【Hackathon 9th No.52】add test_dynamic_per_token_scaled_fp8_quant (#4015)

* add test_dynamic_per_token_scaled_fp8_quant

* fix

* add bfloat16

* ci

Author: co63oc

tests/operators/test_dynamic_per_token_scaled_fp8_quant.py

@@ -0,0 +1,151 @@
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.ops.gpu import dynamic_per_token_scaled_fp8_quant
+
+
+class TestDynamicPerTokenScaledFp8Quant(unittest.TestCase):
+    def setUp(self):
+        paddle.seed(42)
+        np.random.seed(42)
+
+    def _run_dynamic_per_token_scaled_fp8_quant(self, input_data, scale_ub=0.0):
+        """
+        Run the dynamic per-token scaled FP8 quantization operator
+
+        Args:
+            input_data: Input data (numpy array)
+            scale_ub: Scale upper bound value
+
+        Returns:
+            Quantized output and scaling factors
+        """
+        input_tensor = paddle.to_tensor(input_data)
+
+        # Determine the output shape
+        num_tokens = input_tensor.shape[0] if len(input_tensor.shape) > 1 else 1
+
+        # Create the output tensor
+        out_tensor = paddle.empty(input_tensor.shape, dtype=paddle.float8_e4m3fn)
+
+        # Create the scales tensor
+        scales_tensor = paddle.empty([num_tokens], dtype="float32")
+
+        inputs = {"out": out_tensor, "input": input_tensor, "scale": scales_tensor}
+        attrs = {"scale_ub": scale_ub}
+        dynamic_per_token_scaled_fp8_quant(*inputs.values(), *attrs.values())
+
+        out_np = out_tensor.cpu().numpy()
+        scales_np = scales_tensor.cpu().numpy()
+
+        return out_np, scales_np
+
+    def _verify_results(self, input_data, output_data, scales, scale_ub=0.0, tol=7e-2):
+        """
+        Verify that the quantization results are correct
+
+        Args:
+            input_data: Original input data
+            output_data: Quantized output data
+            scales: Scaling factors used
+            scale_ub: Scale upper bound value
+            tol: Allowed tolerance range
+        """
+        # Check if the output data type is FP8
+        self.assertEqual(output_data.dtype, "float8_e4m3fn")  # FP8 is stored as float8_e4m3fn
+
+        # For each token, verify the quantization process
+        num_tokens = input_data.shape[0] if len(input_data.shape) > 1 else 1
+
+        for i in range(num_tokens):
+            # Get the current token's input and output
+            if len(input_data.shape) > 1:
+                token_input = input_data[i]
+                token_output = output_data[i] if len(output_data.shape) > 1 else output_data
+            else:
+                token_input = input_data
+                token_output = output_data
+
+            # Get the current token's scaling factor
+            token_scale = scales[i] if num_tokens > 1 else scales[0]
+
+            # If there is a scale upper limit, check if it is respected
+            if scale_ub > 0:
+                max_val = np.max(np.abs(token_input))
+                expected_scale = min(max_val, scale_ub) / 448.0
+                self.assertAlmostEqual(token_scale, expected_scale, delta=tol)
+            else:
+                max_val = np.max(np.abs(token_input))
+                expected_scale = max_val / 448.0
+                self.assertAlmostEqual(token_scale, expected_scale, delta=tol)
+
+            # Verify that the quantized values are reasonable
+            # The FP8 range is typically -1.0 to 1.0, quantized values should be within this range
+            reconstructed = token_output.astype(np.float32) * token_scale
+            diff = np.abs(reconstructed - token_input.astype(np.float32))
+            self.assertTrue(np.all(diff <= tol * np.max(np.abs(token_input))))
+
+    def test_fp32_input(self):
+        """Test FP32 input"""
+        input_data = np.array([0.1, -0.2, 0.3, -0.4], dtype=np.float32)
+
+        # Test the case without a scale upper limit
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data)
+        self._verify_results(input_data, output_data, scales)
+
+        # Test the case with a scale upper limit
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data, scale_ub=1.5)
+        print(output_data, scales)
+        self._verify_results(input_data, output_data, scales, scale_ub=1.5)
+
+        # Test the single-token case
+        single_token = input_data[0:1]
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(single_token)
+        self._verify_results(single_token, output_data, scales)
+
+    def test_large_values(self):
+        """Test large value input"""
+        input_data = np.array([100.0, -200.0, 300.0, -320.0], dtype=np.float32)
+
+        # Test no scale upper limit - should use max_value / 448 as the scaling factor
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data)
+        self._verify_results(input_data, output_data, scales)
+
+        # Test with scale upper limit
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data, scale_ub=310.0)
+        self._verify_results(input_data, output_data, scales, scale_ub=310.0)
+
+    def test_edge_cases(self):
+        """Test edge cases"""
+        # Test all-zero input
+        zero_input = np.zeros((2, 4), dtype=np.float32)
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(zero_input)
+        self._verify_results(zero_input, output_data, scales)
+
+        # Test single-element input
+        single_element = np.array([[5.0]], dtype=np.float32)
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(single_element)
+        self._verify_results(single_element, output_data, scales)
+
+        # Test very large number of tokens
+        large_input = np.random.randn(1024, 16).astype(np.float32)
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(large_input)
+        self._verify_results(large_input, output_data, scales)
+
+    def test_dynamic_per_token_scaled_fp8_quant_fp16(self):
+        # Test float16
+        input_data = np.array([0.1, -0.2, 0.3, -0.4], dtype="float16")
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data)
+        self._verify_results(input_data, output_data, scales)
+
+    def test_dynamic_per_token_scaled_fp8_quant_bf16(self):
+        # Test bfloat16
+        input_data = np.array([0.1, -0.2, 0.3, -0.4], dtype="bfloat16")
+        output_data, scales = self._run_dynamic_per_token_scaled_fp8_quant(input_data)
+        self._verify_results(input_data, output_data, scales)
+
+
+if __name__ == "__main__":
+    unittest.main()