[bugfix] Fix static asymmetric quantization case (#10334)

Signed-off-by: Daniël de Kok <me@danieldk.eu>
Signed-off-by: luka <luka@neuralmagic.com>
Co-authored-by: Daniël de Kok <me@danieldk.eu>

tests/kernels/test_int8_quant.py

@@ -86,10 +86,7 @@ def test_dynamic_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
     assert torch_out.min() >= int8_traits.min and torch_out.max(
     ) <= int8_traits.max
 
-    ops_out = torch.empty_like(x, dtype=torch.int8)
-    scales_out = torch.empty_like(scales, dtype=torch.float32)
-    azp_out = torch.empty_like(azps, dtype=torch.int32)
-    torch.ops._C.dynamic_scaled_int8_quant(ops_out, x, scales_out, azp_out)
+    ops_out, scales_out, azp_out = scaled_int8_quant(x, symmetric=False)
 
     if (not torch.allclose(scales_out, scales)):
         print(torch.argmax(torch.abs(scales_out - scales)))
@@ -119,7 +116,8 @@ def test_static_scaled_int8_quant(num_tokens: int, hidden_size: int,
 
     out1 = (x / scale_arg).round().clamp(int8_traits.min,
                                          int8_traits.max).to(torch.int8)
-    out2, _, _ = scaled_int8_quant(x, scale_arg)
+    out2, scale2, _ = scaled_int8_quant(x, scale_arg)
+    assert scale2 is scale_arg
 
     # big atol to account for rounding errors
     torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
@@ -145,11 +143,15 @@ def test_static_scaled_int8_azp_quant(num_tokens: int, hidden_size: int,
 
     out1 = ((x / scale).round() + azp).clamp(int8_traits.min,
                                              int8_traits.max).to(torch.int8)
-    out2 = torch.empty_like(x, dtype=torch.int8)
     scale_arg = torch.tensor([scale], dtype=torch.float32, device="cuda")
     azp_arg = torch.tensor([azp], dtype=torch.int32, device="cuda")
 
-    torch.ops._C.static_scaled_int8_quant(out2, x, scale_arg, azp_arg)
+    out2, scale2, azp2 = scaled_int8_quant(x,
+                                           scale_arg,
+                                           azp_arg,
+                                           symmetric=False)
+    assert scale2 is scale_arg
+    assert azp2 is azp_arg
 
     # big atol to account for rounding errors
     torch.testing.assert_close(out1, out2, atol=1, rtol=0.0)
@@ -184,6 +186,5 @@ def test_static_scaled_int8_azp_quant_saturating_cast(is_max: bool) -> None:
     val_i8 = int8_traits.max if is_max else int8_traits.min
     expected = torch.full((1, 5), val_i8, dtype=torch.int8, device="cuda")
 
-    out = torch.empty_like(expected)
-    torch.ops._C.static_scaled_int8_quant(out, x, scale, azp)
+    out, _, _ = scaled_int8_quant(x, scale, azp, symmetric=False)
     torch.testing.assert_close(expected, out, atol=0, rtol=0)

tests/quantization/test_compressed_tensors.py

@@ -8,6 +8,7 @@ import pytest
 import torch
 from compressed_tensors.quantization import QuantizationType
 
+from tests.models.utils import check_logprobs_close
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
     CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24,
     CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
@@ -74,6 +75,35 @@ def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args):
         assert output
 
 
+@pytest.mark.parametrize(
+    "model_path",
+    [
+        "neuralmagic/Llama-3.2-1B-quantized.w8a8"
+        # TODO static & asymmetric
+    ])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [10])
+def test_compressed_tensors_w8a8_logprobs(hf_runner, vllm_runner,
+                                          example_prompts, model_path,
+                                          max_tokens, num_logprobs):
+    dtype = "bfloat16"
+
+    with hf_runner(model_path, dtype=dtype) as hf_model:
+        hf_outputs = hf_model.generate_greedy_logprobs_limit(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_path, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
 def test_compressed_tensors_no_enforce_eager(vllm_runner):
     model_path = "nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change"
     with vllm_runner(model_path) as llm:

vllm/_custom_ops.py

@@ -510,10 +510,16 @@ def cutlass_scaled_mm_azp(a: torch.Tensor,
                           azp_adj: torch.Tensor,
                           azp: Optional[torch.Tensor] = None,
                           bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    """
+    :param azp_adj: In the per-tensor case, this should include the azp.
+    Always per-channel.
+    :param azp: Only set in the per-token case. Per-token if set.
+    """
     assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
     assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
     assert bias is None or bias.numel(
     ) == b.shape[1] and bias.dtype == out_dtype
+    assert azp is None or azp.numel() == a.shape[0]
 
     m = a.shape[0]
     n = b.shape[1]
@@ -735,7 +741,7 @@ def scaled_int8_quant(
             azp is
             None), "azp must only be provided for asymmetric quantization."
         torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
-        return output, scale, None
+        return output, scale, azp
 
     # dynamic-per-token quantization.
     input_scales = torch.empty((input.numel() // input.shape[-1], 1),

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -82,9 +82,13 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
         # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
         # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
         if not self.input_symmetric:
-            layer.azp_adj = layer.weight.sum(dim=0,
-                                             keepdim=True,
-                                             dtype=torch.int32)
+            azp_adj = layer.weight.sum(dim=0, keepdim=True, dtype=torch.int32)
+            if self.is_static_input_scheme:
+                # cutlass_w8a8 requires azp to be folded into azp_adj
+                #  in the per-tensor case
+                azp_adj = layer.input_zero_point * azp_adj
+
+            layer.azp_adj = azp_adj
         else:
             layer.azp_adj = None
 
@@ -138,7 +142,6 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
 
     def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
-
         return apply_int8_linear(input=x,
                                  weight=layer.weight,
                                  weight_scale=layer.weight_scale,

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -211,13 +211,16 @@ def apply_int8_linear(
                                                symmetric=symmetric)
 
     if x_zp is not None:
+        # Currently, static is always per-tensor and dynamic is per-token
+        static = input_zero_point is not None
+        azp = None if static else x_zp
         return ops.cutlass_scaled_mm_azp(x_q,
                                          weight,
                                          scale_a=x_scale,
                                          scale_b=weight_scale,
                                          out_dtype=input.dtype,
                                          azp_adj=azp_adj,
-                                         azp=x_zp,
+                                         azp=azp,
                                          bias=bias)
     return ops.cutlass_scaled_mm(x_q,
                                  weight,