Improve the handling of quantized weights (#2250)
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
2024-07-19 07:37:39 +00:00
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import pytest
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@pytest.fixture(scope="module")
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def flash_llama_marlin24_handle(launcher):
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with launcher(
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"nm-testing/Llama-2-7b-pruned2.4-Marlin_24", quantize="marlin"
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) as handle:
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yield handle
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@pytest.fixture(scope="module")
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async def flash_llama_marlin(flash_llama_marlin24_handle):
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await flash_llama_marlin24_handle.health(300)
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return flash_llama_marlin24_handle.client
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2025-11-18 17:29:21 +00:00
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@pytest.mark.skip(reason="Issue with the model access")
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Improve the handling of quantized weights (#2250)
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
2024-07-19 07:37:39 +00:00
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@pytest.mark.release
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_marlin(flash_llama_marlin, response_snapshot):
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response = await flash_llama_marlin.generate(
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"Test request", max_new_tokens=10, decoder_input_details=True
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)
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assert response.details.generated_tokens == 10
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assert response == response_snapshot
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2025-11-18 17:29:21 +00:00
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@pytest.mark.skip(reason="Issue with the model access")
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Improve the handling of quantized weights (#2250)
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
2024-07-19 07:37:39 +00:00
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@pytest.mark.release
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_marlin24_all_params(flash_llama_marlin, response_snapshot):
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response = await flash_llama_marlin.generate(
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"Test request",
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max_new_tokens=10,
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repetition_penalty=1.2,
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return_full_text=True,
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temperature=0.5,
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top_p=0.9,
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top_k=10,
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truncate=5,
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typical_p=0.9,
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watermark=True,
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decoder_input_details=True,
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seed=0,
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)
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assert response.details.generated_tokens == 10
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assert response == response_snapshot
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2025-11-18 17:29:21 +00:00
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@pytest.mark.skip(reason="Issue with the model access")
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Improve the handling of quantized weights (#2250)
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
2024-07-19 07:37:39 +00:00
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@pytest.mark.release
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_llama_marlin24_load(
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flash_llama_marlin, generate_load, response_snapshot
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):
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responses = await generate_load(
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flash_llama_marlin, "Test request", max_new_tokens=10, n=4
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)
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assert len(responses) == 4
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assert all([r.generated_text == responses[0].generated_text for r in responses])
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assert responses == response_snapshot
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