README syntax highlighting (#365)

* README syntax highlighting

Adds syntax highlighting to README

* Update README.md

README.md

@@ -50,7 +50,7 @@ cores. With `ninja` compiling takes 3-5 minutes on a 64-core machine.
 pip install flash-attn --no-build-isolation
 ```
 Alternatively you can compile from source:
-```
+```sh
 python setup.py install
 ```
 
@@ -58,7 +58,7 @@ If your machine has less than 96GB of RAM and lots of CPU cores, `ninja` might
 run too many parallel compilation jobs that could exhaust the amount of RAM. To
 limit the number of parallel compilation jobs, you can set the environment
 variable `MAX_JOBS`:
-```
+```sh
 MAX_JOBS=4 pip install flash-attn --no-build-isolation
 ```
 
@@ -76,11 +76,11 @@ FlashAttention-2 currently supports:
 
 The main functions implement scaled dot product attention (softmax(Q @ K^T *
 softmax_scale) @ V):
-```
+```python
 from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
 ```
 
-```
+```python
 flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False):
 """dropout_p should be set to 0.0 during evaluation
 If Q, K, V are already stacked into 1 tensor, this function will be faster than
@@ -94,9 +94,10 @@ Arguments:
     causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
 Return:
     out: (batch_size, seqlen, nheads, headdim).
+"""
 ```
 
-```
+```python
 flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False):
 """dropout_p should be set to 0.0 during evaluation
 Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
@@ -114,6 +115,7 @@ Arguments:
     causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
 Return:
     out: (batch_size, seqlen, nheads, headdim).
+"""
 ```
 
 To see how these functions are used in a multi-head attention layer (which
@@ -128,10 +130,10 @@ These functions have been renamed:
 
 If the inputs have the same sequence lengths in the same batch, it is simpler
 and faster to use these functions:
-```
+```python
 flash_attn_qkvpacked_func(qkv, dropout_p, softmax_scale=None, causal=False)
 ```
-```
+```python
 flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False)
 ```
 
@@ -205,7 +207,7 @@ of a baseline implementation in Pytorch (for different head dimensions, input
 dtype, sequence length, causal / non-causal).
 
 To run the tests:
-```
+```sh
 pytest -q -s tests/test_flash_attn.py
 ```
 ## When you encounter issues