Introduce apply_xla_patch_to_nn_linear and test that in a scan

test/scan/test_scan_spmd.py

@@ -1,9 +1,14 @@
+from copy import deepcopy
 import sys
+import re
 import unittest
 
 import torch
 import torch_xla
+import torch.nn as nn
+from torch_xla.distributed.spmd.xla_sharding import apply_xla_patch_to_nn_linear
 from torch_xla.experimental.scan import scan
+from torch_xla.experimental.scan_layers import scan_layers
 from torch_xla.distributed.spmd import mark_sharding, set_global_mesh, get_1d_mesh
 import torch_xla.runtime as xr
 
@@ -54,6 +59,84 @@ def fn(carry, x):
                     f'devices=[1,{N}]0,',
                     torch_xla._XLAC._get_xla_tensor_debug_info(tensor))
 
+  @unittest.skipUnless(xr.global_runtime_device_count() >= 4,
+                       "Multiple devices required")
+  def test_scan_xla_patched_linear(self):
+    """
+    When we use scan to trace `XLAPatchedLinear` layers, the lowered HLO should
+    consist of einsum instead of reshapes and transposes. This is important for
+    sharding constraint propagation.
+    """
+
+    # Create a model with a few linear layers.
+    class MyModel(nn.Module):
+
+      def __init__(self):
+        super().__init__()
+        self.layers = nn.Sequential(*[nn.Linear(128, 128) for _ in range(4)])
+        self.use_scan = True
+
+      def forward(self, x: torch.Tensor):
+        if self.use_scan:
+          return scan_layers(self.layers, x)
+        else:
+          return self.layers(x)
+
+    model = MyModel().to('xla')
+    # High dimensional input whose last dim is the contraction dim.
+    torch_xla.manual_seed(42)
+    x = torch.randn((3, 4, 5, 128), device='xla')
+    torch_xla.sync()
+
+    # If we trace the `nn.Linear` without applying the einsum patch, the lowered
+    # HLO will contain a `dot` operation where the input is flattened to 2D:
+    # the `3, 4, 5, 128` shape is flattened to `60, 128`. This destroys any sharding
+    # constraint applied to the first 3 dims.
+    self.check_dots_in_model(
+        model, x, expect_pattern=r'%dot\.\d+ = f32\[60,128\]')
+
+    # Once we patch the `nn.Linear` modules to use `einsum` and ensure that einsum is
+    # lowered without getting unnecessarily decomposed, the HLO should contain a
+    # `dot` operation that preserves the high dimensional structure. In turn, the
+    # compiler will be able to preserve the sharding constraints on those dimensions.
+    model = apply_xla_patch_to_nn_linear(model)
+    self.check_dots_in_model(
+        model, x, expect_pattern=r'%dot\.\d+ = f32\[3,4,5,128\]')
+
+    # Finally, test the numerics against an eager CPU impl.
+    x = x.bfloat16()
+    model = model.bfloat16()
+    model_cpu = MyModel().bfloat16()
+    model_cpu.load_state_dict(model.state_dict())
+    model_cpu.to('cpu')
+    model_cpu.use_scan = False
+    torch_xla.sync()
+    y_cpu = model_cpu(x.cpu())
+    y_xla = model(x)
+
+    torch_xla.sync()
+    torch.testing.assert_close(y_cpu, y_xla.cpu())
+
+  def check_dots_in_model(self, model, x, expect_pattern):
+    # Trace the model to get the HLO.
+    y = model(x)
+    hlo_text: str = torch_xla._XLAC._get_xla_tensors_hlo([y])
+
+    count = self.count_regex(hlo_text, expect_pattern)
+    assert count == 0 or count == 1, f"count = {count}"
+
+    if count == 1:
+      # This is the expected case.
+      pass
+    else:
+      raise RuntimeError(
+          f"""Expected `nn.Linear` lowering to contain `{expect_pattern}`. Full HLO:
+{hlo_text}
+""")
+
+  def count_regex(self, hlo_text, regex_str):
+    return len(re.findall(regex_str, hlo_text))
+
 
 if __name__ == '__main__':
   test = unittest.main()

test/spmd/test_xla_sharding.py

@@ -1421,6 +1421,60 @@ def test_fallback(self):
     self.assertIn("Data Shape: c64[2048,8]\n  OpSharding: {replicated}",
                   torch_xla._XLAC._get_xla_tensor_debug_info(freqs_cis))
 
+  def test_xla_patched_linear(self):
+    """
+    Test the numerical accuracy of XLAPatchedLinear.
+    """
+
+    from torch_xla.distributed.spmd.xla_sharding import XLAPatchedLinear
+    import torch_xla.runtime
+    import torch.nn.functional as F
+
+    with torch_xla.runtime.xla_device():
+      torch_xla.manual_seed(42)
+      x0 = torch.randn(2, 3, requires_grad=True)
+      w0 = torch.randn(4, 3, requires_grad=True)
+      b0 = torch.randn(4, requires_grad=True)
+      torch_xla.sync()
+
+    # Run `XLAPatchedLinear`.
+
+    x = x0.clone().detach().requires_grad_()
+    w = w0.clone().detach().requires_grad_()
+    b = b0.clone().detach().requires_grad_()
+
+    y = XLAPatchedLinear.apply(x, w, b)
+    assert y is not None
+    loss = y.sum()
+    loss.backward()
+    torch_xla.sync()
+
+    assert x.grad is not None
+    assert w.grad is not None
+    assert b.grad is not None
+    y1, xg1, wg1, bg1 = y.clone().detach(), x.grad.clone().detach(
+    ), w.grad.clone().detach(), b.grad.clone().detach()
+
+    # Compare with `F.linear`.
+
+    x = x0.clone().detach().requires_grad_()
+    w = w0.clone().detach().requires_grad_()
+    b = b0.clone().detach().requires_grad_()
+
+    y = F.linear(x, w, b)
+    loss = y.sum()
+    loss.backward()
+
+    assert x.grad is not None
+    assert w.grad is not None
+    assert b.grad is not None
+    y2, xg2, wg2, bg2 = y.clone().detach(), x.grad.clone().detach(
+    ), w.grad.clone().detach(), b.grad.clone().detach()
+    torch.testing.assert_close(y1, y2)
+    torch.testing.assert_close(xg1, xg2)
+    torch.testing.assert_close(wg1, wg2)
+    torch.testing.assert_close(bg1, bg2)
+
 
 if __name__ == '__main__':
   test = unittest.main()

torch_xla/csrc/init_python_bindings.cpp

@@ -1848,6 +1848,16 @@ void InitXlaModuleBindings(py::module m) {
         });
   m.def("_xla_optimization_barrier_",
         [](std::vector<at::Tensor>& inputs) { OptimizationBarrier_(inputs); });
+  // TODO(https://github.com/pytorch/xla/issues/8713): torch.einsum is getting
+  // decomposed when inside a custom op. This C++ op is an escape hatch to call
+  // XLA einsum without going through torch.einsum. We should remove this
+  // operation when the linked bug is fixed.
+  m.def("_xla_einsum",
+        [](const std::string& equation, const std::vector<at::Tensor>& inputs) {
+          std::vector<XLATensorPtr> xla_tensors = bridge::GetXlaTensors(inputs);
+          XLATensorPtr output = tensor_methods::einsum(equation, xla_tensors);
+          return bridge::AtenFromXlaTensor(output);
+        });
   m.def("_xla_set_default_device", [](const std::string& device) {
     return SetCurrentThreadDevice(device);
   });

torch_xla/distributed/spmd/xla_sharding.py

@@ -1,15 +1,17 @@
 import collections
 from collections.abc import Generator, MutableMapping
 import math
-import os
 from collections import OrderedDict, defaultdict
 from dataclasses import dataclass, field
 import torch
+from torch import Tensor
+from torch.library import custom_op
 import torch_xla
 import torch_xla.core.xla_model as xm
 import torch_xla._internal.utils as _utils
 from torch_xla.distributed.spmd import XLAShardedTensor, XLAShard
 import torch_xla.runtime as xr
+import torch_xla.debug.profiler as xp
 
 import numpy as np
 import functools
@@ -663,17 +665,106 @@ def apply(self, t: torch.Tensor):
     mark_sharding(t, self.mesh, self.partition_spec)
 
 
+### Linear layer implementation backed by einsum.
+
+
+# A custom forward op that uses einsum internally
+@custom_op(
+    "xla::einsum_linear_forward",
+    schema="(Tensor input, Tensor weight, Tensor? bias) -> Tensor",
+    mutates_args=())
+def _einsum_linear_forward(input: Tensor, weight: Tensor,
+                           bias: Optional[Tensor]):
+  with xp.Trace('einsum_linear_forward'):
+    # TODO(https://github.com/pytorch/xla/issues/8713): torch.einsum is getting
+    # decomposed when inside a custom op. This C++ op is an escape hatch to call
+    # XLA einsum without going through torch.einsum. We should remove this
+    # _einsum escape hatch when the linked bug is fixed.
+    product = torch_xla._XLAC._xla_einsum('...n,mn->...m', (input, weight))
+    if bias is not None:
+      return product + bias
+    return product
+
+
+@_einsum_linear_forward.register_fake
+def _einsum_linear_forward_fake(input: Tensor, weight: Tensor,
+                                bias: Optional[Tensor]):
+  product = torch.einsum('...n,mn->...m', input, weight)
+  if bias is not None:
+    return product + bias
+  return product
+
+
+@custom_op(
+    "xla::einsum_linear_backward",
+    schema="(Tensor grad_output, Tensor input, Tensor weight, Tensor? bias, bool needs_input_grad_input, bool needs_input_grad_weight, bool needs_input_grad_bias) -> (Tensor, Tensor, Tensor)",
+    mutates_args=())
+def _einsum_linear_backward(grad_output: Tensor, input: Tensor, weight: Tensor,
+                            bias: Optional[Tensor],
+                            needs_input_grad_input: bool,
+                            needs_input_grad_weight: bool,
+                            needs_input_grad_bias: bool):
+  with xp.Trace('einsum_linear_backward'):
+    grad_input = grad_weight = grad_bias = None
+
+    if needs_input_grad_input:
+      grad_input = torch_xla._XLAC._xla_einsum('...m,mn->...n',
+                                               (grad_output, weight))
+    else:
+      grad_input = None
+
+    if needs_input_grad_weight:
+      grad_weight = torch_xla._XLAC._xla_einsum('...m,...n->mn',
+                                                (grad_output, input))
+    else:
+      grad_weight = None
+
+    if bias is not None and needs_input_grad_bias:
+      grad_bias = torch_xla._XLAC._xla_einsum('...m->m', (grad_output,))
+    else:
+      grad_bias = None
+
+    return grad_input, grad_weight, grad_bias
+
+
+@_einsum_linear_backward.register_fake
+def _einsum_linear_backward_fake(grad_output: Tensor, input: Tensor,
+                                 weight: Tensor, bias: Optional[Tensor],
+                                 needs_input_grad_input: bool,
+                                 needs_input_grad_weight: bool,
+                                 needs_input_grad_bias: bool):
+  grad_input = grad_weight = grad_bias = None
+
+  if needs_input_grad_input:
+    grad_input = torch.einsum('...m,mn->...n', grad_output, weight)
+  else:
+    grad_input = None
+
+  if needs_input_grad_weight:
+    grad_weight = torch.einsum('...m,...n->mn', grad_output, input)
+  else:
+    grad_weight = None
+
+  if bias is not None and needs_input_grad_bias:
+    grad_bias = torch.einsum('...m->m', grad_output)
+  else:
+    grad_bias = None
+
+  return grad_input, grad_weight, grad_bias
+
+
+# Now define the XLAPatchedLinear function that uses the custom ops
 class XLAPatchedLinear(torch.autograd.Function):
   """
   A patched version of `torch.nn.functional.linear` that uses einsum instead
   of torch.matmul which will flatten the tensors to 2D and collide the sharded
   dimensions. The torch.matmul default behavior makes it very hard for XLA compiler
   to propagate the sharding annotation.
 
-  Autocast decorators @custom_fwd and @custom_bwd used as per autocast docs [1] to bring this class/layer within 
+  Autocast decorators @custom_fwd and @custom_bwd used as per autocast docs [1] to bring this class/layer within
   autocast context, when autocast is enabled.
   torch.get_autocast_dtype() fetches datatype for ops run in autocast [2], with the specified device (here, 'xla').
-  
+
   References: 
   [1] https://pytorch.org/docs/stable/notes/amp_examples.html#functions-with-multiple-inputs-or-autocastable-ops 
   [2] https://github.com/pytorch/pytorch/blob/2cc01cc6d3ad2aff47e8460667ba654b2e4c9f21/torch/amp/autocast_mode.py#L500
@@ -683,35 +774,71 @@ class XLAPatchedLinear(torch.autograd.Function):
 
   @staticmethod
   @custom_fwd(device_type='xla', cast_inputs=torch.get_autocast_dtype('xla'))
-  def forward(ctx, input, weight, bias=None):
-    # bias is an optional argument
+  def forward(ctx,
+              input: Tensor,
+              weight: Tensor,
+              bias: Optional[Tensor] = None):
     ctx.save_for_backward(input, weight, bias)
-    with torch.no_grad():
-      product = torch.einsum('...n,mn->...m', input, weight)
-      if bias is None:
-        return product
-      return product + bias
+    # Call our custom forward op. By wrapping the einsum in custom ops,
+    # AOTAutograd won't decompose the einsum.
+    return torch.ops.xla.einsum_linear_forward(input, weight, bias)
 
   @staticmethod
   @custom_bwd(device_type='xla')
-  def backward(ctx, grad_output):
+  def backward(ctx, grad_output: Tensor):
     input, weight, bias = ctx.saved_tensors
-    grad_input = grad_weight = grad_bias = None
+    needs_input_grad_input = ctx.needs_input_grad[0]
+    needs_input_grad_weight = ctx.needs_input_grad[1]
+    needs_input_grad_bias = False
+    if bias is not None:
+      needs_input_grad_bias = ctx.needs_input_grad[2]
 
-    if ctx.needs_input_grad[0]:
-      grad_input = torch.einsum('...m,mn->...n', grad_output, weight)
-    if ctx.needs_input_grad[1]:
-      grad_weight = torch.einsum('...m,...n->mn', grad_output, input)
-    if bias is not None and ctx.needs_input_grad[2]:
-      grad_bias = torch.einsum('...m->m', grad_output)
-
-    return grad_input, grad_weight, grad_bias
+    # Call our custom backward op with the boolean flags
+    grad_input, grad_weight, grad_bias = torch.ops.xla.einsum_linear_backward(
+        grad_output, input, weight, bias, needs_input_grad_input,
+        needs_input_grad_weight, needs_input_grad_bias)
+    return grad_input, grad_weight, grad_bias, None
 
 
 def xla_patched_nn_linear_forward(m, input):
   return XLAPatchedLinear.apply(input, m.weight, m.bias)
 
 
+class EinsumLinear(torch.nn.Linear):
+  """
+  A `torch.nn.Linear` subclass implemented with `einsum`.
+  """
+
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+
+  def forward(self, input):
+    t = xla_patched_nn_linear_forward(self, input)
+    assert isinstance(t, torch.Tensor)
+    return t
+
+
+def apply_xla_patch_to_nn_linear(module: torch.nn.Module):
+  """
+  Recursively replace `nn.Linear` layers with `EinsumLinear` in the module.
+
+  Without this patch, an `nn.Linear` module in PyTorch/XLA will lower to reshapes
+  and transposes instead of einsum, thus compromising sharding propagation.
+  """
+  for name, child in module.named_children():
+    if isinstance(child,
+                  torch.nn.Linear) and not isinstance(child, EinsumLinear):
+      einsum_linear = EinsumLinear(
+          child.in_features, child.out_features, bias=child.bias is not None)
+      einsum_linear.load_state_dict(
+          child.state_dict(), strict=True, assign=True)
+      setattr(module, name, einsum_linear)
+    else:
+      apply_xla_patch_to_nn_linear(child)
+
+  return module
+
+
 def apply_backward_optimization_barrier(m: torch.nn.Module):
   """
   Register a full backward hook that apply an optimization barrier to the given module.