support hardware scale for gaudi2 (#1637)

Signed-off-by: xin3he <xin3.he@intel.com>

Author: xin3he

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/habana_fp8/run_llm.py

@@ -1,7 +1,8 @@
 import os
 os.environ["EXPERIMENTAL_WEIGHT_SHARING"] = "False"
 os.environ["USE_GAUDI2_SCALE"] = "True"
-os.environ.pop("USE_GAUDI2_SCALE")  # gaudi scale work
+# USE_GAUDI2_SCALE requires PT_USE_FP8_AMAX for torch.mm/bmm, or got failure
+os.environ["PT_USE_FP8_AMAX"] = "True"  
 # os.environ["GRAPH_VISUALIZATION"] = "True"
 # import shutil
 # shutil.rmtree(".graph_dumps", ignore_errors=True)
@@ -173,7 +174,7 @@
         args.model,
         trust_remote_code=args.trust_remote_code
     )
-
+tokenizer.pad_token = tokenizer.eos_token
 
 user_model.eval()
 
@@ -219,6 +220,7 @@ def calib_func(model):
 
         user_model = quantize(user_model, qconfig, calib_func, inplace=True)
         # saving
+        print(user_model)
         if args.save and local_rank in [-1, 0]:
             user_model.save("saved_results")
 

neural_compressor/torch/algorithms/habana_fp8/modules.py

@@ -55,7 +55,7 @@ def forward(self, x):
 
 ##################### FP8 modules #######################
 def _map_guadi2_scale(scale):
-    USE_GAUDI2_SCALE = os.environ.get("USE_GAUDI2_SCALE")
+    USE_GAUDI2_SCALE = bool(os.getenv("USE_GAUDI2_SCALE", False))
     if USE_GAUDI2_SCALE:
         scale_list = torch.tensor([16, 1, 1 / 16, 1 / 256])
         for i in scale_list:
@@ -135,6 +135,7 @@ def forward(self, inp):
         if inp.dtype not in [torch.float8_e4m3fn, torch.float8_e5m2]:
             if self.use_amax:
                 input_scale = self.dtype_amax / inp.abs().max()
+                input_scale = _map_guadi2_scale(input_scale)
                 input_scale_inv = torch.reciprocal(input_scale)
             else:
                 input_scale, input_scale_inv = None, None
@@ -183,6 +184,7 @@ def forward(self, input1, input2):
             self.out_dtype = input1.dtype
             if self.use_amax:
                 input1_scale = self.dtype_amax / input1.data.abs().max()
+                input1_scale = _map_guadi2_scale(input1_scale)
                 input1_scale_inv = torch.reciprocal(input1_scale)
             else:
                 input1_scale, input1_scale_inv = None, None
@@ -195,6 +197,7 @@ def forward(self, input1, input2):
             self.out_dtype = input2.dtype
             if self.use_amax:
                 input2_scale = self.dtype_amax / input2.data.abs().max()
+                input2_scale = _map_guadi2_scale(input2_scale)
                 input2_scale_inv = torch.reciprocal(input2_scale)
             else:
                 input2_scale, input2_scale_inv = None, None

neural_compressor/torch/amp/fp8/functions.py

@@ -20,7 +20,8 @@
 import torch
 from torch.nn import functional as F
 
-from neural_compressor.common import logger
+from neural_compressor.torch.algorithms.habana_fp8.modules import _map_guadi2_scale
+from neural_compressor.torch.utils import logger
 
 _F_linear = F.linear
 _torch_matmul = torch.matmul
@@ -32,7 +33,7 @@
 E5M2_AMAX = torch.tensor(57344, dtype=torch.float).to("hpu")
 
 DTYPE_AMAX = E4M3_AMAX if DATA_TYPE == torch.float8_e4m3fn else E5M2_AMAX
-USE_AMAX = False if os.getenv("PT_USE_FP8_AMAX") is None else True
+USE_AMAX = bool(os.getenv("PT_USE_FP8_AMAX", False))
 
 
 def fp8_linear_forward(input, weight, bias=None):
@@ -44,6 +45,7 @@ def fp8_linear_forward(input, weight, bias=None):
         out_dtype = input.dtype
         if USE_AMAX:
             input_scale = DTYPE_AMAX / input.data.abs().max()
+            input_scale = _map_guadi2_scale(input_scale)
             input_scale_inv = torch.reciprocal(input_scale)
         else:
             input_scale, input_scale_inv = None, None
@@ -56,6 +58,7 @@ def fp8_linear_forward(input, weight, bias=None):
         out_dtype = weight.dtype
         if USE_AMAX:
             weight_scale = DTYPE_AMAX / weight.data.abs().max()
+            weight_scale = _map_guadi2_scale(weight_scale)
             weight_scale_inv = torch.reciprocal(weight_scale)
         else:
             weight_scale, weight_scale_inv = None, None
@@ -86,6 +89,7 @@ def fp8_matmul(input1, input2):
         out_dtype = input1.dtype
         if USE_AMAX:
             input1_scale = DTYPE_AMAX / input1.data.abs().max()
+            input1_scale = _map_guadi2_scale(input1_scale)
             input1_scale_inv = torch.reciprocal(input1_scale)
         else:
             input1_scale, input1_scale_inv = None, None
@@ -98,6 +102,7 @@ def fp8_matmul(input1, input2):
         out_dtype = input2.dtype
         if USE_AMAX:
             input2_scale = DTYPE_AMAX / input2.data.abs().max()
+            input2_scale = _map_guadi2_scale(input2_scale)
             input2_scale_inv = torch.reciprocal(input2_scale)
         else:
             input2_scale, input2_scale_inv = None, None