[reland] Add support for Int8DynActInt4WeightQuantizer (#66)

Summary:
att

Test Plan: python test/quantization/test_quant_api.py -k test_8da4w_quantizer

Reviewed By: cpuhrsch

Differential Revision: D55101038

Pulled By: jerryzh168

ghstack-source-id: 4b83757687d2241dd811cc3127d9e8dd6b02a2dc
Pull Request resolved: #74

test/quantization/test_quant_api.py

@@ -24,6 +24,8 @@
     Quantizer,
     TwoStepQuantizer,
     Int8DynActInt4WeightGPTQQuantizer,
+    Int8DynActInt4WeightQuantizer,
+    Int8DynActInt4WeightLinear,
 )
 from pathlib import Path
 from sentencepiece import SentencePieceProcessor
@@ -85,8 +87,11 @@ def quantize(self, model: torch.nn.Module) -> torch.nn.Module:
 class M(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.linear1 = torch.nn.Linear(5, 5).to(torch.float)
-        self.linear2 = torch.nn.Linear(5, 5).to(torch.float)
+        self.linear1 = torch.nn.Linear(64, 32, bias=False).to(torch.float)
+        self.linear2 = torch.nn.Linear(32, 64, bias=False).to(torch.float)
+
+    def example_inputs(self):
+        return (torch.randn(1, 64).to(torch.float),)
 
     def forward(self, x):
         x = self.linear1(x)
@@ -97,8 +102,7 @@ class TestQuantFlow(unittest.TestCase):
     def test_dynamic_quant_gpu_singleline(self):
         m = M().eval()
         m = _apply_dynamic_quant(m)
-        example_inputs = (torch.randn(1, 5).to(dtype=torch.float32),)
-        quantized = m(*example_inputs)
+        quantized = m(*m.example_inputs())
         # AssertionError: Expecting input to have dtype torch.float32, but got dtype: torch.float64
         # While executing %choose_qparams_tensor_1 : [num_users=2] = call_function[target=torch.ops.quantized_decomposed.choose_qparams.tensor](args = (%arg0_3, -128, 127, 0.000244140625, torch.int8), kwargs = {})
         # m = torch.compile(m, mode="max-autotune")
@@ -110,9 +114,9 @@ def test_dynamic_quant_gpu_singleline(self):
     def test_dynamic_quant_gpu_unified_api_unified_impl(self):
         quantizer = XNNPackDynamicQuantizer()
         m = M().eval()
+        example_inputs = m.example_inputs()
         m = quantizer.prepare(m)
         m = quantizer.convert(m)
-        example_inputs = (torch.randn(1, 5).to(dtype=torch.float32),)
         quantized = m(*example_inputs)
         # AssertionError: Expecting input to have dtype torch.float32, but got dtype: torch.float64
         # While executing %choose_qparams_tensor_1 : [num_users=2] = call_function[target=torch.ops.quantized_decomposed.choose_qparams.tensor](args = (%arg0_3, -128, 127, 0.000244140625, torch.int8), kwargs = {})
@@ -125,15 +129,24 @@ def test_dynamic_quant_gpu_unified_api_unified_impl(self):
     def test_dynamic_quant_gpu_unified_api_eager_mode_impl(self):
         quantizer = TorchCompileDynamicQuantizer()
         m = M().eval()
+        example_inputs = m.example_inputs()
         m = quantizer.quantize(m)
-        example_inputs = (torch.randn(1, 5).to(dtype=torch.float32),)
         quantized = m(*example_inputs)
         m = torch.compile(m, mode="max-autotune")
         compiled = m(*example_inputs)
         torch.testing.assert_close(quantized, compiled, atol=0, rtol=0)
 
+    def test_8da4w_quantizer(self):
+        quantizer = Int8DynActInt4WeightQuantizer(group_size=32)
+        m = M().eval()
+        example_inputs = m.example_inputs()
+        m = quantizer.quantize(m)
+        assert isinstance(m.linear1, Int8DynActInt4WeightLinear)
+        assert isinstance(m.linear2, Int8DynActInt4WeightLinear)
+        m(*example_inputs)
+
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
-    def test_gptq(self):
+    def test_gptq_quantizer(self):
         # should be similar to TorchCompileDynamicQuantizer
         precision = torch.bfloat16
         device = "cpu"

torchao/quantization/GPTQ.py

@@ -19,6 +19,7 @@
 
 # from model import Transformer  # pyre-ignore[21]
 from torch.utils._pytree import tree_flatten, tree_unflatten
+import logging
 
 # pyre-fixme[5]: Global expression must be annotated.
 aten = torch.ops.aten
@@ -63,7 +64,7 @@ def model_forward(model, x, input_pos):
         get_task_dict = tasks.get_task_dict
         evaluate = evaluator.evaluate
 else:
-    print("lm_eval is not installed, GPTQ may not be usable")
+    logging.info("lm_eval is not installed, GPTQ may not be usable")
 
 # pyre-fixme[3]: Return type must be annotated.
 def setup_cache_padded_seq_input_pos_max_seq_length_for_prefill(

torchao/quantization/quant_api.py

@@ -36,8 +36,10 @@
 from .quant_primitives import (
     get_group_qparams_symmetric,
     per_token_dynamic_quant,
+    group_quantize_tensor_symmetric,
 )
-from typing import Dict, Tuple
+from typing import Dict, Tuple, Any
+import logging
 
 __all__ = [
     "apply_weight_only_int8_quant",
@@ -54,21 +56,18 @@
 ############################# Unified Quantization APIs ##############################
 # API 1, single quantize call to create a quantized model with quantized state_dict
 class Quantizer:
-    # pyre-fixme[2]: Parameter must be annotated.
-    def quantize(self, model: torch.nn.Module, *args, **kwargs) -> torch.nn.Module:
+    def quantize(self, model: torch.nn.Module, *args: Any, **kwargs: Any) -> torch.nn.Module:
         # pyre-fixme[7]: Expected `Module` but got implicit return value of `None`.
         pass
 
 
 # API 2, flow that needs calibration or training
 class TwoStepQuantizer:
-    # pyre-fixme[2]: Parameter must be annotated.
-    def prepare(self, model: torch.nn.Module, *args, **kwargs) -> torch.nn.Module:
+    def prepare(self, model: torch.nn.Module, *args: Any, **kwargs: Any) -> torch.nn.Module:
         # pyre-fixme[7]: Expected `Module` but got implicit return value of `None`.
         pass
 
-    # pyre-fixme[2]: Parameter must be annotated.
-    def convert(self, model: torch.nn.Module, *args, **kwargs) -> torch.nn.Module:
+    def convert(self, model: torch.nn.Module, *args: Any, **kwargs: Any) -> torch.nn.Module:
         # pyre-fixme[7]: Expected `Module` but got implicit return value of `None`.
         pass
 
@@ -260,7 +259,7 @@ def replace_conv2d_1x1(conv):
         MultiInput,
     )
 else:
-    print("lm_eval not available, skip defining GPTQQuantizer")
+    logging.info("lm_eval not available, skip defining GPTQQuantizer")
 
 
 class GPTQQuantizer(Quantizer):
@@ -442,11 +441,7 @@ def _convert_for_runtime(self, model: torch.nn.Module) -> "nn.Module":
 
     @torch.no_grad()
     # pyre-fixme[14]: `quantize` overrides method defined in `Quantizer` inconsistently.
-    def quantize(
-        self,
-        # pyre-fixme[2]: Parameter must be annotated.
-        model,
-    ) -> torch.nn.Module:
+    def quantize(self, model: torch.nn.Module, **kwargs: Any) -> torch.nn.Module:
         state_dict = self._create_quantized_state_dict(
             model,
             # pyre-fixme[16]: `GPTQQuantizer` has no attribute `tokenizer`.
@@ -686,6 +681,91 @@ def replace_linear_8da4w(
             )
 
 
+class Int8DynActInt4WeightQuantizer(Quantizer):
+    def __init__(
+        self,
+        group_size: int = 256,
+        padding_allowed: bool = False,
+        precision: torch.dtype = torch.float32,
+        scales_precision: torch.dtype = torch.float32,
+    ) -> None:
+        self.group_size: int = group_size
+        self.padding_allowed: bool = padding_allowed
+        self.precision: torch.dtype = precision
+        self.scales_precision: torch.dtype = scales_precision
+        # assert group_size in [32, 64, 128, 256]
+
+    @torch.no_grad()
+    def _create_quantized_state_dict(self, model: torch.nn.Module) -> Dict[str, torch.Tensor]:
+        cur_state_dict = model.state_dict()
+        for fqn, mod in model.named_modules():
+            if isinstance(mod, torch.nn.Linear):
+                assert not mod.bias
+                out_features = mod.out_features
+                in_features = mod.in_features
+                # assert out_features % 8 == 0, "require out_features % 8 == 0"
+                print(f"linear: {fqn}, in={in_features}, out={out_features}")
+
+                assert (
+                    in_features % self.group_size == 0
+                ), f"require in_features:{in_features} % self.group_size:{self.group_size} == 0"
+
+                weight = mod.weight.data
+                """
+                if not _check_linear_int4_k(
+                    in_features, self.group_size
+                ):
+                    if self.padding_allowed:
+                        print(
+                            f"warning: {fqn} is padded to satisfy in_features % 1024 == 0"
+                        )
+                        padded_in_features = _calc_padded_size_linear_int4(
+                            in_features, self.group_size
+                        )
+                        weight = F.pad(
+                            weight, pad=(0, padded_in_features - in_features)
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, "
+                            + "and that group_size"
+                        )
+                """
+                (
+                    weight_int8,
+                    scales,
+                    zeros,
+                ) = group_quantize_tensor_symmetric(
+                    weight.to(self.precision),
+                    4,  # n_bit
+                    self.group_size,
+                    self.scales_precision,
+                )
+                cur_state_dict[f"{fqn}.weight"] = weight_int8.to("cpu")
+                cur_state_dict[f"{fqn}.scales"] = scales.to("cpu")
+                cur_state_dict[f"{fqn}.zeros"] = zeros.to("cpu")
+                # TODO: support bias?
+
+        return cur_state_dict
+
+    def _convert_for_runtime(self, model: torch.nn.Module) -> torch.nn.Module:
+        replace_linear_8da4w(
+            model,
+            self.group_size,
+            self.padding_allowed,
+            self.precision,
+            self.scales_precision,
+        )
+        return model
+
+    def quantize(self, model: torch.nn.Module, *args: Any, **kwargs: Any) -> torch.nn.Module:
+        state_dict = self._create_quantized_state_dict(model)
+        model = self._convert_for_runtime(model)
+        # TODO: make it strict
+        model.load_state_dict(state_dict, strict=False)
+        return model
+
+
 class Int8DynActInt4WeightGPTQQuantizer(GPTQQuantizer):
     # pyre-fixme[3]: Return type must be annotated.
     def __init__(