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Include output embedding as well with include_embedding flag #37935

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Merged
MekkCyber merged 6 commits into from
May 16, 2025
Merged

Include output embedding as well with include_embedding flag #37935

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bbf88b5
Include output embedding as well with `include_embedding` flag
jerryzh168 May 2, 2025
b66730f
format
jerryzh168 May 2, 2025
927ef1f
rename include_embedding to include_input_output_embeddings
jerryzh168 May 7, 2025
2299706
Merge branch 'main' into quantize-lm-head
jerryzh168 May 7, 2025
cf64410
Merge branch 'main' into quantize-lm-head
jerryzh168 May 13, 2025
040e2e0
Merge branch 'main' into quantize-lm-head
MekkCyber May 16, 2025
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6 changes: 5 additions & 1 deletion src/transformers/quantizers/quantizer_torchao.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,11 @@ def _process_model_before_weight_loading(
if self.quantization_config.include_embedding:
input_emb = model.get_input_embeddings()
input_emb_names = [name for name, module in model.named_modules() if id(module) == id(input_emb)]
self.modules_to_not_convert = [x for x in self.modules_to_not_convert if x not in input_emb_names]
output_emb = model.get_output_embeddings()
output_emb_names = [name for name, module in model.named_modules() if id(module) == id(output_emb)]
self.modules_to_not_convert = [
x for x in self.modules_to_not_convert if x not in input_emb_names + output_emb_names
]
Comment on lines +191 to +195
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@MekkCyber MekkCyber May 5, 2025

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I'm not sure if it's a good idea to quantize the lm_head when the flag include_embedding is set 🤔 , it's a bit misleading

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@jerryzh168 jerryzh168 May 5, 2025

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lm_head is the output embedding right?
e.g. https://github.com/vllm-project/vllm/blob/aea302be6c3c323207502a973fe341c3bcf7288f/vllm/model_executor/models/llama.py#L457

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@jerryzh168 jerryzh168 May 5, 2025

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also:

transformers/src/transformers/models/llama/modeling_llama.py

Lines 736 to 743 in 46c0e1f

def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head

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@MekkCyber MekkCyber May 6, 2025

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Yes it is, but it's still a nn.Linear not a nn.Embedding

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@SunMarc SunMarc May 6, 2025

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About embeddings and lm_head, there are some edge cases we need to be aware of.
If they are tied:

  1. if we quantize the embeddings, the lm-head will also be quantized unless we break the tied weights. This will lead to reduce memory consumption but quality will be reduced.
  2. if we decide to remove the tied weights and quantize the embeddings / keep the lm_head as is, the memory consumption will increase (due to the lm-head) but maybe we have latency improvement ?. Maybe you also want to quantize the lm-head differently ?

Do we have a specific use case for 2) as I think this is what you wanted to do @jerryzh168 ?

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@jerryzh168 jerryzh168 May 6, 2025 •
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yeah we have a use case in ExecuTorch, where we quantize both input embedding and lm_head, and we quantize them differently, the way we are doing it right now is:

(1) manually break ties
(2) quantize the input embedding and lm_head separately

see details in https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w#quantization-recipe

quant_config = AOPerModuleConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True, untie_embedding_weights=True, modules_to_not_convert=[])

right now we need to set modules_to_not_convert and this PR will allow use to remove modules_to_not_convert

Also I feel we might be able to remove the untie_embedding_weights flag now since we have an alternative solution.

Please also take a look our solution for manually untying the weights, it might be useful to have some API for it as well

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@jerryzh168 jerryzh168 May 6, 2025

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@MekkCyber how about changing the name to include_input_output_embeddings to be more specific on what we are referring to?

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@MekkCyber MekkCyber May 7, 2025

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Yes, I think it’s fine as long as the user is aware that they’re quantizing the lm_head.

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@jerryzh168 jerryzh168 May 7, 2025

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makes sense, just updated

return

def check_quantized_param(
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5 changes: 4 additions & 1 deletion tests/quantization/torchao_integration/test_torchao.py
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Expand Up @@ -210,7 +210,9 @@ def test_include_embedding(self):
granularity=granularity,
mapping_type=mapping_type,
)
config = AOPerModuleConfig({"_default": None, "model.embed_tokens": embedding_config})
config = AOPerModuleConfig(
{"_default": None, "model.embed_tokens": embedding_config, "lm_head": embedding_config}
)
# need set `include_embedding` to True
quant_config = TorchAoConfig(quant_type=config, include_embedding=True)
quantized_model = AutoModelForCausalLM.from_pretrained(
Expand All @@ -220,6 +222,7 @@ def test_include_embedding(self):
)
# making sure embedding is quantized
self.assertTrue(isinstance(quantized_model.model.embed_tokens.weight, AffineQuantizedTensor))
self.assertTrue(isinstance(quantized_model.lm_head.weight, AffineQuantizedTensor))
tokenizer = AutoTokenizer.from_pretrained(self.model_name)

input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)
Expand Down