test: Add FP8 model tests and tiny model generator

- Add fp8_aware_dense layer unit tests
- Add FP8 Qwen3 model loading test using roulis/tiny-fp8-qwen3
- Include Python script to generate tiny FP8 test models

Author: nyo16

test/bumblebee/layers_test.exs

@@ -0,0 +1,138 @@
+defmodule Bumblebee.LayersTest do
+  use ExUnit.Case, async: true
+
+  import Bumblebee.TestHelpers
+
+  describe "fp8_aware_dense/3" do
+    test "dequantizes FP8 kernel with scale_inv" do
+      # Create a simple model with fp8_aware_dense
+      model =
+        Axon.input("input", shape: {nil, 4})
+        |> Bumblebee.Layers.fp8_aware_dense(8, name: "dense", block_size: 2)
+
+      # Create params with known values
+      # kernel: [4, 8] - input_features x output_features
+      # scale_inv: [2, 4] - ceil(4/2) x ceil(8/2) blocks
+      kernel = Nx.tensor([
+        [1, 2, 3, 4, 5, 6, 7, 8],
+        [1, 2, 3, 4, 5, 6, 7, 8],
+        [1, 2, 3, 4, 5, 6, 7, 8],
+        [1, 2, 3, 4, 5, 6, 7, 8]
+      ], type: {:f, 32})
+
+      # Scale of 2.0 for all blocks means output should be 2x what it would be without scaling
+      scale_inv = Nx.tensor([
+        [2.0, 2.0, 2.0, 2.0],
+        [2.0, 2.0, 2.0, 2.0]
+      ], type: {:f, 32})
+
+      params = %{
+        "dense" => %{
+          "kernel" => kernel,
+          "scale_inv" => scale_inv
+        }
+      }
+
+      input = Nx.tensor([[1.0, 1.0, 1.0, 1.0]])
+
+      output = Axon.predict(model, params, %{"input" => input})
+
+      # Without scaling: input [1,1,1,1] dot kernel gives [4, 8, 12, 16, 20, 24, 28, 32]
+      # With scale_inv of 2.0: [8, 16, 24, 32, 40, 48, 56, 64]
+      expected = Nx.tensor([[8.0, 16.0, 24.0, 32.0, 40.0, 48.0, 56.0, 64.0]])
+
+      assert_all_close(output, expected)
+    end
+
+    test "dequantizes with identity scale (1.0)" do
+      model =
+        Axon.input("input", shape: {nil, 4})
+        |> Bumblebee.Layers.fp8_aware_dense(4, name: "dense", block_size: 2)
+
+      kernel = Nx.tensor([
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1]
+      ], type: {:f, 32})
+
+      # Identity scale
+      scale_inv = Nx.tensor([
+        [1.0, 1.0],
+        [1.0, 1.0]
+      ], type: {:f, 32})
+
+      params = %{
+        "dense" => %{
+          "kernel" => kernel,
+          "scale_inv" => scale_inv
+        }
+      }
+
+      input = Nx.tensor([[2.0, 3.0, 4.0, 5.0]])
+      output = Axon.predict(model, params, %{"input" => input})
+
+      # Identity matrix with scale 1.0 should return input unchanged
+      assert_all_close(output, input)
+    end
+
+    test "handles non-block-aligned dimensions" do
+      # 3 input features, 5 output features with block_size 2
+      # This tests the slicing logic for non-aligned dimensions
+      model =
+        Axon.input("input", shape: {nil, 3})
+        |> Bumblebee.Layers.fp8_aware_dense(5, name: "dense", block_size: 2)
+
+      # kernel: [3, 5]
+      kernel = Nx.broadcast(1.0, {3, 5})
+
+      # scale_inv: [ceil(3/2), ceil(5/2)] = [2, 3]
+      scale_inv = Nx.broadcast(1.0, {2, 3})
+
+      params = %{
+        "dense" => %{
+          "kernel" => kernel,
+          "scale_inv" => scale_inv
+        }
+      }
+
+      input = Nx.tensor([[1.0, 1.0, 1.0]])
+      output = Axon.predict(model, params, %{"input" => input})
+
+      # Sum of 3 ones = 3.0 for each output
+      expected = Nx.tensor([[3.0, 3.0, 3.0, 3.0, 3.0]])
+
+      assert_all_close(output, expected)
+    end
+
+    test "includes bias when use_bias is true" do
+      model =
+        Axon.input("input", shape: {nil, 2})
+        |> Bumblebee.Layers.fp8_aware_dense(2, name: "dense", block_size: 2, use_bias: true)
+
+      kernel = Nx.tensor([
+        [1, 0],
+        [0, 1]
+      ], type: {:f, 32})
+
+      scale_inv = Nx.tensor([[1.0]], type: {:f, 32})
+      bias = Nx.tensor([10.0, 20.0], type: {:f, 32})
+
+      params = %{
+        "dense" => %{
+          "kernel" => kernel,
+          "scale_inv" => scale_inv,
+          "bias" => bias
+        }
+      }
+
+      input = Nx.tensor([[1.0, 2.0]])
+      output = Axon.predict(model, params, %{"input" => input})
+
+      # [1, 2] with identity kernel = [1, 2], plus bias [10, 20] = [11, 22]
+      expected = Nx.tensor([[11.0, 22.0]])
+
+      assert_all_close(output, expected)
+    end
+  end
+end

test/bumblebee/text/qwen3_test.exs

@@ -75,4 +75,32 @@ defmodule Bumblebee.Text.Qwen3Test do
       Nx.tensor([[-0.1487, -0.0071]])
     )
   end
+
+  test ":for_causal_language_modeling with FP8 weights" do
+    assert {:ok, %{model: model, params: %Axon.ModelState{data: params_data} = params, spec: spec}} =
+             Bumblebee.load_model(
+               {:hf, "roulis/tiny-fp8-qwen3"},
+               preserve_source_types: true
+             )
+
+    assert %Bumblebee.Text.Qwen3{architecture: :for_causal_language_modeling} = spec
+
+    # Verify FP8 weights are preserved
+    q_proj_kernel = params_data["decoder.blocks.0.self_attention.query"]["kernel"]
+    assert Nx.type(q_proj_kernel) == {:f8_e4m3fn, 8}
+
+    # Verify scale_inv is loaded
+    q_proj_scale = params_data["decoder.blocks.0.self_attention.query"]["scale_inv"]
+    assert Nx.type(q_proj_scale) == {:f, 32}
+
+    inputs = %{
+      "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]),
+      "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
+    }
+
+    # Model should run without error (dequantization happens internally)
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.logits) == {1, 10, 1024}
+  end
 end