added retrying downloads logic for stability (quic#370)

Signed-off-by: Onkar Chougule <[email protected]>

Author: ochougul

QEfficient/base/common.py

@@ -12,13 +12,15 @@
 QEFFAutoModel provides a common interface for loading the HuggingFace models using either the HF card name of local path of downloaded model.
 """
 
+import os
 from typing import Any
 
 from transformers import AutoConfig
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 
 from QEfficient.base.modeling_qeff import QEFFBaseModel
 from QEfficient.transformers.models.modeling_auto import QEFFAutoModelForCausalLM
+from QEfficient.utils import login_and_download_hf_lm
 
 
 class QEFFCommonLoader:
@@ -50,6 +52,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: str, *args, **kwargs) ->
             )
 
         local_model_dir = kwargs.pop("local_model_dir", None)
+        if not os.path.isdir(pretrained_model_name_or_path) and local_model_dir is None:
+            pretrained_model_name_or_path = login_and_download_hf_lm(pretrained_model_name_or_path, *args, **kwargs)
         hf_token = kwargs.pop("hf_token", None)
         continuous_batching = True if kwargs.pop("full_batch_size", None) else False
 

QEfficient/utils/constants.py

@@ -95,7 +95,7 @@ class Constants:
     INPUT_STR = ["My name is"]
     GB = 2**30
     MAX_QPC_LIMIT = 30
-    MAX_RETRIES = 5  # This constant will be used set the maximum number of retry attempts for downloading a model using huggingface_hub snapshot_download
+    MAX_RETRIES = 10  # This constant will be used set the maximum number of retry attempts for downloading a model using huggingface_hub snapshot_download
     NUM_SPECULATIVE_TOKENS = 2
     SDK_APPS_XML = "/opt/qti-aic/versions/apps.xml"  # This xml file is parsed to find out the SDK version.
 

scripts/replicate_kv_head/replicate_kv_heads.py

@@ -15,6 +15,7 @@
 from QEfficient.transformers.quantizers.awq import WQLinear_GEMM
 from QEfficient.transformers.quantizers.gptq import QuantLinearGPTQ
 from QEfficient.transformers.quantizers.quantizer_compressed_tensors import FP8DeQuantLinear
+from QEfficient.utils._utils import login_and_download_hf_lm
 
 
 def duplicate_weights_for_linear_layer(
@@ -79,7 +80,9 @@ def main(args):
     model_kwargs = {"attn_implementation": "eager"}
     if args.num_hidden_layers:
         model_kwargs["num_hidden_layers"] = args.num_hidden_layers
-    model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs)
+
+    pretrained_model_name_or_path = login_and_download_hf_lm(model_name)
+    model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **model_kwargs)
 
     # Undo the effect of replace_transformers_quantizers
     undo_transformers_quantizers()

tests/transformers/spd/test_pld_inference.py

@@ -145,9 +145,9 @@ def get_padded_input_len(input_len: int, prefill_seq_len: int, ctx_len: int):
     """
     num_chunks = -(input_len // -prefill_seq_len)  # ceil divide without float
     input_len_padded = num_chunks * prefill_seq_len  # Convert input_len to a multiple of prefill_seq_len
-    assert input_len_padded <= ctx_len, (
-        "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
-    )
+    assert (
+        input_len_padded <= ctx_len
+    ), "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
     return input_len_padded
 
 
@@ -202,6 +202,7 @@ def find_candidate_pred_tokens(
     return np.full(num_pred_tokens, fill_tok, dtype=np.int64), has_empty_tokens
 
 
+@pytest.mark.on_qaic
 @pytest.mark.parametrize(
     "prompts, num_speculative_tokens, prefill_seq_len, ctx_len, prefill_bsz, target_model_name, full_batch_size, max_ngram_size",
     configs,

tests/transformers/spd/test_spd_inference.py

@@ -75,9 +75,9 @@ def get_padded_input_len(input_len: int, prefill_seq_len: int, ctx_len: int):
     """
     num_chunks = -(input_len // -prefill_seq_len)  # ceil divide without float
     input_len_padded = num_chunks * prefill_seq_len  # Convert input_len to a multiple of prefill_seq_len
-    assert input_len_padded <= ctx_len, (
-        "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
-    )
+    assert (
+        input_len_padded <= ctx_len
+    ), "input_len rounded to nearest prefill_seq_len multiple should be less than ctx_len"
     return input_len_padded
 
 
@@ -93,6 +93,7 @@ def split_dlm_bonus_token_inputs(dlm_decode_inputs):
     return bonus_token_inputs, dlm_decode_inputs
 
 
+@pytest.mark.on_qaic
 @pytest.mark.skip()  # remove when the SDK 1.20.0 issue solved for compiling this model
 @pytest.mark.parametrize(
     "prompts, num_speculative_tokens, prefill_seq_len, ctx_len, prefill_bsz, draft_model_name, target_model_name, full_batch_size",
@@ -319,9 +320,9 @@ def test_spec_decode_inference(
     for prompt, generation in zip(prompts, batch_decode):
         print(f"{prompt=} {generation=}")
     # validation check
-    assert mean_num_accepted_tokens == float(num_speculative_tokens + 1), (
-        f"mean number of accepted tokens is {mean_num_accepted_tokens} but should be {num_speculative_tokens + 1}"
-    )
+    assert mean_num_accepted_tokens == float(
+        num_speculative_tokens + 1
+    ), f"mean number of accepted tokens is {mean_num_accepted_tokens} but should be {num_speculative_tokens + 1}"
     del target_model_session
     del draft_model_session
     generated_ids = np.asarray(generated_ids[0]).flatten()