Support fp32 activation for quantizing llama2 to int8 activation and …

…int4 weight (pytorch#2032)

Summary:
Pull Request resolved: pytorch#2032

Previously we only supported quantizing fp16 activations to int8.
This adds support for quantizing fp32 activations as well to enable testing.

Representation:
https://www.internalfb.com/intern/everpaste/?handle=GAoWXBlf5O8T4TMBAP8Cps4UiVx7bsIXAAAz

Reviewed By: digantdesai

Differential Revision: D54032932

fbshipit-source-id: baaddbd385985240689444041c5de33245c86dcf

examples/models/llama2/builder.py

@@ -46,6 +46,15 @@ class DType(Enum):
     fp32 = "fp32"
     fp16 = "fp16"
 
+    def to_torch_dtype(self) -> torch.dtype:
+        mapping = {
+            DType.fp32: torch.float32,
+            DType.fp16: torch.float16,
+        }
+        if self not in mapping:
+            raise ValueError(f"Unsupported dtype {self}")
+        return mapping[self]
+
 
 def load_llama_model(
     *,
@@ -145,13 +154,10 @@ def to_dtype(self, dtype_override: Optional[DType]) -> "LlamaEdgeManager":
         assert not dtype_override or isinstance(
             dtype_override, DType
         ), "Override dtype needs to be of type <DType>"
-        if dtype_override == DType.fp16 and self.dtype != DType.fp16:
-            logging.info("model.to torch.float16")
-            self.model = self.model.to(dtype=torch.float16)
-            self.dtype = dtype_override
-        elif dtype_override == DType.fp32 and self.dtype != DType.fp32:
-            logging.info("model.to torch.float32")
-            self.model = self.model.to(dtype=torch.float32)
+        if dtype_override is not None and dtype_override != self.dtype:
+            torch_dtype = dtype_override.to_torch_dtype()
+            logging.info(f"model.to {torch_dtype}")
+            self.model = self.model.to(dtype=torch_dtype)
             self.dtype = dtype_override
         return self
 

examples/models/llama2/export_llama_lib.py

@@ -11,7 +11,7 @@
 import shlex
 from functools import partial
 from pathlib import Path
-from typing import List
+from typing import List, Optional
 
 import pkg_resources
 import torch
@@ -94,7 +94,11 @@ def check_embedding_byte_registered():
     return quantizers
 
 
-def quantize(model: torch.nn.Module, qmode: str) -> torch.nn.Module:
+def quantize(
+    model: torch.nn.Module,
+    qmode: str,
+    activation_dtype: Optional[DType],
+) -> torch.nn.Module:
     """
     Quantizes a model by converting all weights to int8.
     Args:
@@ -103,14 +107,21 @@ def quantize(model: torch.nn.Module, qmode: str) -> torch.nn.Module:
     Returns:
         A quantized model.
     """
+    if activation_dtype is not None:
+        torch_dtype = activation_dtype.to_torch_dtype()
+    else:
+        torch_dtype = torch.float16
+
     if qmode == "int8":
         model_int8 = WeightOnlyInt8QuantHandler(model)
         model_int8_state_dict = model_int8.create_quantized_state_dict()
         model_int8 = model_int8.convert_for_runtime()
         model_int8.load_state_dict(model_int8_state_dict)
         return model_int8
     elif qmode == "int4":
-        model_int4 = Int8DynActInt4WeightQuantHandler(model)
+        model_int4 = Int8DynActInt4WeightQuantHandler(
+            model, activation_precision=torch_dtype
+        )
         model_int4_state_dict = model_int4.create_quantized_state_dict()
         model_int4 = model_int4.convert_for_runtime()
         print("quantized model:", model_int4)
@@ -269,28 +280,29 @@ def _export_llama(modelname, args) -> str:  # noqa: C901
     output_dir_path = canonical_path(args.output_dir, dir=True)
     modelname = "llama2"
     weight_type = WeightType.FAIRSEQ2 if args.fairseq2 else WeightType.LLAMA
+
+    # dtype override
+    if args.dtype_override is not None:
+        dtype_override = DType[args.dtype_override]
+    else:
+        dtype_override = DType["fp16"] if args.quantization_mode == "int4" else None
+
     # source transforms
     transforms = []
     if args.quantized_ckpt or args.quantization_mode:
         modelname = f"{modelname}_q"
-        transforms.append(partial(quantize, qmode=args.quantization_mode))
+        transforms.append(
+            partial(
+                quantize, qmode=args.quantization_mode, activation_dtype=dtype_override
+            )
+        )
 
     if args.embedding_quantize:
         modelname = f"{modelname}_e"
         transforms.append(
             lambda model: EmbeddingOnlyInt8QuantHandler(model).convert_for_runtime()
         )
 
-    # dtype override
-    if args.dtype_override:
-        override = (
-            DType["fp16"]
-            if args.quantization_mode == "int4"
-            else DType[args.dtype_override]
-        )
-    else:
-        override = None
-
     # export_to_edge
     quantizers = get_pt2e_quantizers(args)
 
@@ -323,7 +335,7 @@ def _export_llama(modelname, args) -> str:  # noqa: C901
         .set_output_dir(output_dir_path)
         .set_metadata(args.metadata)
         .source_transform(transforms)
-        .to_dtype(override)
+        .to_dtype(dtype_override)
         .export_to_edge(quantizers)
         .to_backend(partitioners)
         .to_executorch()

examples/models/llama2/quantize.py

@@ -791,7 +791,14 @@ def _calc_padded_size_linear_int4(k, groupsize=1, inner_k_tiles=1):
     return find_multiple(k, groupsize, inner_k_tiles * 16)
 
 
-def replace_linear_8da4w(module, group_size, inner_k_tiles, padding_allowed):
+def replace_linear_8da4w(
+    module,
+    group_size,
+    inner_k_tiles,
+    padding_allowed,
+    activation_precision,
+    weight_precision,
+):
     for name, child in module.named_children():
         if isinstance(child, nn.Linear):
             if (
@@ -807,20 +814,37 @@ def replace_linear_8da4w(module, group_size, inner_k_tiles, padding_allowed):
                         bias=False,
                         group_size=group_size,
                         inner_k_tiles=inner_k_tiles,
+                        activation_precision=activation_precision,
+                        weight_precision=weight_precision,
                     ),
                 )
         else:
-            replace_linear_8da4w(child, group_size, inner_k_tiles, padding_allowed)
+            replace_linear_8da4w(
+                child,
+                group_size,
+                inner_k_tiles,
+                padding_allowed,
+                activation_precision,
+                weight_precision,
+            )
 
 
 class Int8DynActInt4WeightQuantHandler:
-    def __init__(self, mod, group_size=128, inner_k_tiles=8, padding_allowed=True):
+    def __init__(
+        self,
+        mod,
+        group_size=128,
+        inner_k_tiles=8,
+        padding_allowed=True,
+        activation_precision=torch.float16,
+        weight_precision=torch.float16,
+    ):
         self.mod = mod
         self.group_size = group_size
         self.inner_k_tiles = inner_k_tiles
         self.padding_allowed = padding_allowed
-        # TODO: make this an argument
-        self.precision = torch.float16
+        self.activation_precision = activation_precision
+        self.weight_precision = weight_precision
         assert group_size in [32, 64, 128, 256]
         assert inner_k_tiles in [2, 4, 8]
 
@@ -861,7 +885,9 @@ def create_quantized_state_dict(self):
                     weight_int4pack,
                     scales_and_zeros,
                 ) = prepare_int4_weight_and_scales_and_zeros(
-                    weight.to(self.precision), self.group_size, self.inner_k_tiles
+                    weight.to(self.weight_precision),
+                    self.group_size,
+                    self.inner_k_tiles,
                 )
                 cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to("cpu")
                 cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to("cpu")
@@ -870,7 +896,12 @@ def create_quantized_state_dict(self):
 
     def convert_for_runtime(self):
         replace_linear_8da4w(
-            self.mod, self.group_size, self.inner_k_tiles, self.padding_allowed
+            self.mod,
+            self.group_size,
+            self.inner_k_tiles,
+            self.padding_allowed,
+            self.activation_precision,
+            self.weight_precision,
         )
         return self.mod
 
@@ -891,6 +922,8 @@ def __init__(
         dtype=None,
         group_size: int = 128,
         inner_k_tiles: int = 8,
+        activation_precision: torch.dtype = torch.float16,
+        weight_precision: torch.dtype = torch.float16,
     ) -> None:
         super().__init__()
         # always pad if needed since it becomes a noop at runtime if not needed
@@ -903,7 +936,8 @@ def __init__(
         assert not bias, "require bias=False"
         self.group_size = group_size
         self.inner_k_tiles = inner_k_tiles
-        self.precision = torch.float16
+        self.weight_precision = weight_precision
+        self.activation_precision = activation_precision
 
         # assert out_features % 8 == 0, "require out_features % 8 == 0"
         assert (
@@ -917,12 +951,13 @@ def __init__(
         self.register_buffer(
             "scales_and_zeros",
             torch.empty(
-                (in_features // group_size, out_features, 2), dtype=self.precision
+                (in_features // group_size, out_features, 2),
+                dtype=self.weight_precision,
             ),
         )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
-        input = input.to(self.precision)
+        input = input.to(self.activation_precision)
         input = F.pad(input, pad=(0, self.in_features - self.origin_in_features))
 
         (
@@ -937,15 +972,21 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
             input, scales, zero_points, quant_min, quant_max, torch.int8
         )
         input = torch.ops.quantized_decomposed.dequantize_per_token(
-            input, scales, zero_points, quant_min, quant_max, torch.int8, self.precision
+            input,
+            scales,
+            zero_points,
+            quant_min,
+            quant_max,
+            torch.int8,
+            self.activation_precision,
         )
 
-        input = input.to(self.precision)
+        input = input.to(self.activation_precision)
         return linear_forward_int4(
             input,
             self.weight,
             self.scales_and_zeros,
             self.out_features,
             self.group_size,
-            self.precision,
+            self.weight_precision,
         )