introduce new int8 quantization API #3241

namgyu-youn · 2025-10-24T12:50:23Z

Summary:
Introduce a new tensor subclass API. Main features are

Int8Tensor: Main API, which handles quantization and dequantization operations
Utility operation functions: Tensor slice, index selection

This api is integrated to global variants (Int8WeightOnlyConfig, Int8DynamicActivationInt8WeightConfig) using version, and not defined as a default.

Related Issue/PR:
This is reopened PR for #3038

Test plan:
test/quantization/quantize_/workflows/int8/test_int8_tensor.py

Performance:
The following are the results of https://github.com/pytorch/ao/blob/main/tutorials/quantize_vit/run_vit_b_quant.py with a batch size of 32:

API	With `torch.compile`	Without `torch.compile`
Old	65.47 ms	234.39 ms
New	63.30 ms	239.30 ms

pytorch-bot · 2025-10-24T12:50:27Z

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3241

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test/quantization/quantize_/workflows/int8/test_int8_tensor.py

jerryzh168 · 2025-10-24T17:43:01Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        )
+
+    @common_utils.parametrize("dtype", [torch.bfloat16, torch.float16])
+    def test_quantization_shapes(self, dtype):


this seems to be a combination of two tests, one for dynamic quant one for static quant, can you use something like this:

ao/test/quantization/quantize_/workflows/float8/test_float8_tensor.py

Line 65 in e9c7bea

@common_utils.parametrize("mode", ["dynamic", "weight-only"])

also I feel it might be better to not add static quant in this PR, and in a separate PR add both the tensor support and config support for static quant

Okay, not sure to remove static flags (although its not fully implemented) before, but small PR should be always better I feel. I will remove static_scale and all those supports.

jerryzh168 · 2025-10-24T17:44:32Z

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

+        if act_quant_kwargs is not None and act_quant_kwargs.static_scale is not None:
+            # INT8 × INT8 (static)
+            scale = act_quant_kwargs.static_scale
+            zero_point = torch.zeros_like(scale, dtype=torch.int8)


I think user should specify static_zero_point as well

but again, it's better to do this in a separate PR, since current state is a half of the static quant feature (no config)

jerryzh168 · 2025-10-24T17:46:37Z

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

+        # Cast fp16 scale to float
+        intermediate_dtype = (
+            torch.float if x_scales.dtype == torch.half else x_scales.dtype
+        )
+        # Note: CUDA doesn't support int32/int64 matmul, so we convert to float
+        # Error message is NotImplementedError: "addmm_cuda" not implemented for 'Int'
+        # This may introduce minor numerical differences compared to int arithmetic
+        y_dot = torch.mm(tmp.to(intermediate_dtype), w_vals_t.to(intermediate_dtype))
+
+        # Apply activation scale
+        is_per_tensor_act = x_scales.numel() == 1
+        if is_per_tensor_act:
+            y_dot.mul_(x_scales.to(intermediate_dtype))
+        else:
+            # For block-wise activation scale, reshape to match y_dot
+            x_scales_reshaped = x_scales.view(y_dot.shape[0], -1)
+            y_dot.mul_(x_scales_reshaped.to(intermediate_dtype))
+
+        # Apply weight scale
+        is_per_tensor_weight = w_scales.numel() == 1
+        if is_per_tensor_weight:
+            result = y_dot.mul_(w_scales.to(intermediate_dtype))
+        else:
+            # Per-row weight scale - transpose and broadcast
+            w_scales_broadcast = w_scales.t().expand_as(y_dot)
+            result = y_dot.mul_(w_scales_broadcast.to(intermediate_dtype))
+
+        # Reshape back to original shape
+        result = result.view(*x_vals.shape[:-1], result.shape[-1])
+        result = result.to(activation_tensor.dtype)


this should follow:

ao/torchao/dtypes/uintx/plain_layout.py

Line 281 in e9c7bea

def _linear_int8_act_int8_weight_impl(input_tensor, weight_tensor, bias):

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

jerryzh168

I think we should

split the static quant support to separate PR
follow what https://github.com/pytorch/ao/blob/main/torchao/dtypes/uintx/plain_layout.py is doing for quantized linear implementation

this should be a refactor PR, not a refactor + some extra modifications + some feature implementations I think

jerryzh168 · 2025-10-30T22:45:53Z

torchao/float8/inference.py

    aten = torch.ops.aten

-    # Unsupported case for now, this would be 1 scale per data element
+    # Per-tensor quantization (scalar scale)


is this change related?

It is updated to support more granularity. Without this change, we can't use per-tensor (0D scale) and per-row (1D scale).

So maybe it's better to move this util function to a common place?

this can be moved to torchao/quantization/quantize_/common/utils.py I think

Okay, then I will move this to torchao/quantization/quantize_/common/utils.py after this PR.

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

jerryzh168 · 2025-10-30T22:51:47Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+
+    @common_utils.parametrize("dtype", [torch.bfloat16, torch.float16])
+    @common_utils.parametrize("has_bias", [True, False])
+    def test_weight_only_linear_with_bias(self, dtype, has_bias):


this can probably be merged into the linear varaints test as well

jerryzh168

thanks, I think the tensor changes looks good, but need to make a linear_variants tests to make sure we cover different aspects of things (e.g. compile), see comments inline

can you also do a e2e perf check with https://github.com/pytorch/ao/blob/main/tutorials/quantize_vit/run_vit_b_quant.py to make sure the performance are the same before and after change for vit model?

also add a kernel check might be useful to make sure we don't regress things:

ao/test/quantization/quantize_/workflows/float8/test_float8_tensor.py

Line 422 in 1e473ed

def test_expected_gpu_kernel_fbgemm(self):

namgyu-youn · 2025-10-31T10:06:08Z

Updated logs:

Support 3D Tensor input using reshape, similar to Flot8Tensor
Build integrated linear variants test: introduce new int8 quantization API #3241 (comment), introduce new int8 quantization API #3241 (comment)
Add available kernel test: introduce new int8 quantization API #3241 (review)
Add PERF result in PR contents: introduce new int8 quantization API #3241 (review)

Xia-Weiwen · 2025-11-03T05:43:10Z

Hi @namgyu-youn Do you plan to submit another PR for static quantization? We also need static quantization for SmoothQuant. So, we are wondering if you have a plan or we should consider adding it ourselves. Thanks. CC @cyxlily

namgyu-youn · 2025-11-03T06:37:52Z

Hi @namgyu-youn Do you plan to submit another PR for static quantization? We also need static quantization for SmoothQuant. So, we are wondering if you have a plan or we should consider adding it ourselves. Thanks. CC @cyxlily

Yeah, static quantization support using static/dynamic flags is planned; I hope to show it to your team in the foreseeable future.

Also, in the SmoothQuant case, validating its support for the new quantization APIs (below) has higher priority, I think. Could you look into it?

W4A16-INT: Int4WeightOnlyConfig(group_size=32, version=2)
W4A16-FP: Float8WeightOnlyConfig(version=2)
W8A8-FP-dynamic: Float8DynamicActivationFloat8WeightConfig(version=2)

Xia-Weiwen · 2025-11-03T06:52:03Z

Yeah, static quantization support using static/dynamic flags is planned; I hope to show it to your team in the foreseeable future.

Thanks. Looking forward to it. If there is anything we can help with, please let us know.

Also, in the SmoothQuant case, validating its support for the new quantization APIs (below) has higher priority, I think. Could you look into it?

W4A16-INT: Int4WeightOnlyConfig(group_size=32, version=2)

W4A16-FP: Float8WeightOnlyConfig(version=2)

W8A8-FP-dynamic: Float8DynamicActivationFloat8WeightConfig(version=2)

By "validating them", do you mean adding test cases? And are W4A16 and W8A16 (I guess there is a typo in your comment) really needed for SmoothQuant? For W4A16 , it would be much the same as AWQ. And for W8A16, I think accuracy is generally good enough without SmoothQuant.

namgyu-youn · 2025-11-03T07:36:21Z

By "validating them", do you mean adding test cases? And are W4A16 and W8A16 (I guess there is a typo in your comment) really needed for SmoothQuant? For W4A16 , it would be much the same as AWQ. And for W8A16, I think accuracy is generally good enough without SmoothQuant.

Oh yes, it was a typo (W8A16 is right), and W4A16-INT (Int4WeightOnlyConfig(group_size=32, version=2)) is of interest. In my last experience and https://arxiv.org/html/2411.02355v3, W4A16-INT is the most efficient choice for synchronous deployments, while W8A8-INT maximize throughput in asynchronous settings.

Because current AWQ/SmoothQuant test is only working with old APIs (version 1), we can replace it with new APIs like Int4WeightOnlyConfig(group_size=32, version=2) I guess.

Xia-Weiwen · 2025-11-03T07:59:00Z

By "validating them", do you mean adding test cases? And are W4A16 and W8A16 (I guess there is a typo in your comment) really needed for SmoothQuant? For W4A16 , it would be much the same as AWQ. And for W8A16, I think accuracy is generally good enough without SmoothQuant.

Oh yes, it was a typo (W8A16 is right), and W4A16-INT (Int4WeightOnlyConfig(group_size=32, version=2)) is of interest. In my last experience and https://arxiv.org/html/2411.02355v3, W4A16-INT is the most efficient choice for synchronous deployments, while W8A8-INT maximize throughput in asynchronous settings.

Because current AWQ/SmoothQuant test is only working with old APIs (version 1), we can replace it with new APIs like Int4WeightOnlyConfig(group_size=32, version=2) I guess.

I see. Thanks. We will evaluate that.

Xia-Weiwen · 2025-11-04T01:32:58Z

Hi @namgyu-youn May I know if you have a timeline to land this? Thanks.

jerryzh168 · 2025-11-04T01:41:59Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+            ((32, 128), 64, 256),  # 3D
+        ],
+    )
+    def test_int8_linear_quantization_accuracy(


can this one be combined with test_int8_linear_variants as well?

jerryzh168 · 2025-11-04T01:42:46Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        with self.assertRaises(NotImplementedError):
+            _ = dummy.weight[::2]
+
+    def test_index_select(self):


I think all the tests should be modified to use config as well

jerryzh168 · 2025-11-04T01:44:08Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        kernels = {}
+
+        # Check for Triton kernels
+        if "torch.ops.triton" in code[0]:


should add some asserts I think

- Configs are updated to global variants

namgyu-youn · 2025-11-04T14:05:04Z

Updated logs:

Replace Int8Tensor to global configs in test: introduce new int8 quantization API #3241 (comment)
Consolidate linear variation tests: introduce new int8 quantization API #3241 (comment)
Update parser for kernel detection test: introduce new int8 quantization API #3241 (comment)

jerryzh168 · 2025-11-06T23:40:14Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        self.assertEqual(weight2.qdata, dummy.weight.qdata.narrow(1, 0, slice_sizes[1]))
+
+        # Int8DynamicActivationInt8WeightConfig uses per-row (PerRow)
+        # Int8WeightOnlyConfig uses per-tensor (PerTensor)


it should be per row I think?

ao/torchao/quantization/quant_api.py

Line 1343 in 6815e57

group_size = weight.shape[-1]

jerryzh168 · 2025-11-06T23:42:29Z

torchao/quantization/quant_api.py

+        )
+    else:
+        assert config.version == 2, f"Unexpected version: {config.version}"
+        block_size = [weight.shape[0], weight.shape[1]]


why does this default to per tensor? I think it should follow the existing logic from L1376-1378?

jerryzh168 · 2025-11-06T23:44:48Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        self.assertLess(
+            torch.abs(dequantized - test_data).max().item(),
+            0.1,
+            msg=f"Dequantization error exceeds tolerance of {0.1}",
+        )


maybe check sqnr with compute_error instead of hardcoded absolute value?

jerryzh168 · 2025-11-06T23:45:44Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        has_triton = "triton" in code[0].lower()  # Trition
+        has_fbgemm = "fbgemm" in code[0].lower()  # FB-GEMM
+        has_int_mm = "_int_mm" in code[0]  # Int8 MatMul
+
+        self.assertTrue(
+            has_triton or has_fbgemm or has_int_mm,
+            f"No int8 quantization kernels found. has_triton={has_triton}, has_fbgemm={has_fbgemm}, has_int_mm={has_int_mm}",
+        )


is it possible to be more strict to spell out the kernels called and their order? like

ao/test/quantization/quantize_/workflows/float8/test_float8_tensor.py

Lines 596 to 600 in 6815e57

FileCheck().check_count(

"torch.ops.triton.quantize_fp8_row.default(", 1

).check_count("torch.ops.fbgemm.f8f8bf16_rowwise.default(", 1).check_not(

".run("

).run(code[0])

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

jerryzh168 · 2025-11-06T23:49:38Z

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

+            )
+
+        x_vals = activation_tensor.qdata.reshape(-1, activation_tensor.qdata.shape[-1])
+        x_scales = preprocess_scale(activation_tensor.scale, x_vals.shape)


why is this called? can you match the code with can you match the code with

ao/torchao/dtypes/uintx/plain_layout.py

Line 281 in 6815e57

def _linear_int8_act_int8_weight_impl(input_tensor, weight_tensor, bias):

exactly?

namgyu-youn · 2025-11-11T10:20:29Z

Updated logs:

Set per-row as a default granularity in both api and test: introduce new int8 quantization API #3241 (comment) introduce new int8 quantization API #3241 (comment)
Update kernel (trition, int_mm) test using parameterization and FileCheck: introduce new int8 quantization API #3241 (comment) introduce new int8 quantization API #3241 (comment)
Fix quantization ops similar to UINT pattern: introduce new int8 quantization API #3241 (comment) introduce new int8 quantization API #3241 (comment) introduce new int8 quantization API #3241 (comment)

jerryzh168 · 2025-11-11T18:04:24Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        self.dtype = torch.bfloat16
+        self.batch_size = 32
+
+        torch.manual_seed(42)


nit: this can probably be moved to TorchAOIntegrationTestCase
can also add 8e3b3da

feel free to do in a separate PR though

jerryzh168 · 2025-11-11T18:05:25Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+            dtype=self.dtype,
+            device="cuda",
+        )
+        linear.weight.data = self.weight_fp.cuda()


why do we do this? doesn't sound very necessary?

jerryzh168 · 2025-11-11T18:06:27Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    def test_per_row_scale_shape(self, dtype, config):


nit: can you merge the checks in this test to previous test test_int8_linear_variants?

jerryzh168 · 2025-11-11T18:10:41Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+            f"Dequantization error is too high to get a SQNR of {compute_error(dequantized, test_data)}"
+        )
+
+    @common_utils.parametrize(


does this have to be parametrize? I think what we need here is to check the code contains a sequence of ops / kernel calls, like this:

ao/test/quantization/quantize_/workflows/float8/test_float8_tensor.py

Lines 827 to 831 in b4ec4cb

FileCheck().check_count(

"torch.ops.triton.quantize_fp8_row.default(", 1

).check_count("torch.ops.fbgemm.f8f8bf16_rowwise.default(", 1).check_not(

".run("

).run(code[0])

I think we can check 1. the quantize op and then 2. the mm op extern_kernels._int_mm, in a single run (see example), that should be enough

I see, thanks. We don't have to go with multiple runs.

jerryzh168 · 2025-11-11T18:12:06Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    def test_dequantization_accuracy(self, config):


additional comments for this test, 1. increase the size of linear? 2. I think we don't need to overwrite the weight, we can just save the floating point weight (deepcopy) before quantization and compare the results

jerryzh168 · 2025-11-11T18:48:42Z

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

+        self.weight_fp = torch.randn(*self.test_shape, dtype=self.dtype)
+        self.input_fp = torch.randn(*self.test_shape, dtype=self.dtype)
+        self.bias = torch.randn(self.test_shape[0], dtype=self.dtype)
+        self.block_size = list(self.test_shape)


I feel we probably don't need these, it's also easier for people to follow to define everything / most of things in the test itself

jerryzh168 · 2025-11-11T18:51:30Z

torchao/float8/inference.py


-    # Unsupported case for now, this would be 1 scale per data element
+    # Per-tensor quantization (scalar scale)
+    if scale.numel() == 1:


note: I think we can just check for ndim consistently everywhere, after #3324 is fixed

also isn't handling for per tensor and per row already included in original code?

ao/torchao/float8/inference.py

Lines 158 to 178 in b4ec4cb

if block_size_for_dim == 1:

# Scale is per-element along this dimension

# Slice away as normal

return aten.slice.Tensor(scale, dim, start, end, step)

else:

# There is blocking in this dimension

# Calculate which scale elements correspond to the sliced data

scale_start = start // block_size_for_dim if start is not None else None

scale_end = (

(end + block_size_for_dim - 1) // block_size_for_dim

if end is not None

else None

)

# Error on Step > 1

if step > 1:

raise NotImplementedError(

"Slicing with step > 1 is not implemented for scale tensors."

)

return aten.slice.Tensor(scale, dim, scale_start, scale_end, 1)

jerryzh168 · 2025-11-11T18:56:24Z

torchao/quantization/quantize_/workflows/int8/int8_tensor.py

+            if len(act_kwargs.block_size) != input_ndim:
+                if input_ndim == 3 and len(act_kwargs.block_size) == 2:
+                    block_size_updated = [1] + list(act_kwargs.block_size)
+                else:
+                    block_size_updated = list(act_kwargs.block_size)[-input_ndim:]


we kind of changed the meaning of block_size used in PerBlock quant recently, check

ao/torchao/quantization/utils.py

Line 707 in b4ec4cb

elif isinstance(granularity, PerBlock):

when is this code needed?

in principle we shouldn't update block_size here, but instead, make sure block_size make sense and is consistent throughout the code base

jerryzh168 · 2025-11-11T18:58:30Z

torchao/quantization/quant_api.py

+        quantized_weight = Int8Tensor.from_hp(
+            weight,
+            block_size,
+            act_quant_kwargs=QuantizeTensorToInt8Kwargs(block_size=block_size),


why does activation use the same block_size as weight?

I think we can change block_size argument to granularity, if the block_size is unknown for activation since the shape is unknown

introduce new int8 quantization API

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Add Int8Tensor for clearer interface #3038

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refactor ops and update test cases

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add 3D support to api, build linear variants test

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refactor kernel test

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Migrating from AffineQuantizedTensor + Layouts to new structure of tensor subclasses #2752

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	FileCheck().check_count(
	"torch.ops.triton.quantize_fp8_row.default(", 1
	).check_count("torch.ops.fbgemm.f8f8bf16_rowwise.default(", 1).check_not(
	".run("
	).run(code[0])

	if block_size_for_dim == 1:
	# Scale is per-element along this dimension
	# Slice away as normal
	return aten.slice.Tensor(scale, dim, start, end, step)
	else:
	# There is blocking in this dimension
	# Calculate which scale elements correspond to the sliced data
	scale_start = start // block_size_for_dim if start is not None else None
	scale_end = (
	(end + block_size_for_dim - 1) // block_size_for_dim
	if end is not None
	else None
	)

	# Error on Step > 1
	if step > 1:
	raise NotImplementedError(
	"Slicing with step > 1 is not implemented for scale tensors."
	)

	return aten.slice.Tensor(scale, dim, scale_start, scale_end, 1)

introduce new int8 quantization API #3241

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