Test and fix bug for convolution

.github/workflows/torch_xla2.yml

@@ -0,0 +1,43 @@
+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 -e .
+          pip install pytest
+      - name: Run tests
+        working-directory: experimental/torch_xla2
+        shell: bash
+        run: |
+          pytest test/

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/torch_xla2/__init__.py

@@ -1,13 +1,16 @@
 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):

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
@@ -508,8 +507,11 @@ 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