CodeLlama 34B errors out after 3+ completions about autoawq HOT 10 CLOSED

Thank you for this! My best guess is that the number of tokens exceed the cache. Will have to investigate this

from autoawq.

I've seen the same with other models. Thanks for the script @abacaj I'm going to run some other models through their paces to see if I can reproduce.

AutoAWQ=0.1.0, python=3.10, cuda=11.8, rtx 3090

I can reproduce the error with any model:

• TheBloke/Llama-2-7b-Chat-AWQ
• TheBloke/vicuna-7B-v1.5-AWQ
• casperhansen/vicuna-7B-v1.5-AWQ

File "/home/alansrobotlab/anaconda3/envs/textgen/lib/python3.11/site-packages/awq/modules/fused/attn.py", line 183, in forward self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :] = values_store ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: The expanded size of the tensor (0) must match the existing size (24) at non-singleton dimension 2. Target sizes: [1, 32, 0, 128]. Tensor sizes: [32, 24, 128]

from autoawq.

I switched up the approach entirely, and we are rolling over the cache now. This seems to produce correct outputs, and we get as close to HF output with FT modules. They are not meant to be the exact same outputs as slight numerical differences will lead to different outputs in some cases - however, they are very close now.

from autoawq.

I have closed this issue as the main error has been solved. However, it seems there is a problem with the fused modules and the CodeLlama models, although it should already be supported as GQA is implemented.

from autoawq.

Setting fuse_layers=False seems to work with the same code (though slower generations).

from autoawq.

Fixed this now in #75, at least I cannot produce this error anymore even when running for 1000 iterations:

@abacaj and @gestalt73, would appreciate it if you could take the time to test out the pull request to see if something else breaks

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_name_or_path = "casperhansen/vicuna-7b-v1.5-awq"
max_new_tokens = 1024

# Load model
model = AutoAWQForCausalLM.from_quantized(
    model_name_or_path,
    quant_filename="awq_model_w4_g128.pt",
    fuse_layers=True,
    trust_remote_code=False,
    safetensors=True,
    max_new_tokens=1024,
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

tokens = tokenizer(
    "# Write a python function to loop to 1000\n\ndef", return_tensors="pt"
).to("cuda")

# Generate output
cumulative_tokens = 0

for i in range(1000):
    if cumulative_tokens > max_new_tokens:
        cumulative_tokens = 0
    
    generation_output = model.generate(
        **tokens,
        do_sample=True,
        temperature=0.2,
        top_p=0.95,
        top_k=0,
        max_new_tokens=512,
    )

    num_tokens = len(generation_output[0])
    cumulative_tokens += num_tokens

    print(i, num_tokens, cumulative_tokens)

    # print(tokenizer.decode(generation_output[0], skip_special_tokens=True))

from autoawq.

Hey @casper-hansen I ran it a bit with TheBloke/Llama-2-7b-Chat-AWQ and things look normal until the first cache clear, then things get weird. It doesn't error out though.

Take a look at the output after the first set of cache clear messages around line 192.

Output is consistent for the first x generations, then after the resetting cache message it starts ok in generation but gets interesting towards the end of line 209. from there on out it's hit or miss, but I'm also seeing the huge amount of newlines which I would occasionally see in 0.1.0.

Fix KV cache shapes error 75 results.txt

from autoawq.

I don't see the expanded tensor error anymore. But model generations using fused=True are different (worse) compared to fused=False

from autoawq.

Added fused_true and fused_false samples here. I turned sampling off so it should be greedy generation. For fused=False the output seems good

https://gist.github.com/abacaj/aefb5e9dd85a6fc8b54b5b655a9a632e

from autoawq.

Thank you all for testing. The fact that the outputs after resetting the cache are getting weird or not working as expected is not good enough for me to merge the PR. I will have to explore:

1. How to reset the cache without weird outputs
2. How to increase allocated cache dynamically as inputs are run through the model

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