Adding gpu quantization workflows and apis

Summary:
Apis and workflows used for quantization and pruning in the
segment-anything-fast and gpt-fast repos.

Test Plan: python /home/cdhernandez/local/ao/ao/quantization/test.py

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 31191a7
Pull Request resolved: #1

Author: HDCharles

ao/quantization/__init__.py

@@ -0,0 +1,39 @@
+from smoothquant import *  # noqa: F403
+from quant_api import *  # noqa: F403
+from subclass import *  # noqa: F403
+from quant_primitives import *  # noqa: F403
+from utils import *  # noqa: F403
+from weight_only import *  # noqa: F403
+
+__all__ = [
+    "DynamicallyPerAxisQuantizedLinear",
+    "replace_with_custom_fn_if_matches_filter",
+    "apply_weight_only_int8_quant",
+    "apply_dynamic_quant",
+    "change_linear_weights_to_dqtensors",
+    "insert_subclass",
+    "safe_int_mm",
+    "dynamically_quantize_per_tensor",
+    "quantize_activation_per_token_absmax",
+    "dynamically_quantize_per_channel",
+    "dequantize_per_tensor",
+    "dequantize_per_channel",
+    "quant_int8_dynamic_linear",
+    "quant_int8_matmul",
+    "quant_int8_dynamic_per_token_linear",
+    "quant_int8_per_token_matmul",
+    "get_scale",
+    "SmoothFakeDynQuantMixin",
+    "SmoothFakeDynamicallyQuantizedLinear",
+    "swap_linear_with_smooth_fq_linear",
+    "smooth_fq_linear_to_inference",
+    "set_smooth_fq_attribute",
+    "DynamicallyQuantizedLinearWeight",
+    "log_with_rank",
+    "clear_logs",
+    "compute_error",
+    "forward_hook",
+    "apply_logging_hook",
+    "get_model_size_in_bytes",
+    "WeightOnlyInt8QuantLinear",
+]

ao/quantization/__pycache__/dynamic_quant.cpython-310.pyc

@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+from quant_primitives import (
+    dynamically_quantize_per_channel,
+    quant_int8_dynamic_per_token_linear,
+)
+
+__all__ = ["DynamicallyPerAxisQuantizedLinear"]
+
+
+class DynamicallyPerAxisQuantizedLinear(torch.nn.Linear):
+    """
+    This class is a replacement for `torch.nn.Linear`, implementing dynamic quantization on
+    the input across all axes except for the last axis.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        use_fused_int_mm=False,
+    ) -> None:
+        super().__init__(in_features, out_features, bias)
+        self.use_fused_int_mm = use_fused_int_mm
+        # note: enabling use_fused_int_mm = True has best perf when additionally setting
+        # torch._inductor.config.force_fuse_int_mm_with_mul = True
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        """
+        Performs the forward pass of the quantized linear layer.
+
+        This method applies dynamic quantization to the input tensor across all axes except
+        the last axis using the `quant_int8_dynamic_per_token_linear` function.
+
+        Args:
+            X (torch.Tensor): The input tensor to the quantized linear layer.
+
+        Returns:
+            torch.Tensor: The output tensor after the quantized matmul and rescale.
+
+        """
+        # The following line mimics the behavior of SmoothFakeDynamicallyQuantizedLinear
+        if not self.use_fused_int_mm:
+            X = X / self.fake_rescale
+        # somehow the inductor fusion that occurs for most transformer models
+        # when this module has an additional div op is faster than when it doesn't
+        # have it although the memory usage is slightly higher. fake_rescale is scalar 1
+        # so it doesn't affect accuracy
+        Y = quant_int8_dynamic_per_token_linear(
+            X, self.W_int_repr_t, self.W_scales, self.bias, X.dtype
+        )
+        return Y
+
+    @classmethod
+    def from_float(
+        cls, mod: torch.nn.Linear, use_fused_int_mm=False
+    ) -> "DynamicallyPerAxisQuantizedLinear":
+        """
+        Converts a `mod` of class `torch.nn.Linear` to the dynamically quantized version of it.
+
+        Note: this class does not require calibration.
+
+        Args:
+            mod (torch.nn.Linear): The original `torch.nn.Linear` module to convert.
+
+        Returns:
+            DynamicallyPerAxisQuantizedLinear: The converted quantized linear module.
+
+        """
+
+        # create the new module with a toy size to ensure initialization is fast
+        fake_in_features, fake_out_features = 8, 8
+        new_mod = cls(
+            fake_in_features,
+            fake_out_features,
+            bias=mod.bias is not None,
+            use_fused_int_mm=use_fused_int_mm,
+        )
+        new_mod.in_features = mod.in_features
+        new_mod.out_features = mod.out_features
+        W_int_repr, W_scales, _W_zps = dynamically_quantize_per_channel(
+            mod.weight, -128, 127, torch.int8
+        )
+        new_mod.register_buffer("W_int_repr_t", W_int_repr.contiguous().t())
+        new_mod.W_scales = nn.Parameter(W_scales)
+        new_mod.bias = mod.bias
+        if not use_fused_int_mm:
+            new_mod.fake_rescale = torch.tensor(
+                [1.0], dtype=mod.weight.dtype, device=mod.weight.device
+            )
+        del new_mod.weight
+
+        device_to_use = next(mod.parameters()).device
+        new_mod.to(device_to_use)
+        return new_mod

ao/quantization/__pycache__/quant_api.cpython-310.pyc

@@ -0,0 +1,74 @@
+"""
+Quantization API stuff which is not specific to SmoothQuant
+
+Note: this is throwaway code for fast results on Blueberry, this is not
+intended to be the actual long term quantization API for server GPUs.
+"""
+
+import torch
+from dynamic_quant import (
+    DynamicallyPerAxisQuantizedLinear,
+)
+from subclass import (
+    DynamicallyQuantizedLinearWeight,
+)
+from weight_only import (
+    WeightOnlyInt8QuantLinear,
+)
+
+__all__ = [
+    "replace_with_custom_fn_if_matches_filter",
+    "apply_weight_only_int8_quant",
+    "apply_dynamic_quant",
+    "change_linear_weights_to_dqtensors",
+]
+
+
+def replace_with_custom_fn_if_matches_filter(
+    model, replacement_fn, filter_fn, cur_fqn=""
+) -> None:
+    """
+    For each `child` in `model`, replaces it with `replacement_fn(child)`
+    if `filter_fn(child)` is `True`
+    """
+    name_to_child = dict(model.named_children())
+    for name, child in name_to_child.items():
+        if cur_fqn == "":
+            new_fqn = name
+        else:
+            new_fqn = f"{cur_fqn}.{name}"
+        if filter_fn(child, new_fqn):
+            new_child = replacement_fn(child)
+            setattr(model, name, new_child)
+        else:
+            replace_with_custom_fn_if_matches_filter(
+                child, replacement_fn, filter_fn, new_fqn
+            )
+
+
+def apply_weight_only_int8_quant(model):
+    replace_with_custom_fn_if_matches_filter(
+        model,
+        WeightOnlyInt8QuantLinear.from_float,
+        lambda mod, fqn: isinstance(mod, torch.nn.Linear),
+    )
+
+
+def apply_dynamic_quant(model, use_fused_int_mm=0):
+    replace_with_custom_fn_if_matches_filter(
+        model,
+        lambda mod: DynamicallyPerAxisQuantizedLinear.from_float(mod, use_fused_int_mm),
+        lambda mod, fqn: isinstance(mod, torch.nn.Linear),
+    )
+
+
+def change_linear_weights_to_dqtensors(model):
+    def insert_subclass(lin):
+        lin.weight = torch.nn.Parameter(
+            DynamicallyQuantizedLinearWeight.from_float(lin.weight), requires_grad=False
+        )
+        return lin
+
+    replace_with_custom_fn_if_matches_filter(
+        model, insert_subclass, lambda mod, fqn: isinstance(mod, torch.nn.Linear)
+    )