[KV Cache] support kv cache int8 per channel quant

[Eviannn]

SUMMARY:
kv cache quant int8 per channel is supported using this pr.
Besieds, compressed-tensors needs to be updated as well: neuralmagic/compressed-tensors#398

TEST PLAN:
specify type=int8 and strategy=channel in recipe

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @Eviannn, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the LLM compression framework by implementing INT8 per-channel quantization for the Key and Value cache. The changes involve strategic tensor reshaping to align with the per-channel strategy and updates to the quantization observer to correctly compute and apply quantization parameters across the appropriate dimension, ultimately aiming to improve the efficiency and memory footprint of LLMs.

Highlights

• KV Cache Quantization: This pull request introduces support for INT8 per-channel quantization for Key and Value states within the KV cache, enhancing memory efficiency and performance for LLMs.
• Tensor Reshaping for Per-Channel Quantization: Key and Value tensors are now reshaped from [batch_size, num_heads, seq_len, head_dim] to [batch_size, seq_len, num_heads * head_dim] before quantization, and then reshaped back after de-quantization. This prepares the tensors for per-channel quantization along the combined head dimension.
• Dynamic Quantization Dimension Selection: The BaseObserver logic has been updated to dynamically select the correct dimension for per-channel quantization. Specifically, for 3-dimensional KV cache tensors, quantization parameters are now calculated along dim=2 (the num_heads * head_dim dimension), while other scenarios continue to use dim=0.
• Scale and Zero Point Handling: Adjustments were made to squeeze() the scale and zero point tensors when storing them, accommodating the output format of per-channel quantization where these parameters might have singleton dimensions.

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[gemini-code-assist]

Code Review

This pull request introduces support for per-channel INT8 quantization for the Key-Value (KV) cache. The changes involve reshaping the key/value tensors before quantization and then reshaping them back after dequantization. Additionally, the observer logic is updated to handle the 3D tensors characteristic of the KV cache.

My review has identified a couple of significant issues. There is a critical typo in the observer code that would prevent the new functionality from working correctly. Furthermore, the tensor reshaping logic within the KV cache is applied unconditionally, which would likely break existing quantization strategies. I've provided detailed comments and suggestions to address these points.

[gemini-code-assist]

There appears to be a typo in the condition here. observed.dim refers to the method object itself, not its return value. To get the number of dimensions, the method needs to be called: observed.dim().

Comparing the method object to an integer (observed.dim == 3) will always evaluate to False, causing dim to be incorrectly set to 0 for 3D tensors. This will prevent per-channel quantization from working correctly for use cases like the KV cache.

dim = 2 if observed.dim() == 3 else 0

[gemini-code-assist]

The reshape logic for key_states and value_states is specific to the per-channel quantization strategy, as noted in the comments. However, it's applied unconditionally, which will likely break other KV cache quantization strategies (e.g., per-tensor).

This reshape block should be wrapped in a conditional check, for example:
if self.quantization_args.strategy == QuantizationStrategy.CHANNEL:

This change will introduce a variable scope issue for num_heads and head_dim, which are defined in this block but also needed for the reverse reshape. You'll need to refactor the update method to handle this, for instance by initializing num_heads and head_dim to None at the beginning of the function.

You will also need to add the following import at the top of the file:
from compressed_tensors.quantization.quant_args import QuantizationStrategy

[Eviannn]

pertensor and perchannel are same.