Add torch.compile for Mistral #30642

zhenglongjiepheonix · 2024-05-03T16:16:39Z

As suggested by the title, this PR attempts to add torch.compile support for mistral, and this is a not-ready-to-merge PR, it tries to replicate what has been done in Llama to Mistral considering the similar arch

Add Sliding Window Cache
Use Static Cache/Sliding Window Cache for torch.compile
Moves attention mask related logic inside _update_causal_mask
Modify _prepare_input_for_generation to make _generate work

HuggingFaceDocBuilderDev · 2024-05-03T16:38:34Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

ArthurZucker

Thanks for working on this!

src/transformers/models/mistral/modeling_mistral.py

ArthurZucker · 2024-05-06T07:56:10Z

Also the list of models that support static cache in the doc probably need an update

ArthurZucker · 2024-05-06T15:32:02Z

BTW mistral will nee a SlidingWindowCache based on the implementation of RecurrentGemma!

zhenglongjiepheonix · 2024-05-07T04:21:39Z

BTW mistral will nee a SlidingWindowCache based on the implementation of RecurrentGemma!

from my understanding, SlidingWindowCache is for memory efficiency, actually I wonder how SlidingWindowCache would address the issue of get_seq_length where the current solution is relying on performing a non-zero check on all previous tokens, another solution is to use cache_position so that we don't have to call past_key_values.get_seq_length(), but this would require cache position is always passed in model.forward right?

ArthurZucker · 2024-05-07T09:15:01Z

Yes, we need to rely on cache positions that have to be passed to the model's forward. They can be initialized like in Llama. NOte that it's not breaking for the dyunamic cache because it will ignore the extra kwargs.

    def _update_cache(self, key_states, value_states, **cache_kwargs):
        """
        torch.compile compatible sliding window.
        Computes the `indices` based on `cache_position >= self.config.attention_window_size - 1`.
        The `to_shift` is only true once we are above attention_window_size. Thus with `attention_window_size==64`:

        indices = (slicing + to_shift[-1].int()-1) % self.config.attention_window_size
        tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
                19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
                37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
                55, 56, 57, 58, 59, 60, 61, 62, 63,  0])

        We overwrite the cache using these, then we always write at cache_position (clamped to `attention_window_size`)
        """
        cache_position = cache_kwargs.get("cache_position")
        if cache_position.shape[0] > self.config.attention_window_size:
            # int indexing -> device sync? in compile, use tensor
            k_out = key_states[:, :, -self.config.attention_window_size :, :]
            v_out = value_states[:, :, -self.config.attention_window_size :, :]
        else:
            slicing = torch.ones(
                self.config.attention_window_size, dtype=torch.long, device=value_states.device
            ).cumsum(0)
            cache_position = cache_position.clamp(0, self.config.attention_window_size - 1)
            to_shift = cache_position >= self.config.attention_window_size - 1
            indices = (slicing + to_shift[-1].int() - 1) % self.config.attention_window_size

            k_out, v_out = self.key_states.to(key_states.device), self.value_states.to(value_states.device)
            k_out = k_out[:, :, indices]
            v_out = v_out[:, :, indices]

            k_out[:, :, cache_position] = key_states
            v_out[:, :, cache_position] = value_states

        self.key_states, self.value_states = k_out, v_out
        return k_out, v_out

updating the cache should be like this

ArthurZucker · 2024-05-07T09:18:24Z

YEah it's hard to debug. merge from main and try to add a print to check where it's failing! Most probably a copied from that is not well placed

gante

in general looks like it's going in the right direction! 💪

Related PR: #30688

docs/source/en/llm_optims.md

zhenglongjiepheonix · 2024-05-09T04:14:22Z

Hi Aurthur, I have add support for Sliding Window Cache, and please take a look at its implementation and also the _update_causal_mask implementation, I have add my thoughts as comments

ArthurZucker

Good work

for all the copied from that were removed, we need to use on of the model as the new base (mixtral for example)
as @gante said, let's add phi to the lsit of model slow tested
let's revert some styling on setup.py and examples run object detection

src/transformers/cache_utils.py

ArthurZucker · 2024-05-09T08:57:38Z

src/transformers/generation/utils.py

@@ -1342,6 +1340,33 @@ def _get_static_cache(self, max_batch_size: int, max_cache_len: int) -> StaticCa
            self._static_cache.reset()  # reset the cache for a new generation
        return self._static_cache

+    # maybe a better way is to use a single factory function to set caches for models ?


yes, you should be able to use a mapping from the cache_implementation to the cache_class, and replace _get_static_cache by _get_cache. Since most of the args are gonna come from the config.

The need_new_cache is not a great naming IMO. If we need to check that for each cache class it makes sense to implement it, but would rather have minimal number of function in the cache class that are not directly related to the cache (here it's a generate trick)

@gante how much gain do we have from not allocating and just reseting? (IMO should be fairly efficient in general so we could just forget this needs new cache arg

I still keep the logic but keep them in _get_cache

src/transformers/generation/utils.py

src/transformers/models/mistral/modeling_mistral.py

tests/models/mistral/test_modeling_mistral.py

tests/models/mixtral/test_modeling_mixtral.py

ArthurZucker · 2024-05-09T09:24:35Z

src/transformers/cache_utils.py

+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        cache_shape = (
+            config.num_hidden_layers,
+            max_batch_size,
+            self.num_key_value_heads,
+            self.sliding_window_size,
+            self.head_dim,
+        )
+
+        self.key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+        self.value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+
+        torch._dynamo.mark_static_address(self.key_cache)
+        torch._dynamo.mark_static_address(self.value_cache)


Not sure what is the best between list of tensor and a full concatenated tensor?
But I think for everything related to generate, assisted decoding etc, list might be better?

Also in the current configuration:

self.key_cache: List[torch.Tensor] = [] self.value_cache: List[torch.Tensor] = []

is useless!

I tried using lists, cudagraph seems to complain about it

Interesting as that works for the fully static cache! But alright 👍🏻

I think it's different because in static cache the tensor we keep in self.key_cache[layer_idx] never changes in terms of address, however in here we have the needs of assigning whole self.key_cache[layer_idx] to the new tensor passed in as key states

src/transformers/cache_utils.py

zhenglongjiepheonix · 2024-05-10T02:01:09Z

Good work

for all the copied from that were removed, we need to use on of the model as the new base (mixtral for example)

as @gante said, let's add phi to the lsit of model slow tested

let's revert some styling on setup.py and examples run object detection

Hi Authur, I made some modifications, and I still can't quite get what we should do to solve this copy consistency issue. I have changed the base to Mixtral for most models that refered to Mistral, but there are still some cases where it doesn't quite fit because Mixtral has itself moe related logic, and I also see this Ignore copy bandaid-stuff everywhere, it becomes very unclear that what the exact thing I need to do is, is it add Ignore copy or is it make modifications to make sources match and which source should be the standard if they disagree ? and make fix-copies does not help much because you can't just use it without being really sure that you actually need exactly the same thing, otherwise it just break stuffs!

Could you please explain more about the second point about phi model? I don't quite get it.

ArthurZucker

For the copied from issue, indeed mixtral is not necessarily the best, the idea is just to do what we can to not break the copied from chain. Not everything has to be from mixtral

setup.py

examples/pytorch/object-detection/run_object_detection.py

src/transformers/cache_utils.py

src/transformers/generation/utils.py

zhenglongjiepheonix · 2024-05-15T21:46:51Z

If except the 4 mentioned failing tests, all other are passing with this PR + test_compile_static_cache is passing on a A10 with torch 2.3, it's OK from my side (in terms of CI)

I don't see it running on the A10-based workflow, is it because it does not trigger on PR?

It's because the torch version there should use torch 2.3 but it is currently torch 2.2. I will update the docker files to use torch 2.3.

I think A10 workflow only trigger on push on main, not on PR ? I mean my commits on this pr are not triggering the test,
do I need to directly work on the transformers repo instead of my forked repo to run the test

name: Slow tests on important models (on Push - A10)

on:
  push:
    branches: [ main ]

ydshieh · 2024-05-16T05:55:42Z

I think A10 workflow only trigger on push on main, not on PR ?
Yes.

You could run workflow manually

https://github.com/huggingface/transformers/actions/workflows/ssh-runner.yml

See DM on slaick

…ompile_for_mistral

zhenglongjiepheonix · 2024-05-16T18:01:11Z

Ok, now the problem is both llama and mistral are failing the compile static cache tests on A10 because of the same error:

============================= FAILURES SHORT STACK =============================
________________ LlamaIntegrationTest.test_compile_static_cache ________________
msg = 'hasattr ConstDictVariable to'
    def unimplemented(msg: str) -> NoReturn:
        assert msg != os.environ.get("BREAK", False)
>       raise Unsupported(msg)
E       torch._dynamo.exc.Unsupported: hasattr ConstDictVariable to
E       
E       from user code:
E          File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/external_utils.py", line 36, in inner
E           return fn(*args, **kwargs)
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/hooks.py", line 161, in new_forward
E           args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/hooks.py", line 356, in pre_forward
E           return send_to_device(args, self.execution_device), send_to_device(
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/utils/operations.py", line 148, in send_to_device
E           if is_torch_tensor(tensor) or hasattr(tensor, "to"):
E       
E       Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
E       
E       
E       You can suppress this exception and fall back to eager by setting:
E           import torch._dynamo
E           torch._dynamo.config.suppress_errors = True

They work fine on my A100 dev and colab T4 env, but here this error seems unrelated with GPUs, rather it's something related with software, I can't reproduce the error, even on an A10 aws machine with python==3.8:

zhenglongjiepheonix · 2024-05-16T23:14:33Z

Ok, now the problem is both llama and mistral are failing the compile static cache tests on A10 because of the same error:

============================= FAILURES SHORT STACK =============================
________________ LlamaIntegrationTest.test_compile_static_cache ________________
msg = 'hasattr ConstDictVariable to'
    def unimplemented(msg: str) -> NoReturn:
        assert msg != os.environ.get("BREAK", False)
>       raise Unsupported(msg)
E       torch._dynamo.exc.Unsupported: hasattr ConstDictVariable to
E       
E       from user code:
E          File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/external_utils.py", line 36, in inner
E           return fn(*args, **kwargs)
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/hooks.py", line 161, in new_forward
E           args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/hooks.py", line 356, in pre_forward
E           return send_to_device(args, self.execution_device), send_to_device(
E         File "/usr/local/lib/python3.8/dist-packages/accelerate/utils/operations.py", line 148, in send_to_device
E           if is_torch_tensor(tensor) or hasattr(tensor, "to"):
E       
E       Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
E       
E       
E       You can suppress this exception and fall back to eager by setting:
E           import torch._dynamo
E           torch._dynamo.config.suppress_errors = True

They work fine on my A100 dev and colab T4 env, but here this error seems unrelated with GPUs, rather it's something related with software, I can't reproduce the error, even on an A10 aws machine with python==3.8:

the interesting fact is, test_compile_static_cache passes with itself along in CI, however if run after test_compile_sliding_window_cache, then it fails because some memory-related strategies in accelerate make dynamo unhappy @ArthurZucker @ydshieh

zhenglongjiepheonix · 2024-05-16T23:44:18Z

tests/models/mistral/test_modeling_mistral.py

+            8: [
+                "My favourite condiment is 100% ketchup. I love it on everything. "
+                "I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
+            ],
+            7: [
+                "My favourite condiment is 100% ketchup. I love it on everything. "
+                "I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
+            ],
+        }
+
+        prompts = ["My favourite condiment is "]
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
+        tokenizer.pad_token = tokenizer.eos_token
+        model = MistralForCausalLM.from_pretrained(
+            "mistralai/Mistral-7B-v0.1", device_map="sequential", torch_dtype=torch.float16
+        )
+        inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
+
+        # Dynamic Cache
+        generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
+        dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], dynamic_text)
+
+        # Static Cache
+        generated_ids = model.generate(
+            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
+        )
+        static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
+
+        # Sliding Window Cache
+        generated_ids = model.generate(
+            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
+        )
+        static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
+
+        # Static Cache + compile
+        forward_function = model.forward
+        model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
+        generated_ids = model.generate(
+            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
+        )
+        static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
+
+        # Sliding Window Cache + compile
+        torch._dynamo.reset()
+        model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
+        generated_ids = model.generate(
+            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
+        )
+        static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
+
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()


I merged two tests into one, and now it passes!

ArthurZucker · 2024-05-17T14:43:06Z

Yep accelerate does not support compile yet

zhenglongjiepheonix · 2024-05-17T16:08:08Z

I merged from current main and again did slow tests on my dev and aws a10 machine, I believe this PR is good to merge now @ArthurZucker @ydshieh

ArthurZucker

Just need to resolve the merge conflicts, 1 nit and feel free to merge!

ArthurZucker · 2024-05-20T12:26:05Z

src/transformers/models/mistral/modeling_mistral.py

+    @property
+    def sin_cached(self):
+        logger.warning_once(
+            "The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "


this is still the wrong version, it should now be 4.43!

I just remove this like in llama

zhenglongjiepheonix · 2024-05-20T14:26:07Z

Please merge this when available @ArthurZucker , for I don't have write access to the library

* first version * fix sliding window * fix style * add sliding window cache * fix style * address comments * fix test * fix style * move sliding window check inside cache init * revert changes on irrelevant files & add comment on SlidingWindowCache * address comments & fix style fix style * update causal mask * [run-slow] mistral * [run-slow] mistral * [run-slow] mistral * [run-slow] mistral * [run-slow] mistral * [run-slow] llama * [run-slow] mistral * [run-slow] mistral * [run-slow] mistral * revert CI from a10 to t4 * wrap up

zhenglongjiepheonix requested review from ArthurZucker and gante May 3, 2024 16:18

zhenglongjiepheonix force-pushed the longjie/add_torch_compile_for_mistral branch from 06fb2d6 to 57842ab Compare May 4, 2024 05:40

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[WIP][FIX] Fix Mixtral model #30658

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zhenglongjiepheonix force-pushed the longjie/add_torch_compile_for_mistral branch from 57842ab to 315becb Compare May 7, 2024 04:29

ArthurZucker mentioned this pull request May 8, 2024

Does the support for the Mistral Model seem inconsistent or incomplete with it official? #29533

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gante reviewed May 8, 2024

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gante mentioned this pull request May 8, 2024

tracker: generate compatibility with torch.compile #28981

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zhenglongjiepheonix force-pushed the longjie/add_torch_compile_for_mistral branch from 315becb to e0e9968 Compare May 9, 2024 04:03

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ArthurZucker mentioned this pull request May 10, 2024

Precision issues in Mistral rotary embeddings #29496

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zhenglongjiepheonix force-pushed the longjie/add_torch_compile_for_mistral branch from 2b7d873 to dd7ff33 Compare May 10, 2024 20:39

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ArthurZucker reviewed May 13, 2024

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zhenglongjiepheonix added 3 commits May 13, 2024 21:21

first version

dcac131

fix sliding window

9afc73b

fix style

3fa9285

zhenglongjiepheonix added 5 commits May 16, 2024 17:17

fix conflict

902fdbc

Merge remote-tracking branch 'upstream/main' into longjie/add_torch_c…

7e1537e

…ompile_for_mistral

Merge remote-tracking branch 'upstream/main' into longjie/add_torch_c…

c065bbe

…ompile_for_mistral

[run-slow] mistral

691b201

[run-slow] llama

6a9ec8c

zhenglongjiepheonix added 2 commits May 17, 2024 00:48

[run-slow] mistral

02cdb1d

[run-slow] mistral

9779556

[run-slow] mistral

b78e2a0

zhenglongjiepheonix commented May 16, 2024

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zhenglongjiepheonix added 2 commits May 17, 2024 17:49

fix conflict

c9de82d

revert CI from a10 to t4

93e14ba

wrap up

ee8f9fb

ArthurZucker reviewed May 20, 2024

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fix conflict

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ArthurZucker merged commit 616bb11 into huggingface:main May 20, 2024
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gante mentioned this pull request May 22, 2024

SlidingWindowCache: reduce differences to other Cache classes #30970

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SunMarc mentioned this pull request May 22, 2024

FIX / TST: Fix expected results on Mistral AWQ test #30971

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psinger mentioned this pull request Jun 4, 2024

FlashAttention2 issue with Mistral/Mixtral related to max length and RotaryEmbedding #31228

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fxmarty mentioned this pull request Jun 28, 2024

Fix mistral ONNX export #31696

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echarlaix mentioned this pull request Jul 2, 2024

Fix sentence transformers modeling patching for export huggingface/optimum#1936

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ArthurZucker mentioned this pull request Oct 11, 2024

Fix FA2 attention for models supporting sliding window #34093

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