quantize_activation_per_token_absmax use general quant primitives (#…

…193)

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test/quantization/test_quant_primitives.py

@@ -126,6 +126,34 @@ def test_choose_qparams_tensor_sym(self):
         self.assertTrue(torch.equal(scale, scale_ref))
         self.assertTrue(torch.equal(zero_point, zp_ref))
 
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "skipping when torch verion is 2.4 or lower")
+    def test_quantize_activation_per_token_abs_max(self):
+        from torchao.quantization.quant_primitives import quantize_activation_per_token_absmax
+        input = torch.randn(10, 10)
+        quantized_ref, scale_ref = quantize_activation_per_token_absmax(input)
+
+        mapping_type = MappingType.SYMMETRIC
+        block_size = list(input.shape)
+        for i in range(len(block_size) - 1):
+            block_size[i] = 1
+        dtype = torch.int8
+        eps = 1e-5
+        quant_min = -127
+        quant_max = 127
+        scale, zero_point = choose_qparams_affine(input, mapping_type, block_size, dtype, quant_min, quant_max, eps=eps, scale_dtype=torch.float)
+
+        quantized = quantize_affine(input, block_size, scale, zero_point, dtype, quant_min, quant_max)
+
+        self.assertTrue(torch.equal(quantized, quantized_ref))
+        self.assertTrue(torch.equal(scale, scale_ref))
+
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "skipping when torch verion is 2.4 or lower")
+    def test_quantize_activation_per_token_abs_max_zero_input(self):
+        from torchao.quantization.quant_primitives import quantize_activation_per_token_absmax
+        input = torch.zeros(10, 10)
+        # make sure it still works
+        quantized_ref, scale_ref = quantize_activation_per_token_absmax(input)
+
 
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "skipping when torch verion is 2.3 or lower")
     def test_quantize_dequantize_group_sym(self):

torchao/quantization/quant_primitives.py

@@ -330,21 +330,23 @@ def dynamically_quantize_per_tensor(
 
 
 def quantize_activation_per_token_absmax(t):
-    n_bits = 8
     # if the shape of t is [B, N, K], the shape of scales will be [B, N, 1]
-
-    scales = t.abs().amax(dim=-1, keepdim=True)
-    if scales.dtype == torch.float16:
-        scales = (
-            scales.float()
-        )  # want float scales to avoid overflows for fp16, (bf16 has wide enough range)
-    q_max = 2 ** (n_bits - 1) - 1
-    scales = scales.clamp(min=1e-5).div(q_max)
+    mapping_type = MappingType.SYMMETRIC
+    block_size = list(t.shape)
+    for i in range(len(block_size) - 1):
+        block_size[i] = 1
+    dtype = torch.int8
+    eps = 1e-5
     # Note: the original smoothquant does not clamp to qmin/qmax here,
     # but some of the tests with bfloat16 ended up with a flipped sign
     # if we don't clamp.  TODO(future) look into this further.
-    t = torch.round(t / scales).clamp(-127, 127).to(torch.int8)
-    return t, scales
+    quant_min = -127
+    quant_max = 127
+    scale, zero_point = choose_qparams_affine(t, mapping_type, block_size, dtype, quant_min, quant_max, eps, scale_dtype=torch.float)
+
+    quantized = quantize_affine(t, block_size, scale, zero_point, dtype, quant_min, quant_max)
+
+    return quantized, scale
 
 
 def dynamically_quantize_per_channel(x, quant_min, quant_max, target_dtype):