Merge branch 'main' into unit_test_int8

Signed-off-by: andrea-fasoli <[email protected]>

Author: andrea-fasoli

.github/workflows/labelpr.yaml

@@ -12,13 +12,9 @@ jobs:
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
-            const pr_welcome_msg = `Thanks for making a pull request! 😃\nOne of the maintainers will review and advise on the next steps.`;
             // https://github.com/commitizen/conventional-commit-types
             const valid_pr_types = ['feat', 'fix', 'docs', 'style', 'refactor', 'perf', 'test', 'build', 'ci', 'chore', 'revert'];
 
-            if(context.payload.pull_request.comments === 0) {
-              await github.issues.createComment({ ...context.repo, issue_number: context.payload.number, body: pr_welcome_msg});
-            }
 
             const title = context.payload.pull_request.title;
             const results = /^(\w+)(\(\w+\))?!?:/.exec(title);
@@ -32,4 +28,4 @@ jobs:
             const labels = context.payload.pull_request.labels;
             const new_labels = labels.filter(label => !valid_pr_types.includes(label.name)); // keep all labels that are not in valid_pr_types
             new_labels.push({name: pr_type});
-            await github.issues.update({ ...context.repo, issue_number: context.payload.number, labels: new_labels });
+            await github.rest.issues.update({ ...context.repo, issue_number: context.payload.number, labels: new_labels });

examples/QAT_INT8/run_qa_no_trainer_qat.py

@@ -390,7 +390,7 @@ def parse_args():
         "--do_lowering",
         choices=["cutlass", "triton"],
         type=str,
-        default="triton",
+        default=None,
         help="convert QAT model to utilize real INT8 GPU kernel, 'cutlass' or 'triton'",
     )
 
@@ -1136,7 +1136,7 @@ def speedtest(model, exam_inp, Ntest=100):
             logger.info(
                 f"\n    {label} {'with' if comp_mode else 'without'} torch.compile"
             )
-            model_copy = deepcopy(model)
+            model_copy = deepcopy(model).half()
 
             if label == "int8":
                 qcfg = qconfig_init(recipe="qat_int8", args=args)
@@ -1178,7 +1178,7 @@ def speedtest(model, exam_inp, Ntest=100):
 
             # Median runtime using fixed input (in msec)
             med_runtime = speedtest(model_copy, exam_inp)
-            metrics = squad_eval(model_copy) if label == "int8" else {"f1": None}
+            metrics = squad_eval(model_copy)  # if label == "int8" else {"f1": None}
 
             summary["precision"].append(label)
             summary["compile mode"].append(comp_mode)

fms_mo/aiu_addons/__init__.py

@@ -235,6 +235,7 @@ def imatmul_kernel(
     accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.int32)
     ## ------ prepare LSB rounding/truncation masks -------
     round_bit = 1 << (chunk_trun_bits - 1) if chunk_trun_bits > 0 else 0
+    # msb_mask = 0x00FFFFFF  # only needed when simulating truncation on MSB
     ## ---------------------------------------------------------
 
     for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
@@ -326,7 +327,7 @@ def grid(META):
         kernel_config = {
             "BLOCK_SIZE_M": 128,
             "BLOCK_SIZE_K": chunk_size,
-            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_N": 128,  # was 32
             "GROUP_SIZE_M": 8,
             "num_warps": 2,
             "num_stages": 5,
@@ -335,7 +336,7 @@ def grid(META):
         kernel_config = {
             "BLOCK_SIZE_M": 128,
             "BLOCK_SIZE_K": chunk_size,
-            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_N": 128,  # was 64
             "GROUP_SIZE_M": 8,
             "num_warps": 4,
             "num_stages": 4,

fms_mo/custom_ext_kernels/triton_kernels.py

@@ -752,7 +752,7 @@ def from_fms_mo(cls, fms_mo_qlinear, **kwargs):
         qlin_int.max_acc_bits = kwargs.get("max_acc_bits", 32)
         qlin_int.accminmax = (
             -(1 << (qlin_int.max_acc_bits - 1)),
-            1 << (qlin_int.max_acc_bits - 1) - 1,
+            (1 << (qlin_int.max_acc_bits - 1)) - 1,
         )
         qlin_int.truncate_lsb = kwargs.get("truncate_lsb", 0)
         qlin_int.chunk_size = kwargs.get("chunk_size", 100000)
@@ -871,16 +871,16 @@ def from_torch_iW(cls, nnlin_iW, prec, a_cv, a_cvn, w_cv, zero_shift, **kwargs):
 
         qlinear_iW.nbits_a = 8  # Only support INT8 for now
         qlinear_iW.nbits_w = 8
-        qlinear_iW.acc_dtype = torch.float16
+        qlinear_iW.acc_dtype = kwargs.get("acc_dtype", torch.float)
         qlinear_iW.usePTnativeQfunc = kwargs.get("use_PT_native_Qfunc", True)
-        qlinear_iW.use_int_kernel = True
+        qlinear_iW.use_int_kernel = kwargs.get("use_int_kernel", "triton")
         qlinear_iW.weight = nn.Parameter(
             nnlin_iW.weight.to(torch.int8), requires_grad=False
         )
         qlinear_iW.max_acc_bits = kwargs.get("max_acc_bits", 32)
         qlinear_iW.accminmax = (
             -(1 << (qlinear_iW.max_acc_bits - 1)),
-            1 << (qlinear_iW.max_acc_bits - 1) - 1,
+            (1 << (qlinear_iW.max_acc_bits - 1)) - 1,
         )
         qlinear_iW.truncate_lsb = kwargs.get("truncate_lsb", False)
         qlinear_iW.chunk_size = kwargs.get("chunk_size", 100000)
@@ -1027,11 +1027,11 @@ def iaddmm_int(self, bias, m1, m2):
         else:
             m1 = self.qa_fmo_mo_qfunc(m1)
 
-        if m1.shape[1] > self.chunk_size:
+        if m1.shape[1] > self.chunk_size and self.use_int_kernel != "triton":
             idx = list(range(0, m1.shape[1], self.chunk_size))
             Nchunk = len(idx)
             idx.append(m1.shape[1])
-            fp16_out = torch.zeros(
+            accumulator = torch.zeros(
                 (m1.shape[0], m2.shape[1]), dtype=torch.float16, device=m1.device
             )
             trun_scale = 1
@@ -1052,11 +1052,11 @@ def iaddmm_int(self, bias, m1, m2):
                     # could cast to smaller data type to further simulate HW behavior, for example,
                     # if HW truncates 8b from both sides of i32 accumulator, the remaining data can
                     # be cast to i16 to be more realistic. pay attention to overflow handling
-                fp16_out += imm_out.to(torch.float16)
+                accumulator += imm_out.to(torch.float16)
 
             return (
-                fp16_out
-                * (trun_scale * self.input_scale * self.w_scale).to(torch.float16)
+                accumulator
+                * (trun_scale * self.input_scale * self.w_scale)  # .to(torch.float16)
                 + bias
             ).to(self.acc_dtype)
         # The safest casting, i32 -> f32
@@ -1145,10 +1145,13 @@ def extra_repr(self) -> str:
         """
         Returns an alternative string representation of the object
         """
-        return (
+        repr_str = (
             f"in={self.in_features}, out={self.out_features}, bias={self.bias is not None}, "
-            f"use_int_kernel={self.use_int_kernel}"
+            f"int_kernel={self.use_int_kernel}"
         )
+        if self.truncate_lsb > 0 or self.max_acc_bits < 32:
+            repr_str += f", acc_bits={self.max_acc_bits}, trun_lsb={self.truncate_lsb}"
+        return repr_str
 
     def __getstate__(self):
         """

fms_mo/modules/linear.py

@@ -25,18 +25,18 @@ dependencies = [
 "numpy>=1.26.4,<2.3.0",
 "accelerate>=0.20.3,!=0.34,<1.4",
 "transformers>=4.45,<4.49",
-"torch>=2.2.0,<2.5",
+"torch>=2.2.0,<2.5", 
+"triton>=3.0,<3.2",
 "tqdm>=4.66.2,<5.0",
 "datasets>=3.0.0,<4.0",
 "ninja>=1.11.1.1,<2.0",
 "tensorboard",
 "notebook",
-"torchvision>=0.8",
+"torchvision>=0.17",
 "evaluate",
 "huggingface_hub",
 "pandas",
 "safetensors",
-"ninja",
 "ibm-fms>=0.0.8"
 ]