Add conv2d and maxpool2d tests. Uplift MLIR to latest main + stablehl…

…o --> TTIR for conv2d and maxpool2d

requirements.txt

@@ -1,6 +1,7 @@
 -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
 jaxlib==0.4.31
 jax
+flax
 cmake
 ninja
 clang-format

tests/TTIR/test_conv2d.py

@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import jax
+import jax.numpy as jnp
+
+from infrastructure import verify_module
+
+
+@pytest.mark.parametrize(
+    "batch_size, output_channels, input_channels, input_height, input_width, filter_height, filter_width, stride_h, stride_w, padding",
+    (
+        # RESNET
+        (1, 64, 3, 224, 224, 7, 7, 2, 2, 3),
+        (1, 256, 64, 56, 56, 1, 1, 1, 1, 0),
+        (1, 64, 64, 56, 56, 1, 1, 1, 1, 0),
+        (1, 64, 64, 56, 56, 3, 3, 1, 1, 1),
+        (1, 64, 256, 56, 56, 1, 1, 1, 1, 0),
+        (1, 512, 256, 56, 56, 1, 1, 2, 2, 0),
+        (1, 128, 256, 56, 56, 1, 1, 2, 2, 0),
+        (1, 128, 128, 28, 28, 3, 3, 1, 1, 1),
+        (1, 512, 128, 28, 28, 1, 1, 1, 1, 0),
+        (1, 128, 512, 28, 28, 1, 1, 1, 1, 0),
+        # (1, 1024, 512, 28, 28, 1, 1, 2, 2, 0), Requires block sharding
+        (1, 256, 512, 28, 28, 1, 1, 2, 2, 0),
+        (1, 256, 256, 14, 14, 3, 3, 1, 1, 1),
+        (1, 1024, 256, 14, 14, 1, 1, 1, 1, 0),
+        (1, 256, 1024, 14, 14, 1, 1, 1, 1, 0),
+        # (1, 2048, 1024, 14, 14, 1, 1, 2, 2, 0), Requires block sharding
+        # (1, 512, 1024, 14, 14, 1, 1, 2, 2, 0), Requires block sharding
+        # (1, 512, 512, 7, 7, 3, 3, 1, 1, 1), Requires block sharding
+        # (1, 2048, 512, 7, 7, 1, 1, 1, 1, 0), Requires block sharding
+        # (1, 512, 2048, 7, 7, 1, 1, 1, 1, 0), Requires block sharding
+
+        # MISCELLANEOUS
+        (1, 64, 16, 115, 115, 4, 4, 1, 1, 0),
+        (1, 64, 64, 56, 56, 3, 3, 1, 1, 1),
+        (1, 128, 128, 28, 28, 3, 3, 1, 1, 1),
+        (1, 256, 256, 14, 14, 3, 3, 1, 1, 1),
+        (1, 64, 64, 8, 8, 3, 3, 1, 1, 1),
+        (1, 64, 64, 16, 16, 3, 3, 1, 1, 1),
+        (1, 256, 256, 7, 7, 3, 3, 1, 1, 1),
+        (1, 256, 64, 56, 56, 1, 1, 2, 2, 0), 
+    ),
+)
+def test_conv2d(
+    batch_size,
+    output_channels,
+    input_channels,
+    input_height,
+    input_width,
+    filter_height,
+    filter_width,
+    stride_h,
+    stride_w,
+    padding
+):
+  def module_conv(img, weights):
+    return jax.lax.conv_general_dilated(img, weights, [stride_h, stride_w], [[padding]*2]*2, dimension_numbers=('NHWC', 'OIHW', 'NHWC'))
+
+
+  img_shape = (batch_size, input_height, input_width, input_channels)
+  weights_shape = (output_channels, input_channels, filter_height, filter_width)
+
+  # Some resnet convolutions seem to require bfloat16, ttnn throws in runtime otherwise.
+  # On another note, MaxPool2d is also only supported for bfloat16 in ttnn, so we have
+  # to run resnet in bfloat16 for the time being.
+  verify_module(module_conv, [img_shape, weights_shape], required_pcc=0.95, required_atol=float("inf"), dtype=jnp.bfloat16)

tests/TTIR/test_maxpool2d.py

@@ -0,0 +1,64 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import jax
+import jax.numpy as jnp
+import flax
+
+from infrastructure import verify_module
+
+
+@pytest.mark.parametrize(
+    "act_shape",  ## NHWC
+    [   
+        (1, 32, 32, 32),
+        (1, 32, 32, 64),
+        (1, 32, 32, 128),
+        (1, 32, 64, 32),
+        (1, 32, 64, 64),
+        (1, 32, 64, 128),
+        (1, 32, 128, 32),
+        (1, 32, 128, 64),
+        (1, 32, 128, 128),
+        (1, 64, 32, 32),
+        (1, 64, 32, 64),
+        (1, 64, 32, 128),
+        (1, 64, 64, 32),
+        (1, 64, 64, 64),
+        (1, 64, 64, 128),
+        (1, 64, 128, 32),
+        (1, 64, 128, 64),
+        (1, 64, 128, 128),
+        (1, 128, 32, 32),
+        (1, 128, 32, 64),
+        (1, 128, 32, 128),
+        (1, 128, 64, 32),
+        (1, 128, 64, 64),
+        (1, 128, 64, 128),
+        (1, 128, 128, 32),
+        (1, 128, 128, 64),
+        (1, 128, 128, 128),
+    ],
+)
+def test_maxpool2d(
+    act_shape,
+):
+    def module_maxpool(img):
+        return flax.linen.max_pool(img, window_shape=(2, 2), strides=(2, 2), padding=((0, 0), (0, 0)))
+
+    verify_module(module_maxpool, [act_shape], required_pcc=0.95, required_atol=float("inf"), dtype=jnp.bfloat16)
+
+def test_resnet_maxpool2d():
+    # This maxpool doesnt work on its own because of the reshape that is inserted on its input
+    # Issue: https://github.com/tenstorrent/tt-metal/issues/12866
+    # It works with the conv on top since the output is already flattened.
+    # In resnet, this is essentially the sequence that occurs. The only difference is that
+    # there are a few eltwise ops in between.
+    def module_resnet_maxpool(act, weights):
+        x = jax.lax.conv_general_dilated(act, weights, [2, 2], ((3, 3), (3, 3)), dimension_numbers=('NHWC', 'OIHW', 'NHWC'))
+        x = flax.linen.max_pool(x, window_shape=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)))
+        return x
+
+    verify_module(module_resnet_maxpool, [(1, 224, 224, 3), (64, 3, 7, 7)], required_pcc=0.95, required_atol=float("inf"), dtype=jnp.bfloat16)

tests/infrastructure.py

@@ -5,9 +5,9 @@
 import jax
 import jax.numpy as jnp
 
-def random_input_tensor(shape, key=42, on_device=False):
+def random_input_tensor(shape, key=42, on_device=False, dtype=jnp.float32):
   def random_input(shape, key):
-      return jax.random.uniform(jax.random.PRNGKey(key), shape=shape)
+      return jax.random.uniform(jax.random.PRNGKey(key), shape=shape, dtype=dtype)
 
   jitted_tensor_creator = jax.jit(random_input, static_argnums=[0,1], backend='cpu')
   tensor = jitted_tensor_creator(shape, key)
@@ -37,9 +37,9 @@ def compare_tensor_to_golden(tensor, golden, required_pcc=0.99, required_atol=1e
 
   return ret
 
-def verify_module(module, input_shapes, key=42, required_pcc=0.99, required_atol=1e-2):
+def verify_module(module, input_shapes, key=42, required_pcc=0.99, required_atol=1e-2, dtype=jnp.float32):
   tt_device = jax.devices()[0]
-  cpu_inputs = [random_input_tensor(shape, key) for shape in input_shapes]
+  cpu_inputs = [random_input_tensor(shape, key, dtype=dtype) for shape in input_shapes]
   tt_inputs = [jax.device_put(cpu_input, tt_device) for cpu_input in cpu_inputs]
   graph = jax.jit(module)
   res = graph(*tt_inputs)

third_party/CMakeLists.txt

@@ -3,9 +3,9 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TT_MLIR_VERSION "5d92bf937bc76b521d39e0fa320b20773905bfc1")
+set(TT_MLIR_VERSION "6dc351c2dc01dbb683617ff53d3cd623f5e87acc")
 set(LOGURU_VERSION "4adaa185883e3c04da25913579c451d3c32cfac1")
-
+set(CMAKE_BUILD_TYPE Debug)
 if (TOOLCHAIN STREQUAL "ON")
     cmake_minimum_required(VERSION 3.20)
     project(ttmlir-toolchain LANGUAGES CXX C)