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hf_text-generation-inference/server/tests/utils/test_weights.py

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feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
import pytest
import torch
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
from text_generation_server.utils.weights import (
DefaultWeightsLoader,
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 01:37:39 -06:00
UnquantizedWeight,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
Weights,
WeightsLoader,
)
from text_generation_server.layers.gptq import GPTQWeight, GPTQWeightsLoader
from text_generation_server.layers.exl2 import Exl2Weight, Exl2WeightsLoader
Split up `layers.marlin` into several files (#2292) The marlin.py file was getting large, split it up.
2024-07-24 08:33:26 -06:00
from text_generation_server.layers.marlin.marlin import (
MarlinWeight,
MarlinWeightsLoader,
)
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
from types import SimpleNamespace
from typing import List, Optional, Dict, Union
from pathlib import Path
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
@pytest.fixture
def gptq_weights_loader():
return GPTQWeightsLoader(
bits=4,
groupsize=-1,
desc_act=False,
quant_method="gptq",
quantize="gptq",
sym=True,
)
@pytest.fixture
def gptq_weights_loader_awq():
return GPTQWeightsLoader(
bits=4,
groupsize=-1,
desc_act=False,
quant_method="awq",
quantize="awq",
sym=True,
)
@pytest.fixture
def marlin_weights_loader():
return MarlinWeightsLoader(bits=4, is_marlin_24=False)
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
dummy_file_system = {
"test_weights": {
"layer.0.weight": torch.tensor(
[
[1, 2],
[3, 4],
],
dtype=torch.float32,
),
},
"test_weights_2": {
"layer.1337.weight": torch.tensor(
[
[1, 2, 3, 4],
[5, 6, 7, 8],
],
dtype=torch.float32,
),
},
"test_get_weights_col_packed": {
"weight.weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
},
"test_get_multi_weights_col": {
"weight.weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
"weight.weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
},
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row": {
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
"weight.weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
},
"test_get_weights_col_gptq": {
"weight.qweight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
"weight.g_idx": torch.tensor([0, 1, 0, 1], dtype=torch.int32),
"weight.qzeros": torch.tensor(
[
[0, 1],
[1, 0],
],
dtype=torch.int32,
),
"weight.scales": torch.tensor(
[
[100.0, 100.0],
[100.0, 100.0],
],
dtype=torch.float16,
),
"gptq_bits": torch.tensor([8], dtype=torch.float32),
"gptq_groupsize": torch.tensor([2], dtype=torch.float32),
},
"test_get_weights_col_marlin": {
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
},
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_gptq": {
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
"weight.qweight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.g_idx": torch.tensor([0, 1, 0, 1], dtype=torch.int32),
"weight.qzeros": torch.tensor(
[
[0, 1],
[1, 0],
],
dtype=torch.int32,
),
"weight.scales": torch.tensor(
[
[100.0, 100.0],
[100.0, 100.0],
],
dtype=torch.float16,
),
"gptq_bits": torch.tensor([8], dtype=torch.float32),
"gptq_groupsize": torch.tensor([2], dtype=torch.float32),
},
"test_get_multi_weights_col_gptq": {
"weight.qweight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.g_idx": torch.tensor([0, 1, 0, 1], dtype=torch.int32),
"weight.qzeros": torch.tensor(
[
[0, 1],
[1, 0],
],
dtype=torch.int32,
),
"weight.scales": torch.tensor(
[
[100.0, 100.0],
[100.0, 100.0],
],
dtype=torch.float16,
),
"gptq_bits": torch.tensor([8], dtype=torch.float32),
"gptq_groupsize": torch.tensor([2], dtype=torch.float32),
},
"test_get_weights_col_packed_gptq": {
"weight.qweight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.g_idx": torch.tensor([0, 1, 0, 1], dtype=torch.int32),
"weight.qzeros": torch.tensor(
[
[0, 1],
[1, 0],
],
dtype=torch.int32,
),
"weight.scales": torch.tensor(
[
[100.0, 100.0],
[100.0, 100.0],
],
dtype=torch.float16,
),
"gptq_bits": torch.tensor([8], dtype=torch.float32),
"gptq_groupsize": torch.tensor([2], dtype=torch.float32),
},
"test_get_weights_col_packed_exl2": {
"weight.q_weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.q_scale": torch.tensor([8], dtype=torch.int32),
"weight.q_invperm": torch.tensor([1, 0, 3, 2], dtype=torch.int32),
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_exl2": {
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
"weight.q_weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.q_scale": torch.tensor([8], dtype=torch.int32),
"weight.q_invperm": torch.tensor([1, 0, 3, 2], dtype=torch.int32),
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
"test_get_multi_weights_col_exl2": {
"weight.q_weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.q_scale": torch.tensor([8], dtype=torch.int32),
"weight.q_invperm": torch.tensor([1, 0, 3, 2], dtype=torch.int32),
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
"test_get_weights_col_exl2": {
"weight.q_weight": torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.int32,
),
"weight.q_scale": torch.tensor([8], dtype=torch.int32),
"weight.q_invperm": torch.tensor([1, 0, 3, 2], dtype=torch.int32),
"weight.q_scale_max": torch.tensor([100], dtype=torch.float16),
"weight.q_groups": torch.tensor([4], dtype=torch.int16),
},
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_marlin": {
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5], [0.25]], dtype=torch.float16),
},
"test_get_multi_weights_col_marlin": {
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5], [0.25]], dtype=torch.float16),
},
"test_get_weights_col_packed_marlin": {
"weight.B": torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
"weight.s": torch.tensor([[0.5], [0.25]], dtype=torch.float16),
},
}
class MockSlice:
def __init__(self, tensor):
self.tensor = tensor
def get_shape(self):
return self.tensor.shape
def __getitem__(self, idx):
return self.tensor[idx]
def mock_get_slice(tensor_name, filename):
tensor = dummy_file_system[filename][tensor_name]
return MockSlice(tensor)
def mock_handle(filename, device, dtype):
return SimpleNamespace(
get_slice=lambda tensor_name: mock_get_slice(tensor_name, filename)
)
class MockSafeOpen:
def __init__(self, filename, framework, dummy_fs):
self.filename = filename
self.framework = framework
self.dummy_fs = dummy_fs
def keys(self):
return list(self.dummy_fs[self.filename].keys())
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
pass
class MockWeights(Weights):
def __init__(
self,
filenames: List[Union[Path, str]],
device,
dtype,
process_group,
dummy_fs,
aliases: Optional[Dict[str, List[str]]] = None,
prefix: Optional[str] = None,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader: Optional[WeightsLoader] = None,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
):
routing = {}
self.dummy_fs = dummy_fs
for filename in filenames:
with MockSafeOpen(filename, framework="pytorch", dummy_fs=dummy_fs) as f:
for k in f.keys():
if k in routing:
raise RuntimeError(
f"Key {k} was found in multiple files: {filename} and {routing[k]}"
)
routing[k] = filename
if aliases is None:
aliases = {}
self.aliases = aliases
self.routing = routing
self.device = device
self.dtype = dtype
self.process_group = process_group
self.prefix = prefix
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
self.weights_loader = (
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 01:37:39 -06:00
# We don't need to get linear layers, so just wrap raw tensors.
DefaultWeightsLoader(lambda x: x)
if weights_loader is None
else weights_loader
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
)
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
self._handles = {}
def _get_handle(self, filename: Union[Path, str]):
if filename in self._handles:
return self._handles[filename]
else:
handle = mock_handle(filename, self.device, self.dtype)
self._handles[filename] = handle
return handle
def get_shape(self, tensor_name: str):
filename, _ = self.get_filename(tensor_name)
handle = self._get_handle(filename)
return handle.get_slice(tensor_name).get_shape()
def get_tensor(self, tensor_name: str):
filename, _ = self.get_filename(tensor_name)
handle = self._get_handle(filename)
return handle.get_slice(tensor_name).tensor
dummy_process_group = SimpleNamespace(rank=lambda: 0, size=lambda: 1)
def test_weights():
weights = MockWeights(
[
"test_weights",
"test_weights_2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
assert weights.get_shape("layer.0.weight") == (2, 2)
assert weights.get_tensor("layer.1337.weight").shape == (2, 4)
def test_get_tensor():
weights = MockWeights(
[
"test_weights",
"test_weights_2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
assert torch.allclose(
weights.get_tensor("layer.0.weight"),
torch.tensor(
[
[1, 2],
[3, 4],
],
dtype=torch.float32,
),
)
assert torch.allclose(
weights.get_tensor("layer.1337.weight"),
torch.tensor(
[
[1, 2, 3, 4],
[5, 6, 7, 8],
],
dtype=torch.float32,
),
)
def test_get_weights_col_packed():
weights = MockWeights(
[
"test_get_weights_col_packed",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
prefix = "weight"
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
block_sizes=block_sizes,
)
assert torch.allclose(
w,
torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
)
def test_get_weights_col_packed_block_size():
weights = MockWeights(
[
"test_get_weights_col_packed",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
prefix = "weight"
block_sizes = 2
w = weights.get_weights_col_packed(
prefix=prefix,
block_sizes=block_sizes,
)
assert torch.allclose(
w,
torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
)
def test_get_weights_col_packed_block_size_arr():
weights = MockWeights(
[
"test_get_weights_col_packed",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
prefix = "weight"
block_sizes = [1, 1]
w = weights.get_weights_col_packed(
prefix=prefix,
block_sizes=block_sizes,
)
assert torch.allclose(
w,
torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
)
def test_get_multi_weights_col():
weights = MockWeights(
[
"test_get_multi_weights_col",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
prefixes = ["weight", "weight"]
w = weights.get_multi_weights_col(
prefixes=prefixes,
dim=0,
)
assert torch.allclose(
w,
torch.tensor(
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[1, 2],
[3, 4],
[5, 6],
[7, 8],
],
dtype=torch.float32,
),
)
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_row():
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row",
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
)
prefix = "weight"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
w = weights.get_weights_row(
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
prefix=prefix,
)
assert torch.allclose(
w,
torch.tensor(
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]],
dtype=torch.float32,
),
)
# test_get_weights_col
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_awq(gptq_weights_loader_awq):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader_awq,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_weights_col(
prefix=prefix,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]]),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor(
[[100.0, 100.0], [100.0, 100.0]],
dtype=torch.float16,
),
g_idx=None,
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=True,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert w.g_idx == expected_weight.g_idx, "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_gtpq(gptq_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_weights_col(
prefix=prefix,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]]),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=torch.tensor([0, 1, 0, 1], dtype=torch.int32),
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=False,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert torch.allclose(w.g_idx, expected_weight.g_idx), "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_weights_col_exl2():
weights = MockWeights(
[
"test_get_weights_col_exl2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=Exl2WeightsLoader(),
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_weights_col(
prefix=prefix,
)
scaled_scale_max = 0.3906 * 256
expected_weight = Exl2Weight(
q_weight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
q_scale=torch.tensor([8], dtype=torch.int32),
q_invperm=torch.tensor([1, 0, 3, 2], dtype=torch.int16),
q_scale_max=torch.tensor([scaled_scale_max], dtype=torch.float16),
q_groups=torch.tensor([4], dtype=torch.int16),
)
assert torch.allclose(w.q_weight, expected_weight.q_weight), "q_weight mismatch"
assert torch.allclose(w.q_scale, expected_weight.q_scale), "q_scale mismatch"
assert torch.allclose(w.q_invperm, expected_weight.q_invperm), "q_invperm mismatch"
assert torch.allclose(
w.q_scale_max, expected_weight.q_scale_max
), "q_scale_max mismatch"
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_marlin(marlin_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_marlin",
],
device="cpu",
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=marlin_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_weights_col(
prefix=prefix,
)
expected_weight = MarlinWeight(
B=torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
s=torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
)
assert torch.allclose(w.B, expected_weight.B), "B mismatch"
assert torch.allclose(w.s, expected_weight.s), "s mismatch"
# test_get_weights_col_packed
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_packed_awq(gptq_weights_loader_awq):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_packed_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader_awq,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
block_sizes=block_sizes,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=None,
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=True,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert w.g_idx == expected_weight.g_idx, "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
@pytest.mark.skip(reason="Review expected functionality")
def test_get_weights_col_packed_exl2():
weights = MockWeights(
[
"test_get_weights_col_packed_exl2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=Exl2WeightsLoader(),
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
block_sizes = 1
w = weights.get_weights_col_packed(
prefix=prefix,
block_sizes=block_sizes,
)
scaled_scale_max = 0.3906 * 256
expected_weight = Exl2Weight(
q_weight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
q_scale=torch.tensor([8], dtype=torch.int32),
q_invperm=torch.tensor([1], dtype=torch.int16),
q_scale_max=torch.tensor([scaled_scale_max], dtype=torch.float16),
q_groups=torch.tensor([4], dtype=torch.int16),
)
assert torch.allclose(w.q_weight, expected_weight.q_weight), "q_weight mismatch"
assert torch.allclose(w.q_scale, expected_weight.q_scale), "q_scale mismatch"
assert torch.allclose(w.q_invperm, expected_weight.q_invperm), "q_invperm mismatch"
assert torch.allclose(
w.q_scale_max, expected_weight.q_scale_max
), "q_scale_max mismatch"
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_packed_gptq(gptq_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_packed_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefixes = ["weight"]
w = weights.get_multi_weights_col(
prefixes=prefixes,
dim=0,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=torch.tensor([0, 1, 0, 1], dtype=torch.int32),
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=False,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert torch.allclose(w.g_idx, expected_weight.g_idx), "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_col_packed_marlin(marlin_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_weights_col_packed_marlin",
],
device="cpu",
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=marlin_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_multi_weights_col(
prefixes=[prefix],
dim=0,
)
expected_weight = MarlinWeight(
B=torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
s=torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
)
print(expected_weight)
assert torch.allclose(w.B, expected_weight.B), "B mismatch"
assert torch.allclose(w.s, expected_weight.s), "s mismatch"
# test_get_multi_weights_col
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_multi_weights_col_awq(gptq_weights_loader_awq):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_multi_weights_col_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader_awq,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefixes = ["weight"]
w = weights.get_multi_weights_col(
prefixes=prefixes,
dim=0,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=None,
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=True,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert w.g_idx == expected_weight.g_idx, "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
def test_get_multi_weights_col_exl2():
weights = MockWeights(
[
"test_get_multi_weights_col_exl2",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=Exl2WeightsLoader(),
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
try:
w = weights.get_multi_weights_col(
prefixes=[prefix],
dim=0,
)
except ValueError as e:
assert e.args[0] == "get_multi_weights_col is not supported for exl2"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_multi_weights_col_gptq(gptq_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_multi_weights_col_gptq",
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefixes = ["weight"]
w = weights.get_multi_weights_col(
prefixes=prefixes,
dim=0,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=torch.tensor([0, 1, 0, 1], dtype=torch.int32),
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=False,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert torch.allclose(w.g_idx, expected_weight.g_idx), "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_multi_weights_col_marlin(marlin_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
"test_get_multi_weights_col_marlin",
],
device="cpu",
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=marlin_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
w = weights.get_multi_weights_col(
prefixes=[prefix],
dim=0,
)
expected_weight = MarlinWeight(
B=torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
s=torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
)
assert torch.allclose(w.B, expected_weight.B), "B mismatch"
assert torch.allclose(w.s, expected_weight.s), "s mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
# test_get_weights_row
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_row_awq(gptq_weights_loader_awq):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_gptq",
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader_awq,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
w = weights.get_weights_row(
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
prefix=prefix,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=None,
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=True,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert w.g_idx == expected_weight.g_idx, "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_row_exl2():
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_exl2",
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=Exl2WeightsLoader(),
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
w = weights.get_weights_row(
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
prefix=prefix,
)
print(w)
scaled_scale_max = 0.3906 * 256
expected_weight = Exl2Weight(
q_weight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
q_scale=torch.tensor([8], dtype=torch.int32),
q_invperm=torch.tensor([1, 0, 3, 2], dtype=torch.int16),
q_scale_max=torch.tensor([scaled_scale_max], dtype=torch.float16),
q_groups=torch.tensor([4], dtype=torch.int16),
)
assert torch.allclose(w.q_weight, expected_weight.q_weight), "q_weight mismatch"
assert torch.allclose(w.q_scale, expected_weight.q_scale), "q_scale mismatch"
assert torch.allclose(w.q_invperm, expected_weight.q_invperm), "q_invperm mismatch"
assert torch.allclose(
w.q_scale_max, expected_weight.q_scale_max
), "q_scale_max mismatch"
assert torch.allclose(w.q_groups, expected_weight.q_groups), "q_groups mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_row_gptq(gptq_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_gptq",
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
],
device="cpu",
dtype=torch.float32,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=gptq_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
w = weights.get_weights_row(
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
prefix=prefix,
)
expected_weight = GPTQWeight(
qweight=torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=torch.int32),
qzeros=torch.tensor([[0, 1], [1, 0]], dtype=torch.int32),
scales=torch.tensor([[100.0, 100.0], [100.0, 100.0]], dtype=torch.float16),
g_idx=torch.tensor([0, 1, 0, 1], dtype=torch.int32),
bits=8.0,
groupsize=2.0,
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 01:37:39 -06:00
use_awq_kernel=False,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
use_exllama=False,
)
assert torch.allclose(w.qweight, expected_weight.qweight), "qweight mismatch"
assert torch.allclose(w.qzeros, expected_weight.qzeros), "qzeros mismatch"
assert torch.allclose(w.scales, expected_weight.scales), "scales mismatch"
assert torch.allclose(w.g_idx, expected_weight.g_idx), "g_idx mismatch"
assert w.bits == expected_weight.bits, "bits mismatch"
assert w.groupsize == expected_weight.groupsize, "groupsize mismatch"
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 01:37:39 -06:00
assert w.use_awq_kernel == expected_weight.use_awq_kernel, "use_awq_kernel mismatch"
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
assert w.use_exllama == expected_weight.use_exllama, "use_exllama mismatch"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
def test_get_weights_row_marlin(marlin_weights_loader):
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
weights = MockWeights(
[
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
"test_get_weights_row_marlin",
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
],
device="cpu",
dtype=torch.float16,
process_group=dummy_process_group,
dummy_fs=dummy_file_system,
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
weights_loader=marlin_weights_loader,
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
)
prefix = "weight"
Move quantized weight handling out of the `Weights` class (#2194) Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
2024-07-09 12:04:03 -06:00
w = weights.get_weights_row(
feat: add simple tests for weights (#2092) * feat: add simple tests for weights * fix: adjust types and add tests * fix: adjust so all tests pass * feat: improve weight tests * fix: add missing tests and renames * fix: tweak shapes
2024-06-25 04:22:59 -06:00
prefix=prefix,
)
expected_weight = MarlinWeight(
B=torch.tensor([[1, 2], [3, 4]], dtype=torch.int32),
s=torch.tensor([[0.5000], [0.2500]], dtype=torch.float16),
)
assert torch.allclose(w.B, expected_weight.B), "B mismatch"
assert torch.allclose(w.s, expected_weight.s), "s mismatch"