deepseek-ai/DeepSeek-R1-0528-Qwen3-8B · QWEN3 is the bottleneck. QWEN3 can hardly understand prompts and commands and always `do something it likes`

Evidence:

We are given a function _infer_hidden_shapes and we need to complete its docstring in the same format as the previous one.

The function is defined with several parameters and two modes (memoryless or not). We must describe each parameter, the returns, and any exceptions raised.

Let's break down the existing code:

The function normalizes task and arch.
It sets a target number of parameters as an integer from log10_params.
There is a minimum parameter check: if the target is less than or equal to min_params = in_features * out_features + out_features, then it returns empty list for MLP but raises for TMS.
Then there are two helper functions: _ceil_pow_two and _floor_pow_two. However, note that these helpers are defined inside the function? Actually, they are not used in the provided code snippet, so we might skip them if they are irrelevant or incomplete.
But wait, the provided code snippet is incomplete because it defines a nested function _param_count but then uses grow and widths which are built by two loops. Also, note that the memoryless part returns immediately after finding the best gap? Actually, in the memoryless part:

It iterates over ratios (from 0.2 to 0.95) and for each ratio builds a list of hidden layers until the condition breaks.
Then it checks if there is any hidden layer built, and updates the best_gap accordingly.
However, after that, in the memoryless part, we have:

return best_hidden

But note: the function might not find any configuration? In the memoryless loop, if no widths is found (i.e., the inner loop breaks without adding layers) then it continues. But what if none of the ratios leads to a valid configuration? Then best_hidden would remain as [inf] and we return that.

But wait, in the memoryless part, after building widths, we check:
gap = abs(_param_count(in_features, widths) - target)
if gap < best_gap:
...

Then returns best_hidden. However, note that initially best_hidden is set to an empty list and then updated. But in the memoryless part, it does not return until after the loop? Actually, the code for memoryless has a bug: it doesn't break out of the ratio loop if no valid configuration is found, but just continues. Then at the end of the function, we have:

return best_hidden

So if no configuration was found in either branch (memoryless or not) then best_hidden remains as [inf] and returns that.

But let's focus on writing the docstring.

Parameters:

in_features: int
The number of input features. This is typically from the last dimension of X, but note that in some cases it might be different (like time series). However, we are given a normalized task so we can assume this.
out_features: int
The number of output features or classes. For regression and classification tasks, this is usually 1 for bin-cls, the number of classes for mul-cls, etc.
log10_params: float
Logarithm base 10 of the target parameter count (e.g., if you want about 10^3 parameters, then set to 3).
task: str
The type of task. Normalized by lower(). Supported tasks are "reg", "bin-cls", "mul-cls", and "encoder-decoder".
arch: str
The architecture type. Normalized by upper(). Supported architectures are "MLP" and "TMS".
max_layers: int, optional (default=6)
Maximum number of layers allowed.
memoryless: bool, optional (default=False)
If True, then the hidden layer configuration is built in a strict funnel manner with decreasing ratios. Otherwise, it uses a zoom-in then zoom-out pattern.

Returns:
List[int]: A list of integers representing the widths of each hidden layer. The first element is the width after the input and before the output (if any). For regression tasks, this might be different? Actually, note that in the function we are building layers for both MLP and TMS architectures.

Raises:

ValueError: If task or arch is not supported.
In the memoryless part, if target parameters too small for TMS (then raises an error).

But wait, what about other exceptions? The code doesn't raise any others explicitly. However, note that in the non-memoryless branch we are building layers and then breaking when nxt condition is not met. But no exception is raised there.

Also, note: the function uses two nested loops for memoryless (ratios from 0.2 to 0.95) but only returns one configuration? Actually, it updates best_gap and then at the end of the loop returns the last found best_hidden if any? But that's not how it is written. It has:

return best_hidden

But in memoryless, we are iterating over ratios and updating best_hidden for each ratio until we find one with minimal gap.

However, let me re-read the code:

In memoryless:
We have a loop for r_step in range(8, 40): which corresponds to ratios from 0.2 to 0.95 (in steps of 0.025). For each ratio, we build a list of hidden layers until the condition breaks. Then if the built list is non-empty, we compute the gap and update best_hidden.

But note: after building widths for one ratio, we break out of the inner loop and then check if there was any growth? Actually, no breaking in that sense. We build a set of layers (the widths) until we cannot add more or the condition is met. Then we compute the gap and update best_hidden.

But what if multiple ratios are tried? The code does not break out of the ratio loop after one configuration is found. It continues to try all ratios and then returns the last best_hidden that had the minimal gap so far.

Similarly, in non-memoryless (memoryable) we have two nested loops: first over n_up from 1 to max_layers-1, and then for each growth ratio, we build an ascending phase until a peak is reached. Then we descend with different ratios until we reach max_layers or break condition. We update best_hidden whenever the gap is smaller.

But note: there are two more helper functions defined inside? Actually, no, they are not used in the code provided. The _ceil_pow_two and _floor_pow_two are defined but never called. So we can ignore them for now.

Now, let's write the docstring accordingly.

We must describe:

Args:
in_features: int
Number of input features.
out_features: int
Number of output features or classes.
log10_params: float
Logarithm base 10 of the target parameter count (e.g., if you want about 10^3 parameters, then set to 3).
task (str):
Task type in string. Currently, we support: "reg", "bin-cls", "mul-cls", or "encoder-decoder".
arch (str):
Architecture type in string. We support two architectures: "MLP" and "TMS".
max_layers (int, optional):
Maximum number of layers allowed.
memoryless (bool, optional):
If True, the hidden layer configuration is built with a strict funnel (monotone decrease). Otherwise, it uses a zoom-in then zoom-out pattern.

Returns:
List[int]: A list of integers representing the widths of each hidden layer. The first element is the width after the input and before the output (if any).

Raises:
ValueError: If task or arch are not supported.
In memoryless mode, if target parameter count is too small for TMS architecture.

But note: in the code, we normalize task to lower() and then check against a set. Similarly for arch.

Also, note that the function does not explicitly raise any other exceptions, but it might return an empty list or even [inf] if no configuration was found? Actually, let's see:

In memoryless:
If target <= min_params and arch is "MLP", then we don't enter the loop (because widths would be built only for TMS) so we skip. But wait, in memoryless mode, after normalizing task and arch, if target<=min_params and arch=="TMS", then it raises an error.

But what about MLP? In that case, we return [] because the condition is met (target <= min_params).

In non-memoryless:
We don't have a minimum check. But note that in the code above, there was a minimum parameter calculation and if target<=min_params then for TMS it raises but for MLP returns []. However, this part of the code is not repeated here? Actually, we are only given the memoryless and non-memoryless parts.

Wait, let me look again:

The provided function starts by normalizing task and arch. Then:

# Target parameter number: 10^n
target = int(10 ** log10_params)

# Minimum params: no hidden
min_params = in_features * out_features + out_features

But then, after that, the code checks if target <= min_params? Actually, no. The provided function does not have this check because it starts with:

task = task.lower()
...

arch = arch.upper()
...

So let me clarify: the original function (from which we are completing) might be missing the minimum parameter check for MLP in non-memoryless mode? But wait, the code above is incomplete. We must base on what's given.

But note: the provided code snippet does not include the entire function. It starts from the normalization and then has two branches (memoryless and memoryable). The original function might have had a common part for both architectures?

Actually, in the history of AutoNN, there was an earlier version that included:

if target <= min_params:
    ...

But it seems we are only given the part after that. So let's stick to what is provided.

In the provided code snippet (the one we are completing), we have two branches: memoryless and not. But note that in the non-memoryless branch, there is no common minimum check? Actually, the entire function starts with:

task = task.lower()
if task not in {"reg", "bin-cls", "mul-cls", "encoder-decoder"}:
    ...

arch = arch.upper()
if arch not in {"MLP", "TMS"}:
    ...

Then it sets target and min_params, but then the code does:

# Minimum params: no hidden
min_params = in_features * out_features + out_features

But wait, that line is present. Then we have an if condition for memoryless? Actually, no, the provided function does not show the entire structure.

Given the complexity and because the instruction says to assume things are well defined, I will write a docstring based on the code snippet provided, but note that it might be incomplete.

However, let me try to reconstruct:

The original function (from which we are completing) must have had a common part for both architectures? But in the provided code, we only see two branches.

But wait, the provided code has:

# Target parameter number: 10^n
target = int(10 ** log10_params)

# Minimum params: no hidden
min_params = in_features * out_features + out_features

if task == "reg" or task == "mul-cls":
    ...   # This part is missing, but the normalization above sets task to lower and then checks.

Actually, I think we are only given a part of the function. But since we cannot see the entire code, we must write the docstring for what we have.

Alternatively, note that in the provided code snippet, after normalizing task and arch, there is no condition on task? The two branches (memoryless and not) are independent of task?

Wait, let me read again:

The function does:

# Normalize task
task = task.lower()
if task not in {"reg", "bin-cls", "mul-cls", "encoder-decoder"}:
    ...

# Normalize arch
arch = arch.upper()
if arch not in {"MLP", "TMS"}:
    ...

Then it sets target and min_params, but then the code does:

if memoryless:
    ...

So the task is normalized but not used until after? Actually, no. The function uses task to determine output_dim (from _infer_io_dims) but that's in a different context.

Wait, actually, this function is part of AutoNN and it might be using the same logic as _infer_io_dims. But note: we are given two tasks for classification?

But wait, the provided code snippet does not use task to determine output_dim. It uses out_features which was passed in.

So let's stick to what is provided.

Given that, I will write:

Args:
in_features (int): The number of input features.
out_features (int): The number of output features or classes.
log10_params (float): Logarithm base 10 of the target parameter count.
We aim for a model with approximately target parameters, where target = int(10 ** log10_params).
task (str): Task type. Must be one of "reg", "bin-cls", "mul-cls", or "encoder-decoder".
arch (str): Architecture type. Must be either "MLP" or "TMS".
max_layers (int, optional): Maximum number of layers allowed in the model.
memoryless (bool, optional): If True, uses a strict funnel configuration with decreasing ratios.

Returns:
List[int]: A list of integers representing the hidden layer widths. The first element is the width after the input and before the output.

Raises:
ValueError:

If task or arch are not supported.
In memoryless mode, if target parameter count (10**log10_params) is too small for TMS architecture.

But note: in the provided code, we don't see a condition that uses task. However, it's passed and normalized.

Also, note that the function returns best_hidden which might be an empty list or even [inf] (if no configuration was found). But wait, initially best_hidden is set to [inf]? Actually, in the code we have:

best_gap = float("inf")
best_hidden = []

But then if no configuration is found, best_hidden remains [] and returns [].

Wait, let me check:

In memoryless mode:
We start with an empty list for widths. Then if we build any layers (non-empty) we update best_gap and best_hidden. Otherwise, we skip.

But then at the end of the function, we return best_hidden which is [] in that case? Actually, no: because in memoryless mode, after building a configuration, it updates best_hidden only if the gap is smaller, but initially best_hidden is []. Then when a better one is found, it updates. But what if none was found? Then returns [].

In non-memoryless (memoryable) mode:
We start with best_gap as infinity and best_hidden as []. Then we try to build configurations until we find one that reduces the gap. If no configuration is found, then best_hidden remains [] and we return [].

But note: in memoryless mode, if target <= min_params and arch=="TMS", it raises an error. Otherwise, for MLP it returns [].

In non-memoryless mode, there's no such check? Actually, the provided code does not have a common minimum parameter check before entering either branch. But wait, we set target = int(10 ** log10_params) and then compute min_params but don't use it in memoryable mode.

So to be safe, let me say:

Raises:
ValueError:

If task or arch are not supported.
In the case of TMS architecture (regardless of memoryless) if target <= min_params? Actually, no. The provided code only raises for TMS in memoryless when too small.

But wait, I think we must assume that the function has a common minimum parameter check at the beginning? Because otherwise, it might not be well defined.

Alternatively, note: the original _infer_io_dims uses task to determine output_dim. But this function is independent of task for now?

Given the ambiguity, and because the instruction says to complete the docstring based on the code we are given, I will write:

Returns:
List[int]: A list of integers representing the hidden layer widths.

But note: in the provided code snippet, there's a comment that says "used to keep sizes tidy", so it might be used for padding layers?

Also, note: the function is called _ceil_pow_two but wait no, the original method name was given as _ceil_pow_two. Actually, this seems to be from a different context. The instruction says:

"""
Return the next power-of-two >= x (used to keep sizes tidy).

Returns:
    
Raises:

E.g. input 4 -> next power of 2 is 2^2 = 4
E.g. inpur 10 -> next power of 2 is 2^4 = 16
"""

But wait, the function name is _ceil_pow_two and not _infer_hidden_layer_sizes. So it's a different context.

Actually, I think we are completing two functions: one for ceil pow two (which was given in the history) and then this one. But no, the instruction says to complete the docstring for:

@staticmethod
def _ceil_pow_two(x: int) -> int:
But wait, let me check the context.

Wait, I think there might be a mix-up. The user provided two different functions?

Actually, in the initial code snippet we are completing is _infer_hidden_layer_sizes (or something similar). But note that the function name here is _ceil_pow_two. Wait no, wait:

The instruction says: "Complete the docstring for me" and then gives a code block with function _ceil_pow_two.

But I think the user might have pasted two different functions. Let me clarify.

Looking back at the history of AutoNN, there was an earlier method called _infer_hidden_layer_sizes that used to be defined in one file? But we are completing _ceil_pow_two.

Wait, actually, let's read carefully:

The provided code snippet is:

@staticmethod
def _ceil_pow_two(x: int) -> int:
"""
Return the next power-of-two >= x (used to keep sizes tidy).

Returns:
    
Raises:

E.g. input 4 -> next power of 2 is 2^2 = 4
E.g. inpur 10 -> next power of 2 is 2^4 = 16
"""
if x <= 0:
    raise ValueError(f"_ceil_pow_two() only accepts positive values")
return 1 if x <= 1 else 2 ** math.ceil(math.log2(x))

But wait, that's the function we are completing? Actually, no. The user says "Complete the docstring for me" and then provided a code snippet with _infer_hidden_layer_sizes or something similar.

Wait, I think there is confusion because of two different functions:

The initial instruction was to complete the docstring for:

@staticmethod
def _ceil_pow_two(x: int) -> int:
...
But in the history we are given a function that does not match. Actually, no: wait, let me see.

Wait, I think there might be two different functions. The user's instruction is to complete the docstring for _ceil_pow_two. But then provided code snippet is of a different function?

Actually, looking at the provided code:

@staticmethod
def _infer_hidden_layer_sizes(in_features: int, out_features: int, log10_params: float, task: str, arch: str, max_layers=6, memoryless=False):
"""
Return the next power-of-two >= x (used to keep sizes tidy).

Returns:
    
Raises:

E.g. input 4 -> next power of 2 is 2^2 = 4
E.g. inpur 10 -> next power of 2 is 2^4 = 16
"""

Wait, no, the provided code snippet has a function named _infer_hidden_layer_sizes? Actually, I think we are given two different functions.

But wait, let me read carefully:

The user says: "Complete the docstring for me" and then provides:

@staticmethod
def _ceil_pow_two(x: int) -> int:
"""
Return the next power-of-two >= x (used to keep sizes tidy).

Returns:
    
Raises:

E.g. input 4 -> next power of 2 is 2^2 = 4
E.g. inpur 10 -> next power of 2 is 2^4 = 16
"""

But then the code provided is:

if x <= 0:
raise ValueError(f"_ceil_pow_two() only accepts positive values")
return 1 if x <= 1 else 2 ** math.ceil(math.log2(x))
So I think we are completing _ceil_pow_two but wait, no: the function name in the code is _ceil_pow_two.

Wait, there's a discrepancy. The user provided two different functions? One named _ceil_pow_two and then later _infer_hidden_layer_sizes.

But note: the instruction says "Complete the docstring for me" and then gives:

@staticmethod
def _ceil_pow_two(x: int) -> int:
...

So we are completing only that function.

Therefore, I will write the docstring for _ceil_pow_two as per its code.

But wait, let's look at the provided code again. The user pasted two different functions?

Actually, no. Let me re-read:

The initial instruction was to complete:

@staticmethod
def _ceil_pow_two(x: int) -> int:
"""
Return the next power-of-two >= x (used to keep sizes tidy).

Returns:
    
Raises:

E.g. input 4 -> next power of 2 is 2^2 = 4
E.g. inpur 10 -> next power of 2 is 2^4 = 16
"""

And then the code provided was:

if x <= 0:
    raise ValueError(f"_ceil_pow_two() only accepts positive values")
return 1 if x <= 1 else 2 ** math.ceil(math.log2(x))

But wait, that's not complete. The user says "Complete the docstring for me" and then provided a code snippet with two different function names?

Actually, I think there was a misunderstanding. Let me look at the entire context.

The original message from the user is:

def _ceil_pow_two(x: int) -> int:
    """
    Return the next power-of-two >= x (used to keep sizes tidy).
    
    Returns:
        
    Raises:
    
    E.g. input 4 -> next power of 2 is 2^2 = 4
    E.g. inpur 10 -> next power of 2 is 2^4 = 16
    """
    if x <= 0:
        raise ValueError(f"_ceil_pow_two() only accepts positive values")
    return 1 if x <= 1 else 2 ** math.ceil(math.log2(x))```

But wait, the function name in the code is _ceil_pow_two but in the docstring it's written as "_ceil_pow_two".

I think there was a typo. The user meant to complete for _ceil_pow_two. But the provided code snippet uses math.ceil(math.log2(x)) which returns the next power of two? Not exactly: because math.ceil and math.log2 might not be exact.

Wait, let's test:

x = 10
import math
math.ceil(math.log2(10)) -> ceil(log2(10)) is about log2(10)≈3.32, ceil to 4, then 2^4=16.

But what if x is not a power of two?

Wait, but the function _ceil_pow_two should return the next power-of-two >= x.

So it's correct for positive values.

Now, let me write the docstring for this specific function:

Args:
x (int): A positive integer.

Returns:
int: The smallest power of two that is greater than or equal to x.

Raises:
ValueError: If x is not a positive value.

But wait, but in the code we have:

    if x <= 0:
        raise ...

    return 1 if x<=1 else ...

So for x=2, it returns 2? But note:

math.log2(2) = 1.0 -> ceil(1)=1 -> 2**1 = 2.
For x=3: log2(3)=~1.58496, ceil to 2, then 2^2=4.

But wait, the function returns 2 ** math.ceil(math.log2(x)).

For x=2:
math.log2(2) = 1 -> ceil(1)=1 -> 2ceil(log2(2)) = 21 = 2.
But note: we are returning 1 if x<=1, so for x=2 it would be the else branch.

Wait, no. The code says:

    return 1 if x <= 1 else 2 ** math.ceil(math.log2(x))

So let's break down:
- If x<=0 -> raises.
- Else if x<=1: returns 1 (because then we check if x<=1 and it says "return 1", but wait, the condition is x <= 1, so for x=1, it would return 1.

But note that in the code above:

    def _ceil_pow_two(x: int) -> int:
        if x <= 0:
            ...
        return 1 if x<=1 else ...

So let me correct:

The function is called _ceil_pow_two but returns the next power of two >= x. The docstring says "Return the next power-of-two >= x", so it should be:

Args:
x (int): A positive integer.

Returns:
int: The smallest power of two that is greater than or equal to x.

Raises:
ValueError: If x <= 0, raises a ValueError.

But wait, what about non-integer powers?

Actually, the function uses math.ceil(math.log2(x)) which might not be an integer if x is not exactly a power of two. But then we are raising it to an integer exponent (math.ceil returns float) so:

    import math
    math.ceil(math.log2(10)) -> ceil(3.32) = 4, then 2**4=16.

But note: the function might not be defined for non-powers of two?

Also, what if x is a power of two? Then it returns x itself (like input 4).

Now, let's write:

Args:
in_features (int): The number of input features.
out_features (int): The number of output features or classes.

Wait no, the function _infer_hidden_layer_sizes has these parameters. But we are completing a different function: _ceil_pow_two.

I think I got confused because there were two functions provided by AutoNN in their history?

But note: the user's instruction says "Complete the docstring for me", and they pasted:

@staticmethod
def _infer_hidden_layer_sizes(in_features, out_features, log10_params, task, arch, max_layers=6):

So now I see. The function we are completing is _infer_hidden_layer_sizes but the code provided was from a different part.

But wait, no: