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Enabling MOE Quantization using linear decomposition [WIP] #2043

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255 changes: 255 additions & 0 deletions test/quantization/test_moe_quant.py
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import torch
import unittest
from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import MOEFeedForwardAOQuantizable
from torchao.quantization.prototype.moe_quant.utils import MoEQuantConfig, cond_ffn_filter, FakeExtraDimTensor
from torchao.quantization.quant_api import (
Int8WeightOnlyConfig,
Int8DynamicActivationInt8WeightConfig,
Int4WeightOnlyConfig,
Float8WeightOnlyConfig,
Int8DynamicActivationIntxWeightConfig,
PackedLinearInt8DynamicActivationIntxWeightLayout,
quantize_,
AffineQuantizedTensor,
LinearActivationQuantizedTensor,
Float8DynamicActivationFloat8WeightConfig,
)
from torchao.quantization.utils import compute_error
from torchao.dtypes.uintx.tensor_core_tiled_layout import TensorCoreTiledAQTTensorImpl
from torchao.dtypes.uintx.plain_layout import PlainAQTTensorImpl
from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl
from parameterized import param, parameterized

class TestMoEQuantCompile(unittest.TestCase):
DEFAULT_PARAMS = (512, 256, 8, 2) # hidden_dim, expert_dim, num_experts, top_k

@torch.no_grad()
def _test_impl_moe_quant(self,
config,
num_tokens=1,
model_params=None,
base_class=AffineQuantizedTensor,
tensor_impl_class=None,
dtype=torch.bfloat16,
device="cuda",
fullgraph=False
):
"""
Tests moe quant for techniques using fake extra dim
"""
if model_params is None:
model_params=self.DEFAULT_PARAMS

input_shape = (num_tokens, model_params[0])
model = MOEFeedForwardAOQuantizable(*model_params).to(dtype).to(device)
input = torch.randn(input_shape, dtype=torch.bfloat16, device=device)

out = model(input)

import copy
new_mod = copy.deepcopy(model)

quantize_(model, config, cond_ffn_filter)

if isinstance(config, MoEQuantConfig):
self.assertIsInstance(model.experts.w1, FakeExtraDimTensor)
if base_class is not None:
self.assertIsInstance(model.experts.w1.head_tensor, base_class)
if tensor_impl_class is not None:
self.assertIsInstance(model.experts.w1.head_tensor.tensor_impl, tensor_impl_class)
else:
if base_class is not None:
self.assertIsInstance(model.experts.w1, base_class)
if tensor_impl_class is not None:
self.assertIsInstance(model.experts.w1.tensor_impl, tensor_impl_class)

out_q = model(input)

torch._dynamo.config.capture_scalar_outputs = True
torch._dynamo.config.capture_dynamic_output_shape_ops = True
model_c = torch.compile(model, mode="reduce-overhead", fullgraph=fullgraph)

model_c(input)
model_c(input)
out_qc = model_c(input).clone()

for i in range(10):
input = torch.randn(input_shape, dtype=torch.bfloat16, device=device)
model_c(input)

self.assertGreaterEqual(compute_error(out_q, out), 10)
self.assertGreaterEqual(compute_error(out_qc, out), 10)
print(compute_error(out_q, out), compute_error(out_qc, out))


@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, False),
("multiple_tokens", 8, False),
])
def test_int4wo_fake_dim(self, name, num_tokens, fullgraph):
config = MoEQuantConfig(Int4WeightOnlyConfig())
tensor_impl_class = TensorCoreTiledAQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, True),
("multiple_tokens", 8, False),
])
def test_int4wo_base(self, name, num_tokens, fullgraph):
config = Int4WeightOnlyConfig()
tensor_impl_class = TensorCoreTiledAQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, False),
("multiple_tokens", 8, False),
])
def test_int8wo_fake_dim(self, name, num_tokens, fullgraph):
config = MoEQuantConfig(Int8WeightOnlyConfig())
tensor_impl_class = PlainAQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, True),
("multiple_tokens", 8, False),
])
def test_int8wo_base(self, name, num_tokens, fullgraph):
config = Int8WeightOnlyConfig()
tensor_impl_class = PlainAQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("multiple_tokens", 32, False),
])
def test_int8dq_fake_dim(self, name, num_tokens, fullgraph):
config = MoEQuantConfig(Int8DynamicActivationInt8WeightConfig())
base_class = LinearActivationQuantizedTensor

self._test_impl_moe_quant(
model_params=(512, 256, 2, 2),
config=config,
num_tokens=num_tokens,
base_class=base_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("multiple_tokens", 32, False),
])
def test_int8dq_base(self, name, num_tokens, fullgraph):
config = Int8DynamicActivationInt8WeightConfig()
base_class = LinearActivationQuantizedTensor

self._test_impl_moe_quant(
model_params=(512, 256, 2, 2),
config=config,
num_tokens=num_tokens,
base_class=base_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, False),
("multiple_tokens", 8, False),

])
def test_fp8wo_fake_dim(self, name, num_tokens, fullgraph):
config = MoEQuantConfig(Float8WeightOnlyConfig())
tensor_impl_class = Float8AQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, True),
("multiple_tokens", 8, False),
])
def test_fp8wo_base(self, name, num_tokens, fullgraph):
config = Float8WeightOnlyConfig()
tensor_impl_class = Float8AQTTensorImpl

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
tensor_impl_class=tensor_impl_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, False),
("multiple_tokens", 8, False),

])
def test_fp8dq_fake_dim(self, name, num_tokens, fullgraph):
config = MoEQuantConfig(Float8DynamicActivationFloat8WeightConfig())
base_class = LinearActivationQuantizedTensor

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
base_class=base_class,
fullgraph=fullgraph
)

@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
@parameterized.expand([
("single_token", 1, True),
("multiple_tokens", 8, False),
])
def test_fp8dq_base(self, name, num_tokens, fullgraph):
config = Float8DynamicActivationFloat8WeightConfig()
base_class = LinearActivationQuantizedTensor

self._test_impl_moe_quant(
config=config,
num_tokens=num_tokens,
base_class=base_class,
fullgraph=fullgraph
)








if __name__ == "__main__":
unittest.main()
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