Add test for choose_qparams for tinygemm ops

Summary:
This is in preparation for replacing tinygemm q/dq ops with the unified quant primitive ops

Test Plan:
python test/quantization/test_quant_primitives.py -k test_tinygemm_get_groupwise_affine_qparams

Reviewers:

Subscribers:

Tasks:

Tags:

test/quantization/test_quant_primitives.py

@@ -10,6 +10,7 @@
 import torch
 from torchao.quantization.quant_primitives import (
     get_group_qparams_symmetric,
+    get_groupwise_affine_qparams,
     quantize_affine,
     dequantize_affine,
     choose_qparams_affine,
@@ -56,8 +57,8 @@ def test_get_group_qparams_symmetric(self):
         scale_obs = scale_obs.reshape(weight.shape[0], -1)
 
         # assert that scales are identical
-        (scale_ao, _) = get_group_qparams_symmetric(weight, n_bit, groupsize)
-        torch.testing.assert_allclose(scale_obs, scale_ao, rtol=0, atol=0)
+        (scale_ao, _) = get_group_qparams_symmetric(weight, n_bit, groupsize, precision=torch.float16)
+        torch.testing.assert_close(scale_obs, scale_ao, rtol=0, atol=0)
 
     def test_choose_qparams_group_sym(self):
         """Note: groupwise asymmetric quant is using a different way of computing zero_points, so
@@ -88,7 +89,7 @@ def test_choose_qparams_token_asym(self):
         scale_ref = scale_ref.squeeze()
         zp_ref = zp_ref.squeeze()
 
-        torch.testing.assert_allclose(scale, scale_ref, atol=10e-3, rtol=10e-3)
+        torch.testing.assert_close(scale, scale_ref, atol=10e-3, rtol=10e-3)
         self.assertTrue(torch.equal(zero_point, zp_ref))
 
     def test_choose_qparams_tensor_asym(self):
@@ -257,7 +258,7 @@ def test_quantize_dequantize_channel_asym_4d_multi_dim_reduction(self):
         quantized = quantize_affine(input, block_size, scale, zero_point, dtype)
         dequantized = dequantize_affine(quantized, block_size, scale, zero_point, dtype, output_dtype=torch.float32)
         # we don't have corresponding ops in existing primitives, so just make sure it runs and it's close to float
-        torch.testing.assert_allclose(dequantized, input, rtol=2, atol=0.02)
+        torch.testing.assert_close(dequantized, input, rtol=2, atol=0.02)
 
     def test_choose_qparams_tensor_asym_eps(self):
         input = torch.zeros(10, 10)
@@ -279,5 +280,42 @@ def test_get_group_qparams_symmetric_memory(self):
         after_choose_qparams_mem_use = torch.cuda.memory_allocated()
         self.assertTrue(after_choose_qparams_mem_use < 1.2 * original_mem_use)
 
+    def test_tinygemm_get_groupwise_affine_qparams(self):
+        input = torch.randn(10, 256)
+        n_bit = 4
+        scale_ref, zero_point_ref = get_groupwise_affine_qparams(input, n_bit=n_bit, groupsize=128, dtype=torch.bfloat16)
+
+        mapping_type = MappingType.ASYMMETRIC
+        dtype = torch.int8
+        block_size = (1, 128)
+        quant_min = 0
+        quant_max = 2**n_bit - 1
+        eps = 1e-6
+        scale_dtype = torch.bfloat16
+        zero_point_dtype = torch.bfloat16
+        scale, zero_point = \
+            choose_qparams_affine(
+                input,
+                mapping_type,
+                block_size,
+                dtype,
+                quant_min,
+                quant_max,
+                eps,
+                scale_dtype=scale_dtype,
+                zero_point_dtype=zero_point_dtype,
+                _is_zero_exactly_representable=False,
+            )
+
+        def int_zero_point_to_float(zero_point, scale, qaunt_min, mid_point):
+            return (quant_min - zero_point + mid_point) * scale
+
+        mid_point = 2 ** (n_bit - 1)
+        zero_point_float = int_zero_point_to_float(zero_point, scale, quant_min, mid_point)
+
+        self.assertTrue(torch.equal(scale, scale_ref))
+        torch.testing.assert_close(zero_point_float, zero_point_ref, rtol=0.00001, atol=torch.max(scale)*0.03)
+
+
 if __name__ == "__main__":
     unittest.main()

torchao/quantization/quant_primitives.py

@@ -251,19 +251,24 @@ def choose_qparams_affine(
    eps: Optional[float] = None,
    scale_dtype: Optional[torch.dtype] = None,
    zero_point_dtype: Optional[torch.dtype] = None,
+   _is_zero_exactly_representable = True,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Args:
         input (torch.Tensor): fp32, bf16, fp16 input Tensor
         mapping_type (MappingType): determines how the qparams are calculated, symmetric or asymmetric
-      block_size: (Tuple[int, ...]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam
-                           e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
+        block_size: (Tuple[int, ...]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam
+          e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
         target_dtype (torch.dtype): dtype for target quantized Tensor
         quant_min (Optional[int]): minimum quantized value for target quantized Tensor
         quant_max (Optioanl[int]): maximum quantized value for target quantized Tensor
         eps (Optional[float]): minimum scale, if not provided, default to eps of input.dtype
         scale_dtype (torch.dtype): dtype for scale Tensor
         zero_point_dtype (torch.dtype): dtype for zero_point Tensor
+        _is_zero_exactly_representable (bool): a private flag to indicate whether we need zero to be exactly
+          representable or not, this is typically required for ops that needs zero_padding, like convolution
+          it's less important for ops that doesn't have zero_padding in the op itself, like linear. if we don't need
+          zero to be exactly representable, we'll not do rounding and clamping for zero_point
 
     Output:
         Tuple of scales and zero_points Tensor with requested dtype
@@ -283,17 +288,26 @@ def choose_qparams_affine(
     min_val = torch.amin(input, dim=reduction_dims, keepdim=False)
     max_val = torch.amax(input, dim=reduction_dims, keepdim=False)
 
-    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
-    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+    if _is_zero_exactly_representable:
+        min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+        max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+    else:
+        min_val_neg = min_val
+        max_val_pos = max_val
 
     if mapping_type == MappingType.SYMMETRIC:
         max_val_pos = torch.max(-min_val_neg, max_val_pos)
         scale = max_val_pos / (float(quant_max - quant_min) / 2)
+        assert _is_zero_exactly_representable, "non-representable zero path is not implemented for symmetric quantization"
         zero_point = torch.full_like(scale, int((quant_min + quant_max + 1) / 2))
     else:
         scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
-        zero_point = quant_min - torch.round(min_val_neg / scale)
-        zero_point = torch.clamp(zero_point, quant_min, quant_max)
+        if _is_zero_exactly_representable:
+            zero_point = quant_min - torch.round(min_val_neg / scale)
+            zero_point = torch.clamp(zero_point, quant_min, quant_max)
+        else:
+            zero_point = quant_min - min_val_neg / scale
+
 
     if eps is None:
         eps = torch.finfo(input.dtype).eps