intx weight only linear quantizer for mps (pytorch#1192)

Summary: Pull Request resolved: pytorch#1192

Differential Revision: D65079774

torchao/experimental/ops/mps/register.mm

@@ -17,54 +17,66 @@
 
 // LowBit Quantized Linear on MPS Backend
 template <int nbit>
-Tensor linear_mps_kernel(
+void check_linear_mps_args(
     const Tensor& A,
     const Tensor& B,
     int64_t group_size,
     const Tensor& SZ) {
-  auto M = A.size(0);
   auto N = B.size(0);
   auto K = A.size(1);
 
-  TORCH_CHECK(
-      A.is_mps(), __func__, "A is on ", A.device(), " but expected on mps");
-  TORCH_CHECK(
-      B.is_mps(), __func__, "B is on ", B.device(), " but expected on mps");
-  TORCH_CHECK(
-      SZ.is_mps(), __func__, "SZ is on ", SZ.device(), " but expected on mps");
-
-  TORCH_CHECK(
+  TORCHAO_CHECK(
       A.dtype() == at::kBFloat16 || A.dtype() == at::kHalf ||
           A.dtype() == at::kFloat,
       __func__,
       " : expect A to be either 32-bit or 16-bit float tensor.");
-  TORCH_CHECK(A.is_contiguous(), __func__, " : expect A to be contiguous.");
-  TORCH_CHECK(A.dim() == 2, __func__, " : expect A to be 2D tensor.");
+  TORCHAO_CHECK(A.is_contiguous(), __func__, " : expect A to be contiguous.");
+  TORCHAO_CHECK(A.dim() == 2, __func__, " : expect A to be 2D tensor.");
 
-  TORCH_CHECK(
+  TORCHAO_CHECK(
       B.dtype() == at::kByte, __func__, " : expect B to be uint8 tensor.");
-  TORCH_CHECK(B.is_contiguous(), __func__, " : expect B to be contiguous.");
-  TORCH_CHECK(
+  TORCHAO_CHECK(B.is_contiguous(), __func__, " : expect B to be contiguous.");
+  TORCHAO_CHECK(
       B.size(1) == (K / 8) * nbit,
       __func__,
       " : expect B.size(1) == ",
       (K / 8) * nbit);
 
-  TORCH_CHECK(K % 8 == 0, __func__, ": expect K to be multiple of 8, got ", K);
+  TORCHAO_CHECK(K % 8 == 0, __func__, ": expect K to be multiple of 8, got ", K);
 
-  TORCH_CHECK(
+  TORCHAO_CHECK(
       group_size == 32 || group_size == 64 || group_size == 128 ||
           group_size == 256,
       __func__,
       ": expect group_size to be 32, 64, 128 or 256, got ",
       group_size);
 
-  TORCH_CHECK(
+  TORCHAO_CHECK(
       SZ.dim() == 3 && SZ.size(1) == N && SZ.size(2) == 2,
       __func__,
       ": expect SZ to be 3d tensor with sizes [:, ",
       N,
       ", 2]");
+}
+
+template <int nbit>
+Tensor linear_mps_kernel(
+    const Tensor& A,
+    const Tensor& B,
+    int64_t group_size,
+    const Tensor& SZ) {
+  TORCHAO_CHECK(
+      A.is_mps(), __func__, "A is on ", A.device(), " but expected on mps");
+  TORCHAO_CHECK(
+      B.is_mps(), __func__, "B is on ", B.device(), " but expected on mps");
+  TORCHAO_CHECK(
+      SZ.is_mps(), __func__, "SZ is on ", SZ.device(), " but expected on mps");
+
+  check_linear_mps_args<nbit>(A, B, group_size, SZ);
+
+  auto M = A.size(0);
+  auto N = B.size(0);
+  auto K = A.size(1);
 
   auto C = at::empty({M, N}, A.options());
 
@@ -82,6 +94,31 @@ Tensor linear_mps_kernel(
   return C;
 }
 
+template <int nbit>
+Tensor linear_mps_kernel_meta(
+    const Tensor& A,
+    const Tensor& B,
+    int64_t group_size,
+    const Tensor& SZ) {
+  TORCHAO_CHECK(
+      A.is_meta(), __func__, "A is on ", A.device(), " but expected on meta");
+  TORCHAO_CHECK(
+      B.is_meta(), __func__, "B is on ", B.device(), " but expected on meta");
+  TORCHAO_CHECK(
+      SZ.is_meta(),
+      __func__,
+      "SZ is on ",
+      SZ.device(),
+      " but expected on meta");
+
+  check_linear_mps_args<nbit>(A, B, group_size, SZ);
+
+  auto M = A.size(0);
+  auto N = B.size(0);
+
+  return at::empty({M, N}, A.options()).to("meta");
+}
+
 // LowBit Packing on CPU Backend
 template <int nbit>
 Tensor pack_weights_cpu_kernel(const Tensor& W) {
@@ -144,4 +181,14 @@ Tensor pack_weights_cpu_kernel(const Tensor& W) {
   m.impl("_linear_fp_act_7bit_weight", &linear_mps_kernel<7>);
 }
 
+TORCH_LIBRARY_IMPL(torchao, Meta, m) {
+  m.impl("_linear_fp_act_1bit_weight", &linear_mps_kernel_meta<1>);
+  m.impl("_linear_fp_act_2bit_weight", &linear_mps_kernel_meta<2>);
+  m.impl("_linear_fp_act_3bit_weight", &linear_mps_kernel_meta<3>);
+  m.impl("_linear_fp_act_4bit_weight", &linear_mps_kernel_meta<4>);
+  m.impl("_linear_fp_act_5bit_weight", &linear_mps_kernel_meta<5>);
+  m.impl("_linear_fp_act_6bit_weight", &linear_mps_kernel_meta<6>);
+  m.impl("_linear_fp_act_7bit_weight", &linear_mps_kernel_meta<7>);
+}
+
 } // namespace torchao::kernels::mps::lowbit::aten

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

@@ -0,0 +1,104 @@
+# 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
+
+torchao_root: Optional[str] = os.getenv("TORCHAO_ROOT")
+assert torchao_root is not None, "TORCHAO_ROOT is not set"
+
+sys.path.insert(0, torchao_root)
+from torchao.experimental.quant_api import IntxWeightOnlyLinearQuantizer
+
+
+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 TestIntxWeightOnlyLinearQuantizer(unittest.TestCase):
+    cases = [(nbit,) for nbit in range(1, 8)]
+
+    def _model_setup(self):
+        k0 = 512
+        k1 = 256
+        k2 = 128
+        k3 = 1024
+        layers = [
+            torch.nn.Linear(k0, k1, bias=False),
+            torch.nn.Linear(k1, k2, bias=False),
+            torch.nn.Linear(k2, k3, bias=False),
+        ]
+        model = torch.nn.Sequential(*layers)
+        return model
+
+    def _quantize_model(self, model, precision, nbit, group_size):
+        quantizer = IntxWeightOnlyLinearQuantizer(
+            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 = self._model_setup()
+        group_size = 32
+        m = 3
+        k0 = 512
+        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 = self._model_setup()
+        group_size = 32
+        m = 3
+        activations = torch.randn(m, 512, 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, 1024))
+
+    @parameterized(cases)
+    def test_3d_output_device_and_shape(self, nbit):
+        model = self._model_setup()
+        group_size = 32
+        leading_shape = (3, 5)
+        activations = torch.randn(*leading_shape, 512, 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, 1024))
+
+
+if __name__ == "__main__":
+    unittest.main()

torchao/experimental/quant_api.py

@@ -516,3 +516,114 @@ def apply(weight):
         )
 
     return _get_linear_subclass_inserter(apply)
+
+
+class IntxWeightOnlyQuantizedLinear(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
+        )
+        weight_qvals = (weight_qvals + (1 << (nbit - 1))).to(torch.uint8)
+
+        self.weight_scales_and_zeros = torch.stack(
+            (weight_scales.t(), weight_zeros.t()), dim=2
+        )
+
+        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_and_zeros
+            )
+
+        lead_shape = x.shape[0:-1]
+        k = x.shape[-1]
+        n = self.weight_scales_and_zeros.shape[1]
+        res = self._linear_op(x.reshape(-1, k), self.packed_weights, self.group_size, self.weight_scales_and_zeros)
+        res = res.reshape(*lead_shape, n)
+        return res
+
+
+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 = IntxWeightOnlyQuantizedLinear(
+                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 IntxWeightOnlyLinearQuantizer:
+    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 IntxWeightOnlyLinearQuantizer"
+            )
+        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 IntxWeightOnlyLinearQuantizer"
+            )
+        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