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Refactor QAT to use common fake_quantize_affine primitive #527

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Jul 22, 2024
Merged

Refactor QAT to use common fake_quantize_affine primitive #527

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14 changes: 10 additions & 4 deletions test/quantization/test_qat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -350,13 +350,19 @@ def test_qat_generic_fake_quantize(self):

ao_input = copy.deepcopy(py_input)
ao_input.grad.data.zero_()
ao_s = copy.deepcopy(py_s).reshape(-1, 1)
ao_zp = copy.deepcopy(py_zp).reshape(-1, 1)
ao_out = _GenericFakeQuantize.apply(ao_input, ao_s, ao_zp, qmin, qmax)
block_size = (1, ao_input.shape[-1])
ao_s = copy.deepcopy(py_s)
ao_zp = copy.deepcopy(py_zp)
ao_out = _GenericFakeQuantize.apply(ao_input, ao_s, ao_zp, qmin, qmax, block_size)
ao_out.sum().backward()

torch.testing.assert_close(py_out, ao_out, atol=0, rtol=0)
torch.testing.assert_close(py_input.grad, ao_input.grad, atol=0, rtol=0)

# Test that gradients are close enough
num_grads = py_input.grad.numel()
num_equal_grads = torch.eq(py_input.grad, ao_input.grad).flatten().sum().item()
num_equal_grad_threshold = 0.8
self.assertGreaterEqual(num_equal_grads / num_grads, num_equal_grad_threshold)

def _assert_close_4w(self, val, ref):
# Note: for int4 weight-only quantization, we do not expect exact match
Expand Down
108 changes: 43 additions & 65 deletions torchao/quantization/prototype/qat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import Any, Optional, Tuple
from typing import Any, List, Optional, Tuple

import torch
import torch.nn.functional as F
Expand All @@ -25,7 +25,10 @@
ZeroPointDomain,
)
from torchao.quantization.unified import TwoStepQuantizer
from torchao.quantization.utils import get_group_qparams_symmetric
from torchao.quantization.utils import (
_get_per_token_block_size,
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@jerryzh168 jerryzh168 Jul 20, 2024 •
edited
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if it's helpful we could have a general util like:

def get_block_size(granularity, **kw_params) -> Callable:
    if granularity == Granularity.PER_BLOCK:
        ...
    elif type == Granularity.PER_TOKEN:
        ...
     ...


block_size = get_block_size(Granularity.PER_TOKEN)(x)

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Sounds good, let's do that separately

get_group_qparams_symmetric,
)


# =================
Expand Down Expand Up @@ -346,8 +349,14 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
scales, zero_points = get_groupwise_affine_qparams(
self.weight, n_bit, self.groupsize, self.scales_precision,
)
w_fq = _Int4WeightOnlyFakeQuantize.apply(
self.weight, scales, zero_points, qmin, qmax, self.groupsize,
w_fq = fake_quantize_per_channel_group(
self.weight,
scales,
zero_points,
qmin,
qmax,
self.groupsize,
ZeroPointDomain.FLOAT,
)
return F.linear(x, w_fq)

Expand All @@ -370,39 +379,6 @@ def disable_4w_fake_quant(mod: torch.nn.Module):
# | QUANT PRIMITIVES |
# ========================

class _Int4WeightOnlyFakeQuantize(torch.autograd.Function):
"""
Implementation of int4 grouped per channel weight-only fake quantize
intended to match the numerics of the efficient int4 tinygemm kernel.
"""

@staticmethod
def forward(ctx, input, scales, zero_points, quant_min, quant_max, groupsize):
assert groupsize > 1
assert input.shape[-1] % groupsize == 0
assert input.dim() == 2
n_bit = 4
block_size = (1, groupsize)
quant_min = 0
quant_max = 2 ** n_bit - 1
(fq, mask) = fake_quantize_affine_cachemask(
input,
block_size,
scales,
zero_points,
torch.int32,
quant_min,
quant_max,
zero_point_domain = ZeroPointDomain.FLOAT,
)
ctx.save_for_backward(mask)
return fq

@staticmethod
def backward(ctx, gy):
(mask,) = ctx.saved_tensors
return gy * mask, None, None, None, None, None

class _GenericFakeQuantize(torch.autograd.Function):
"""
Implementation of generic fake quantize with backward STE.
Expand All @@ -412,71 +388,73 @@ class _GenericFakeQuantize(torch.autograd.Function):
"""

@staticmethod
def forward(ctx, input, scales, zero_points, quant_min, quant_max):
def forward(
ctx: torch.autograd.function.FunctionCtx,
input: torch.Tensor,
scales: torch.Tensor,
zero_points: torch.Tensor,
quant_min: int,
quant_max: int,
block_size: List[int],
zero_point_domain: ZeroPointDomain=ZeroPointDomain.INT,
) -> torch.Tensor:
# Note: for bf16 inputs, casting them to fp32 has the unexpected
# side effect of reducing memory footprint significantly, presumably
# because bf16 * fp32 kernels are not as memory efficient
assert input.dtype == torch.float32
assert scales.dtype == torch.float32
assert zero_points.dtype == torch.int32
q = input.mul(1.0 / scales).round().add(zero_points)
dq = q.clamp(quant_min, quant_max).sub(zero_points).mul(scales)
mask = torch.logical_and((q >= quant_min), (q <= quant_max))

(fq, mask) = fake_quantize_affine_cachemask(
input,
block_size,
scales,
zero_points,
torch.int32,
quant_min,
quant_max,
zero_point_domain,
)

ctx.save_for_backward(mask)
return dq
return fq

@staticmethod
def backward(ctx, gy):
(mask,) = ctx.saved_tensors
return gy * mask, None, None, None, None, None

# TODO: move this to core
quantized_decomposed_lib.define(
"fake_quantize_per_channel_group(Tensor input, Tensor scales, Tensor zero_points, "
"int quant_min, int quant_max, int group_size) -> Tensor"
)
return gy * mask, None, None, None, None, None, None

@impl(quantized_decomposed_lib, "fake_quantize_per_channel_group", "CompositeImplicitAutograd")
def fake_quantize_per_channel_group(
input: torch.Tensor,
scales: torch.Tensor,
zero_points: torch.Tensor,
quant_min: int,
quant_max: int,
group_size: int,
zero_point_domain: ZeroPointDomain=ZeroPointDomain.INT,
) -> torch.Tensor:
assert group_size > 1
assert input.shape[-1] % group_size == 0
assert input.dim() == 2
grouped_input = input.reshape(-1, group_size).to(torch.float32)
scales = scales.reshape(-1, 1)
zero_points = zero_points.reshape(-1, 1)
fq = _GenericFakeQuantize.apply(
grouped_input, scales, zero_points, quant_min, quant_max,
block_size = (1, group_size)
return _GenericFakeQuantize.apply(
input, scales, zero_points, quant_min, quant_max, block_size, zero_point_domain,
)
return fq.reshape_as(input).to(input.dtype)

# TODO: move this to core
quantized_decomposed_lib.define(
"fake_quantize_per_token(Tensor input, Tensor scales, Tensor zero_points, "
"int quant_min, int quant_max) -> Tensor"
)

@impl(quantized_decomposed_lib, "fake_quantize_per_token", "CompositeImplicitAutograd")
def fake_quantize_per_token(
input: torch.Tensor,
scales: torch.Tensor,
zero_points: torch.Tensor,
quant_min: int,
quant_max: int,
) -> torch.Tensor:
# TODO: we won't need this import anymore once we move this to core
from torch.ao.quantization.fx._decomposed import _per_token_quant_qparam_dim_check

_per_token_quant_qparam_dim_check(input, scales, zero_points)
block_size = _get_per_token_block_size(input)
fq_input = input.to(torch.float32)
fq = _GenericFakeQuantize.apply(
fq_input, scales, zero_points, quant_min, quant_max,
fq_input, scales, zero_points, quant_min, quant_max, block_size,
)
return fq.reshape_as(input).to(input.dtype)

Expand Down
21 changes: 3 additions & 18 deletions torchao/quantization/quant_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,6 @@
TORCH_VERSION_AFTER_2_4,
unwrap_tensor_subclass,
)

from .subclass import (
QuantizedLinearWeightBase,
LinearActQuantizedTensor,
Expand All @@ -42,6 +41,7 @@
Int4WeightOnlyGPTQQuantizer,
Int4WeightOnlyQuantizer,
)
from .utils import _get_per_token_block_size
import logging
from .autoquant import autoquant, AutoQuantizableLinearWeight

Expand Down Expand Up @@ -343,19 +343,10 @@ def apply_int8_dynamic_activation_int4_weight_quant(weight):
quant_min = -8
quant_max = 7

# TODO: make a general helper function?
# input settings
def get_per_token_block_size(x):
block_size = []
for i in range(len(x.shape)-1):
block_size.append(1)
block_size.append(x.shape[-1])
return block_size

# input settings
input_mapping_type = MappingType.ASYMMETRIC
input_target_dtype = torch.int8
input_quant_func = lambda x: to_affine_quantized(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype)
input_quant_func = lambda x: to_affine_quantized(x, input_mapping_type, _get_per_token_block_size(x), input_target_dtype)

weight = to_affine_quantized(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps)
weight = to_linear_act_quantized(weight, input_quant_func)
Expand Down Expand Up @@ -441,18 +432,12 @@ def get_weight_block_size(x):
zero_point_dtype = torch.int64

# input settings
def get_per_token_block_size(x):
block_size = list(x.shape)
for i in range(len(block_size)-1):
block_size[i] = 1
return block_size

input_mapping_type = MappingType.SYMMETRIC
input_target_dtype = torch.int8
input_eps = 1e-5
input_quant_min = -127
input_quant_max = 127
input_quant_func = lambda x: to_affine_quantized(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype, eps=input_eps, quant_min=input_quant_min, quant_max=input_quant_max, scale_dtype=torch.float32 if x.dtype == torch.float16 else None)
input_quant_func = lambda x: to_affine_quantized(x, input_mapping_type, _get_per_token_block_size(x), input_target_dtype, eps=input_eps, quant_min=input_quant_min, quant_max=input_quant_max, scale_dtype=torch.float32 if x.dtype == torch.float16 else None)

block_size = get_weight_block_size(weight)
weight = to_affine_quantized(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
Expand Down
9 changes: 8 additions & 1 deletion torchao/quantization/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, Optional, Tuple
from typing import Dict, List, Optional, Tuple

import torch
from torch.utils._python_dispatch import TorchDispatchMode
Expand Down Expand Up @@ -475,3 +475,10 @@ def recommended_inductor_config_setter():
torch._inductor.config.fx_graph_cache = True
torch._inductor.config.triton.unique_kernel_names = True
torch.set_float32_matmul_precision("high")

def _get_per_token_block_size(x: torch.Tensor) -> List[int]:
block_size = []
for i in range(len(x.shape)-1):
block_size.append(1)
block_size.append(x.shape[-1])
return block_size
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