Implemented range setting in QNN llama flow

Summary:
`llama.py` now has the `--range_setting` flag, for which there are the options `mse_weight_only` and `mse_with_act_loss`. There is also an eval script for computing perplexity called `eval_llama_qnn.py`. This script also has a flag --quant_linear_only to only quantize linear/conv nodes, to run faster experiments.

(for faster eval, try seq length 1024)

Reviewed By: cccclai

Differential Revision: D78127727

Author: rohansjoshi

examples/qualcomm/oss_scripts/llama/TARGETS

@@ -34,6 +34,16 @@ python_library(
     ],
 )
 
+python_library(
+    name = "range_setting_pt2e",
+    srcs = [
+        "range_setting_pt2e.py",
+    ],
+    deps = [
+        "//caffe2:torch",
+    ],
+)
+
 python_binary(
     name = "llama",
     main_function = "executorch.examples.qualcomm.oss_scripts.llama.llama.main",
@@ -42,6 +52,7 @@ python_binary(
     ],
     deps = [
         ":llama_lib",
+        "//executorch/examples/qualcomm/oss_scripts/llama:range_setting_pt2e",
     ],
 )
 
@@ -55,6 +66,7 @@ python_binary(
     deps = [
         ":llama_lib",
         "//executorch/examples/models/llama:eval_library",
+        "//executorch/examples/qualcomm/oss_scripts/llama:range_setting_pt2e",
         "fbsource//third-party/pypi/lm-eval:lm-eval",
     ],
 )

examples/qualcomm/oss_scripts/llama/eval_llama_qnn.py

@@ -5,16 +5,13 @@
 # LICENSE file in the root directory of this source tree.
 
 import argparse
-import copy
 import json
 
 import logging
 import sys
 
-from typing import List, Tuple
-
 import torch
-import torch.nn as nn
+
 from executorch.backends.qualcomm.quantizer.custom_annotation import (
     annotate_linear_16a8w_in_affine_layer,
     annotate_matmul_16a8w,
@@ -46,14 +43,18 @@
     LlamaModel,
     ModelArgs,
 )
-
-from executorch.examples.qualcomm.utils import make_quantizer
+from executorch.examples.qualcomm.oss_scripts.llama.range_setting_pt2e import (
+    compute_scales,
+    make_custom_quantizer,
+    reverse_quantize_module_swap,
+    set_scales,
+    WrappedLlamaModel,
+)
 
 from lm_eval.evaluator import simple_evaluate
 
 from pytorch_tokenizers import get_tokenizer
 
-from torchao.quantization.pt2e import MinMaxObserver
 from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
 from torchao.quantization.pt2e.quantizer import QuantizationSpec
 
@@ -64,30 +65,6 @@
 logging.getLogger().setLevel(logging.INFO)
 
 
-class WrappedLlamaModel(nn.Module):
-    def __init__(
-        self, model, atten_mask, use_kv_cache=False, max_seq_len=512, device="cuda"
-    ):
-        super(WrappedLlamaModel, self).__init__()
-        self.model = model
-        self.max_seq_len = max_seq_len
-        self.use_kv_cache = use_kv_cache
-        self.device = device
-        self.atten_mask = atten_mask
-
-    def forward(
-        self,
-        tokens: torch.Tensor,
-        *args,
-    ) -> Tuple[torch.Tensor, List[torch.Tensor], List[torch.Tensor]]:
-        # Pad input if necessary, since LlamaModel requires static shape
-        if tokens.shape[1] != self.max_seq_len:
-            tokens = torch.nn.functional.pad(
-                tokens, (0, self.max_seq_len - tokens.shape[1])
-            )
-        return self.model.forward(tokens, self.atten_mask)
-
-
 def add_mse_weight_observer(quant_dtype, quantizer):
     weight_dtype = (
         torch.int4
@@ -115,24 +92,16 @@ def add_mse_weight_observer(quant_dtype, quantizer):
     )
 
 
-def gen_eval_wrapper(model_name, args):
-    tokenizer = get_tokenizer(args.tokenizer_path)
+def prepare_model(model_name, args):
     with open(args.params) as f:
-        kv_config = ModelArgs(**json.load(f))
+        prefill_config = ModelArgs(**json.load(f))
         # TODO: support batch inputs if necessary
-        kv_config.max_batch_size = 1
-        kv_config.max_seq_len = args.max_seq_length
-        kv_config.use_kv_cache = True
-
-        prefill_config = copy.copy(kv_config)
+        prefill_config.max_batch_size = 1
         prefill_config.max_seq_len = args.max_seq_length
-        prefill_config.use_kv_cache = (
-            False if args.max_seq_length == args.prefill_ar_len else True
-        )
-    config = prefill_config
+        prefill_config.use_kv_cache = False
     use_i64_token = args.embedding_quantize is not None
     model = LlamaModel(
-        config,
+        prefill_config,
         ar_len=args.prefill_ar_len,
         output_new_cache_only=True,
         output_cache=False,
@@ -173,57 +142,72 @@ def permute(w, heads):
     if "model" in state_dict:
         state_dict = state_dict["model"]
 
+    # TODO: use dtype of model checkpoint
+    model = model.to(device=args.device, dtype=torch.float)
+    inputs = model.get_example_inputs(use_kv_cache=False)
+    tokens, atten_mask = inputs
+
+    scales_state_dict = {}
+    if args.range_setting == "mse_with_act_loss":
+        wrapped_model = WrappedLlamaModel(
+            model, atten_mask, args.use_kv_cache, args.max_seq_length, args.device
+        )
+        act_bits, weight_bits = {
+            "8a8w": (8, 8),
+            "16a4w": (16, 4),
+            "16a4w_block": (16, 4),
+        }[args.ptq]
+        scales_state_dict = compute_scales(
+            wrapped_model, tokens, weight_bits, act_bits, 1600
+        )
+        torch.save(scales_state_dict, "scales_state_dict.pth")
+        logging.info("Saved scales to scales_state_dict.pth!")
+        reverse_quantize_module_swap(wrapped_model)
+
     for layer in model.layers:
         if getattr(layer.attention, "prepare_sha", None):
             layer.attention.prepare_sha()
         if getattr(layer.feed_forward, "prepare_feedfoward_conv", None):
             layer.feed_forward.prepare_feedfoward_conv()
-
-    model.to(dtype=torch.float)
-    model.to(device=args.device)
-
-    tokens, atten_mask = model.get_example_inputs(use_kv_cache=False)
-    tokens = tokens.to(device=args.device)
-    atten_mask = atten_mask.to(device=args.device)
-    atten_mask = atten_mask.to(dtype=torch.float)
-    inputs = (tokens, atten_mask)
-
     if args.embedding_quantize:
         model = get_quant_embedding_transform(
             embedding_quantize=args.embedding_quantize
         )(model)
 
     model = convert_linear_to_conv2d(model)
+    return model, prefill_config, inputs, scales_state_dict
+
 
-    if args.ptq:
+def gen_eval_wrapper(model_name, args):
+    tokenizer = get_tokenizer(args.tokenizer_path)
+    model, config, inputs, scales_state_dict = prepare_model(model_name, args)
+    tokens, atten_mask = inputs
+    use_i64_token = args.embedding_quantize is not None
+
+    if args.ptq is not None:
         quant_dtype = getattr(QuantDtype, f"use_{args.ptq}")
 
         custom_annotations = (annotate_matmul_16a8w,)
         if args.llama_model == "stories110m":
             custom_annotations = custom_annotations + (
                 annotate_linear_16a8w_in_affine_layer,
             )
-        quantizer = make_quantizer(
-            quant_dtype=quant_dtype,
-            per_channel_conv=True,
-            per_channel_linear=True,
-            act_observer=MinMaxObserver,
-        )
-        quantizer.add_custom_quant_annotations(custom_annotations)
 
-        if args.range_setting == "mse_weight":
-            add_mse_weight_observer(quant_dtype, quantizer)
+        quantizer = make_custom_quantizer(
+            quant_dtype, args.range_setting, custom_annotations, args.quant_linear_only
+        )
 
         with torch.no_grad():
+            logging.info("Starting export...")
             model = torch.export.export(model, inputs, strict=True).module()
             if quant_dtype == QuantDtype.use_16a4w_block:
                 conv_nodes = [n for n in model.graph.nodes if "conv" in n.name]
                 block_size_map = {n.name: (1, 64, 1, 1) for n in conv_nodes}
                 quantizer.set_block_size_map(block_size_map)
-
+            logging.info("Finished export, adding observers (prepare_pt2e)...")
             model = prepare_pt2e(model, quantizer)
 
-        logging.info("Quantizing the model...")
+        logging.info("Observers added, starting calibration...")
 
         calibrate(
             inputs,
@@ -236,7 +220,24 @@ def permute(w, heads):
             use_i64_token=use_i64_token,
         )
 
+        if args.range_setting == "mse_with_act_loss":
+            # scales_state_dict = torch.load("scales_state_dict.pth")
+            set_scales(model, scales_state_dict, config.head_dim)
+
+        logging.info("Quantizing the model...")
         model = convert_pt2e(model)
+        logging.info("Quantization complete! Here is some sample generated text:")
+
+        calibrate(
+            inputs,
+            "Could you tell me about Facebook?",
+            model,
+            tokenizer=tokenizer,
+            ar_len=args.prefill_ar_len,
+            max_seq_len=args.max_seq_len,
+            kv_updater=None,
+            use_i64_token=use_i64_token,
+        )
 
     model = WrappedLlamaModel(
         model, atten_mask, args.use_kv_cache, args.max_seq_length, args.device
@@ -248,7 +249,7 @@ def permute(w, heads):
         max_seq_length=args.calibration_seq_length,
         use_kv_cache=args.use_kv_cache,
         generate_full_logits=args.generate_full_logits,
-        enable_dynamic_shape=args.enable_dynamic_shape,
+        enable_dynamic_shape=False,
     )
 
 
@@ -271,6 +272,7 @@ def eval_llama(
             model=eval_wrapper,
             tasks=args.tasks,
             num_fewshot=args.num_fewshot,
+            limit=args.fraction,
         )
 
     for task, res in eval_results["results"].items():
@@ -290,9 +292,19 @@ def main() -> None:
     )
     parser.add_argument(
         "--range_setting",
-        help="Choose which range setting method (e.g. mse_weight). If not specified, will do minmax for weights and activations",
+        help="Choose which range setting method for weight quantization (e.g. mse_weight_only or mse_with_act_loss). If not specified, defaults to minmax",
         type=str,
     )
+    parser.add_argument(
+        "--fraction",
+        help="the fraction of examples per task (only use this for testing)",
+        type=float,
+    )
+    parser.add_argument(
+        "--quant_linear_only",
+        help="if you select this option we quantize linear layers only",
+        action="store_true",
+    )
 
     args = parser.parse_args()
     args.llama_model = "llama3_2"