Fix linear

.github/workflows/ci-sharktank.yml

@@ -22,6 +22,45 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  test_punet:
+    name: "Integration Tests - punet"
+    runs-on: nodai-amdgpu-mi250-x86-64
+    env:
+      PIP_CACHE_DIR: "${{ github.workspace }}/.pip-cache"
+    steps:
+      - name: "Setting up Python"
+        id: setup_python
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+        with:
+          python-version: 3.11
+
+      - name: "Checkout Code"
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Cache Pip Packages
+        uses: actions/cache@6849a6489940f00c2f30c0fb92c6274307ccb58a # v4.1.2
+        id: cache-pip
+        with:
+          path: ${{ env.PIP_CACHE_DIR }}
+          key: pip-${{ steps.setup_python.outputs.python-version }}-${{ hashFiles('*requirements*.txt','sharktank/requirements*.txt') }}
+
+      - name: Install pip deps
+        run: |
+          python -m pip install --no-compile --upgrade pip
+          # Note: We install in three steps in order to satisfy requirements
+          # from non default locations first. Installing the PyTorch CPU
+          # wheels saves multiple minutes and a lot of bandwidth on runner setup.
+          pip install --no-compile -r pytorch-cpu-requirements.txt
+          pip install --no-compile -r requirements.txt -r sharktank/requirements-tests.txt -e sharktank/
+
+          # Update to the latest iree packages.
+          pip install -f https://iree.dev/pip-release-links.html --upgrade --pre \
+            iree-base-compiler iree-base-runtime --src deps \
+            -e "git+https://github.com/iree-org/iree-turbine.git#egg=iree-turbine"
+      - name: Run punet tests
+        run: |
+          pytest -v sharktank/ -m model_punet
+
   test:
     name: "Unit Tests and Type Checking"
     strategy:

sharktank/integration/models/punet/integration_test.py

@@ -89,12 +89,13 @@ def sdxl_fp16_dataset(sdxl_fp16_base_files, temp_dir):
 def sdxl_int8_base_files():
     from huggingface_hub import hf_hub_download
 
-    REPO_ID = "amd-shark/sdxl-quant-models"
-    REVISION = "942e771bf0c2657a8b33380103d04747a75dfa4a"
+    REPO_ID = "amd-shark/sdxl-quant-int8"
+    SUBFOLDER = "mi300_all_sym_8_step14_fp32"
+    REVISION = "efda8afb35fd72c1769e02370b320b1011622958"
 
     def download(filename):
         return hf_hub_download(
-            repo_id=REPO_ID, subfolder="unet/int8", filename=filename, revision=REVISION
+            repo_id=REPO_ID, subfolder=SUBFOLDER, filename=filename, revision=REVISION
         )
 
     return {

sharktank/sharktank/layers/linear.py

@@ -31,9 +31,8 @@ class LinearLayer(ThetaLayer):
       x = x * premul_input
     matmul(x, weight.T) + bias
 
-    fake_quant exists to allow export without adding dequant ops.
-    when fake_quant is True, the op will in quant dequant fashion.
-    When false, it will keep quantized types.
+    fake quant only exists in order to allow for q_input to act as qdq.
+    when fake quant is false, q_input will quantize normally.
     ```
     """
 
@@ -43,7 +42,7 @@ def __init__(
         *,
         weight_name: str = "weight",
         bias_name: str = "bias",
-        fake_quant: bool = True,
+        fake_quant: bool = False,
     ):
         super().__init__(theta)
         self._simulate_native_quant = True
@@ -74,21 +73,23 @@ def forward(self, x):
             x = q_input.quantize(x)
             if self.fake_quant:
                 x = x.unpack().dequant()
-        elif qdq_input is not None and self.fake_quant:
+
+        elif qdq_input is not None:
             x = qdq_input.quantize(x).unpack().dequant()
 
         y = ops.linear(x, weight, bias)
 
         # Unconditionally dequantize.
-        if isinstance(y, QuantizedTensor) and not self.fake_quant:
+        if isinstance(y, QuantizedTensor):
             y = y.unpack().dequant()
         # Note that f8_e4m3fnuz types on AMD GPUs accumulate to fp32.
         # We can truncate to fp16 in iree, so we do a cast here
         # to account for this in the IR. This is may not be the right
         # level to do this, but for now its here.
-        if not self.fake_quant and y.dtype == torch.float8_e4m3fnuz:
-            y = ops.to(y, torch.float16)
-            return y
-        if qdq_output is not None and self.fake_quant:
+        if not isinstance(y, QuantizedTensor):
+            if y.dtype == torch.float8_e4m3fnuz:
+                y = ops.to(y, torch.float16)
+                return y
+        if qdq_output is not None:
             y = qdq_output.quantize(y).unpack().dequant()
         return y