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Add configs to run int4 inference #37

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Add configs to run int4 inference #37

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572e644
Add configs to run int4 inference
Nov 18, 2022
132d99d
fix quantization-bit config & turn off ds_sample
Nov 18, 2022
99cd7c9
change the quantization config format to work with the new style at D…
Nov 19, 2022
32779e8
Update bloom-ds-inference.py
awan-10 Nov 21, 2022
b472e48
Merge pull request #1 from awan-10/patch-1
RezaYazdaniAminabadi Nov 21, 2022
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7 changes: 4 additions & 3 deletions bloom-inference-scripts/bloom-ds-inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@
parser = ArgumentParser()

parser.add_argument("--name", required=True, type=str, help="model_name")
parser.add_argument("--dtype", type=str, help="float16 or int8", choices=["int8", "float16"], default="float16")
parser.add_argument("--dtype", type=str, help="float16 or int8 or int4", choices=["int8", "float16", "int4"], default="float16")
parser.add_argument("--local_rank", required=False, type=int, help="used by dist launchers")
parser.add_argument("--batch_size", default=1, type=int, help="batch size")
parser.add_argument("--benchmark", action="store_true", help="additionally run benchmark")
Expand Down Expand Up @@ -100,7 +100,7 @@ def get_checkpoint_files(model_name_or_path):


model_name = args.name
infer_dtype = args.dtype
infer_dtype = args.dtype if args.dtype != 'int4' else 'int8'
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@stas00 stas00 Nov 18, 2022 •
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would it make for a more user-friendly API to

  1. keep the dtype intact
  2. drop quantization_bits
  3. let deepspeed.init_inference derive the number of bits from dtype?

not only the currently suggested override is confusing, I fail to see what purpose serves carrying the same information in dtype and and quantization_bits twice

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oh, wait, torch.init4 still doesn't exist, does it?

let's find the feature request.

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still not implemented pytorch/pytorch#74627

so that's why you had to do the odd workarounds, right?

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I guess we can drop it once its implemented @stas00 ?
For now, this might be the best way to do it.

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see #37 (comment)

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it's pointless to wait, since they won't have int3 and int12

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would it make for a more user-friendly API to

  1. keep the dtype intact
  2. drop quantization_bits
  3. let deepspeed.init_inference derive the number of bits from dtype?

not only the currently suggested override is confusing, I fail to see what purpose serves carrying the same information in dtype and and quantization_bits twice

@stas00 and @RezaYazdaniAminabadi - just clarifying that we have introduced a new DeepSpeedInferenceConfig that can be passed to init_inference. We are keeping it backwards compatible but if we are okay to make changes to this file, I would advocate for writing a config dictionary for DeepSpeed and pass that to init_inference instead of the various kwargs. Please see here for an example: https://gist.github.com/awan-10/6e3d5c756be3a876522e860c6bbf702d#file-bloom-ds-inference-py-L173

Also, see the docs for the new config: https://deepspeed.readthedocs.io/en/latest/inference-init.html

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That definitely works.

@awan-10, may I suggest you make the inference config accept dict_or_path just like zero does? it might be for some users easier to write out a separate file.

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@stas00 - thanks for the suggestion. Created an issue so we can track it: deepspeedai/DeepSpeed#2532. Mike and I will work on it.

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Thank you very much, @awan-10


tp_presharded_mode = True if model_name in tp_presharded_models else False

Expand Down Expand Up @@ -191,6 +191,7 @@ def write_checkponts_json():
mp_size=world_size,
base_dir=repo_root,
dtype=getattr(torch, infer_dtype),
quantization_bits=8 if args.dtype == 'int8' else 4,
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what happens with --dtype float16?

probably best to set this in kwargs only if quantization dtype is provided

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The quabtization-bit should not be used when running in half-precision. But, I agree we can do it in the kwargs and only for qunatized inference mode.

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@stas00 stas00 Nov 18, 2022 •
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these demos are already used by many users so let's make those nice and clean configuration-wise, so it's clear to the reader when what bits should be enabled.

checkpoint=checkpoints_json,
**kwargs,
)
Expand Down Expand Up @@ -227,7 +228,7 @@ def write_checkponts_json():
# dynamically extend to support larger bs by repetition
input_sentences *= math.ceil(args.batch_size / len(input_sentences))

generate_kwargs = dict(max_new_tokens=num_tokens, do_sample=False)
generate_kwargs = dict(max_new_tokens=num_tokens, do_sample=True)
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this is already a very different type of change.

If int4 requires do_sample=True, then again, let's change it only if it's --dtype int4

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Sure, I will double check the do_sample=False again to see if the text generated makes sense. If not, I set it to true for int4

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I just checked with do_sample=False and I see the text is produced in the same way as for FP16 and INT8:

in=DeepSpeed is a machine learning framework
out=DeepSpeed is a machine learning framework for deep learning. It is a Python library, and it is also a framework. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and it is a library. It is a framework, and

So, I am gonna turn it off for now.



print_rank0(f"Generate args {generate_kwargs}")
Expand Down