Fix a bug in convolution for torch_xla2. Enable tests

.github/workflows/torch_xla2.yml

@@ -0,0 +1,44 @@
+on:
+  pull_request:
+    branches:
+      - master
+      - r[0-9]+.[0-9]+
+    paths:
+      - 'experimental/torch_xla2/**'
+  push:
+    branches:
+      - master
+      - r[0-9]+.[0-9]+
+    paths:
+      - 'experimental/torch_xla2/**'
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
+  cancel-in-progress: true
+
+jobs:
+  torchxla2-cpu:
+    runs-on: ubuntu-20.04
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          sparse-checkout: |
+            experimental/torch_xla2
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Install
+        shell: bash
+        working-directory: experimental/torch_xla2
+        run: |
+          pip install pytest absl-py jax[cpu] flatbuffers tensorflow
+          pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
+          pip install -e .
+      - name: Run tests
+        working-directory: experimental/torch_xla2
+        shell: bash
+        run: |
+          pytest test/

experimental/torch_xla2/requirements.txt

@@ -1,5 +1,22 @@
-jax==0.4.24.dev20240202
-jaxlib==0.4.24.dev20240202
-numpy==1.26.3
-torch==2.2.0
-typing_extensions==4.9.0
+certifi==2022.12.7
+charset-normalizer==2.1.1
+filelock==3.9.0
+fsspec==2023.4.0
+idna==3.4
+jax==0.4.24
+jaxlib==0.4.24
+Jinja2==3.1.2
+MarkupSafe==2.1.3
+ml-dtypes==0.3.2
+mpmath==1.2.1
+networkx==3.0rc1
+numpy==1.24.1
+opt-einsum==3.3.0
+Pillow==9.3.0
+requests==2.28.1
+scipy==1.12.0
+sympy==1.11.1
+--pre
+torch
+typing_extensions==4.8.0
+urllib3==1.26.13

experimental/torch_xla2/test/llama/llama_model.py

@@ -244,6 +244,10 @@ def forward(
 
     k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
     v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+    print('q=', q.shape)
+    print('k=', k.shape)
+    print('v=', v.shape)
+    print('mask=', mask.shape)
     y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
 
     y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)

experimental/torch_xla2/test/test_conv.py

@@ -0,0 +1,79 @@
+import torch
+from torch import nn
+import torch_xla2
+from . import test_base
+
+class CustomConv1(torch.nn.Module):
+
+  def __init__(
+      self,
+      channels_conv1=3,
+      width_conv1=3,
+      channels_conv2=5,
+      width_conv2=5,
+      hidden_layer_size=50,
+  ):
+    super(CustomConv1, self).__init__()
+    self.conv1 = nn.Conv1d(1, channels_conv1, width_conv1)
+    self.conv2 = nn.Conv1d(channels_conv1, channels_conv2, width_conv2)
+    self.fc1 = nn.Linear(hidden_layer_size, 2)
+
+  def forward(self, x):
+    x = nn.functional.max_pool1d(nn.functional.relu(self.conv1(x)), 2, stride=2)
+    x = nn.functional.max_pool1d(nn.functional.relu(self.conv2(x)), 2, stride=2)
+    x = torch.flatten(x, 1)
+    x = nn.functional.softmax(self.fc1(x), dim=1)
+    return x
+
+
+class CustomConv2(nn.Module):
+
+  def __init__(self):
+    super().__init__()
+    inp = 4
+    out = 16
+
+    self.conv = nn.Conv2d(inp, out, kernel_size=3, padding=1)
+
+    # This is supposed to be a squeeze and excitation block.
+    self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
+
+    self.scale = nn.Sequential(nn.Linear(out, out), nn.Sigmoid())
+
+  def forward(self, x):
+    x = self.conv(x)
+
+    b = x.shape[0]
+    ap = self.avg_pool(x).view(b, -1)
+    ap = self.scale(ap)
+    ap = ap.view(b, -1, 1, 1)
+
+    return x * ap
+
+
+class ConvTest(test_base.TestCase):
+
+    def test_conv1(self):
+        m = CustomConv1()
+        arg = torch.randn((20, 1, 50))
+        res = m(arg)
+
+        jax_weights, jax_func = torch_xla2.extract_jax(m) 
+        arg = torch_xla2.tensor.t2j(arg)
+        res2 = jax_func(jax_weights, (arg, ))
+        res2_torch = torch_xla2.tensor.j2t(res2)
+        self.assertTrue(torch.allclose(res, res2_torch))
+
+    def test_conv2(self):
+        m = CustomConv2()
+        arg = torch.randn((20, 4, 50, 100))
+        res = m(arg)
+        jax_weights, jax_func = torch_xla2.extract_jax(m) 
+        arg = torch_xla2.tensor.t2j(arg)
+        res2 = jax_func(jax_weights, (arg, ))
+        res2_torch = torch_xla2.tensor.j2t(res2)
+        self.assertTrue(torch.allclose(res, res2_torch, atol=1e-4, rtol=1e-4))
+
+
+if __name__ == '__main__':
+    test_base.main() 

experimental/torch_xla2/test/test_core_aten_ops.py

@@ -1762,6 +1762,18 @@ def test_aten_index_put_2(self):
     kwargs = dict()
     run_export_and_compare(self, torch.ops.aten.index_put, args, kwargs)
 
+  def test_aten_index_put_3(self):
+    args = (
+        torch.randint(0, 10, (10, 10)).to(torch.int32),
+        [
+            torch.randint(0, 10, (1,)).to(torch.int64),
+        ],
+        torch.randint(0, 10, (10,)).to(torch.int32),
+        True,
+    )
+    kwargs = dict()
+    run_export_and_compare(self, torch.ops.aten.index_put, args, kwargs)
+
   def test_aten_index_select_0(self):
     args = (
         torch.randn((2, 10)).to(torch.float32),

experimental/torch_xla2/torch_xla2/__init__.py

@@ -1,19 +1,23 @@
 import jax
 import torch
-import torch._functorch
+from torch._functorch import make_functional
+from torch.utils import _pytree as pytree
 from torch_xla2 import tensor
+from torch_xla2 import export, ops, ops_registry, tensor, tf_integration
 
 
 def extract_jax(mod: torch.nn.Module):
   """Returns a pytree of jax.ndarray and a jax callable."""
-  func, weights, buffer = torch._functorch.make_functional_with_buffers(mod)
+  func, weights, buffer = make_functional.make_functional_with_buffers(mod)
   states = (weights, buffer)
+  states = pytree.tree_map_only(torch.Tensor, tensor.t2j, states)
 
   @jax.jit
   def jax_func(states, inputs):
     (states, inputs) = tensor.wrap((states, inputs))
     weights, buffer = states
-    res = func(weights, buffer, *inputs)
+    with tensor.XLADispatchMode():
+      res = func(weights, buffer, *inputs)
     return tensor.unwrap(res)
 
   return states, jax_func

experimental/torch_xla2/torch_xla2/export.py

@@ -156,7 +156,7 @@ def call_function(self, target, args: Tuple, kwargs: Dict) -> Any:
     ):
       return super().call_function(target, args, kwargs)
 
-    print('Running ', target.name(), '--------')
+    # print('Running ', target.name(), '--------')
 
     op = ops_registry.lowerings.lookup(target)
     if op is None:
@@ -166,13 +166,18 @@ def call_function(self, target, args: Tuple, kwargs: Dict) -> Any:
 
   def run_node(self, n) -> Any:
     res = super().run_node(n)
-    if n.op == 'call_function':
-      if hasattr(res, 'shape'):
-        print('Meta:', n.meta.get('val').shape, 'REAL: ', res.shape)
+    #if n.op == 'call_function':
+     # if hasattr(res, 'shape'):
+     #   print('Meta:', n.meta.get('val').shape, 'REAL: ', res.shape)
     return res
 
+from torch._decomp import get_decompositions
+import torch._refs
+_extra_decomp = get_decompositions(
+  [torch.ops.aten.unfold]
+)
+
 
-_extra_decomp = {}
 
 
 def exported_program_to_jax(exported_program):

experimental/torch_xla2/torch_xla2/ops.py

@@ -2,7 +2,6 @@
 """Torch ops implemented using jax."""
 import sys
 
-import flax
 import jax
 from jax import numpy as jnp
 import numpy as np
@@ -298,10 +297,13 @@ def _aten_empty(sizes, **kwargs):
 
 @op(torch.ops.aten.index_put_)
 @op(torch.ops.aten.index_put)
-def _aten_index_put(self, indexes, values):
+def _aten_index_put(self, indexes, values, accumulate=False):
   indexes = [slice(None, None, None) if i is None else i for i in indexes]
   indexes = tuple(indexes)
-  return self.at[indexes].set(values)
+  if accumulate:
+    return self.at[indexes].add(values)
+  else:
+    return self.at[indexes].set(values)
 
 
 @op(torch.ops.aten.index)
@@ -508,8 +510,11 @@ def create_default_conv_dimension_numbers(num_spatial_dims):
   )
 
   if bias is not None:
-    # TODO(qihqi): this is wrong
-    bias = bias.reshape(bias.shape + (1,))
+    # TODO(qihqi): bias always on channel?
+    if len(bias.shape) == 1:
+      shape = [1] * len(res.shape)
+      shape[1] = bias.shape[0]
+      bias = bias.reshape(tuple(shape))
     res = res + bias
   return res
 
@@ -1200,19 +1205,21 @@ def _aten_arange(
     start,
     end=None,
     step=1,
+    *,
     dtype=None,
     layout=None,
+    requires_grad=False,
     device=None,
-    pin_memory=False,
+    pin_memory=False
 ):
+  if end is None:
+    end = start
+    start = 0
   return jnp.arange(
       start,
       end,
       step,
       dtype=dtype,
-      layout=layout,
-      device=device,
-      pin_memory=pin_memory,
   )
 
 

experimental/torch_xla2/torch_xla2/tensor.py

@@ -131,10 +131,12 @@ def __new__(cls, elem):
         shape[i] = 1
     if dtype is None:
       dtype = torch.float32
-    return torch.Tensor._make_subclass(
+    return torch.Tensor._make_wrapper_subclass(
         cls,
-        torch.empty(shape, dtype=dtype, device="meta"),
-        require_grad=False,
+        shape,
+        dtype=dtype,
+        device='meta',
+        requires_grad=False,
     )
 
   def __init__(self, elem: jax.Array):