                setattr(module, func_name, func)


@contextmanager
def no_init_weights():
    """
    Disable weight initialization both at the torch-level, and at the transformers-level (`init_weights`).
    This is used to speed-up initializing an empty model with deepspeed, as we do not initialize the model on meta device
    with deepspeed, but we still don't need to run expensive weight initializations as we are loading params afterwards.
    """
    from .modeling_utils import PreTrainedModel

    def empty_func(*args, **kwargs):
        pass

    originals = defaultdict(dict)
    try:
        # Replace all torch funcs by empty ones
        for module_name in TORCH_MODULES_TO_PATCH:
            if module_name in sys.modules:
                module = sys.modules[module_name]
                for func_name in TORCH_INIT_FUNCTIONS.keys():
                    if hasattr(module, func_name):
                        originals[module][func_name] = getattr(module, func_name)
                        setattr(module, func_name, empty_func)

        # Also patch our own `init_weights`
        original_init_weights = PreTrainedModel.init_weights
        PreTrainedModel.init_weights = empty_func

        yield
    finally:
        # Set back the original torch functions on all modules
        for module, functions in originals.items():
            for func_name, func in functions.items():
                setattr(module, func_name, func)
        # Set back `init_weights`
        PreTrainedModel.init_weights = original_init_weights


@contextmanager
def no_tie_weights():
    """
    Disable weight tying during loading with `from_pretrained`. This is needed as we want to have access to ALL
    weights in the state_dict during `from_pretrained`, and otherwise tying them would remove them from it, as it's
    called in `post_init` when instantiating.
    """
    from .modeling_utils import PreTrainedModel

    def empty_func(*args, **kwargs):
        pass

    try:
        original_tie_weights = PreTrainedModel.tie_weights
        PreTrainedModel.tie_weights = empty_func

        yield
    finally:
        # Set back the original
        PreTrainedModel.tie_weights = original_tie_weights


@contextmanager
def meta_device_safe_creation_ops():
    """
    During meta-device model initialisation, ``torch.linspace`` produces meta
    tensors that have no data.  Custom models loaded from the Hub (remote code)
    often call ``.item()`` on these tensors to compute scalar hyperparameters
    (e.g. stochastic-depth / drop-path schedules).  Native transformers models
    already pass ``device="cpu"`` explicitly for such calls (see e.g.
    ``modeling_swin.py``, ``modeling_pvt_v2.py``), but remote-code models
    written before v5 do not.
    This context manager patches ``torch.linspace`` to default to
    ``device="cpu"`` when no explicit device is requested, matching the best
    practice already used throughout transformers.  Calls that supply an
    explicit ``device`` argument (e.g. ``device=self.logits.device``) are left
    untouched.  ``torch.arange`` is intentionally NOT patched because it is
    used in RoPE computations where the device must match model parameters.
    """
    original_linspace = torch.linspace

    def _safe_linspace(*args, **kwargs):
        kwargs.setdefault("device", "cpu")
        return original_linspace(*args, **kwargs)

    torch.linspace = _safe_linspace
    try:
        yield
    finally:
        torch.linspace = original_linspace

	# Copyright 2025 The HuggingFace 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.
	import math
	import sys
	from collections import defaultdict
	from contextlib import contextmanager

	import torch


	# Record all the torch primitives in advance, so that we can use them without them being modified when we patch torch
	# in context managers
	TORCH_INIT_FUNCTIONS = {
	"uniform_": torch.nn.init.uniform_,
	"normal_": torch.nn.init.normal_,
	"constant_": torch.nn.init.constant_,
	"ones_": torch.nn.init.ones_,
	"zeros_": torch.nn.init.zeros_,
	"eye_": torch.nn.init.eye_,
	"dirac_": torch.nn.init.dirac_,
	"xavier_uniform_": torch.nn.init.xavier_uniform_,
	"xavier_normal_": torch.nn.init.xavier_normal_,
	"kaiming_uniform_": torch.nn.init.kaiming_uniform_,
	"kaiming_normal_": torch.nn.init.kaiming_normal_,
	"trunc_normal_": torch.nn.init.trunc_normal_,
	"orthogonal_": torch.nn.init.orthogonal_,
	"sparse_": torch.nn.init.sparse_,
	}


	def uniform_(
	tensor: torch.Tensor, a: float = 0.0, b: float = 1.0, generator: torch.Generator \| None = None
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["uniform_"](tensor, a=a, b=b, generator=generator)
	return tensor


	def normal_(
	tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, generator: torch.Generator \| None = None
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["normal_"](tensor, mean=mean, std=std, generator=generator)
	return tensor


	def constant_(tensor: torch.Tensor, val: float) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["constant_"](tensor, val=val)
	return tensor


	def ones_(tensor: torch.Tensor) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["ones_"](tensor)
	return tensor


	def zeros_(tensor: torch.Tensor) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["zeros_"](tensor)
	return tensor


	def eye_(tensor: torch.Tensor) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["eye_"](tensor)
	return tensor


	def dirac_(tensor: torch.Tensor, groups: int = 1) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["dirac_"](tensor, groups=groups)
	return tensor


	def xavier_uniform_(tensor: torch.Tensor, gain: float = 1.0, generator: torch.Generator \| None = None) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["xavier_uniform_"](tensor, gain=gain, generator=generator)
	return tensor


	def xavier_normal_(tensor: torch.Tensor, gain: float = 1.0, generator: torch.Generator \| None = None) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["xavier_normal_"](tensor, gain=gain, generator=generator)
	return tensor


	def kaiming_uniform_(
	tensor: torch.Tensor,
	a: float = 0,
	mode: str = "fan_in",
	nonlinearity: str = "leaky_relu",
	generator: torch.Generator \| None = None,
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["kaiming_uniform_"](
	tensor, a=a, mode=mode, nonlinearity=nonlinearity, generator=generator
	)
	return tensor


	def kaiming_normal_(
	tensor: torch.Tensor,
	a: float = 0,
	mode: str = "fan_in",
	nonlinearity: str = "leaky_relu",
	generator: torch.Generator \| None = None,
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["kaiming_normal_"](
	tensor, a=a, mode=mode, nonlinearity=nonlinearity, generator=generator
	)
	return tensor


	def trunc_normal_(
	tensor: torch.Tensor,
	mean: float = 0.0,
	std: float = 1.0,
	a: float = -2.0,
	b: float = 2.0,
	generator: torch.Generator \| None = None,
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["trunc_normal_"](tensor, mean=mean, std=std, a=a, b=b, generator=generator)
	return tensor


	def orthogonal_(
	tensor: torch.Tensor,
	gain: float = 1,
	generator: torch.Generator \| None = None,
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["orthogonal_"](tensor, gain=gain, generator=generator)
	return tensor


	def sparse_(
	tensor: torch.Tensor, sparsity: float, std: float = 0.01, generator: torch.Generator \| None = None
	) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	return TORCH_INIT_FUNCTIONS["sparse_"](tensor, sparsity=sparsity, std=std, generator=generator)
	return tensor


	def copy_(tensor: torch.Tensor, other: torch.Tensor) -> torch.Tensor:
	if not getattr(tensor, "_is_hf_initialized", False):
	with torch.no_grad():
	return tensor.copy_(other)
	return tensor


	def _variance_scaling(tensor, mode="fan_in", distribution="normal"):
	fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(tensor)
	if mode == "fan_in":
	denom = fan_in
	elif mode == "fan_out":
	denom = fan_out
	elif mode == "fan_avg":
	denom = (fan_in + fan_out) / 2

	variance = 1.0 / denom

	if distribution == "truncated_normal":
	trunc_normal_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
	elif distribution == "normal":
	normal_(tensor, std=math.sqrt(variance))
	elif distribution == "uniform":
	bound = math.sqrt(3 * variance)
	uniform_(tensor, -bound, bound)
	else:
	raise ValueError(f"invalid distribution {distribution}")


	def lecun_normal_(tensor):
	if not getattr(tensor, "_is_hf_initialized", False):
	_variance_scaling(tensor, mode="fan_in", distribution="truncated_normal")
	return tensor


	def default_flax_embed_init_(tensor):
	if not getattr(tensor, "_is_hf_initialized", False):
	_variance_scaling(tensor, mode="fan_in", distribution="normal")
	return tensor


	# Here, we need to check several modules imported, and hot patch all of them, as sometimes torch does
	# something like `from torch.nn.init import xavier_uniform_` in their internals (e.g in torch.nn.modules.activations,
	# where MultiHeadAttention lives), so the function name is binded at import time and just doing
	# `setattr(torch.nn.init, name, globals()[name])` is thus not enough
	# The following list should be enough for all torch versions we work with
	TORCH_MODULES_TO_PATCH = (
	"torch.nn.init",
	"torch.nn.modules.activation",
	"torch.nn.modules.transformer",
	"torch.nn.modules.linear",
	"torch.nn.modules.loss",
	"torch.nn.modules.batchnorm",
	"torch.nn.modules.conv",
	"torch.nn.modules.normalization",
	"torch.nn.modules.rnn",
	"torch.nn.modules.sparse",
	)


	@contextmanager
	def guard_torch_init_functions():
	"""
	Guard the `torch.nn.init` primitive functions to behave exactly like the functions in this file, i.e. be
	protected against the `_is_hf_initialized` flag to avoid re-init if the param was already loaded.

	Usually, all models are using the init from `transformers` which are already guarded, but just to make extra sure
	and for remote code, we also use this context manager.
	"""
	originals = defaultdict(dict)
	try:
	# Replace all torch funcs by the ones in this file
	for module_name in TORCH_MODULES_TO_PATCH:
	if module_name in sys.modules:
	module = sys.modules[module_name]
	for func_name in TORCH_INIT_FUNCTIONS.keys():
	if hasattr(module, func_name):
	originals[module][func_name] = getattr(module, func_name)
	setattr(module, func_name, globals()[func_name])
	yield
	finally:
	# Set back the original functions on all modules
	for module, functions in originals.items():
	for func_name, func in functions.items():
	setattr(module, func_name, func)


	@contextmanager
	def no_init_weights():
	"""
	Disable weight initialization both at the torch-level, and at the transformers-level (`init_weights`).
	This is used to speed-up initializing an empty model with deepspeed, as we do not initialize the model on meta device
	with deepspeed, but we still don't need to run expensive weight initializations as we are loading params afterwards.
	"""
	from .modeling_utils import PreTrainedModel

	def empty_func(args, *kwargs):
	pass

	originals = defaultdict(dict)
	try:
	# Replace all torch funcs by empty ones
	for module_name in TORCH_MODULES_TO_PATCH:
	if module_name in sys.modules:
	module = sys.modules[module_name]
	for func_name in TORCH_INIT_FUNCTIONS.keys():
	if hasattr(module, func_name):
	originals[module][func_name] = getattr(module, func_name)
	setattr(module, func_name, empty_func)

	# Also patch our own `init_weights`
	original_init_weights = PreTrainedModel.init_weights
	PreTrainedModel.init_weights = empty_func

	yield
	finally:
	# Set back the original torch functions on all modules
	for module, functions in originals.items():
	for func_name, func in functions.items():
	setattr(module, func_name, func)
	# Set back `init_weights`
	PreTrainedModel.init_weights = original_init_weights


	@contextmanager
	def no_tie_weights():
	"""
	Disable weight tying during loading with `from_pretrained`. This is needed as we want to have access to ALL
	weights in the state_dict during `from_pretrained`, and otherwise tying them would remove them from it, as it's
	called in `post_init` when instantiating.
	"""
	from .modeling_utils import PreTrainedModel

	def empty_func(args, *kwargs):
	pass

	try:
	original_tie_weights = PreTrainedModel.tie_weights
	PreTrainedModel.tie_weights = empty_func

	yield
	finally:
	# Set back the original
	PreTrainedModel.tie_weights = original_tie_weights


	@contextmanager
	def meta_device_safe_creation_ops():
	"""
	During meta-device model initialisation, ``torch.linspace`` produces meta
	tensors that have no data. Custom models loaded from the Hub (remote code)
	often call ``.item()`` on these tensors to compute scalar hyperparameters
	(e.g. stochastic-depth / drop-path schedules). Native transformers models
	already pass ``device="cpu"`` explicitly for such calls (see e.g.
	``modeling_swin.py``, ``modeling_pvt_v2.py``), but remote-code models
	written before v5 do not.

	This context manager patches ``torch.linspace`` to default to
	``device="cpu"`` when no explicit device is requested, matching the best
	practice already used throughout transformers. Calls that supply an
	explicit ``device`` argument (e.g. ``device=self.logits.device``) are left
	untouched. ``torch.arange`` is intentionally NOT patched because it is
	used in RoPE computations where the device must match model parameters.
	"""
	original_linspace = torch.linspace

	def _safe_linspace(args, *kwargs):
	kwargs.setdefault("device", "cpu")
	return original_linspace(args, *kwargs)

	torch.linspace = _safe_linspace
	try:
	yield
	finally:
	torch.linspace = original_linspace