intx weight only linear quantizer for mps (#1192)

Summary: Pull Request resolved: #1192

Differential Revision: D65079774

torchao/experimental/ops/mps/test/test_quantizer.py

@@ -0,0 +1,140 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+import copy
+import os
+import sys
+
+import torch
+import torchao_mps_ops
+import unittest
+
+from torchao.experimental.quant_api import UIntxWeightOnlyLinearQuantizer
+from torchao.experimental.quant_api import _quantize
+
+
+def parameterized(test_cases):
+    def decorator(func):
+        def wrapper(self):
+            for case in test_cases:
+                with self.subTest(case=case):
+                    func(self, *case)
+
+        return wrapper
+
+    return decorator
+
+
+class TestUIntxWeightOnlyLinearQuantizer(unittest.TestCase):
+    cases = [(nbit,) for nbit in range(1, 8)]
+
+    # Currently, the quantization code in quant_api.py only supports K values
+    # multiple of group_size.
+    # TODO(mcandales): Generalize the code in quant_api.py and add tests to
+    # cover values of K not multiple of group_size.
+    def _model_setup(self):
+        group_size = 32
+        k0 = 96
+        k1 = 224
+        k2 = 160
+        n = 47
+        layers = [
+            torch.nn.Linear(k0, k1, bias=False),
+            torch.nn.Linear(k1, k2, bias=False),
+            torch.nn.Linear(k2, n, bias=False),
+        ]
+        model = torch.nn.Sequential(*layers)
+        return model, group_size, k0, n
+
+    def _quantize_model(self, model, precision, nbit, group_size):
+        quantizer = UIntxWeightOnlyLinearQuantizer(
+            device="mps",
+            precision=precision,
+            bitwidth=nbit,
+            groupsize=group_size,
+        )
+        quantized_model = copy.deepcopy(model)
+        quantized_model = quantizer.quantize(quantized_model)
+        return quantized_model
+
+    @parameterized(cases)
+    def test_export(self, nbit):
+        model, group_size, k0, n = self._model_setup()
+        m = 3
+        activations = torch.randn(m, k0, dtype=torch.float32, device="mps")
+
+        quantized_model = self._quantize_model(model, torch.float32, nbit, group_size)
+        exported = torch.export.export(quantized_model, (activations,))
+
+        for node in exported.graph.nodes:
+            if node.op == "call_function":
+                self.assertTrue(
+                    str(node.target)
+                    == f"torchao._linear_fp_act_{nbit}bit_weight.default"
+                )
+
+    @parameterized(cases)
+    def test_2d_output_device_and_shape(self, nbit):
+        model, group_size, k0, n = self._model_setup()
+        m = 3
+        activations = torch.randn(m, k0, dtype=torch.float32, device="mps")
+
+        quantized_model = self._quantize_model(model, torch.float32, nbit, group_size)
+        result = quantized_model(activations)
+        self.assertTrue(result.is_mps)
+        self.assertTrue(result.shape == (m, n))
+
+    @parameterized(cases)
+    def test_3d_output_device_and_shape(self, nbit):
+        model, group_size, k0, n = self._model_setup()
+        leading_shape = (3, 5)
+        activations = torch.randn(
+            *leading_shape, k0, dtype=torch.float32, device="mps"
+        )
+
+        quantized_model = self._quantize_model(model, torch.float32, nbit, group_size)
+        result = quantized_model(activations)
+        self.assertTrue(result.is_mps)
+        self.assertTrue(result.shape == (*leading_shape, n))
+
+    # TODO(mcandales): Consolidate with the reference impl in test_lowbit.py
+    def _reference_linear_lowbit_quant_weights(self, A, W, group_size, S, Z):
+        N = W.shape[0]
+        K = W.shape[1]
+        W = W.to(torch.float32)
+        scales = S.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K]
+        zeros = Z.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K]
+        W = scales * W + zeros
+        return torch.mm(A, W.t())
+
+    @parameterized(cases)
+    def test_accuracy(self, nbit):
+        group_size = 32
+        m = 3
+        n = 7
+        k = 64
+        with torch.no_grad():
+            activations = torch.rand(m, k, dtype=torch.float32, device="mps")
+            model = torch.nn.Sequential(*[torch.nn.Linear(k, n, bias=False)])
+            quantized_model = self._quantize_model(model, torch.float32, nbit, group_size)
+            result = quantized_model(activations)
+
+            # Compute expected result
+            weight_cpu = model[0].weight.data
+            weight_qvals_cpu, weight_scales_cpu, weight_zeros_cpu = _quantize(
+                weight_cpu, group_size, nbit, True, torch.uint8
+            )
+            weight_scales_cpu = weight_scales_cpu.t()
+            weight_zeros_cpu = -weight_zeros_cpu.t() * weight_scales_cpu
+            expected = self._reference_linear_lowbit_quant_weights(activations.cpu(), weight_qvals_cpu, group_size, weight_scales_cpu, weight_zeros_cpu)
+
+            # Compare results
+            torch.testing.assert_close(result.cpu(), expected, rtol=0.001, atol=0.001)
+
+
+if __name__ == "__main__":
+    unittest.main()

torchao/experimental/quant_api.py

@@ -25,10 +25,16 @@
 logger.addHandler(handler)
 
 
-def _quantize(vals: torch.Tensor, group_size: int, nbit: int, has_weight_zeros: bool):
+def _quantize(vals: torch.Tensor, group_size: int, nbit: int, has_weight_zeros: bool, dtype=torch.int8):
     assert nbit >= 1 and nbit <= 8
-    qmin = -(1 << (nbit - 1))
-    qmax = (1 << (nbit - 1)) - 1
+    if dtype == torch.int8:
+        qmin = -(1 << (nbit - 1))
+        qmax = (1 << (nbit - 1)) - 1
+    elif dtype == torch.uint8:
+        qmin = 0
+        qmax = (1 << nbit) - 1
+    else:
+        raise ValueError(f"Unsupported dtype {dtype}")
 
     n, k = vals.shape
     vals = vals.reshape(-1, group_size)
@@ -51,7 +57,7 @@ def _quantize(vals: torch.Tensor, group_size: int, nbit: int, has_weight_zeros:
         zero_points=group_zeros,
         quant_min=qmin,
         quant_max=qmax,
-        dtype=torch.int8,
+        dtype=dtype,
         group_size=group_size,
     )
 
@@ -516,3 +522,113 @@ def apply(weight):
         )
 
     return _get_linear_subclass_inserter(apply)
+
+
+class UIntxWeightOnlyQuantizedLinear(nn.Module):
+    def __init__(
+        self,
+        pack_weight_op,
+        linear_op,
+    ):
+        super().__init__()
+        self._pack_weights_op = pack_weight_op
+        self._linear_op = linear_op
+
+    def quantize_and_pack_weights(self, weights, nbit, group_size):
+        self.nbit = nbit
+        self.group_size = group_size
+
+        weight_qvals, weight_scales, weight_zeros = _quantize(
+            weights, self.group_size, self.nbit, True, torch.uint8
+        )
+        weight_scales = torch.transpose_copy(weight_scales, 1, 0)
+        weight_zeros = torch.transpose_copy(weight_zeros, 1, 0)
+        self.weight_scales = weight_scales
+        self.weight_zeros = -weight_zeros * weight_scales
+
+        self.packed_weights = self._pack_weights_op(weight_qvals.cpu()).to(device="mps")
+
+    def forward(self, x):
+        assert x.dim() >= 2
+        if x.dim() == 2:
+            return self._linear_op(
+                x, self.packed_weights, self.group_size, self.weight_scales, self.weight_zeros
+            )
+
+        lead_shape = x.shape[0:-1]
+        k = x.shape[-1]
+        n = self.weight_scales.shape[1]
+        return self._linear_op(
+            x.reshape(-1, k), self.packed_weights, self.group_size, self.weight_scales, self.weight_zeros
+        ).reshape(*lead_shape, n)
+
+
+def _replace_linear_with_quantized_linear_mps(module: nn.Module, kwargs={}):
+    group_size = kwargs["group_size"]
+    nbit = kwargs["nbit"]
+
+    assert not isinstance(module, nn.Linear)
+    assert nbit >= 1 and nbit <= 7
+
+    for name, child in module.named_children():
+        if not isinstance(child, nn.Linear):
+            _replace_linear_with_quantized_linear_mps(child, kwargs)
+        else:
+            assert child.bias is None
+            qlinear = UIntxWeightOnlyQuantizedLinear(
+                pack_weight_op=getattr(torch.ops.torchao, f"_pack_weight_{nbit}bit"),
+                linear_op=getattr(
+                    torch.ops.torchao, f"_linear_fp_act_{nbit}bit_weight"
+                ),
+            )
+            setattr(module, name, qlinear)
+            getattr(module, name).quantize_and_pack_weights(
+                child.weight, nbit, group_size
+            )
+
+
+class UIntxWeightOnlyLinearQuantizer:
+    def __init__(
+        self,
+        device,
+        precision,
+        *,
+        bitwidth: Optional[int] = None,
+        groupsize: Optional[int] = None,
+    ):
+        if device != "mps":
+            raise NotImplementedError(
+                "Only device=mps is currently supported in UIntxWeightOnlyLinearQuantizer"
+            )
+        else:
+            self.device = device
+
+        if precision not in [torch.float32, torch.float16, torch.bfloat16]:
+            raise NotImplementedError(
+                "Only precisions float32, float16 & bfloat16 are currently supported in UIntxWeightOnlyLinearQuantizer"
+            )
+        else:
+            self.precision = precision
+
+        if bitwidth is None:
+            self.bitwidth = 4
+            logger.warning(f"bitwidth not specified, defaulting to {self.bitwidth}.")
+        else:
+            self.bitwidth = bitwidth
+
+        if groupsize is None:
+            self.groupsize = 128
+            logger.warning(f"groupsize not specified, defaulting to {self.groupsize}.")
+        else:
+            self.groupsize = groupsize
+
+    def quantize(self, model: nn.Module) -> nn.Module:
+        model = model.to(self.device).to(self.precision)
+        _replace_linear_with_quantized_linear_mps(
+            model,
+            kwargs={
+                "group_size": self.groupsize,
+                "nbit": self.bitwidth,
+            },
+        )
+        return model